KR20180091765A - Method for providing the information for diagnosing of prostate cancer - Google Patents

Method for providing the information for diagnosing of prostate cancer Download PDF

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KR20180091765A
KR20180091765A KR1020180014783A KR20180014783A KR20180091765A KR 20180091765 A KR20180091765 A KR 20180091765A KR 1020180014783 A KR1020180014783 A KR 1020180014783A KR 20180014783 A KR20180014783 A KR 20180014783A KR 20180091765 A KR20180091765 A KR 20180091765A
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KR102049319B1 (en
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정병하
이광석
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연세대학교 산학협력단
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Abstract

The present invention relates to a method for providing information on prostate cancer which can be applied to various fields related to prostate cancer diagnosis and makes it very easy to understand and apply an evaluation result due to quantitative calculation, since the prostate cancer can be diagnosed by using an image obtained by using transrectal ultrasonography and/or magnetic resonance imaging (MRI) as well as information related to malignancy of the prostate cancer can be provided. The method comprises the following steps: obtaining a prostate image through MRI; extracting a lesion region image from the obtained image; individually calculating a red color average value (pixel R), the number of pixels of the lesion region (pixel count), a minimum value of a Hounsfield unit (pixel minimum Hounsfield unit), a maximum value of the Hounsfield unit (pixel maximum Hounsfield unit), an average value of the Hounsfield unit (pixel average Hounsfield unit), and a standard deviation of the Hounsfield unit (pixel standard deviation Hounsfield unit) from the extracted image; and applying the calculated values to the following equation, X = K1*pixel R+K2*pixel count+K3*pixel minimum Hounsfield unit+K4*pixel maximum Hounsfield unit+K5*pixel average Hounsfield unit+K6*pixel standard deviation Hounsfield unit+K7.

Description

전립선암 진단에 관한 정보제공방법{Method for providing the information for diagnosing of prostate cancer}The present invention relates to a method for diagnosing prostate cancer,

본 발명은 전립선암 진단에 관한 정보제공방법에 관한 것이다.The present invention relates to a method for providing information on diagnosis of prostate cancer.

전립선(prostate) 또는 전립샘은 샘조직과 섬유근조직으로 구성된 부속생식샘으로, 정액을 생성, 분비하는 역할을 하고 있다. 전립선액은 정액의 약 1.5~2%를 차지하고 있는데 다량의 구연산염(citrate), 아연(zinc), 전립선 특이항원(prostate specific antigen; PSA) 등으로 구성되어 있다. 이 중 전립선 특이항원은 정액의 액화와 관여되는 것으로 알려져 있고, 최근에는 전립선암(prostate cancer)을 진단하는데도 사용되고 있다. 고령화 사회가 되어가며 지난 20년간 우리나라에서도 전립선암 발생률이 급격하게 증가되고 있는 추세이며, 이에 따라 전립선암에 대한 관심이 증가되고 있다.Prostate or prostate gland is a gonadal gland composed of glandular tissue and fibroid tissue, which is responsible for the production and secretion of semen. Prostate fluid accounts for about 1.5% to 2% of semen, and it is composed of citrate, zinc and prostate specific antigen (PSA). Among these, prostate specific antigen is known to be involved in liquefaction of semen, and recently it is also used to diagnose prostate cancer. As an aging society, the incidence of prostate cancer has risen sharply in Korea for the last 20 years, and interest in prostate cancer is increasing.

전립선암이란, 전립선에 발생하는 모든 암을 의미하며, 대부분은 샘 세포(조직에서 분비물을 가지고 있거나 밖으로 내보내는 세포로 세포질의 항상성을 유지하는 세포를 의미함)에서 발생되고 있으며, 샘 세포에서 발생되는 암을 생암종이라고 합니다. 이외에도 육종, 소세포 암종, 이행세포 암종 등 다양한 종류가 있습니다.Prostate cancer refers to all cancers that occur in the prostate gland. Most of them occur in glandular cells (cells that have secretions in tissues or cells that excrete them, meaning cells that maintain cytostatic homeostasis) Cancer is called live carcinoma. In addition, there are various kinds such as sarcoma, small cell carcinoma, transitional cell carcinoma.

전립선암을 진단하는 방법은 현재 국제적인 임상 가이드라인에 따라 전립선 특이항체를 통해 진단하고 있으며, 조기 진단적인 목적에 따라 국제 권고안에 따라 PSA 수치가 3 이상인 사람에 대해서는 전립선 조직 검사를 권고하고 있습니다. 그러나 PSA 10 미만의 환자에게서 전립선암이 진단될 확률은 20%가 되지 않습니다. 최근 전립선 조직 검사를 받는 환자들의 약 70% 이상이 PSA 10 미만 임을 고려할 때 상당수의 환자들이 불필요한 조직 검사를 받고 있다는 것을 알 수 있습니다. 또한, PSA 수치가 4 미만인 환자 중 전립선암일 경우도 15%에 달하기 때문에 PSA 수치 만으로는 전립선암을 정확히 진단하기에는 어려운 실정이다. 이를 해결하기 위하여 전립선암을 진단하기 위한 다양한 바이오마커들이 활발히 개발되고 있지만(국내등록특허 10-1141190), 실생활에 직접 이용될 수 있는 바이오마커의 수는 매우 한정적일 뿐만 아니라, 검사 가격이 100만원을 호가하기 때문에 환자들이 쉽게 접하기 어려운 실정이다. 이외에 경직장 초음파 검사(trans-rectal ultrasonography; TRUS)를 통하여 진단하는 방법이 있지만, 전립선암의 경우에는 다른 갑상선암, 간암 등과 다르게 음영이 낮아지지 않는 경우도 다수이고, 암이 아니라 염증 등의 다른 원인에 의해서도 음영이 낮아지기 때문에 진단 정확성이 43.0 정도 밖에 되지 않기 때문에 경직장 초음파 검사 만으로는 전립선암을 진단하기에는 한계가 있다.Diagnosis of prostate cancer is based on current international clinical guidelines and is recommended for prostate biopsy for those with an PSA level of 3 or more according to international recommendations for early diagnostic purposes. However, the probability of having prostate cancer diagnosed in a patient under 10 PSA is not 20%. Considering that more than 70% of patients undergoing prostate biopsy are under PSA 10, a significant number of patients are undergoing unnecessary biopsy. In addition, PSA levels in prostate cancer patients are less than 4% of the prostate cancer reaches 15%, so it is difficult to accurately diagnose prostate cancer by PSA alone. In order to solve this problem, various biomarkers for diagnosing prostate cancer have been actively developed (Korean Patent No. 10-1141190), but the number of biomarkers that can be directly used in real life is very limited, And it is difficult for patients to easily access. In addition, transrectal ultrasonography (TRUS) can be used to diagnose prostate cancer. However, in contrast to other thyroid cancer and liver cancer, there are many cases in which the shadow is not lowered. The diagnostic accuracy is only about 43.0 because the shadow is lowered. Therefore, it is not enough to diagnose prostate cancer by only transrectal ultrasonography.

이에 본 발명자들은 자기공명영상(MRI)을 통하여 전립선암을 용이하게 진단하기 위하여, 객관적이고 정확성을 증가시킬 수 있는 진단용 알고리즘을 개발하고자 노력하였다. Accordingly, the present inventors have sought to develop a diagnostic algorithm that can increase the objective and accuracy in order to easily diagnose prostate cancer through magnetic resonance imaging (MRI).

본 발명은 상기와 같은 종래 기술상의 문제점을 해결하기 위해 안출된 것으로, 경직장초음파 영상 및/또는 장기공명영상을 이용하여 전립선암에 관한 정보를 제공하는 방법 및 이를 이용한 진단 장치를 제공하는 것을 그 목적으로 한다.SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-mentioned problems in the prior art, and it is an object of the present invention to provide a method of providing information on prostate cancer using a transrectal ultrasound image and / .

그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당업계에서 통상의 지식을 가진 자에게 명확하게 이해될 수 있을 것이다.However, the technical problem to be solved by the present invention is not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description.

이하, 본원에 기재된 다양한 구체예가 도면을 참조로 기재된다. 하기 설명에서, 본 발명의 완전한 이해를 위해서, 다양한 특이적 상세사항, 예컨대, 특이적 형태, 조성물, 및 공정 등이 기재되어 있다. 그러나, 특정의 구체예는 이들 특이적 상세 사항 중 하나 이상 없이, 또는 다른 공지된 방법 및 형태와 함께 실행될 수 있다. 다른 예에서, 공지된 공정 및 제조 기술은 본 발명을 불필요하게 모호하게 하지 않게 하기 위해서, 특정의 상세사항으로 기재되지 않는다. "한 가지 구체예" 또는 "구체예"에 대한 본 명세서 전체를 통한 참조는 구체예와 결부되어 기재된 특별한 특징, 형태, 조성 또는 특성이 본 발명의 하나 이상의 구체예에 포함됨을 의미한다. 따라서, 본 명세서 전체에 걸친 다양한 위치에서 표현된 "한 가지 구체예에서" 또는 "구체예"의 상황은 반드시 본 발명의 동일한 구체예를 나타내지는 않는다. 추가로, 특별한 특징, 형태, 조성, 또는 특성은 하나 이상의 구체예에서 어떠한 적합한 방법으로 조합될 수 있다.Hereinafter, various embodiments described herein will be described with reference to the drawings. In the following description, for purposes of complete understanding of the present invention, various specific details are set forth, such as specific forms, compositions, and processes, and the like. However, certain embodiments may be practiced without one or more of these specific details, or with other known methods and forms. In other instances, well-known processes and techniques of manufacture are not described in any detail, in order not to unnecessarily obscure the present invention. Reference throughout this specification to "one embodiment" or "embodiment" means that a particular feature, form, composition, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Accordingly, the appearances of the phrase " in one embodiment "or" an embodiment "in various places throughout this specification are not necessarily indicative of the same embodiment of the present invention. In addition, a particular feature, form, composition, or characteristic may be combined in any suitable manner in one or more embodiments.

