KR101862167B1 - Method for providing the information for diagnosing of the disease related to bladder - Google Patents

Abstract

The present invention relates to a method for providing information about a bladder-related disease, which can be applied to various fields using a bladder endoscope because bladder cancer, cystitis, and border bladder cancer can be diagnosed by using an image obtained using the bladder endoscope and various information on whether the bladder cancer reoccurs and a treatment is failed can be provided, and it is expected to be applied to various endoscopes in addition to the bladder endoscope. Also, it is expected that an evaluation result is very easily understood and applied by objective calculation. The method for providing information about a bladder-related disease includes the steps of: obtaining the image through the bladder endoscope; extracting an image of a lesion region from the obtained image; calculating an average value of each of red, green, and blue in the extracted image; and applying the calculated average value to a formula.

Description

The present invention relates to a method for providing information on bladder-related diseases,

The present invention relates to a method for providing information on a bladder-related disease.

The urinary bladder is a pouch-like organ that stores urine from the kidney and discharges it when it reaches a certain amount. It is a part of the muscular enlargement in the urinary tract. The shape, size, and thickness of the wall can change according to the amount of urine. Major diseases related to the bladder include carcinoma in situ, bladder cancer, neurogenic bladder (NB), cystitis, interstitial cystitis (IC), bladder rupture, Bladder stones, and bladder tuberculosis are known.

Of these, bladder cancer is the most common cancer of the genitourinary tract. It is known that smoking and various chemical substances (dyeing paints such as leather, air pollutants, artificial sweeteners, nitrate) are absorbed into the body and excreted in the urine, thereby stimulating the bladder wall and causing cancer. The traditional method of diagnosing bladder cancer is to detect abnormal cells in the urine, but this is very low accuracy. In addition, a catheter (catheter) is inserted into the bladder to examine suspicious tissue, and there is a cystoscopy that examines the test. In this case, it is relatively accurate as an invasive method, but due to the risk of bladder perforation, This is difficult because it can not be easily administered to patients. Recently, various markers for effectively diagnosing bladder cancer have been developed, but there are few examples that have been commercialized yet (Korean Patent No. 10-1573467). In addition, there is a method of diagnosing using a medical medical image such as an endoscope. However, since the diagnosis method depends only on the subjective visual judgment of the practitioner, the accuracy differs according to the diagnostic method depending on the individual.

Therefore, the inventors of the present invention have found that by using an endoscope which is most easily used for diagnosing a bladder-related disease, the diagnosis using the endoscope (upper gastrointestinal tract (esophagus / stomach / duodenum) and lower gastrointestinal tract (rectum / colon) We have tried to develop a diagnostic algorithm that can increase both the objective and accuracy of diagnosis.

Disclosure of Invention Technical Problem [8] Accordingly, the present invention has been made keeping in mind the above problems occurring in the prior art, and an object of the present invention is to provide a method for providing information on bladder-related diseases using an image acquired using a bladder endoscope, .

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.

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.

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.

In the present specification, the term " bladder-related disease " refers collectively to all diseases occurring in the bladder, preferably a bladder that can be examined through a cystoscopy, more preferably a neurogenic bladder, Interstitial cystitis, bladder cancer, border bladder cancer, and the like, but the present invention is not limited thereto.

As used herein, the term " lesion " refers to a tissue, body fluid, or the like that has undergone a change due to pathological action. Preferably, the endoscopic lesion is different from other normal tissues in shade, Refers to any site that differs under endoscopy from other tissues, such as being observed, having a different light transmittance, or having microvessels concentrated, but not limited to, if the difference can be observed under endoscopy. In other words, it is also called a lesion.

In the present specification, the "method of providing information on the bladder-related disease" refers to a method of providing information on diseases occurring in the bladder, and preferably includes information on diagnosis of bladder cancer, cystitis, , Prediction of treatment efficacy, prediction of outcome by bladder washing cytology, prediction of cancer progression, and the like, but not limited to, information on bladder-related diseases.

In the present specification, the term "bladder cancer" refers to a tumor originating from the bladder, and is defined as a malignant tumor pathologically through a histological examination of a lesion in the bladder. The term "bladder cancer" In situ is an intraepithelial carcinoma in which the epithelial cells constituting the epithelium are caused by malignant pathology and develop cancer. The epithelial cells reside in the epithelium and can not escape from the underlying basement membrane. It can show a redness spot. In other words, it is also called superficial bladder cancer. Low grade bladder cancer refers to border bladder cancer or benign bladder cancer. Benign bladder cancer is a cancer that does not metastasize to other tissues and can be easily removed because of no risk of recurrence. Malignant bladder cancer, on the other hand, , Metastasis, and recurrence of cancer. High grade bladder cancer refers to myoclonal bladder cancer and metastatic bladder cancer. Nearly invasive bladder cancer refers to cancer whose cancer has progressed to the muscular layer of the bladder. A highly malignant bladder cancer is metastatic bladder cancer, which is not confined to the bladder but refers to a cancer that has metastasized to nearby tissues or other organs. As used herein, the term " bladder washing cytology " refers to a method for diagnosing bladder cancer through a tumor marker test (a test for a substance showing the presence of cancer cells) contained in a urine of a patient, Tumor markers are not limited as long as they are known to be capable of diagnosing bladder cancer.

In the present specification, "prognosis" refers to a prospect of a future symptom or progress that has been diagnosed by diagnosing a disease. Prognosis in cancer patients usually refers to the time of metastasis or survival within a period of time after cancer or surgery. Preferably means progression and cure of the disease, such as bladder cancer-induced death or the likelihood of progress, including recurrence of metastatic bladder cancer, metastatic spread, and drug resistance. For the purpose of the present invention, the prognosis means the possibility of systemic or local recurrence after treatment of bladder cancer, and preferably means prediction of systemic or local recurrence within 2 years after surgery or chemotherapy of bladder cancer.

As used herein, " prediction " means that a patient responds favorably or non-preferentially to various therapies, such as chemotherapy, radiation therapy, and the like, so that the patient can be treated, for example, by treatment with a particular therapeutic agent and / Elimination, and / or survival and / or likelihood after treatment with chemotherapy for a certain period of time without recurrence of cancer. The predictive method of the present invention can be used clinically by selecting and applying the most appropriate treatment regimen for any particular patient. The predictive method of the present invention can be used to ascertain whether a patient responds favorably to therapy, such as, for example, a prescribed therapeutic or combination, a surgical intervention, a prescribed treatment regimen including administration of chemotherapy, Term survival or systemic or local recurrence of the disease. In addition, it can be planned to use more effective adjuvant chemotherapy for patients who minimize unnecessary adjuvant chemotherapy or predict a systemic or local recurrence.

