KR102280204B1 - Method and apparatus for providing information on the pathological grade of tumors of renal cell carcinoma - Google Patents

Abstract

The present invention relates to a method and apparatus for providing information on the pathological grade of a tumor of renal cell carcinoma (RCC). According to the present invention, there is provided a method for providing information on the pathological grade of a tumor of renal cell carcinoma, comprising: acquiring a computed tomography (CT) image of the kidney; an image of the tumor from the CT image extracting , obtaining a value of at least one image parameter from the image of the extracted tumor, and determining the pathological grade of the tumor based on a comparison of the value of the at least one image parameter with a predetermined threshold value There is provided a method comprising the steps of: and outputting the determined pathological grade.

Description

METHOD AND APPARATUS FOR PROVIDING INFORMATION ON THE PATHOLOGICAL GRADE OF TUMORS OF RENAL CELL CARCINOMA

The present invention relates to a method and apparatus for providing information on the pathological grade of a tumor of renal cell carcinoma. Specifically, the present invention relates to methods and devices for providing information on pathological grades based on image characteristics of renal cell carcinoma tumors.

Small renal masses (SRMs) range from mass benign tumors to cancerous tumors. According to the guidelines of the US National Comprehensive Cancer Network (NCCN), when clinically indicated, the only method for initial operation is a kidney biopsy. However, renal biopsy is clinically useful in differentiating benign and malignant tumors, but provides insufficient information regarding pathological characteristics.

Surgical resection remains an effective treatment for SRMs, including regional renal cell carcinoma (RCC), and radical nephrectomy and partial nephrectomy are available. In recent years, active surveillance (AS) has emerged as an alternative strategy, especially for the elderly and patients with health risks.

Strategies for optimal patient selection, monitoring protocols, and definitions for disease reclassification and progression of AS to SRM, including RCC, are unclear. The Fuhrman grade, which influences pathologic symptoms and disease recurrence, size, and type of history, is a predictive factor that determines the prognosis of RCC patients. However, if the renal biopsy of the SRM is not pathological, the clinician cannot recommend specific AS guidelines for the patient.

Therefore, there is a need for a method that can compensate for the shortcomings of histopathological methods by providing information on the pathological characteristics of SRM.

The present invention aims to solve the following problems in order to solve the above problems.

An object of the present invention is to provide a method and apparatus capable of providing information on the pathological characteristics of renal cell carcinoma (RCC).

An object of the present invention is to provide a method and apparatus capable of providing information on the pathological characteristics of RCC non-invasively through the analysis of imaging characteristics.

The problems to be solved of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

A method for providing information on a pathological grade of a tumor of a renal cell carcinoma (RCC) according to various embodiments of the present invention includes computed tomography (computed tomography, computed tomography) of a tumor of RCC, CT) acquiring an image, acquiring a value of at least one image parameter from the acquired image, determining the pathological grade of the tumor based on a comparison of the value of the at least one image parameter with a predetermined threshold value determining, and outputting the determined pathological grade.

An apparatus for providing information on a pathological grade of a tumor of renal cell carcinoma (RCC) according to an embodiment of the present invention includes a memory storing an image, an input/output unit, and the memory and at least one processor functionally connected to the input/output unit, wherein the at least one processor acquires a computed tomography (CT) image of a tumor of the RCC, and at least one processor from the acquired image generate a value of an image parameter, determine a pathological grade of the tumor based on a comparison of the value of the at least one image parameter with a predetermined threshold value, and output the determined pathological grade.

A computer program according to various embodiments of the present invention performs a method for providing information on a pathological grade of a tumor of renal cell carcinoma (RCC) according to an embodiment of the present invention and is recorded on a computer-readable storage medium.

The present invention may provide a method and apparatus capable of providing information on the pathological characteristics of renal cell carcinoma (RCC).

The present invention can provide a method and an apparatus capable of providing information on the pathological characteristics of RCC non-invasively through the analysis of imaging characteristics.

Effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

1A and 1B show examples of computed tomography (CT) images for renal cell carcinoma (RCC).
2 is a flowchart of a method for providing information on the pathological grade of a tumor of renal cell carcinoma (RCC) according to various embodiments of the present disclosure.
3 is a block diagram of an apparatus for providing information on the pathological grade of a tumor of renal cell carcinoma (RCC) according to various embodiments of the present disclosure.
4 illustrates baseline clinical and demographic characteristics according to various embodiments of the present invention.
5 depicts characterization of solid kidney tumors based on histological subtype in accordance with various embodiments of the present invention.
6 is a tumor size between Fuhrman grade 2 or less and grade 3 or higher and Hounsfield units according to subtypes of renal cell carcinoma (RCC) according to various embodiments of the present disclosure; unit, HU) shows a comparison of the measured values.
7 illustrates univariate and multivariate analysis of clear cell renal cell carcinoma (CCRCC) of Fuhrman grade 3 or higher according to various embodiments of the present disclosure;

Hereinafter, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them. The present invention may be embodied in several different forms and is not limited to the embodiments described herein.