명세서에서 특별한 정의가 없으면 본 명세서에 사용된 모든 과학적 및 기술적인 용어는 본 발명이 속하는 기술분야에서 당업자에 의하여 통상적으로 이해되는 것과 동일한 의미를 가진다.Unless defined otherwise in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

본 명세서에 있어서, "전립선암(prostate cancer)"이란 전립선에서 발생하는 종양을 총칭하며, 바람직하게는 악성 종양을 의미하나, 전립선에서 발생된 종양이라면 이에 제한되지 않는다. 본 명세서 내에는 전립선암, proatate cancer(+) 등으로 표시될 수 있으며, 대조군으로는 전립선암이 아닌 양성암(benign cancer), prostate cancer(-) 등으로 표시될 수 있으나, 당업자에게 있어서 전립선암을 표시하는 기재라면 이에 제한되지 않는다.As used herein, the term "prostate cancer" refers to tumors originating from the prostate gland, preferably malignant tumors, but not limited thereto. The prostate cancer may be represented by prostate cancer, prostate cancer (+), etc. The control group may be represented by benign cancer, prostate cancer (-) or the like rather than prostate cancer. But the present invention is not limited thereto.

본 명세서에 있어서, "병변(lesion)"이란, 병적 작용으로 인해 변화가 일어난 조직, 체액 등을 의미하며, 바람직하게는 내시경, 경직장초음파, 자기공명영상 하에서 다른 일반 조직과 다르게 음영이 다르거나, 명암이 다르거나, 출혈이 관찰되거나, 빛의 투과율이 다르거나, 미세혈관이 집중되어 있거나 하는 등, 일반적인 다른 조직과 차이를 보이는 모든 부위를 의미하며, 바람직하게는 저음영(low shade) 부위이나, 영상에서 차이를 관찰할 수 있다면 이에 제한되지 않는다. 다른 말로는 병소라고도 한다.As used herein, the term " lesion " refers to a tissue, body fluids, or the like that has undergone a change due to a pathological action. Preferably, the lesion differs from other general tissues under endoscopic, Refers to any site that differs from other tissues in general such that the contrast is different, bleeding is observed, the light transmittance is different, microvessels are concentrated, or the like, preferably low shade, But the present invention is not limited thereto as long as the difference can be observed in the image. In other words, it is also called a lesion.

본 명세서에 있어서, "진단 장치"란, 내시경, 경직장초음파(TRUS), 자기공명영상(MRI) 등에서 습득한 영상으로부터 질환 진단, 질환 정보 제공이 가능한 장비를 의미하며, 영상을 수치화하여 분석할 수 있는 형태라면 제한이 없다. 바람직하게는 (a) 획득한 전립선 영상을 저장하는 저장부; (b) 상기 영상 중 병변(lesion) 영역의 영상을 선별하는 필터부; (c) 상기 선별된 영상의 적색 평균값(pixel R), 녹색 평균값(pixel G), 청색 평균값(pixel B), 적색 평균값에 대한 청색 평균값의 비율(Ratio R to B), 병변 역역의 픽셀수(pixel count), 하운스필드 유닛의 최소값(pixel minimum Hounsfield unit), 하운스필드 유닛의 최대값(pixel maximum Hounsfield unit), 하운스필드 유닛의 평균값(pixel average Hounsfield unit), 및 하운스필드 유닛의 표준편차(pixel standard deviation Hounsfield unit)를 각각 산출하는 산출부; (d) 상기 산출된 값을 수식에 대입하는 연산부; 및 (e) 상기 연산부의 결과값을 보여주는 표시부로 구성된다면 이에 제한되지 않는다. 상기 선별은 영상을 통해 다른 부위와 다른 특이사항이 인식될 수 있는, 색, 명암, 음영, 미세혈관, 출혈 등으로 인한 차이로 발생하는 부위를 선별하는 것을 의미하며, 영상에서 차이를 확인할 수 있다면 제한이 없다. 바람직하게는 저음영 부위이나, 이에 제한되지 않는다.In the present specification, the term "diagnosis device " means equipment capable of diagnosing diseases and providing disease information from images acquired by an endoscope, TRUS, magnetic resonance imaging (MRI) There is no restriction in the form. (A) a storage unit for storing the acquired prostate image; (b) a filter unit for selecting an image in a lesion region of the image; (c) a ratio of a blue average value (Ratio R to B) to a red average value (pixel R), a green average value (pixel G), a blue average value (pixel B) pixel count, a pixel minimum Hounsfield unit, a pixel maximum Hounsfield unit, a pixel average Hounsfield unit, and a Hounsfield unit. A calculation unit for calculating a pixel standard deviation Hounsfield unit; (d) an operation unit for substituting the calculated value into an equation; And (e) a display unit for displaying the result of the operation unit. The selection means to select a region where differences due to color, contrast, shade, micro-vein, hemorrhage and the like can be recognized from other regions through the image, and if the difference can be confirmed in the image no limits. Preferably, it is a low-sound region, but is not limited thereto.

본 발명은 (a) 자기공명영상(magnetic resonance imaging; MRI)를 통해 전립선 영상을 획득하는 단계; (b) 상기 획득된 영상에서 병변(lesion) 영역의 영상을 추출하는 단계; (c) 상기 추출된 영상에서 적색 평균값(pixel R), 병변 역역의 픽셀수(pixel count), 하운스필드 유닛의 최소값(pixel minimum Hounsfield unit), 하운스필드 유닛의 최대값(pixel maximum Hounsfield unit), 하운스필드 유닛의 평균값(pixel average Hounsfield unit), 및 하운스필드 유닛의 표준편차(pixel standard deviation Hounsfield unit)를 각각 산출하는 단계; 및 (d) 상기 산출된 값을 수식 "X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계를 포함하는, 전립선암 진단에 관한 정보제공방법을 제공한다. 바람직하게는 상기 X 값은 0 내지 1의 값이며, 상기 K1은 -0.052 내지 0.217의 값이며, 상기 K2는 -0.035 내지 0.003의 값이며, 상기 K3은 -0.086 내지 0.009의 값이며, 상기 K4는 -0.015 내지 -0.003의 값이며, 상기 K5는 -0.006 내지 0.108의 값이며, 상기 K6은 -0.098 내지 0의 값이며, 상기 K7은 -18.219 내지 2.414의 값이나, 이에 제한되지 않는다.(A) acquiring a prostate image through magnetic resonance imaging (MRI); (b) extracting an image of a lesion region from the acquired image; (c) calculating a red average value (pixel R), a pixel count (pixel count), a minimum minimum pixel unit, and a maximum maximum pixel unit (pixel maximum pixel unit) Calculating a pixel average Hounsfield unit and a pixel standard deviation Hounsfield unit of the Hounsfield unit, respectively; And (d) calculating the calculated value by the following equation: X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 " pixel standard deviation Hounsfield unit + K 7 ". Preferably, the X value is a value of 0 to 1, K 1 is a value of -0.052 to 0.217, K 2 is a value of -0.035 to 0.003, K 3 is a value of -0.086 to 0.009, K 4 is a value of -0.015 to -0.003, K 5 is a value of -0.006 to 0.108, K 6 is a value of -0.098 to 0, K 7 is a value of -18.219 to 2.414, It is not limited.

본 발명의 일 구체예에서, 상기 (d) 단계의 수식의 K1은 -0.050 내지 -0.052이며, K2는 0.001 내지 0.003이며, K3은 0.007 내지 0.009이며, K4는 -0.003 내지 -0.005이며, K5는 -0.004 내지 -0.006이며, K6은 0이며, K7은 2.412 내지 2.414인 것을 특징으로 하는 전립선암 진단에 관한 정보제공방법에 있어서, 상기 X값이 -3.1 이상의 값을 가질 때 전립선암으로 진단하는 것을 특징으로 한다.In one embodiment of the present invention, K 1 of the formula (d) is -0.050 to -0.052, K 2 is 0.001 to 0.003, K 3 is 0.007 to 0.009, K 4 is -0.003 to -0.005 , K 5 is from -0.004 to -0.006, K 6 is 0, and K 7 is from 2.412 to 2.414. In the method of providing information on diagnosis of prostate cancer, the X value has a value of -3.1 or more It is characterized by the diagnosis of prostate cancer.