In the present specification, the term " diagnostic apparatus " means apparatus capable of diagnosing a disease and providing disease information from an image acquired by an endoscope, and there is no limitation as long as the image can be quantified and analyzed. (A) a storage unit for storing an image acquired through the bladder endoscope; (b) a filter unit for selecting an image in a lesion region of the image; (c) a calculating unit for calculating an average value of each of red (R), green (G), and blue (B) of the selected image; (d) an operation unit for substituting the calculated RGB average value into an equation; And (e) a display unit for displaying the result of the operation unit. The selection means to select a site which is different from the other site due to color, contrast, shade, micro-vein, hemorrhage or the like through the endoscope, and if the difference can be confirmed under the endoscope no limits.

(A) acquiring an image through a bladder endoscope; (b) extracting an image of a lesion region from the acquired image; (c) calculating an average value of each of red (R), green (G), and blue (B) in the extracted image; And (d) applying the calculated mean value to the formula " X = k ₁ * R + k ₂ * G + k ₃ * B + K ₄ ", . Preferably, the value X is a value of 0 to _1, k 1 is the value of -0.007 to 0.046, k ₂ is the value of -0.089 to 0.022, k ₃ is the value of -7.795 to 0.012, K ₄ is -0.776 to 0, but is not limited thereto. The RGB value is a value that is automatically measured when the bladder endoscopy is observed and is a vector representing the values of three basic colors Red, Green, and Blue as a number between 0 and 255. For example, the RGB value of the basic color is red: (255, 0, 0) Green: (0, 255, 0) Blue: (0, 0, 255) Black: (0, 0, 0) White: (255, 255, 255)

In one embodiment of the present invention, k ₁ of the formula (d) is -0.007 to -0.005, k ₂ is 0.020 to 0.022, k ₃ is -0.012 to -0.010, K ₄ is -0.776 -0.774. In the method of providing information on bladder-related diseases, when the value of X in the formula is more than 0.5 and less than 1.0, it is diagnosed as borderline bladder cancer.

In another embodiment of the present invention, k ₁ of the formula (d) is 0.005 to 0.007, k ₂ is -0.020 to -0.018, k ₃ is 0.010 to 0.012, and K ₄ is -0.190 to -0.188 The bladder washing cytology of the patient with borderline bladder cancer can be predicted when the X value of the formula is more than 0.5 to less than 1.0. .

In another embodiment of the present invention, k ₁ of the formula (d) is 0.038 to 0.040, k ₂ is -0.056 to -0.054, k ₃ is -3.294 to -3.292, and K ₄ is 0 Wherein the bladder washing cytology positive result of the bladder cancer patient can be predicted when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0 .

In another embodiment of the present invention, k ₁ of the formula (d) is 0.044 to 0.046, k ₂ is -0.032 to -0.030, k ₃ is -5.372 to -5.370, and K ₄ is 0 Wherein the X value of the formula is a low grade bladder cancer having a value of 0 or more and 0.5 or less, and has a value of 0.5 or more and 1.0 or less And can be diagnosed as high grade bladder cancer.

In another embodiment of the present invention, k ₁ of the formula (d) is 0.032 to 0.034, k ₂ is -0.089 to -0.087, k ₃ is -7.795 to -7.793, and K ₄ is 0 Wherein the bladder cancer is diagnosed as bladder cancer when the X value is a value of 0 or more and 0.5 or less and a value of 0.5 or more and 1.0 or less as a borderline bladder cancer .

(A) acquiring an image through a bladder endoscope; (b) extracting an image of a lesion region from the acquired image; (c) calculating an average value of each of red (R) and green (G) in the extracted image; And (d) applying the calculated mean value to the formula " X = k ₁ * R + k ₂ * G + k ₃ ". Preferably, the X value is a value of 0 to 1, k ₁ is a value of -0.67 to 0.074, k ₂ is a value of -0.094 to 0.082, and k ₃ is 0 to 8.326.

In one embodiment of the present invention, the k ₁ of the formula (d) is 0.072 to 0.074, k ₂ is -0.094 to -0.092, and k ₃ is 0.906 to 0.908. Wherein the diagnosis of interstitial cystitis is made when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0.

In another embodiment of the present invention, the k ₁ of the formula (d) is -0.67 to -0.65, k ₂ is 0.080 to 0.082, and k ₃ is 8.324 to 8.326. The present invention is characterized in that it can be diagnosed as borderline bladder cancer when the X value of the above formula has a value of 0 or more and 0.5 or less and a value of 0.5 or more and less than 1.0 as cystitis.

In another embodiment of the present invention, the k ₁ of the formula (d) is 0.034 to 0.036, k ₂ is 0.050 to 0.052, and k ₃ is 0 The present invention is characterized in that it can be diagnosed as malignancy bladder cancer when the value of X in the above formula is 0 or more and 0.5 or less and is a benign bladder cancer and a value of 0.5 or more and 1.0 or less.

In another embodiment of the present invention, the method for providing information on bladder-related diseases, wherein k ₁ of the formula of step (d) is 0.009 to 0.011, k ₂ is 0, and k ₃ is 0 Therefore, when the X value of the formula is greater than 0.5 and less than or equal to 1.0, it can be diagnosed that the bladder cancer has recurred.

In another embodiment of the present invention, the method of providing information on the bladder-related disease, wherein k ₁ of the formula (d) is 0, k ₂ is 0.004 to 0.006, and k ₃ is 0 Wherein the BCG injection treatment is predicted to fail when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0.

(A) a storage unit for storing an image acquired through the bladder endoscope; (b) a filter unit for selecting an image in a lesion region of the image; (c) a calculating unit for calculating an average value of each of red (R), green (G), and blue (B) of the selected image; (d) an arithmetic unit for substituting the calculated RGB average value into the formula "X = k ₁ * R + k ₂ * G + k ₃ * B + K ₄ "; And (e) a display unit for displaying a result of the operation unit. Preferably, X value is a value of 0 to _1, k 1 is the value of -0.007 to 0.046, k ₂ is the value of -0.089 to 0.022, k ₃ is the value of -7.795 to 0.012, K ₄ is - 0.776 to 0, but is not limited thereto.