1A and 1B show examples of computed tomography (CT) images for renal cell carcinoma (RCC).

1A is a computed tomography (CT) image of the kidney of a 43-year-old male. The portion indicated by the arrow indicates a tumor of clear cell renal cell carcinoma (CCRCC) with a Fuhrman grade of grade 2.

1B is a computed tomography (CT) image of the kidney of a 45-year-old male. The portion indicated by the arrow indicates a tumor of clear cell renal cell carcinoma (CCRCC) with a Fuhrman grade of grade 3.

2 is a flowchart of a method for providing information on the pathological grade of a tumor of renal cell carcinoma (RCC) according to various embodiments of the present disclosure.

In step S201, a computed tomography (CT) image of the RCC tumor is acquired. According to an embodiment, after receiving a CT image of the kidney, a CT image of the tumor of the RCC may be obtained by extracting a portion of the tumor image of the RCC from the CT image of the kidney. Alternatively, according to an embodiment, it may be obtained by receiving a tumor image portion of the RCC from the CT image of the kidney. According to an embodiment, the acquired CT image is a non-contrast enhanced CT (NCCT) image. According to one embodiment, the tumor is a tumor of clear cell renal cell carcinoma (CCRCC).

In step S203, a value of at least one image parameter is generated from the acquired image. According to an embodiment, the at least one image parameter may include an average Hounsfield unit (HU) value of the acquired image, the size of the tumor based on the acquired image, or a HU value of the acquired image. may include at least one of the standard deviations of .

In step S205, the pathological grade of the tumor is determined based on the comparison of the value of at least one image parameter with a predetermined threshold value. According to an embodiment, when the average HU value of the acquired image is 29.1 or more, it may be determined that the Fuhrman grade of the tumor is grade 3 or more. According to an embodiment, when the size of the tumor based on the acquired image is 3.8 cm or more, it may be determined that the Fuhrman grade of the tumor is grade 3 or more. According to an embodiment, when the standard deviation of the HU value of the acquired image is 14.2 or more, it may be determined that the Fuhrman grade of the tumor is grade 3 or more.

In step S207, the determined pathological grade is output. According to an embodiment, the determined pathological grade may be output through a display. According to an embodiment, the determined pathological grade may be transmitted using a communication module.

3 is a block diagram of an apparatus for providing information on the pathological grade of a tumor of renal cell carcinoma (RCC) according to various embodiments of the present disclosure.

Referring to FIG. 3 , an apparatus 300 for providing information on a pathological grade of an RCC tumor according to various embodiments of the present disclosure includes a memory 310 , an input/output unit 330 , and a processor 350 . do.

The memory 310 is connected to the processor 350 and stores data such as a basic program, an application program, and setting information for the operation of the processor 350 . The memory 350 may be configured as a volatile memory, a non-volatile memory, or a combination of a volatile memory and a non-volatile memory. In addition, the memory 350 provides stored data according to the request of the processor 230 . According to an embodiment, the memory 310 stores an image and a value of at least one image parameter calculated from the image.

The input/output unit 330 is connected to the processor 350 and inputs and/or outputs a CT image, information of a determined pathological grade, and the like. All or part of the input/output unit 330 may be referred to as an input device, an output device, or an input/output device. According to an embodiment, the input/output unit 330 may input and/or output images, information, etc. with other devices through a wired connection or a network. According to an embodiment, the input/output unit 330 may receive an image or information through an input device such as a keyboard or a mouse, and may output an image or information through a display device such as a display.

The processor 350 may be configured to implement the procedures and/or methods proposed in the present invention. The processor 350 controls overall operations of the apparatus 300 for providing information on the pathological grade of the tumor of the RCC. For example, the processor 350 inputs and outputs images and information through the input/output unit 330 . In addition, the processor 350 writes data to and reads data from the memory 310 . The processor 350 may include at least one processor.