본 발명의 다른 구체예에서, 상기 방법은 추가로 하기 단계에 의해 글리슨 등급을 진단하는 것을 특징으로 하는 전립선암 진단 방법으로 e) 상기 수식에 의하여 전립선암으로 선별된 환자에 대하여 수식 “X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계로서, 상기 (e) 단계의 수식의 K1은 0.215 내지 0.217이며, K2는 -0.033 내지 -0.035이며, K3은 -0.084 내지 -0.086이며, K4는 -0.013 내지 -0.015이며, K5는 0.106 내지 0.108이며, K6은 -0.096 내지 -0.098이며, K7은 -18.217 내지 -18.219인 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법에 있어서, 상기 X값이 -1 이상의 값을 가질 때 전립선암의 글리슨 등급(Gleason Score)이 7 내지 10으로 진단하는 것을 특징으로 한다.In another embodiment of the present invention, the method further comprises the step of diagnosing the Gleason's grade by the following steps: e) the patient is screened for prostate cancer by the formula: " X = K1 (e) applying the method of (e) to the Hounsfield unit + K7 "pixel average Hounsfield unit + K5 * pixel average Hounsfield unit + Wherein K1 is from 0.215 to 0.217, K2 is from -0.033 to -0.035, K3 is from -0.084 to -0.086, K4 is from -0.013 to -0.015, K5 is from 0.106 to 0.108, -0.098, and K7 is from -18.217 to -18.219. In the method of providing information on diagnosis of prostate cancer, when the X value has a value of -1 or more, the Gleason score of the prostate cancer is 7 10 < / RTI >

또한 본 발명은 (a) 자기공명영상(magnetic resonance imaging; MRI)을 통해 획득한 전립선 영상을 저장하는 저장부; (b) 상기 영상 중 병변(lesion) 영역의 영상을 선별하는 필터부; (c) 상기 선별된 영상의 적색 평균값(pixel R), 병변 역역의 픽셀수(pixel count), 하운스필드 유닛의 최소값(pixel minimum Hounsfield unit), 하운스필드 유닛의 최대값(pixel maximum Hounsfield unit), 하운스필드 유닛의 평균값(pixel average Hounsfield unit), 및 하운스필드 유닛의 표준편차(pixel standard deviation Hounsfield unit)를 각각 산출하는 산출부; 및 (d) 상기 산출된 값을 수식 "X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 대입하는 연산부; 및 (e) 상기 연산부의 결과값을 보여주는 표시부로 구성되는 전립선암 진단 장치를 제공한다. 바람직하게는 상기 X 값은 0 내지 1의 값이며, 상기 K1은 -0.052 내지 0.217의 값이며, 상기 K2는 -0.035 내지 0.003의 값이며, 상기 K3은 -0.086 내지 0.009의 값이며, 상기 K4는 -0.015 내지 -0.003의 값이며, 상기 K5는 -0.006 내지 0.108의 값이며, 상기 K6은 -0.098 내지 0의 값이며, 상기 K7은 -18.219 내지 2.414의 값이나, 이에 제한되지 않는다.(A) a storage unit for storing a prostate image acquired through magnetic resonance imaging (MRI); (b) a filter unit for selecting an image in a lesion region of the image; (c) calculating a red average value (pixel R), a pixel count (pixel count), a minimum minimum pixel value, A calculation unit for calculating a pixel average Hounsfield unit and a pixel standard deviation Hounsfield unit of the Hounsfield unit, And (d) calculating the calculated value by the following equation: X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 "; And (e) a display unit for displaying a result of the operation unit. Preferably, the X value is a value of 0 to 1, K 1 is a value of -0.052 to 0.217, K 2 is a value of -0.035 to 0.003, K 3 is a value of -0.086 to 0.009, K 4 is a value of -0.015 to -0.003, K 5 is a value of -0.006 to 0.108, K 6 is a value of -0.098 to 0, K 7 is a value of -18.219 to 2.414, It is not limited.

본 발명의 일 구체예에서, 상기 (d) 단계의 수식의 K1은 -0.050 내지 -0.052이며, K2는 0.001 내지 0.003이며, K3은 0.007 내지 0.009이며, K4는 -0.003 내지 -0.005이며, K5는 -0.004 내지 -0.006이며, K6은 0이며, K7은 2.412 내지 2.414인 것을 특징으로 하는, 전립선암 진단 장치에 있어서, 상기 X값이 -3.1 이상의 값을 가질 때 전립선암으로 진단하는 것을 특징으로 한다.In one embodiment of the present invention, K 1 of the formula (d) is -0.050 to -0.052, K 2 is 0.001 to 0.003, K 3 is 0.007 to 0.009, K 4 is -0.003 to -0.005 , Wherein K 5 is between -0.004 and -0.006, K 6 is 0, and K 7 is between 2.412 and 2.414. In the prostate cancer diagnosis apparatus, when the X value is equal to or greater than -3.1, .

본 발명의 다른 구체예에서, 상기 방법은 추가로 하기 단계에 의해 글리슨 등급을 진단하는 것을 특징으로 하는 전립선암 진단 장치로 e) 상기 수식에 의하여 전립선암으로 선별된 환자에 대하여 수식 “X= K1*pixel R + K2*pixel count + K3*pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계로서, 상기 (e) 단계의 수식의 K1은 0.215 내지 0.217이며, K2는 -0.033 내지 -0.035이며, K3은 -0.084 내지 -0.086이며, K4는 -0.013 내지 -0.015이며, K5는 0.106 내지 0.108이며, K6은 -0.096 내지 -0.098이며, K7은 -18.217 내지 -18.219인 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법에 있어서, 상기 X값이 -1 이상의 값을 가질 때 전립선암의 글리슨 등급(Gleason Score)이 7 내지 10으로 진단하는 것을 특징으로 한다.In another embodiment of the present invention, the method further comprises the step of diagnosing the Gleason grade by the following steps: e) determining the formula " X = K applied to 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 " Wherein K 1 of the step (e) is 0.215 to 0.217, K 2 is -0.033 to -0.035, K 3 is -0.084 to -0.086, K 4 is -0.013 to -0.015, and K 5 is 0.106 to 0.108, K 6 is -0.096 to -0.098, and K 7 is -18.217 to -18.219. In the method of providing information on diagnosis of prostate cancer, (Gleason score) of prostate cancer is 7 to 10 It characterized.

본원 발명의 정보제공방법은 단순히 경직장초음파 또는 자기공명영상을 통하여 획득된 영상을 수치화하고 이를 적용한 알고리즘을 이용하여 객관적으로 전립선암에 관한 정보를 제공할 수 있기 때문에, 불필요한 검사를 줄일 수 있을 뿐만 아니라, 영상 획득과 동시에 객관적인 진단 결과를 볼 수 있기 때문에 진단에 소요되는 시간을 단축시킬 수 있다. 또한, 기존의 다른 전립선암 검사와 보조적으로 이용할 경우 진단 정확성을 현저히 향상시킬 수 있기 때문에 평가 결과에 대한 이해 및 적용이 매우 용이할 것으로 기대된다.Since the information providing method of the present invention can provide information about the prostate cancer objectively by using numerical values obtained by merely using the transverse ultrasound or magnetic resonance images and using an algorithm using the numerical values, unnecessary examinations can be reduced , It is possible to shorten the time required for diagnosis since the objective diagnosis result can be seen at the same time as the image acquisition. In addition, it is expected that understanding and application of the evaluation results will be very easy because it can significantly improve the diagnostic accuracy when used in conjunction with other existing prostate cancer tests.

도 1은 본 발명의 일 실시예에 따른 자기공명영상(MRI)를 이용하여 획득한 전립선 영상을 나타낸 도면이다.1 is a view showing a prostate image obtained using a magnetic resonance imaging (MRI) according to an embodiment of the present invention.

이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하고자 한다. 이들 실시예는 오로지 본 발명을 보다 구체적으로 설명하기 위한 것으로서, 본 발명의 요지에 따라 본 발명의 범위가 이들 실시예에 의해 제한되지 않는다는 것은 당업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.Hereinafter, the present invention will be described in more detail with reference to Examples. It will be apparent to those skilled in the art that these embodiments are only for describing the present invention in more detail and that the scope of the present invention is not limited by these embodiments in accordance with the gist of the present invention .