In one embodiment of the invention, k ₁ of the formula in the step (d) is a -0.007 to -0.005, the k ₂ is 0.020 to 0.022, wherein k ₃ is -0.012 to -0.010, K ₄ is - Wherein the diagnosis is made as borderline bladder cancer when the X value is greater than 0.5 and less than or equal to 1.0.

In another embodiment of the present invention, k ₁ of the formula (d) is 0.005 to 0.007, k ₂ is -0.020 to -0.018, k ₃ is 0.010 to 0.012, K ₄ is -0.190 to Wherein the bladder washing cytology positive result of the bladder bladder cancer patient can be predicted when the X value is in the range of 0.5 to less than 1.0. .

In still another embodiment of the present invention, k ₁ of the formula in the step (d) it is 0.038 to 0.040, and wherein k ₂ is -0.056 to -0.054, -3.294 to -3.292, and wherein k is _3, K ₄ is Wherein the bladder washing cytology positive result of the bladder cancer patient can be predicted when the X value is greater than 0.5 and less than or equal to 1.0.

In another embodiment of the present invention, k ₁ of the formula (d) is 0.044 to 0.046, k ₂ is -0.032 to -0.030, k ₃ is -5.372 to -5.370, and K ₄ is Wherein the X value is a low grade bladder cancer having a value of 0 or more and 0.5 or less and a value of 0.5 or more and 1.0 or less, (high grade) bladder cancer.

In another embodiment of the present invention, k ₁ of the formula (d) is 0.032 to 0.034, k ₂ is -0.089 to -0.087, k ₃ is -7.795 to -7.793, and K ₄ is 0 in the bladder bladder-related disease diagnosis apparatus, wherein the bladder cancer is diagnosed as bladder cancer when the X value is 0 or more and 0.5 or less, .

(A) a storage unit for storing an image acquired through the bladder endoscope; (b) a filter unit for selecting an image in a lesion region of the image; (c) a calculating unit for calculating average values of red (R) and green (G) of the selected image; And (d) substituting the calculated average value into the equation " X = k ₁ * R + k ₂ * G + k _{3 &} quot ;; 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 -0.67 to 0.074, k ₂ is a value of -0.094 to 0.082, and k ₃ is 0 to 8.326.

In one embodiment of the present invention, the k ₁ of the formula (d) is 0.072 to 0.074, the k ₂ is -0.094 to -0.092, and the k ₃ is 0.906 to 0.908. Wherein the diagnosis is made by interstitial cystitis when the value of X is greater than 0.5 and less than or equal to 1.0.

In another embodiment of the present invention, the k ₁ of the formula (d) is -0.67 to -0.65, the k ₂ is 0.080 to 0.082, and the k ₃ is 8.324 to 8.326. The diagnostic apparatus of the present invention is characterized by being capable of diagnosing a borderline bladder cancer when the value X has a value of 0 or more and 0.5 or less and a value of 0.5 or more and less than 1.0 as cystitis.

In another embodiment of the present invention, the k ₁ of the formula of step (d) is 0.034 to 0.036, the k ₂ is 0.050 to 0.052, and the k ₃ is 0. Is benign bladder cancer when the value of X is 0 or more and 0.5 or less, and is diagnosed as malignancy bladder cancer when the value is 0.5 or more and less than 1.0.

In another embodiment of the present invention, the k ₁ of the formula of step (d) is 0.009 to 0.011, k ₂ is 0, and k ₃ is 0 , And when the X value is greater than 0.5 and less than or equal to 1.0, it is diagnosed that the bladder cancer has recurred.

In another embodiment of the present invention, the k ₁ of the formula (d) is 0, the k ₂ is 0.004 to 0.006, and the k ₃ is 0 , And when the X value has a value of more than 0.5 but less than 1.0, it is predicted that BCG injection treatment will fail.

Since the information providing method of the present invention can acquire images through an endoscope simply and objectively, it can provide information about various bladder related diseases with high accuracy, so that not only unnecessary examination can be reduced, but also diagnosis results can be obtained simultaneously with the endoscope The time required for diagnosis can be shortened. Therefore, it can be applied to various fields using the bladder endoscopy, and it is expected that understanding and application of the evaluation result is very easy due to quantitative calculation. It is expected to be used for various diagnoses using endoscopes such as upper gastrointestinal tract (esophagus / stomach / duodenum) and lower gastrointestinal tract (rectum / colon) and bronchoscopy.

FIG. 1 is a diagram showing distribution of RGB average values of a normal part and an abnormal part of each disease according to an embodiment of the present invention.
FIG. 2 is a graph showing distribution of RGB average values of a control group, a cystitis, an interstitial cystitis, and a bladder cancer patient according to an embodiment of the present invention.
FIG. 3 is a graph showing distribution of RGB average values of a control group and interstitial cystitis according to an embodiment of the present invention.
FIG. 4 is a graph showing distribution of RGB average values of a control group, a cystitis, and a bladder cancer patient according to an embodiment of the present invention.
FIG. 5 is a graph showing distribution of RGB values of bladder cancer and normal for a suspected cancer in a patient with recurrent bladder cancer according to an embodiment of the present invention. FIG.
FIG. 6 is a graph showing the distribution of the RGB values of bladder cancer, border bladder cancer, and normal in relation to suspected cancer in a patient with recurrent bladder cancer according to an embodiment of the present invention.
FIG. 7 is a graph showing distribution of RGB values according to cancer malignancy in a patient with recurrent bladder cancer according to an embodiment of the present invention, according to bladder cancer, border bladder cancer, and normal classification.
FIG. 8 is a graph showing ROC curves and AUC values according to an embodiment of the present invention.

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 .