Example

Experimental method

Data were collected from patients who underwent nephrectomy for clinically localized renal tumors from September 2011 to August 2014. Age, sex, body mass index (BMI), clinical tumor size, pathologic tumor size, tumor-lymph node-metastasis (TNM) stage, Fuhrman grade, and histology of tumor Patient characteristics such as subtype were evaluated. The patient's preoperative radiographic data (CT) was interpreted by a urologist-radiologist in the Department of Urology. The final pathology was determined using surgical specimens and reported by a urinary pathologist. Of the initial 423 patients, 50 were excluded from the study because they underwent the CT protocol without a non-contrast enhanced CT (NCCT) step.

Considering issues such as radiation dose and diagnostic efficacy, the inclusion of additional NCCT steps in contrast-enhanced CT to differentiate small renal mass (SRM) in CT protocols varies between physicians and institutions. In addition, in multiphasic contrast-enhanced CT, the time from injection to CT imaging was variable. To present reproducible and representative results, we tried to measure the HU parameters of SRM in NCCT. To this end, we collected data from patients who underwent contrast-enhanced CT in parallel with the NCCT phase.

imaging analysis

All images were collected and stored using a picture archiving and communication system (PACS) (GE Healthcare, Barrington, IL, USA). The SRM location identified on the contrast CT was used to match the location of the SRM at the NCCT stage. HU values for the elliptical region of interest (ROI) obtained from single slice measurements representing the widest section of the SRM among successive slices were maintained. The pixel count, minimum HU, maximum HU, mean HU, and standard deviation (SD) of HUs were recorded for each ROI using dedicated functions built into PACS.

Validation for confirmation of results for tumor differentiation

The most common among patients diagnosed with RCC was the clear cell type. Accordingly, from September 2014 to August 2016, the median time between renal biopsy and partial nephrectomy was 28.0 days, and renal biopsy for small renal mass (SRMs) prior to partial nephrectomy. We investigated the characteristics of 58 patients with clear cell RCC who underwent the test. Twelve of these patients were excluded as they performed a CT protocol without an NCCT step. Finally, 46 patients were evaluated.

The focus of the invention

It is the focus of the present invention to provide criteria for HU parameters to differentiate between tumor characteristics and histopathological characteristics in patients undergoing partial nephrectomy. In addition, the use of HU in clear cell RCC is a secondary focus to provide a predictive criterion of Fuhrman grade 3 or higher (≥III).

statistical analysis

Upgrade was defined as a higher Fuhrman grade at the final pathological examination than at the renal biopsy. Nipple type 1 or type 2 was classified as papillary type. Tumor grades were bifurcated into Fuhrman grade 2 or less (≤II) and 3 or higher (≥III) grades. Variables by category were evaluated using Fisher's exact test. Differences in variables in a continuous distribution across categories were assessed using the Mann-Whitney U test. The receiver operating characteristic (ROC) curve and the area under the ROC curves (AUC) were used to obtain the cutoff value. These optimal cutoff values were based on predefined values according to sensitivity analysis using Youden's Index. Multivariate regression analysis was performed for predictors of tumor aggressiveness with p values of 0.05 or less in univariate analysis. In the efficacy analysis, paired samples were tested to confirm that there was no difference between the pathological Fuhrman grade 3 or higher (≥III) and latent variables. All reported p-values were two-sided, and statistical significance was set to less than 0.05. Statistical analysis was performed using statistical software package version 23.0 on Windows (SPSS, Chicago, IL, USA).

4 illustrates baseline clinical and demographic characteristics according to various embodiments of the present invention.

Of the 373 patients (median age 54.0 years, tumor size 2.8 cm) included in this study, 357 (95.7%) were diagnosed with RCC. Among benign lesions, angiomyolipoma (12/16, 75.0%) was the most common tumor. Oncocytoma (1/16, 6.25%), leiomyoma (1/16, 6.25%), and metanephric adenoma (1/16, 6.25%) were also reported. Among cancer lesions, the clear cell type (323/357, 90.5%) was the most common subtype. There were no differences in age, BMI, aspect, or type of surgery between benign and cancerous lesions.

With increasing SRM, approximately 20% of patients report surgical removal of benign lesions. The outcome of surgical resection for benign lesions was 4.3%, which is lower than in previous studies.

5 depicts characterization of solid kidney tumors based on histological subtype in accordance with various embodiments of the present invention.

Figure 5 shows the clinicopathological features and values of HU-related variables in NCCT CT among hemangiomyolipomas and RCC subtypes. The tumor size of angiomyolipomas was lower compared to the subtypes of RCC. Among RCC subtypes, Fuhrman grade 3 or higher (≥III) were reported more frequently in the papillary or chromophobe type than in the clear cell type. Among the image analysis variables, there was a significant difference in the maximum and mean values of HU for angiomyolipoma and SRM of RCC subtypes. Notably, the mean HU of angiomyolipomas was the highest among HUs of the RCC subtype.