실시예Example

실시예 1: 영상 획득 및 알고리즘 확인Example 1: Image acquisition and algorithm verification

2011년도부터 2015년도까지 전립선암으로 의심되는 환자 57명을 대상으로 하여 전립선 특이항원(PSA)의 수치를 측정하고, 자기공명영상(magnetic resonance imaging; MRI; 3.0 Tesla MRI system equipped with phased array coil-6 channel(Intera Achieva 3.0T, Phillips Medical System))을 이용하여 MRI-cognitive-target biopsy(TBx) 72 지역의 T2 이미지(영상)을 획득하였다. 그리고 실험 정확성을 높이기 위하여 동일한 저음영 부위 안에서 3 point의 값을 측정하였으며, 서로 다른 영역의 저음영 부위 3 부위(1 point)를 각각 측정하였다. Picture archiving and communication system(PACS)에 의하여 각각의 영상에서 자동적으로 획득되는 적색(Red; R), 녹색(Green; G), 청색(Blue; B) 값 및 흑백 모드(gray scale)를 통하여 빛의 투과성 정도를 나타내는 하운스필드 유닛(hounsfield unit; HU)을 이용하여 진단 알고리즘을 작성하였다. 진단의 정확성 여부를 확인하기 위하여, 회귀선(Y=X)으로 설명된 부분(SSR)이 총변동(SST) 중에서 어느 정도 차지하는지를 나타내는 값, 즉, 분석 결과 값이 회귀선에 얼마나 가깝게 적합한지를 보여주는 R-square(R') 값을 이용하였으며, R-square 값이 1에 가까울수록 적합하다는 것을 의미한다. 신뢰성을 확인하기 위하여 독립표본 t-검정(independent sample t-test)을 수행하였으며, "p<0.05"이면 유의성이 있는 것으로 분석하였고, 진단 알고리즘 작성을 위해서는 단순 선형 회귀 분석(Simple linear regression analysis) 모형 및 로지스틱 회귀분석을 이용하였으며, 0 ≤ Z ≤ 1로 정의하였고, B 값은 로지스틱 회귀분석에 나오는 B값으로 정의하여 알고리즘을 작성하였다(http://www.cbgstat.com/method_logistic_regression_analysis/logistic_regression_analysis.php). 실시예의 모든 통계분석은 IBM PSS statistics ver. 21(IBM Korea corporation, Seoul, Korea) 및 MedCalc Ver. 11.6(MedCalc Software)을 이용하여 실시하였다. 각각의 진단 알고리즘은 하기와 같으며, 하기 알고리즘에서 “pixel R”은 각각의 영상에서 획득한 1X1 픽셀의 평균 적색값(R)을 의미하며, “pixel G”는 각각의 영상에서 획득한 1X1 픽셀의 평균 녹색값(G)을 의미하며, “pixel B”는 각각의 영상에서 획득한 1X1 픽셀의 평균 청색값(B)을 의미하며, “pixel count”는 하운스필드 유닛 값이 측정된 부위의 총 픽셀 수를 의미하며, “pixel minimum Hounsfield unit”은 하운스필드 유닛 측정 값 중 최소값을 의미하며, “pixel maximum Hounsfield unit”은 하운스필드 유닛 측정 값 중 최대값을 의미하며, “pixel average Hounsfield unit”은 하운스필드 유닛 측정값의 평균값을 의미하며, “pixel standard deviation Hounsfield unit”은 하운스필드 유닛값의 평균값에 대한 표준편차를 의미한다. 또한, “Delta R to B”는 평균 적색값(R)에서 평균 청색값(B)을 뺀 값을 의미하며, “Ratio R to B”는 평균 청색값(B)을 평균 적색값(R)으로 나눈 값을 의미한다. 획득된 각각의 값을 단순 선형 회귀 분석에 적용하여 각각의 진단용 알고리즘을 작성하였다.The prostate-specific antigen (PSA) level was measured in 57 patients suspected to have prostate cancer from 2011 to 2015, and magnetic resonance imaging (MRI) was performed using 3.0 Tesla MRI system equipped with phased array coil- Cognitive-target biopsy (TBx) 72 region T2 images (images) were acquired using a 6-channel (Intera Achieva 3.0T, Phillips Medical System). In order to improve the accuracy of the experiment, 3 points were measured in the same low frequency region and 3 points (1 point) in the low frequency region were measured. (R), green (G), blue (B) values and a gray scale, which are automatically acquired from each image by a picture archiving and communication system (PACS) A diagnostic algorithm was constructed using a hounsfield unit (HU), which represents the degree of permeability. In order to check the accuracy of the diagnosis, a value indicating how much the portion (SSR) described by the regression line (Y = X) occupies in the total variation (SST), that is, R The value of -quare (R ') is used, and the closer the value of R-square is to 1, the more suitable. The independent sample t-test was performed to confirm the reliability, and it was analyzed that "p <0.05" was significant. For the writing of the diagnostic algorithm, a simple linear regression analysis And logistic regression were used, and 0 ≤ Z ≤ 1, and the B value was defined as the B value in the logistic regression analysis (http://www.cbgstat.com/method_logistic_regression_analysis/logistic_regression_analysis.php ). All statistical analyzes of the examples were performed using IBM PSS statistics ver. 21 (IBM Korea corporation, Seoul, Korea) and MedCalc Ver. 11.6 (MedCalc Software). Each of the diagnostic algorithms is as follows. In the following algorithm, &quot; pixel R &quot; means an average red color value (R) of 1x1 pixels obtained in each image, &quot; pixel G &quot; Pixel B &quot; means an average blue value (B) of 1 x 1 pixels obtained in each image, and &quot; pixel count &quot; means an average green value Pixel minimum Hounsfield unit &quot; means the minimum value among the Hounsfield unit measurements, &quot; pixel maximum Hounsfield unit &quot; means the maximum value among the Hounsfield unit measurements, and &quot; pixel average Hounsfield quot; unit &quot; means the average value of the Hounsfield unit measurement, and &quot; pixel standard deviation Hounsfield unit &quot; means the standard deviation of the mean value of the Hounsfield unit value. "Ratio R to B" means a value obtained by subtracting the average blue value B from the average red value R, and "Ratio R to B" means a value obtained by subtracting the average blue value B from the average red value R It means divided value. Each of the obtained values was applied to simple linear regression analysis, and each diagnostic algorithm was created.

(1) MRI를 이용한 양성(benign)암(prostate cancer(-)) 또는 전립선암(prostate cancer(+)) 진단용 알고리즘: (1) Diagnostic algorithm for benign cancer (prostate cancer (-)) or prostate cancer (+) using MRI:

F(pixel R, pixel count, pixel minimum Hounsfield unit, pixel maximum Hounsfield unit, pixel average Hounsfield unit, pixel standard deviation Hounsfield unit)F (pixel R, pixel count, pixel minimum Hounsfield unit, pixel maximum Hounsfield unit, pixel average Hounsfield unit, pixel standard deviation Hounsfield unit)

= -0.051*pixel R + 0.002*pixel count + 0.008*pixel minimum Hounsfield unit - 0.004*pixel maximum Hounsfield unit - 0.005* pixel average Hounsfield unit + 2.413= -0.051 * pixel R + 0.002 * pixel count + 0.008 * pixel minimum Hounsfield unit - 0.004 * pixel maximum Hounsfield unit - 0.005 * pixel average Hounsfield unit + 2.413

MRI 영상을 통해 획득한 각각의 값을 대입하였을 때, -3.1 이상의 값을 가질 때는 전립선암으로 진단할 수 있다는 것을 확인하였다(p<0.001).When each value obtained from the MRI image was assigned, it was confirmed that prostate cancer could be diagnosed when the value was above -3.1 (p <0.001).

(2) MRI를 이용한 전립선암의 악성도 진단용 알고리즘:(2) Algorithm for the diagnosis of malignancy of prostate cancer using MRI:

F(pixel R, pixel count, pixel minimum Hounsfield unit, pixel maximum Hounsfield unit, pixel average Hounsfield unit, pixel standard deviation Hounsfield unit)F (pixel R, pixel count, pixel minimum Hounsfield unit, pixel maximum Hounsfield unit, pixel average Hounsfield unit, pixel standard deviation Hounsfield unit)

= 0.216*pixel R - 0.034*pixel count - 0.085*pixel minimum Hounsfield unit - 0.014*pixel maximum Hounsfield unit + 0.107*pixel average Hounsfield unit - 0.097*pixel standard deviation Hounsfield unit - 18.218= 0.216 * pixel R - 0.034 * pixel count - 0.085 * pixel minimum Hounsfield unit - 0.014 * pixel maximum Hounsfield unit + 0.107 * pixel average Hounsfield unit - 0.097 * pixel standard deviation Hounsfield unit - 18.218

전립선 암 환자의 MRI 영상에서 획득한 각각의 값을 대입하였을 때, -1 이상의 값을 가질 때는 글리슨 등급 7 내지 10으로 진단할 수 있다는 것을 확인하였다(p<0.05).When each of the values obtained from the MRI images of the prostate cancer patients was substituted, it was confirmed that Glyson grade 7 to 10 can be diagnosed when having a value of -1 or more (p <0.05).

MRI를 이용한 분석 결과는 표 1에 나타내었다.The results of the analysis using MRI are shown in Table 1.