Example

Example 1: Algorithm for diagnosis of bladder related diseases

In order to establish an algorithm for objectively diagnosing bladder-related diseases such as carcinoma in situ (CIS), interstitial cystitis (IC), and neurogenic bladder (nb) From January to August 2015, we analyzed the images of 140 patients (EVIS LUCERA SPECTRUM system, CV-260SL, Olympus). Of the 140 patients, 86 (61.4%) were diagnosed with borderline bladder cancer after transurethral resection of bladder (TURB) and 19 (13.6%) were diagnosed with benign prostatic hyperplasia (BPH) ), 26 (18.6%) were diagnosed with localized cervical cancer, and 5 (3.6%) were diagnosed with interstitial cystitis (IC) , And 4 patients (2.9%) were diagnosed with neurogenic bladder (nb). Patients with benign prostatic hyperplasia and cervical cancer were included in the control group. Each patient's bladder was examined with an endoscope to randomly select three points out of the abnormally seen areas, that is, the areas showing abnormality under the endoscope and the normal areas, and the average RGB (red-green -blue), respectively. Finally, the RGB values of 840 regions were obtained. The obtained RGB values were divided into a normal region and an abnormal region according to each disease and displayed in three dimensions, and are shown in FIGS. In the drawings, "NL_Baseline" refers to the normal lesion of a female cervical cancer patient, "NL_BPH" refers to the normal region of the male prostate hyperplasia, "NL_Cystitis" refers to the normal region of the cystitis patient, NL_NB "refers to the normal area of the neurogenic bladder (NB) patient," NL_IC "refers to the normal area of the interstitial cystitis (IC) patient," NL_BCa "refers to the bladder tumor "AbNL" refers to the abnormal region of each disease, and "abNL" refers to the normal region of the patient who has been diagnosed with borderline bladder cancer later. The independent sample t-test was performed to confirm the reliability of the difference between the RGB values of each group, and it was analyzed to be significant when "p <0.05" Logistic regression analysis was used in Statistics version 21 (IBM Corporation, Armonk, NY, USA). More particularly, "Z = B ₀ + B ₁ + B ₂ X ₁ X ₂ X ₃ + B ₃ + ... B _n X _n" in the equation Z is the defined as the "0≤Z≤1", the value of B Was defined as the B value from the logistic regression analysis. 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).

As shown in FIG. 1 to FIG. 4, it was first confirmed that they can be classified into a certain pattern according to each disease, and the average value of each RGB value was measured in order to create a diagnostic algorithm. The mean RGB values of the abnormal regions of the borderline bladder cancer patients were 182.52 ± 31.96, 116.03 ± 26.16, and 96.83 ± 26.28, respectively, for R (red): G (green) The mean values of R, G, and B in the abnormal regions of the patients were 192.70 ± 22.15, 167.84 ± 23.45 and 140.05 ± 29.62, respectively. The reliability analysis for the comparison of RGB mean values between the borderline bladder cancer patients and the control group showed that there was a significant difference between the two values of p <0.001. Based on this, it is possible to diagnose borderline bladder cancer patients I was able to confirm that it was.

As a result of comparing the RGB average values of the abnormal region of the borderline bladder cancer patient and the normal region of the borderline bladder cancer patient, the average RGB value of the normal region of the borderline bladder cancer patient was R: 182.59 ± 31.85 (p = 0.982 ), G: 146.91 ± 31.27 (p <0.001), and B: 124.51 ± 31.056 (p <0.001), showing a reliable difference between green and blue abnormal regions of patients with borderline bladder cancer. We confirmed that the diagnosis of borderline bladder cancer can be quantified. The mean value of RGB at the normal site was 174.75 ± 26.63 (p = 0.408) and 132.83 ± 46.14 (p = 0.408) at the normal site of the cystitis patients in the borderline bladder cancer patients. 0.236) and B: 106.00 ± 36.82 (p = 0.411), respectively. The mean values of the RGB values of the abnormal parts of patients with borderline bladder cancer and those of the abnormal parts of cystitis patients were 209.22 ± 23.83 (p = 0.014), 95.56 ± 22.04 (g), 67.89 ± 22.41 (p = 0.001), indicating a significant difference in RGB mean between patients with bladder cancer and those with cystitis. Through the above results, it was confirmed that each disease can be diagnosed by using an algorithm that compares the average value of RGB by observing regions that are seen abnormally through an endoscope.

Each algorithm was applied to simple linear regression analysis. The " R " value of each algorithm was calculated by averaging the red values of three arbitrary three red pixels as the average red value of the 3x3 pixels of the region where the abnormality was seen. The " G " The green value was obtained by averaging the green values of three arbitrary portions as a green value, and the "B" value was obtained by averaging the blue values of three arbitrary portions as an average blue value of 3 × 3 pixels of the portion which is seen as abnormal.

(1) Algorithm for the diagnosis of borderline bladder cancer: F (R, G, B) = - 0.006 * R + 0.021 * G-0.011 * B-0.775

When each value of R, G, B was substituted, it was confirmed that it could be diagnosed as borderline bladder cancer when the value is more than 0.5 and less than 1.0 (p <0.001).

(2) Algorithm for the diagnosis of interstitial cystitis: F (R, G, B) = 0.073 * R-0.093 * G + 0.907

When each value of R and G was substituted, it was confirmed that it could be diagnosed as interstitial cystitis when the value was more than 0.5 and less than 1.0 (p <0.05).

(3) Algorithm for discrimination between cystitis and borderline bladder cancer: F (R, G, B) = - 0.66 * R + 0.081 * G + 8.325

When each value of R and G was substituted, it could be diagnosed as borderline bladder carcinoma when the value was between 0 and 0.5 but less than 0.5 above 1.0 (p <0.05).

Example  2: Algorithm for Predicting Borderline Bladder Cancer Prognosis

To establish an algorithm for predicting the prognosis of borderline carcinoma in situ (CIS), 86 patients (mean age: 70.5 years) from January 2014 to August 2015 were followed up for white light imaging cystoscopy (EVIS LUCERA SPECTRUM system, CV-260SL, Olympus). Basic information on 86 patients is shown in Table 1. Each patient's bladder was examined with an endoscope to randomly select three points out of the abnormally seen areas, that is, the areas showing abnormality under the endoscope and the normal areas, and the average RGB (red-green -blue) were measured and the RGB values of 516 regions were obtained. A multivariate Cox proportional hazards model was used to evaluate the correlation between RGB mean value and recurrence of bladder cancer and / or failure of BCG treatment. Independent sample t-test was performed to confirm reliability, and "p <0.05" was considered to be significant. All statistical analyzes were performed using IBM PSS statistics ver. 21 (IBM Korea corporation, Seoul, Korea).

The average RGB values of the normal and abnormal regions of the borderline bladder cancer patients were R: 185.0 (158.8-203.3), G: 148 (123.8-166.0) and B: 124.0 (103.0-146.0) The mean values of RGB in the abnormal region were R: 180.0 (159.0-207.0) (p = 0.675), G: 113.0 (98.0-131.0), B: 97.0 (78.0-113.0) ) Showed significant differences in green and blue, indicating that there was a significant difference in the diagnosis of early borderline bladder cancer and recurrent borderline bladder cancer (see Table 1).

In the multivariate analysis, bladder washing cytology using urine cells as an independent factor showed HR = 0.526, p = 0.027. The red values of RGB mean values were HR = 1.009, p <0.001, The value of HR was 0.87, p <0.001, and it was confirmed that the accuracy was increased compared with urinary bladder cancer diagnosis. This confirmed the same result in recurrent borderline bladder cancer. The results are shown in Table 2.

The results of Cox analysis for predicting the recurrence of bladder cancer in patients undergoing urethral transurethral resection of the bladder tumor are shown in Table 3. As shown in Table 3, HR = 4.12, p <0.001 for the patients diagnosed with bladder cancer, HR = 1.01, p <0.001 for the RGB mean value, It is possible to predict the recurrence of bladder cancer.

In addition, the results of the Cox analysis for predicting the failure of BCG infusion therapy among the relapsed patients are shown in Table 4. As shown in Table 4, the green value was HR = 1.007, p = 0.014, confirming that the failure of BCG infusion therapy in the bladder could be predicted independently.

From the above results, it was confirmed that prediction of bladder cancer recurrence or diagnosis and failure of BCG injection treatment can be predicted using the RGB average value of the endoscope.

(1) Bladder washing cytology in patients with borderline bladder cancer. Prediction algorithm: F (R, G, B) = 0.005 * R-0.019 * G + 0.011 * B-0.189

When the values of R, G, and B were assigned, values of more than 0.5 and less than 1.0 were predictive of bladder washing cytology results (p <0.001). The algorithm was created using a simple linear regression analysis model.

(2) F (R, G, B) = 0.010 * R for the prognosis of borderline bladder cancer,

(P <0.001). We conclude that the risk of recurrence of borderline bladder cancer can be predicted to be higher if the value of red (R) is more than 0.5 and less than 1.0.

(3) BCG infusion treatment failure prediction algorithm: F (R, G, B) = 0.005 * G

When the green value (G) was added, it was confirmed that BCG infusion treatment was more likely to fail if the value was above 0.5 but less than 1.0 (p <0.05).

Total
Initial diagnosed bladder cancer Recurrent bladder cancer Normal bladder wall CIS lesion p-value Normal bladder wall CIS lesion p-value Normal bladder wall CIS lesion p-value N 86 15 71 Age (median, IQR) 70.5 (63.4-78.9) 71.1 (64.7-79.3) 64.8 (63.2-70.9) Sex (M: F) 71:15 10: 5 61:10 Concomitant bladder tumor (n) 70 (81.4%) 8 (53.3%) 27 (38.0%) positive bladder washing cytology (n) 54 (62.8%) 8 (53.3%) 46 (64.8%) Surface change with papillary (n) 41 (47.7%) 5 (33.3%) 36 (50.7%) R (median, IQR) 185.0 (158.8-203.3) 180.0 (159.0-207.0) 0.675 183.0 (154.5-198.0) 186.0 (168.5-208.0) 0.366 185.0 (159.5-204.5) 177.0 (158.0-205.5) 0.405 G (median, IQR) 148 (123.8-166.0) 113.0 (98.0-131.0) <0.001 145.0 (129.5-160.5) 113.0 (91.5-130.0) <0.001 150.0 (122.0-166.5) 113.0 (99.0-131.0) <0.001 B (median, IQR) 124.0 (103.0-146.0) 97.0 (78.0-113.0) <0.001 125.0 (108.0-145.5) 98.0 (78.5-111.0) <0.001 122.0 (101.0-146.5) 97.0 (77.5-113.0) <0.001

Total Initial diagnosed bladder cancer Recurrent bladder cancer Univariate Multivariate Univariate Multivariate Univariate Multivariate HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p Age * 1,000 0.985 1.015 > 0.999 1,000 0.964 1.038 > 0.999 1,000 0.984 1.016 > 0.999 Sex (F vs M) 1,000 0.635 1.576 > 0.999 1,000 0.416 2.403 > 0.999 1,000 0.579 1.726 > 0.999 Previous bladder cancer diagnosed (Yes vs No) 1,000 0.635 1.576 > 0.999 Concomitant bladder tumor (Yes vs No) 1,000 0.704 1.421 > 0.999 1,000 0.437 2.289 > 0.999 1,000 0.676 1.479 > 0.999 Positive bladder washing cytology (Yes vs No) 1,000 0.689 1.452 > 0.999 0.526 0.297 0.930 0.027 1,000 0.375 2.664 > 0.999 1,000 0.668 1.496 > 0.999 0.533 0.299 0.950 0.033 R * 1,000 0.994 1.005 0.946 1.009 1.087 1.133 <0.001 1.005 0.991 1.019 0.486 0.999 0.993 1.005 0.713 1.096 1.074 1.119 <0.001 G * 0.962 0.954 0.969 <0.001 0.870 0.849 0.892 <0.001 0.935 0.908 0.963 <0.001 0.927 0.895 0.961 <0.001 0.965 0.957 0.973 <0.001 0.884 0.863 0.906 <0.001 B * 0.967 0.959 0.974 <0.001 0.943 0.919 0.968 <0.001 0.969 0.962 0.977 <0.001

Total Initial diagnosed bladder cancer Recurrent bladder cancer Univariate Multivariate Univariate Multivariate Univariate Multivariate HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p Age * 0.999 0.986 1.011 0.846 0.968 0.936 1.001 0.057 0.998 0.983 1.013 0.795 Sex (F vs M) 0.762 0.494 1.177 0.221 0.289 0.093 0.898 0.032 0.743 0.450 1.226 0.246 Previous bladder cancer diagnosed (Yes vs No) 3.647 2.207 6.027 <0.001 4.123 2.423 7.015 <0.001 Concomitant bladder tumor (Yes vs No) 0.844 0.610 1.168 0.305 0.018 0.000 1.018 0.051 1.038 0.743 1.450 0.828 Positive bladder washing cytology (Yes vs No) 1.120 0.819 1.531 0.480 3.017 1.055 8.624 0.039 0.793 0.571 1.101 0.166 0.569 0.396 0.819 0.002 Surface change with papillary 0.949 0.664 1.357 0.773 1.607 0.597 4.327 0.348 0.942 0.641 1.385 0.762 R * 1.010 1.005 1.015 <0.001 1.013 1.008 1.018 <0.001 1.012 0.991 1.032 0.272 1.012 1.007 1.018 <0.001 1.018 1.011 1.025 <0.001 G * 1.006 1.001 1.010 0.017 1.009 0.989 1.029 0.384 0.927 0.895 0.961 <0.001 1.006 1.001 1.010 0.016 0.978 0.968 0.989 <0.001 B * 1.006 1.002 1.011 0.008 0.971 0.945 0.998 0.034 1.008 1.004 1.013 <0.001 1.018 1.009 1.027 <0.001 Intraveiscal BCG treatment 0.783 0.573 1.071 0.126 0.677 0.49 0.934 0.017 1.405 0.523 3.777 0.500 0.663 0.475 0.925 0.016 0.553 0.391 0.781 0.001