　 TotalTotal Prostate cancer(-)Prostate cancer (-) Prostate cancer(+)Prostate cancer (+) p-valuep-value No. of patients (n. %)No. of patients (n.%) 5252 2929 2323 No. of cases (n. %)No. of cases (n.%) 7272 4242 3030 Age (years) (median, IQR)Age (years) (median, IQR) 65.6(61.1-71.3)65.6 (61.1-71.3) 64.9(59.4-69.5)64.9 (59.4-69.5) 68.1(63.9-76.6)68.1 (63.9-76.6) <0.001<0.001 BMI (kg/m2) (median, IQR)BMI (kg / m 2 ) (median, IQR) 24.7(22.9-28.1)24.7 (22.9-28.1) 25.2(22.9-28.7)25.2 (22.9-28.7) 24.5(22.9-25.8)24.5 (22.9-25.8) 0.008 0.008 Diabetes mellitus (n. %)Diabetes mellitus (n.%) 9(12.5)9 (12.5) 4(9.5)4 (9.5) 5(16.7)5 (16.7) 0.007 0.007 number of prostate biopsy history　 (n. %)number of prostate biopsy history (n.%) 0.302 0.302 　1One 10(13.8)10 (13.8) 7(16.7)7 (16.7) 3(10.0)3 (10.0) 22 31(43.1)31 (43.1) 15(35.7)15 (35.7) 16(53.3)16 (53.3) ≥3≥3 31(43.1)31 (43.1) 20(47.6)20 (47.6) 11(36.7)11 (36.7) PSA (ng/ml) (median, IQR)PSA (ng / ml) (median, IQR) 8.43(6.18-13.62)8.43 (6.18-13.62) 8.35(6.30-14.2)8.35 (6.30-14.2) 9.30(6.14-13.3)9.30 (6.14-13.3) 0.666 0.666 Prostate volume (cc) (median, IQR)Prostate volume (cc) (median, IQR) 43.2(35.7-59.1)43.2 (35.7-59.1) 50.7(40.9-64.1)50.7 (40.9-64.1) 39.5(26.4-44.8)39.5 (26.4-44.8) <0.001<0.001 PSA density (ng/ml/cc) (median, IQR)PSA density (ng / ml / cc) (median, IQR) 0.21(0.13-0.33)0.21 (0.13-0.33) 0.19(0.12-0.28)0.19 (0.12-0.28) 0.23(0.19-0.35)0.23 (0.19-0.35) <0.001<0.001 Average of R value for 3 point of lesion in MRI (median, IQR)Average of R value for 3 points of lesion in MRI (median, IQR) 66.5(54.1-78.9)66.5 (54.1-78.9) 73.7(53.0-84.0)73.7 (53.0-84.0) 64.4(54.3-69.7)64.4 (54.3-69.7) <0.001<0.001 Average of pixel number for 3 point of lesion in MRI (median, IQR)Average of pixel number for 3 points of lesion in MRI (median, IQR) 183.2(97.5-281.3)183.2 (97.5-281.3) 179.5(98.0-273.0)179.5 (98.0-273.0) 193.0(97.3-290.0)193.0 (97.3-290.0) 0.836 0.836 Average of minimum hounsfield unit value for 3 point of lesion in MRI (median, IQR)Average of minimum hounsfield unit value for 3 points of lesion in MRI (median, IQR) 372.5(218.0-469.3)372.5 (218.0-469.3) 364.2(173.0-469.7)364.2 (173.0-469.7) 379.2(304.7-468.0)379.2 (304.7468.0) 0.005 0.005 Average of maximum hounsfield unit value for 3 point of lesion in MRI (median, IQR)Average of maximum hounsfield unit value for 3 points of lesion in MRI (median, IQR) 674.7(465.9-870.9)674.7 (465.9-870.9) 657.9(390.7-878.0)657.9 (390.7-878.0) 678.0(535.0-843.0)678.0 (535.0-843.0) 0.121 0.121 Average of average hounsfield unit value for 3 point of lesion in MRI (median, IQR)Average of average hounsfield unit value for 3 points of lesion in MRI (median, IQR) 509.7(346.8-662.3)509.7 (346.8-662.3) 506.3(322.0-665.7)506.3 (322.0-665.7) 534.0(402.6-661.8)534.0 (402.6-661.8) 0.002 0.002 Average of standard deviation for hounsfield unit value for 3 point of lesion in MRI (median, IQR)Average of standard deviation for hounsfield unit value for 3 points of lesion in MRI (median, IQR) 57.6(49.3-74.7)57.6 (49.3-74.7) 57.2(47.5-73.9)57.2 (47.5-73.9) 61.6(53.9-75.1)61.6 (53.9-75.1) 0.011 0.011 R value for 1 point of lesion in MRI (median, IQR)R value for 1 point of lesion in MRI (median, IQR) 66(54.0-80.0)66 (54.0-80.0) 73.0(54.0-85.0)73.0 (54.0-85.0) 62.0(53.0-69.0)62.0 (53.0-69.0) <0.001<0.001 Fixel number for 1 point of lesion in MRI (median, IQR)Fixel number for 1 point of lesion in MRI (median, IQR) 185.0(96.5-283.0)185.0 (96.5-283.0) 180.5(98.0-273.0)180.5 (98.0-273.0) 200.0(96.0-284-0)200.0 (96.0-284-0) 0.850 0.850 Minimum hounsfield unit value for 1 point of lesion in MRI (median, IQR)Minimum hounsfield unit value for 1 point of lesion in MRI (median, IQR) 239.0(374.5-460.0)239.0 (374.5-460.0) 361.0(173.0-454.0)361.0 (173.0-454.0) 382.0(295.0-465.0)382.0 (295.0-465.0) 0.004 0.004 Maximum hounsfield unit value for 1 point of lesion in MRI (median, IQR)Maximum hounsfield unit value for 1 point of lesion in MRI (median, IQR) 670.5(477.0-855.0)670.5 (477.0-855.0) 657.5(403.0-869.0)657.5 (403.0-869.0) 693.5(535.0-834.0)693.5 (535.0-834.0) 0.068 0.068 Average hounsfield unit value for 1 point of lesion in MRI (median, IQR)Average hounsfield unit value for 1 point in lesion in MRI (median, IQR) 508.5(354.5-656.0)508.5 (354.5-656.0) 500.0(316.0-656.0)500.0 (316.0-656.0) 539.5(411.0-651.0)539.5 (411.0-651.0) 0.004 0.004 Standard deviation for hounsfield unit value for 1 point of lesion in MRI (median, IQR)Standard deviation for hounsfield unit value for 1 point of lesion in MRI (median, IQR) 58.0(48.0-73.0)58.0 (48.0-73.0) 56.0(46.0-70.0)56.0 (46.0-70.0) 59.0(50.0-74.0)59.0 (50.0-74.0) 0.011 0.011 PathologyPathology Gleason score 7 미만 (n. %)Gleason score less than 7 (n.%) 20(66.7)20 (66.7) 20(66.7)20 (66.7) Gleason score 7-10 (n. %)Gleason score 7-10 (n.%) 10(33.3)10 (33.3) 10(33.3)10 (33.3)

표 1에 따른 각각의 모든 요인(factor)들에 따른 R-square 값, 신뢰성 여부(95% CI, P value), 콕스비례위험모형(Cox proportional hazard model)을 이용하여 HR을 확인하였다. 그 결과는 하기 표 2 내지 4에 나타내었다. 의학적인 요소들(Clinical factors)만을 고려하였을 때 R square 값은 0.753(95% CI: 0.707-0.798)이고, MRI를 이용하여 획득한 영상 요소들(Image factors)만을 고려하였을 때 R square 값은 0.849(95% CI: 0.812-0.885)이고, 의학적인 요소들과 영상 요소들을 모두 합하여 복합 분석한 경우의 R square 값은 0.883(95% CI: 0.852-0.914)로 복합 분석하는 경우 MRI를 통한 전립선암 진단에 관한 정확성이 증가되는 것을 확인하였다.HR was confirmed using R-square, reliability (95% CI, P value), and Cox proportional hazards model with all the factors according to Table 1. The results are shown in Tables 2 to 4 below. The R square value was 0.753 (95% CI: 0.707-0.798) when only the clinical factors were considered, and the R square value was 0.849 when only the image factors obtained using MRI were considered (95% CI: 0.812-0.885). When combined analysis of medical factors and image elements combined, R square value was 0.883 (95% CI: 0.852-0.914) And the accuracy of diagnosis was increased.

　 The factors related with Total : Nagelkerke R Square (0.883)The factors related with Total: Nagelkerke R Square (0.883) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp Age*Age * 1.087 1.087 1.063 1.063 1.112 1.112 <0.001<0.001 1.132 1.132 1.096 1.096 1.168 1.168 <0.001<0.001 BMI*BMI * 0.909 0.909 0.868 0.868 0.953 0.953 <0.001<0.001 Diabetes mellitus Diabetes mellitus 1.900 1.900 1.188 1.188 3.038 3.038 0.007 0.007 number of prostate biopsy history (≥2 vs. 1)number of prostate biopsy history (≥2 vs. 1) 1.800 1.800 1.113 1.113 2.912 2.912 0.017 0.017 2.368 2.368 1.277 1.277 4.394 4.394 0.006 0.006 PSA*PSA * 0.981 0.981 0.962 0.962 1.001 1.001 0.063 0.063 Prostate volume*Prostate volume * 0.941 0.941 0.929 0.929 0.953 0.953 <0.001<0.001 0.939 0.939 0.924 0.924 0.953 0.953 <0.001<0.001 PSA density*PSA density * 15.005 15.005 5.440 5.440 41.386 41.386 <0.001<0.001 78.066 78.066 17.905 17.905 340.372 340.372 <0.001<0.001 Average of R value for 3 point of lesion in MRI*Average of R value for 3 points of lesion in MRI * 0.972 0.972 0.962 0.962 0.981 0.981 <0.001<0.001 0.963 0.963 0.950 0.950 0.976 0.976 <0.001<0.001 Average of fixel number for 3 point of lesion in MRI*Average of fixer number for 3 points of lesion in MRI * 0.999 0.999 0.998 0.998 1.000 1,000 0.078 0.078 Average of minimum hounsfield unit value for 3 point of lesion in MRI*Average of minimum hounsfield unit value for 3 points of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.010 0.010 1.001 1.001 0.999 0.999 1.003 1.003 0.215 0.215 Average of maximum hounsfield unit value for 3 point of lesion in MRI*Average of maximum hounsfield unit value for 3 points of lesion in MRI * 1.000 1,000 1.000 1,000 1.001 1.001 0.228 0.228 Average of average hounsfield unit value for 3 point of lesion in MRI*Average of average hounsfield unit value for 3 points of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.017 0.017 0.998 0.998 0.994 0.994 1.003 1.003 0.541 0.541 Average of standard deviation for hounsfield unit value for 3 point of lesion in MRI*Average of standard deviation for hounsfield unit value for 3 points of lesion in MRI * 0.996 0.996 0.992 0.992 1.000 1,000 0.062 0.062 R value for 1 point of lesion in MRI*R value for 1 point of lesion in MRI * 0.974 0.974 0.965 0.965 0.984 0.984 <0.001<0.001 1.000 1,000 0.962 0.962 1.040 1.040 0.984 0.984 Fixel number for 1 point of lesion in MRI*Fixel number for 1 point of lesion in MRI * 0.999 0.999 0.998 0.998 1.000 1,000 0.079 0.079 Minimum hounsfield unit value for 1 point of lesion in MRI*Minimum hounsfield unit value for 1 point of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.011 0.011 1.000 1,000 0.993 0.993 1.007 1.007 0.994 0.994 Maximum hounsfield unit value for 1 point of lesion in MRI*Maximum hounsfield unit value for 1 point of lesion in MRI * 1.000 1,000 1.000 1,000 1.001 1.001 0.246 0.246 Average hounsfield unit value for 1 point of lesion in MRI*Average hounsfield unit value for 1 point in lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.018 0.018 1.000 1,000 0.988 0.988 1.013 1.013 0.987 0.987 Standard deviation for hounsfield unit value for 1 point of lesion in MRI*Standard deviation for hounsfield unit value for 1 point in lesion in MRI * 0.998 0.998 0.996 0.996 1.001 1.001 0.261 0.261