Total Recurrent bladder cancer Univariate Multivariate Univariate Multivariate HR 95% CI p HR 95% CI p HR 95% CI p HR 95% CI p Age * 0.967 0.953 0.982 <0.001 0.970 0.953 0.987 0.001 0.977 0.961 0.994 0.007 0.980 0.961 0.998 0.034 Sex (F vs M) 0.578 0.312 1.069 0.081 1.436 0.623 3.311 0.396 Previous bladder cancer diagnosed (Yes vs No) 3.852 2.106 7.043 <0.001 2.849 1.511 5.372 0.001 Concomitant bladder tumor (Yes vs No) 1.025 0.705 1.490 0.899 1.550 1.051 2.286 0.027 Positive bladder washing cytology (Yes vs No) 1.107 0.767 1.598 0.587 0.456 0.985 0.670 0.042 Surface change with papillary 0.730 0.467 1.140 0.167 0.778 0.483 1.251 0.299 R * 1.005 0.999 1.011 0.078 1.008 1.002 1.014 0.007 G * 1.004 0.999 1.009 0.138 1.007 1.002 1.013 0.014 1.003 0.998 1.008 0.236 B * 1.004 0.999 1.009 0.102 1.006 1.001 1.011 0.019 1.008 1.002 1.014 0.008

Example  3: Algorithm for Recurrence of Bladder Cancer

In general, after the diagnosis of bladder cancer, a urethral dystrophic tumor resection is performed to remove only a few lesions locally. Then, the patient is checked periodically for recurrence through a bladder endoscope. At this time, It is difficult to confirm the recurrence of bladder cancer through an endoscope. In order to develop an algorithm that can accurately and objectively diagnose the recurrence of bladder cancer even after resection, a white light imaging (WLI) endoscope and a narrow-band imaging (NBI) endoscope (Olympus VISERA ELITE system). Narrowband endoscopy can enhance the image quality by increasing the transmittance of light with narrow wavelength range of 415nm to 540nm. Eighty patients with suspected or suspected bladder cancer were treated with WLI endoscopy and NBI endoscopy by two urology specialists. Twenty-seven patients with WLI and NBI and 27 with NBI alone And a total of 172 abnormal sites were identified. Basic information about the patients is shown in Table 5 below. Malignancies were observed in 17 areas (63%) and borderline bladder cancer (53.3%) by NBI alone. The distribution of the RGB values for the lesion (site) in which the cancer is suspected in patients with recurrent bladder cancer is shown in Figs. 5-7. In the figure, the term "no cancer" refers to the average value of the patient's non-cancer RGB. Cancer means bladder cancer, CIS means borderline bladder cancer, low grade means low malignant bladder cancer and high grade means malignant bladder cancer. The independent sample t-test was performed to confirm the reliability of the difference of RGB values between the groups, and it was analyzed to be significant when "p <0.05" Simple linear regression analysis was used. All statistical analyzes of the examples were performed using IBM PSS statistics ver. 21 (IBM Korea corporation, Seoul, Korea). The results are shown in Table 6.

(1) Bladder washing cytology of bladder cancer patients Predictive results: F (R, G, B) = 0.039 * R-0.055 * G-3.293 * B

When the values of R, G, and B were assigned, values of more than 0.5 and less than 1.0 were predictive of bladder washing cytology results (p <0.001).

(2) Benign or malignancy diagnosis algorithm for bladder cancer: F (R, G, B) = 0.035 * R + 0.051 * G

(P <0.001). When the values of red (R) and green (G) were assigned to each of the groups, a positive value between 0 and 0.5 and a positive value between 0.5 and 1.0 were diagnosed as malignant.

(3) Algorithm for classification of malignancy (low grade vs. high grade) of bladder cancer patients: F (R, G, B) = 0.045 * R-0.031 * G-5.371 * B

When each value of R, G, B was substituted, it was confirmed that bladder cancer can be classified as high malignant bladder cancer when the value is more than 0.5 and less than 1.0 (p <0.05).

(4) Algorithm for discriminating borderline bladder cancer or bladder cancer: F (R, G, B) = 0.033 * R-0.088 * G-7.794 * B

When the values of R, G, and B were substituted, bladder cancer was predicted to be bladder cancer when the value was between 0 and 0.5, and the value was more than 0.5 and less than 1.0 (p <0.05).

Total WLI + / NBI + WLI- / NBI + p value No. site (n,%) 172 145 (84.3) 27 (15.7) Interventions 100 88 (88.0) 12 (12.0) Patients 80 65 (81.3) 15 (18.7) Sex (n,%) 0.150  Male 70 (87.5) 60 (75.0) 10 (12.5)  Female 10 (12.5) 8 (10.0) 2 (2.5) Age, mean ± SD 69.1 ± 12.2 68.5 ± 12.4 69.4 ± 8.2 0.737 Bladder tumor (n,%) 0.695  Primary 16 (20.0) 12 (15.0) 4 (5.0)  Recurrent 64 (80.0) 50 (62.5) 14 (17.5) Stage (n,%) 0.007  pTx / 0 51 (29.7) 41 (28.3) 10 (37.0)  pTis 15 (8.8) 7 (4.8) 8 (29.6)  pTa 16 (9.3) 12 (8.3) 4 (14.8)  pT1 82 (47.7) 77 (53.1) 5 (18.5)  pT2 88 (4.7) 8 (5.5) 0 (0.0) Bladder washing cytology (+) (n,%) 72 (41.9) 58 (40.0) 14 (51.9) 0.254 Recurrent tumor (n,%) 149 (86.6) 125 (86.2) 24 (88.9) 0.695 Previous BCG instillaation Hx (n,%) 6 (3.5) 6 (4.1) 0 (0.0) 0.078 Benign lesions 0.025  Cystitis 40 (23.3) 34 (23.4) 6 (22.2)  Urothelial hyperplasia / inverted papilloma 11 (6.4) 7 (4.8) 4 (14.8) papillary type (n,%) 77 (44.8) 73 (50.3) 4 (14.8) <0.001 Grade (n,%) 0.001  low grade 50 (29.1) 45 (31.0) 5 (18.5)  high grade 57 (33.1) 53 (36.6) 4 (14.8)