　 The factors related with prostate : Nagelkerke R Square (0.753)The factors related with prostate: Nagelkerke R Square (0.753) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp Age*Age * 1.087 1.087 1.063 1.063 1.112 1.112 <0.001<0.001 1.157 1.157 1.120 1.120 1.195 1.195 <0.001<0.001 BMI*BMI * 0.909 0.909 0.868 0.868 0.953 0.953 <0.001<0.001 0.981 0.981 0.921 0.921 1.045 1.045 0.554 0.554 Diabetes mellitus Diabetes mellitus 1.900 1.900 1.188 1.188 3.038 3.038 0.007 0.007 0.348 0.348 0.184 0.184 0.658 0.658 0.001 0.001 number of prostate biopsy history (≥2 vs. 1)number of prostate biopsy history (≥2 vs. 1) 1.800 1.800 1.113 1.113 2.912 2.912 0.017 0.017 1.198 1.198 0.610 0.610 2.354 2.354 0.600 0.600 PSA*PSA * 0.981 0.981 0.962 0.962 1.001 1.001 0.063 0.063 Prostate volume*Prostate volume * 0.941 0.941 0.929 0.929 0.953 0.953 <0.001<0.001 0.935 0.935 0.921 0.921 0.950 0.950 <0.001<0.001 PSA density*PSA density * 15.005 15.005 5.440 5.440 41.386 41.386 <0.001<0.001 56.084 56.084 12.746 12.746 246.765 246.765 <0.001<0.001

　 The factors related with Image : Nagelkerke R Square (0.849)The factors related with Image: Nagelkerke R Square (0.849) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp Average of R value for 3 point of lesion in MRI*Average of R value for 3 points of lesion in MRI * 0.972 0.972 0.962 0.962 0.981 0.981 <0.001<0.001 0.960 0.960 0.948 0.948 0.972 0.972 <0.001<0.001 Average of fixel number for 3 point of lesion in MRI*Average of fixer number for 3 points of lesion in MRI * 0.999 0.999 0.998 0.998 1.000 1,000 0.078 0.078 Average of minimum hounsfield unit value for 3 point of lesion in MRI*Average of minimum hounsfield unit value for 3 points of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.010 0.010 1.003 1.003 1.002 1.002 1.004 1.004 <0.001<0.001 Average of maximum hounsfield unit value for 3 point of lesion in MRI*Average of maximum hounsfield unit value for 3 points of lesion in MRI * 1.000 1,000 1.000 1,000 1.001 1.001 0.228 0.228 Average of average hounsfield unit value for 3 point of lesion in MRI*Average of average hounsfield unit value for 3 points of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.017 0.017 0.999 0.999 0.995 0.995 1.002 1.002 0.444 0.444 Average of standard deviation for hounsfield unit value for 3 point of lesion in MRI*Average of standard deviation for hounsfield unit value for 3 points of lesion in MRI * 0.996 0.996 0.992 0.992 1.000 1,000 0.062 0.062 R value for 1 point of lesion in MRI*R value for 1 point of lesion in MRI * 0.974 0.974 0.965 0.965 0.984 0.984 <0.001<0.001 1.000 1,000 0.970 0.970 1.032 1.032 0.976 0.976 Fixel number for 1 point of lesion in MRI*Fixel number for 1 point of lesion in MRI * 0.999 0.999 0.998 0.998 1.000 1,000 0.079 0.079 Minimum hounsfield unit value for 1 point of lesion in MRI*Minimum hounsfield unit value for 1 point of lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.011 0.011 1.000 1,000 0.992 0.992 1.008 1.008 0.986 0.986 Maximum hounsfield unit value for 1 point of lesion in MRI*Maximum hounsfield unit value for 1 point of lesion in MRI * 1.000 1,000 1.000 1,000 1.001 1.001 0.246 0.246 Average hounsfield unit value for 1 point of lesion in MRI*Average hounsfield unit value for 1 point in lesion in MRI * 1.001 1.001 1.000 1,000 1.002 1.002 0.018 0.018 1.000 1,000 0.990 0.990 1.010 1.010 0.984 0.984 Standard deviation for hounsfield unit value for 1 point of lesion in MRI*Standard deviation for hounsfield unit value for 1 point in lesion in MRI * 0.998 0.998 0.996 0.996 1.001 1.001 0.261 0.261

글리슨 등급에 따른 MRI 결과는 표 5에 나타내었다. MRI를 이용하여 전립선암의 악성도를 감별한 분석 결과에 대하여 각각의 모든 요인(factor)들에 따른 R-square 값, 신뢰성 여부(95% CI, P value), 콕스비례위험모형(Cox proportional hazard model)을 이용하여 HR을 확인하였다. 그 결과는 하기 표 6 내지 9에 나타내었다. 글리슨 등급에 따라 전립선암의 악성도를 분석한 결과, 의학적인 요소들만을 대상으로한 R square 값은 0.684이고, 영상 요소들만을 대상으로 하였을 때의 R square 값은 0.842이며, 의학적인 요소들과 영상 요소들을 복합적으로 분석한 경우의 R square 값은 0.874인 것을 확인하였다. 상기 결과를 통하여, MRI을 이용한 영상 분석 결과를 복합적으로 이용할 경우 전립선암의 악성도 측정에 대한 정확성이 현저히 증가되는 것을 확인할 수 있었으며, 본원 발명의 "(2) MRI를 이용한 전립선암의 악성도 진단용 알고리즘"을 이용하여 전립선암의 악성도를 진단할 수 있다는 것을 확인할 수 있었다.MRI results according to Gleason grade are shown in Table 5. (95% CI, P value), Cox proportional hazards (Cox proportional hazards), and Cox proportional hazards (Cox proportional hazards) were used for the analysis of malignancy of prostate cancer using MRI model) was used to confirm HR. The results are shown in Tables 6 to 9 below. According to the Gleason grade, the R square value for the medical factors was 0.684, the R square value for the image elements only was 0.842, and the medical factors The R square value of image elements was 0.874. From the above results, it can be seen that the accuracy of the measurement of the malignancy of the prostate cancer is significantly increased when the image analysis results using the MRI are used in combination. In the "(2) diagnosis of malignancy of prostate cancer using MRI Algorithm can be used to diagnose malignancy of prostate cancer.

　 ≤ GS 6≤ GS 6 GS 7-10GS 7-10 p-valuep-value No. of cases No. of cases 7 (30.4)7 (30.4) 16 (69.6)16 (69.6) Age (years) Age (years) 65.7 (60.1-66.2)65.7 (60.1-66.2) 71.8 (62.6-79.2)71.8 (62.6-79.2) <0.001<0.001 Previous prostate biopsy history (yes)Previous prostate biopsy history (yes) 6 (85.7)6 (85.7) 14 (87.5)14 (87.5) 0.2860.286 PSA (ng/ml) PSA (ng / ml) 6.42 (4.99-9.28)6.42 (4.99-9.28) 9.53 (8.16-13.79)9.53 (8.16-13.79) <0.001<0.001 Prostate volume (cc) Prostate volume (cc) 32.0 (25.5-42.9)32.0 (25.5-42.9) 33.8 (25.1-43.2)33.8 (25.1-43.2) <0.001<0.001 PSA density (ng/ml/cc) PSA density (ng / ml / cc) 0.22 (0.18-0.32)0.22 (0.18-0.32) 0.31 (0.20-0.41)0.31 (0.20-0.41) <0.001<0.001 PSA velocity (ng/ml/yr)PSA velocity (ng / ml / yr) 0.52 (-0.47-1.13)0.52 (-0.47-1.13) 0.74 (0.09-3.09)0.74 (0.09-3.09) 0.0240.024 Pixel R Pixel R 56.0 (54.7-60.3)56.0 (54.7-60.3) 66.0 (54.0-77.0)66.0 (54.0-77.0) 0.7000.700 Pixel number Pixel number 290.0 (159.3-538.3)290.0 (159.3-538.3) 201.3 (97.3-297.0)201.3 (97.3-297.0) 0.5980.598 Minimum HUMinimum HU 371.7 (304.7-387.3)371.7 (304.7-387.3) 457.0 (290.7-497.3)457.0 (290.7-497.3) <0.001<0.001 Maximum HU Maximum HU 707.0 (583.3-811.0)707.0 (583.3-811.0) 752.7 (532.3 (864.7)752.7 (532.3 (864.7) 0.0200.020 Average HU Average HU 524.5 (425.5-599.1)524.5 (425.5-599.1) 632.2 (402.6-664.5)632.2 (402.6-664.5) <0.001<0.001 SD of HU SD of HU 52.2 (53.0-90.2)52.2 (53.0-90.2) 64.3 (54.9-81.5)64.3 (54.9-81.5) <0.001<0.001 Clinical stageClinical stage <0.001<0.001 T2 T2 7 (100.0)7 (100.0) 10 (63.8)10 (63.8) T3 T3 0 (0.0)0 (0.0) 6 (36.2)6 (36.2)