Univariate analysis Multivariate analysis Hazard ratio (95% CI) p value
Hazard ratio (95% CI) p value Age * 1.01 (0.977-1.033) 0.736 Sex (M vs. F) 2.32 (0.839-6.390) 0.105 Bladder washing cytology (+) (n,%) 2.43 (1.195-4.954) 0.014
3.87 (1.654-9.023) 0.002 Recurrent tumor (n,%) 0.46 (0.147-1.418) 0.175 grossly papillary feature 4.30 (2.217-8.356) <0.001 6.80 (3.028-15.255) <0.001 Average Red of lesion for WLI 0.23 (0.999-1.027) 0.258
Average Green of lesion for WLI 18.10 (0.969-1.001) 0.013
1.01 (0.960-1.062) 0.709 Average Blue of lesion for WLI 6.83 (0.978-1.002) 0.006
0.96 (0.939-0.989) 0.006 Average Red of lesion for NBI 11.99 (0.978-1.001) 0.009
0.99 (0.972-1.001) 0.069 Average Green of lesion for NBI 4.89 (0.983-1.004) 0.020
1.02 (0.998-1.048) 0.074 Average Blue of lesion for NBI 5.34 (0.977-1.005) 0.017
0.92 (0.676-1.256) 0.604

Example  4: Diagnostic accuracy

In order to confirm the accuracy of the diagnosis using the algorithm, ROC (receiver operating characteristic) curve and area under the curve (AUC) were calculated. The results are shown in FIG. 8 and Table 7.

As shown in FIG. 8, the AUC value according to the bladder cleansing cytology was 0.600, the AUC value according to observation of the grossly papillary feature was 0.681 using the narrow band image, the diagnosis was made by the combination of the bladder washing cytology and the method using the narrow band image The AUC value was 0.740, and the AUC value when the algorithm of the present invention was combined with the bladder cleansing cytology and the narrowband image was 0.790, indicating that the accuracy was remarkably increased. Thus, it can be confirmed that the diagnosis accuracy can be significantly increased if the algorithm of the present invention is used in combination with the existing bladder-related disease diagnosis method.

Cytology (+) & grossly papillary feature & Using RGB value .790 Cytology (+) & grossly papillary feature .740 Cytology (+) .600 Grossly papillary feature .681

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 (24)

(a) acquiring an image through a bladder endoscope;
(b) extracting an image of a lesion region from the acquired image;
(c) calculating an average value of each of red (R), green (G), and blue (B) in the extracted image; And
according to (d) providing information about, comprising the step of applying said calculated mean value of the formula _{"X = k 1 * R +} k 2 * G + k 3 * B + k 4", bladder-related diseases method,
The value X is a value of 0 to 1, k ₁ is a value of -0.007 to 0.046, k ₂ is a value of -0.089 to 0.022, k ₃ is a value of -7.795 to 0.012, k ₄ is a value of -0.776 to 0.012, 0, information on bladder-related diseases The method according to claim 1,
Wherein k ₁ of the formula (d) is -0.005 to -0.007, k ₂ is 0.020 to 0.022, k ₃ is -0.010 to -0.012, and k ₄ is -0.774 to -0.776. In a method for providing information on a bladder-related disease,
Wherein the diagnosis is made to be a borderline bladder cancer when the X value of the formula has a value of more than 0.5 to less than 1.0. The method according to claim 1,
Wherein the k ₁ of the formula (d) is 0.005 to 0.007, k ₂ is -0.018 to -0.020, k ₃ is 0.010 to 0.012, and k ₄ is -0.188 to -0.190. In a method for providing information about a disease,
Wherein the positive result of bladder washing cytology in a patient with borderline bladder cancer can be predicted when the X value of the formula is greater than 0.5 and less than or equal to 1.0. The method according to claim 1,
Wherein the k ₁ of the formula (d) is 0.038 to 0.040, k ₂ is -0.054 to -0.056, k ₃ is -3.292 to -3.294, and k ₄ is 0. A method of providing information about a content,
Wherein the positive result of the bladder washing cytology of a bladder cancer patient can be predicted when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0. The method according to claim 1,
Wherein the k ₁ of the formula (d) is 0.044 to 0.046, k ₂ is -0.030 to -0.032, k ₃ is -5.370 to -5.372, and k ₄ is 0. A method of providing information about a content,
The present invention is characterized by being a low grade bladder cancer having an X value of 0 or more and 0.5 or less and a diagnosis of a high grade bladder cancer having a value of 0.5 or more and less than 1.0 Information providing method. The method according to claim 1,
Wherein the k ₁ of the formula (d) is 0.032 to 0.034, k ₂ is -0.087 to -0.089, k ₃ is -7.793 to -7.795, and k ₄ is 0 A method of providing information about a content,
Wherein the cancer is diagnosed as bladder cancer when the value of X in the formula is a value of 0 or more and 0.5 or less and a value of 0.5 or more and 1.0 or less as a borderline bladder cancer. (a) acquiring an image through a bladder endoscope;
(b) extracting an image of a lesion region from the acquired image;
(c) calculating an average value of each of red (R) and green (G) in the extracted image; And
(d) applying the calculated average value to the formula " X = k ₁ * R + k ₂ * G + k _{3 &} quot ;, comprising:
Wherein the X value is a value of 0 to 1, k ₁ is a value of -0.67 to 0.074, k ₂ is a value of -0.094 to 0.082, and k ₃ is a value of 0 to 8.326. 8. The method of claim 7,
The method for providing information on a bladder-related disease according to claim 1, wherein k ₁ of the equation (d) is 0.072 to 0.074, k ₂ is -0.092 to -0.094, and k ₃ is 0.906 to 0.908.
Wherein the diagnosis is made as interstitial cystitis when the X value of the formula has a value of more than 0.5 to less than 1.0. 8. The method of claim 7,
The method of providing information on a bladder-related disease according to claim 1, wherein k ₁ of the equation (d) is -0.65 to -0.67, k ₂ is 0.080 to 0.082, and k ₃ is 8.324 to 8.326.
Wherein the bladder cancer is diagnosed as borderline bladder cancer when the X value of the above formula has a value of 0 or more and 0.5 or less and a value of 0.5 or more and less than 1.0 as cystitis. 8. The method of claim 7,
The method of providing information on a bladder-related disease according to claim 1, wherein k ₁ of the equation (d) is 0.034 to 0.036, k ₂ is 0.050 to 0.052, and k ₃ is 0,
Wherein the cancer is diagnosed as benign bladder cancer when the X value of the formula is 0 or more and 0.5 or less, and malignancy bladder cancer when the value is 0.5 or more and 1.0 or less. 8. The method of claim 7,
Wherein the k ₁ of the formula of step (d) is 0.009 to 0.011, k ₂ is 0, and k ₃ is 0,
Characterized in that it is possible to diagnose the recurrence of the border bladder cancer when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0. 8. The method of claim 7,
The method for providing information on a bladder-related disease according to claim 1, wherein k ₁ in the equation (d) is 0, k ₂ is 0.004 to 0.006, and k ₃ is 0,
Is predicted to fail in BCG infusion treatment when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0. (a) a storage unit for storing an image acquired through a bladder endoscope;
(b) a filter unit for selecting an image in a lesion region of the image;
(c) a calculating unit for calculating an average value of each of red (R), green (G), and blue (B) of the selected image;
(d) an operation unit for substituting the calculated RGB average value into the formula " X = k ₁ * R + k ₂ * G + k ₃ * B + k _{4 &} quot ;; And
(e) a display unit for displaying a result value of the operation unit,
The value X is a value of 0 to 1, k ₁ is a value of -0.007 to 0.046, k ₂ is a value of -0.089 to 0.022, k ₃ is a value of -7.795 to 0.012, k ₄ is a value of -0.776 to 0.012, 0, a device for diagnosing a bladder-related disease. 14. The method of claim 13,
Wherein k ₁ of the formula (d) is -0.005 to -0.007, k ₂ is 0.020 to 0.022, k ₃ is -0.010 to -0.012, and k ₄ is -0.774 to -0.776. A device for diagnosing a bladder-related disease,
Wherein the diagnosis of borderline bladder cancer is made when the value of X in the above formula is more than 0.5 and less than or equal to 1.0. 14. The method of claim 13,
Wherein the k ₁ of the formula (d) is 0.005 to 0.007, k ₂ is -0.018 to -0.020, k ₃ is 0.010 to 0.012, and k ₄ is -0.188 to -0.190. A disease diagnosis apparatus comprising:
Characterized in that the positive result of bladder washing cytology of a patient with borderline bladder cancer can be predicted when the value of X in the above formula is more than 0.5 and less than or equal to 1.0. 14. The method of claim 13,
Wherein the k ₁ of the formula (d) is 0.038 to 0.040, k ₂ is -0.056 to -0.054, k ₃ is -3.294 to -3.292, and k ₄ is 0 In the apparatus,
Wherein the bladder washing cytology positive result of the bladder cancer patient can be predicted when the X value of the above formula has a value of more than 0.5 and less than or equal to 1.0. 14. The method of claim 13,
Wherein the k ₁ of the formula of step (d) is 0.044 to 0.046, k ₂ is -0.030 to -0.032, k ₃ is -5.370 to -5.372, and k ₄ is 0 In the apparatus,
The present invention is characterized by being a low grade bladder cancer having an X value of 0 or more and 0.5 or less and a diagnosis of a high grade bladder cancer having a value of 0.5 or more and less than 1.0 A device for diagnosing a bladder-related disease. 14. The method of claim 13,
Wherein the k ₁ of the formula of step (d) is 0.032 to 0.034, k ₂ is -0.087 to -0.089, k ₃ is -7.793 to -7.795, and k ₄ is 0 In the apparatus,
Wherein the bladder cancer is diagnosed as bladder cancer when the value of X in the formula is a value of 0 or more and 0.5 or less and a value of 0.5 or more and 1.0 or less as a border bladder cancer. (a) a storage unit for storing an image acquired through a bladder endoscope;
(b) a filter unit for selecting an image in a lesion region of the image;
(c) a calculating unit for calculating average values of red (R) and green (G) of the selected image; And
(d) an operation unit for substituting the calculated average value into the formula " X = k ₁ * R + k ₂ * G + k _{3 &} quot ;; And
(e) a display unit for displaying a result value of the operation unit, the apparatus comprising:
Wherein the X value is a value from 0 to 1, K1 is a value from -0.67 to 0.074, K2 is a value from -0.094 to 0.082, and K3 is from 0 to 8.326. 20. The method of claim 19,
Wherein the k ₁ of the equation (d) is 0.072 to 0.074, k ₂ is -0.094 to -0.092, and k ₃ is 0.906 to 0.908.
Wherein the diagnosis is made as interstitial cystitis when the X value of the formula is greater than 0.5 and less than or equal to 1.0. 20. The method of claim 19,
Wherein the k ₁ of the equation (d) is -0.67 to -0.65, k ₂ is 0.080 to 0.082, and k ₃ is 8.324 to 8.326.
Wherein the bladder cancer is diagnosed as borderline bladder cancer when the X value of the above formula has a value of 0 or more and 0.5 or less and a value of 0.5 or more and less than 1.0 as cystitis. 20. The method of claim 19,
Wherein the k ₁ of the formula (d) is 0.034 to 0.036, k ₂ is 0.050 to 0.052, and k ₃ is 0,
A benign bladder cancer having a value of 0 or more and 0.5 or less in the above formula is diagnosed as malignancy bladder cancer when having a value of more than 0.5 and less than 1.0. Device. 20. The method of claim 19,
Wherein the k ₁ of the formula of step (d) is 0.009 to 0.011, k ₂ is 0, and k ₃ is 0,
Wherein the bladder-related disease diagnosis device diagnoses that bladder cancer has recurred when the X value of the formula has a value of more than 0.5 and less than or equal to 1.0. 20. The method of claim 19,
Wherein the k ₁ of the formula of step (d) is 0, k ₂ is 0.004 to 0.006, and k ₃ is 0,
Wherein when the X value of the formula is greater than 0.5 and less than or equal to 1.0, it is predicted that the BCG injection treatment will fail.

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