　 The factors related with Total : Nagelkerke R Square (0.952)The factors related with Total: Nagelkerke R Square (0.952) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp Age*Age * 1.110 1.110 1.067 1.067 1.155 1.155 <0.001<0.001 12.432 12.432 2.678 2.678 57.707 57.707 0.001 0.001 BMI*BMI * 1.271 1.271 1.131 1.131 1.428 1.428 <0.001<0.001 Diabetes mellitus Diabetes mellitus 2.133 2.133 1.077 1.077 4.227 4.227 0.030 0.030 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.002 number of prostate biopsy history (≥2 vs. 1)number of prostate biopsy history (≥2 vs. 1) 0.706 0.706 0.299 0.299 1.665 1.665 0.426 0.426 PSA*PSA * 1.212 1.212 1.146 1.146 1.281 1.281 <0.001<0.001 6.145 6.145 2.059 2.059 18.334 18.334 0.001 0.001 Prostate volume*Prostate volume * 1.026 1.026 1.002 1.002 1.051 1.051 0.035 0.035 PSA density*PSA density * 99.678 99.678 19.118 19.118 519.705 519.705 <0.001<0.001 　 　 　 　 Average of R value for 3 point of lesion in MRI*Average of R value for 3 points of lesion in MRI * 1.100 1.100 1.067 1.067 1.134 1.134 <0.001<0.001 　 　 　 　 Average of fixel number for 3 point of lesion in MRI*Average of fixer number for 3 points of lesion in MRI * 0.997 0.997 0.994 0.994 0.999 0.999 0.012 0.012 　 　 　 　 Average of minimum hounsfield unit value for 3 point of lesion in MRI*Average of minimum hounsfield unit value for 3 points of lesion in MRI * 1.003 1.003 1.001 1.001 1.006 1.006 0.006 0.006 　 　 　 　 Average of maximum hounsfield unit value for 3 point of lesion in MRI*Average of maximum hounsfield unit value for 3 points of lesion in MRI * 1.002 1.002 1.000 1,000 1.004 1.004 0.018 0.018 　 　 　 　 Average of average hounsfield unit value for 3 point of lesion in MRI*Average of average hounsfield unit value for 3 points of lesion in MRI * 1.004 1.004 1.002 1.002 1.006 1.006 0.001 0.001 　 　 　 　 Average of standard deviation for hounsfield unit value for 3 point of lesion in MRI*Average of standard deviation for hounsfield unit value for 3 points of lesion in MRI * 1.047 1.047 1.027 1.027 1.067 1.067 <0.001<0.001 　 　 　 　 R value for 1 point of lesion in MRI*R value for 1 point of lesion in MRI * 1.084 1.084 1.054 1.054 1.114 1.114 <0.001<0.001 2.296 2.296 1.349 1.349 3.910 3.910 0.002 0.002 Fixel number for 1 point of lesion in MRI*Fixel number for 1 point of lesion in MRI * 0.997 0.997 0.995 0.995 0.999 0.999 0.014 0.014 Minimum hounsfield unit value for 1 point of lesion in MRI*Minimum hounsfield unit value for 1 point of lesion in MRI * 1.003 1.003 1.001 1.001 1.005 1.005 0.007 0.007 Maximum hounsfield unit value for 1 point of lesion in MRI*Maximum hounsfield unit value for 1 point of lesion in MRI * 1.002 1.002 1.000 1,000 1.003 1.003 0.033 0.033 Average hounsfield unit value for 1 point of lesion in MRI*Average hounsfield unit value for 1 point in lesion in MRI * 1.003 1.003 1.001 1.001 1.005 1.005 0.001 0.001 Standard deviation for hounsfield unit value for 1 point of lesion in MRI*Standard deviation for hounsfield unit value for 1 point in lesion in MRI * 1.031 1.031 1.015 1.015 1.047 1.047 <0.001<0.001 0.851 0.851 0.767 0.767 0.944 0.944 0.002 0.002

　 The factors related with prostate : Nagelkerke R Square (0.726)The factors related with prostate: Nagelkerke R Square (0.726) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp Age*Age * 1.110 1.110 1.067 1.067 1.155 1.155 <0.001<0.001 1.563 1.563 1.281 1.281 1.907 1.907 <0.001<0.001 BMI*BMI * 1.271 1.271 1.131 1.131 1.428 1.428 <0.001<0.001 1.338 1.338 1.132 1.132 1.581 1.581 0.001 0.001 Diabetes mellitus Diabetes mellitus 2.133 2.133 1.077 1.077 4.227 4.227 0.030 0.030 0.025 0.025 0.003 0.003 number of prostate biopsy history (≥2 vs. 1)number of prostate biopsy history (≥2 vs. 1) 0.706 0.706 0.299 0.299 1.665 1.665 0.426 0.426 0.228 0.228 0.001 0.001 PSA*PSA * 1.212 1.212 1.146 1.146 1.281 1.281 <0.001<0.001 1.688 1.688 1.399 1.399 2.035 2.035 <0.001<0.001 Prostate volume*Prostate volume * 1.026 1.026 1.002 1.002 1.051 1.051 0.035 0.035 0.951 0.951 0.904 0.904 1.000 1,000 0.049 0.049 PSA density*PSA density * 99.678 99.678 19.118 19.118 519.705 519.705 <0.001<0.001

　 The factors related with Image : Nagelkerke R Square (0.524)The factors related with Image: Nagelkerke R Square (0.524) UnivariateUnivariate MultivariateMultivariate 　 HRHR 95% CI95% CI pp HRHR 95% CI95% CI pp R value for 1 point of lesion in MRI*R value for 1 point of lesion in MRI * 1.084 1.084 1.054 1.054 1.114 1.114 <0.001<0.001 1.139 1.139 1.094 1.094 1.086 1.086 <0.001<0.001 Fixel number for 1 point of lesion in MRI*Fixel number for 1 point of lesion in MRI * 0.997 0.997 0.995 0.995 0.999 0.999 0.014 0.014 0.986 0.986 0.981 0.981 0.991 0.991 <0.001<0.001 Minimum hounsfield unit value for 1 point of lesion in MRI*Minimum hounsfield unit value for 1 point of lesion in MRI * 1.003 1.003 1.001 1.001 1.005 1.005 0.007 0.007 0.985 0.985 0.974 0.974 0.997 0.997 0.012 0.012 Maximum hounsfield unit value for 1 point of lesion in MRI*Maximum hounsfield unit value for 1 point of lesion in MRI * 1.002 1.002 1.000 1,000 1.003 1.003 0.033 0.033 Average hounsfield unit value for 1 point of lesion in MRI*Average hounsfield unit value for 1 point in lesion in MRI * 1.003 1.003 1.001 1.001 1.005 1.005 0.001 0.001 1.017 1.017 1.006 1.006 1.028 1.028 0.002 0.002 Standard deviation for hounsfield unit value for 1 point of lesion in MRI*Standard deviation for hounsfield unit value for 1 point in lesion in MRI * 1.031 1.031 1.015 1.015 1.047 1.047 <0.001<0.001 　 　 　 　

ParameterParameter AUCAUC 95% CI95% CI Prostate cancer detectionProstate cancer detection Clinical parameter Clinical parameter 0.7530.753 0.707-0.7980.707-0.798 Image parameterImage parameter 0.8490.849 0.812-0.8850.812-0.885 Combination of clinical and image parameter Combination of clinical and image parameters 0.8830.883 0.852-0.9140.852-0.914 High grade diseaseHigh grade disease Clinical parameter Clinical parameter 0.6840.684 0.633-0.7340.633-0.734 Image parameterImage parameter 0.8420.842 0.807-0.8770.807-0.877 Combination of clinical and image parameter Combination of clinical and image parameters 0.8740.874 0.840-0.9080.840-0.908

이상으로 본 발명의 특정한 부분을 상세히 기술하였는바, 당업계의 통상의 지식을 가진 자에게 있어서 이러한 구체적인 기술은 단지 바람직한 구현 예일 뿐이며, 이에 본 발명의 범위가 제한되는 것이 아닌 점은 명백하다. 따라서 본 발명의 실질적인 범위는 첨부된 청구항과 그의 등가물에 의하여 정의된다고 할 것이다.While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the present invention. It is therefore intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

Claims (6)

(a) 자기공명영상(magnetic resonance imaging; MRI)를 통해 전립선 영상을 획득하는 단계;
(b) 상기 획득된 영상에서 병변(lesion) 영역의 영상을 추출하는 단계;
(c) 상기 추출된 영상에서 적색 평균값(pixel R), 병변 역역의 픽셀수(pixel count), 하운스필드 유닛의 최소값(pixel minimum Hounsfield unit), 하운스필드 유닛의 최대값(pixel maximum Hounsfield unit), 하운스필드 유닛의 평균값(pixel average Hounsfield unit), 및 하운스필드 유닛의 표준편차(pixel standard deviation Hounsfield unit)를 각각 산출하는 단계; 및
(d) 상기 산출된 값을 수식 "X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계를 포함하는, 전립선암 진단에 관한 정보제공방법에 있어서,
상기 X 값은 0 내지 1의 값이며, 상기 K1은 -0.052 내지 0.217의 값이며, 상기 K2는 -0.035 내지 0.003의 값이며, 상기 K3은 -0.086 내지 0.009의 값이며, 상기 K4는 -0.015 내지 -0.003의 값이며, 상기 K5는 -0.006 내지 0.108의 값이며, 상기 K6은 -0.098 내지 0의 값이며, 상기 K7은 -18.219 내지 2.414의 값인, 전립선암 진단에 관한 정보제공방법.(a) acquiring a prostate image through magnetic resonance imaging (MRI);
(b) extracting an image of a lesion region from the acquired image;
(c) calculating a red average value (pixel R), a pixel count (pixel count), a minimum minimum pixel unit, and a maximum maximum pixel unit (pixel maximum pixel unit) Calculating a pixel average Hounsfield unit and a pixel standard deviation Hounsfield unit of the Hounsfield unit, respectively; And
(d) Calculate the calculated value as: "X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 & quot ;, the method comprising:
The X value is a value of 0 to 1, wherein K 1 is the value of -0.052 to 0.217, wherein the K 2 is a value of -0.035 to 0.003, wherein K 3 is a value of -0.086 to 0.009, wherein the K 4 is a value of -0.015 to -0.003, -0.006 to the K 5 has a value of 0.108, wherein the K 6 is -0.098 to a value of 0, wherein K is about 7 value, the diagnosis of prostate cancer to 2.414 -18.219 Information delivery method. 제 1 항에 있어서,
상기 (d) 단계의 수식의 K1은 -0.050 내지 -0.052이며, K2는 0.001 내지 0.003이며, K3은 0.007 내지 0.009이며, K4는 -0.003 내지 -0.005이며, K5는 -0.004 내지 -0.006이며, K6은 0이며, K7은 2.412 내지 2.414인 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법에 있어서,
상기 X값이 -3.1 이상의 값을 가질 때 전립선암으로 진단하는 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법.The method according to claim 1,
K 1 of the formula (d) is -0.050 to -0.052, K 2 is 0.001 to 0.003, K 3 is 0.007 to 0.009, K 4 is -0.003 to -0.005, K 5 is -0.004 to -0.006 is, K is 0 6, K 7 is provided in the information about cancer, prostate cancer diagnosis, characterized in that 2.412 to 2.414 method,
And diagnosing prostate cancer when the X value is equal to or greater than -3.1. 제 1 항에 있어서,
상기 정보제공방법은 추가로 하기 단계를 포함하는, 전립선암 진단에 관한 정보제공방법:
(e) 상기 수식에 의하여 전립선암으로 선별된 환자에 대하여 수식 “X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계로서,
상기 (e) 단계의 수식의 K1은 0.215 내지 0.217이며, K2는 -0.033 내지 -0.035이며, K3은 -0.084 내지 -0.086이며, K4는 -0.013 내지 -0.015이며, K5는 0.106 내지 0.108이며, K6은 -0.096 내지 -0.098이며, K7은 -18.217 내지 -18.219인 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법에 있어서,
상기 X값이 -1 이상의 값을 가질 때 전립선암의 글리슨 등급(Gleason Score)이 7 내지 10으로 진단하는 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법.The method according to claim 1,
Wherein the information providing method further comprises the steps of: providing information about diagnosis of prostate cancer,
(e) For a patient selected by prostate cancer according to the above formula, " X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 "
K 1 of the equation (e) is 0.215 to 0.217, K 2 is -0.033 to -0.035, K 3 is -0.084 to -0.086, K 4 is -0.013 to -0.015, and K 5 is 0.106 and to 0.108, and K 6 are -0.096 to -0.098, K 7 is provided in the information about cancer, prostate cancer diagnosis, characterized in that to -18.217 -18.219 method,
Wherein the Gleason score of the prostate cancer is 7 to 10 when the value X has a value of -1 or more. (a) 자기공명영상(magnetic resonance imaging; MRI)을 통해 획득한 전립선 영상을 저장하는 저장부;
(b) 상기 영상 중 병변(lesion) 영역의 영상을 선별하는 필터부;
(c) 상기 선별된 영상의 적색 평균값(pixel R), 병변 역역의 픽셀수(pixel count), 하운스필드 유닛의 최소값(pixel minimum Hounsfield unit), 하운스필드 유닛의 최대값(pixel maximum Hounsfield unit), 하운스필드 유닛의 평균값(pixel average Hounsfield unit), 및 하운스필드 유닛의 표준편차(pixel standard deviation Hounsfield unit)를 각각 산출하는 산출부; 및
(d) 상기 산출된 값을 수식 "X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 대입하는 연산부; 및
(e) 상기 연산부의 결과값을 보여주는 표시부로 구성되는 전립선암 진단 장치에 있어서,
상기 X 값은 0 내지 1의 값이며, 상기 K1은 -0.052 내지 0.217의 값이며, 상기 K2는 -0.035 내지 0.003의 값이며, 상기 K3은 -0.086 내지 0.009의 값이며, 상기 K4는 -0.015 내지 -0.003의 값이며, 상기 K5는 -0.006 내지 0.108의 값이며, 상기 K6은 -0.098 내지 0의 값이며, 상기 K7은 -18.219 내지 2.414의 값인, 전립선암 진단 장치.(a) a storage unit for storing a prostate image acquired through magnetic resonance imaging (MRI);
(b) a filter unit for selecting an image in a lesion region of the image;
(c) calculating a red average value (pixel R), a pixel count (pixel count), a minimum minimum pixel value, A calculation unit for calculating a pixel average Hounsfield unit and a pixel standard deviation Hounsfield unit of the Hounsfield unit, And
(d) Calculate the calculated value as: "X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 "; And
(e) a display unit for displaying a result of the operation unit,
The X value is a value of 0 to 1, wherein K 1 is the value of -0.052 to 0.217, wherein the K 2 is a value of -0.035 to 0.003, wherein K 3 is a value of -0.086 to 0.009, wherein the K 4 is -0.015 to -0.003, and the value of the K is 5 and the value of -0.006 to 0.108, wherein the K 6 is -0.098 to a value of 0, the value of K is 7 -18.219 2.414 to, prostate cancer diagnostic device. 제 4 항에 있어서,
상기 (d) 단계의 수식의 K1은 -0.050 내지 -0.052이며, K2는 0.001 내지 0.003이며, K3은 0.007 내지 0.009이며, K4는 -0.003 내지 -0.005이며, K5는 -0.004 내지 -0.006이며, K6은 0이며, K7은 2.412 내지 2.414인 것을 특징으로 하는, 전립선암 진단 장치에 있어서,
상기 X값이 -3.1 이상의 값을 가질 때 전립선암으로 진단하는 것을 특징으로 하는, 전립선암 진단 장치.5. The method of claim 4,
K 1 of the formula (d) is -0.050 to -0.052, K 2 is 0.001 to 0.003, K 3 is 0.007 to 0.009, K 4 is -0.003 to -0.005, K 5 is -0.004 to -0.006 is, K is 0 6, K 7 is in prostate cancer diagnostic device, characterized in that 2.412 to 2.414,
Wherein the diagnosis of prostate cancer is made when the X value is equal to or greater than -3.1. 제 4 항에 있어서,
상기 진단 장치는 추가로 하기 단계에 의해 글리슨 등급을 진단하는 것을 특징으로 하는 전립선암 진단 장치:
e) 상기 수식에 의하여 전립선암으로 선별된 환자에 대하여 수식 “X= K1*pixel R + K2*pixel count + K3* pixel minimum Hounsfield unit + K4*pixel maximum Hounsfield unit + K5*pixel average Hounsfield unit + K6*pixel standard deviation Hounsfield unit + K7"에 적용하는 단계로서,
상기 (e) 단계의 수식의 K1은 0.215 내지 0.217이며, K2는 -0.033 내지 -0.035이며, K3은 -0.084 내지 -0.086이며, K4는 -0.013 내지 -0.015이며, K5는 0.106 내지 0.108이며, K6은 -0.096 내지 -0.098이며, K7은 -18.217 내지 -18.219인 것을 특징으로 하는, 전립선암 진단에 관한 정보제공방법에 있어서,
상기 X값이 -1 이상의 값을 가질 때 전립선암의 글리슨 등급(Gleason Score)이 7 내지 10으로 진단하는 것을 특징으로 하는, 전립선암 진단 장치.5. The method of claim 4,
Wherein the diagnostic device further diagnoses the Gleason grade by the following steps:
e) For a patient selected as prostate cancer by the above formula, the formula " X = K 1 * pixel R + K 2 * pixel count + K 3 * pixel minimum Hounsfield unit + K 4 * pixel maximum Hounsfield unit + K 5 * pixel average Hounsfield unit + K 6 * pixel standard deviation Hounsfield unit + K 7 "
K 1 of the equation (e) is 0.215 to 0.217, K 2 is -0.033 to -0.035, K 3 is -0.084 to -0.086, K 4 is -0.013 to -0.015, and K 5 is 0.106 and to 0.108, and K 6 are -0.096 to -0.098, K 7 is provided in the information about cancer, prostate cancer diagnosis, characterized in that to -18.217 -18.219 method,
Wherein the Gleason score of the prostate cancer is diagnosed as 7 to 10 when the X value has a value of -1 or more.
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