One of the purposes of the present invention was to determine whether it could be helpful in distinguishing between benign and cancerous lesions through image analysis, but the effective factors could not be identified. In particular, HU values vary between subtypes in both benign and cancerous lesions. In addition, the contrast enhancement pattern and the shape of the tumor should be considered. Therefore, the HU-related variables of NCCT alone were not useful for differentiating benign and cancerous lesions.

6 shows tumor sizes and Hounsfield units between Fuhrman grade 2 or less and grade 3 or higher according to subtypes of renal cell carcinoma (RCC) according to various embodiments of the present disclosure; unit, HU) shows a comparison of the measured values.

6 shows a comparison of Fuhrman grade 2 or less (≤II) versus 3 or greater (≥III) tumor size and HU measurements according to tumor type after partial nephrectomy in RCC patients. In particular, tumor size, number of pixels, mean HU, and SD of HU were significantly different between Fuhrman grade 2 or less (≤II) and 3 or higher (≥III) grades in patients with clear cell types. .

To confirm the pathological aggressiveness according to cancer subtype, we further investigated several variables related to HU, including number of pixels, minimum, maximum, mean HU, and SD of HU. The difference between mean HU and SD of HU in clear cell RCC was statistically significant. However, in other subtypes of RCC, we could not confirm that the difference was statistically significant due to the small number of cases. Therefore, the present invention more accurately investigated clear cell RCC.

7 illustrates univariate and multivariate analyzes of clear cell renal cell carcinoma (CCRCC) of Fuhrman grade 3 or higher according to various embodiments of the present disclosure;

Referring to FIG. 7 , clear cell RCCs with Fuhrman grade 3 or higher (≥III) were found in SRMs. Here, tumor size ≥3.8 cm (≥3.8 cm) (Odds ratio (OR) = 2.15 (95% confidence interval (CI): 1.259-3.680, 9 = 0.005) and mean HU (≥29.1) (OR = 3.21 (1.990-5.172), p < 0.001) was an independent predictor. Excluding the number of pixels as a potential variable, detrimental collinearity, as confirmed by observing that the variance expansion coefficient was less than 1.8, was ) was not present.

Accordingly, we determined that tumor size and mean HU are important predictors for tumor grade. Previous studies have reported that tumor aggressiveness increases with size. The US National Comprehensive Cancer Network (NCCN) guidelines also recommend active surveillance (AS) only for tumors within 4 cm.

Regarding the fact that a tumor size of 3.8 cm or more can be identified as a distinguishing factor between Fuhrman grade 2 or less (≤II) and 3 or more (≥III) grades, the results of the present invention are similar to those of previous studies. similar to the result. However, several cases with Fuhrman grade 3 or higher (≥III) were found when the tumor was less than 4 cm in size. In addition, when diagnosed with RCC on kidney biopsy, data from studies including kidney biopsy, including the present invention, show that no Fuhrman grade was reported in about 10-25% of cases. . Moreover, the grade difference between biopsy and final pathology was 57.6%, similar to the results of the previous study (43.3-69.8%). However, mean HU has been shown to be helpful in predicting final pathological grade, whether or not biopsy tumor grade is reported.

The present invention has two important clinical implications. First, the present invention is useful for selecting AS patients diagnosed with clear cell RCC on renal biopsy. In addition, in clear-cell RCC patients who did not present a valid or low Fuhrman grade on renal biopsy, the mean HU predicts the correct Fuhrman grade at the final pathology to determine whether the patient is suitable for AS. It will be useful to decide whether or not Second, by adding only the NCCT step without an invasive procedure such as an identifiable kidney biopsy during the subsequent treatment period, the present invention is useful for predicting whether a cancer will worsen. Therefore, it can provide monitoring and termination criteria for interventions.

To date, published data with tumor grade in imaging analysis when RCC is first diagnosed after partial nephrectomy are rare. A pathological downgrade of renal biopsy was confirmed in several studies. However, the present invention presents for the first time a new criterion for predicting Fuhrman grade in surgical specimens.

As a result of the experiment, 357 patients (95.7%) were diagnosed with RCC in 373 patients (median tumor size of 2.8 cm). The clear cell type (90.5%) was the most common subtype. Tumor size (≥3.8 cm) and mean HU (≥29.1) were independent predictors of clear cell RCC with Fuhrman grade 3 or higher (≥III). Of the 46 clear-cell RCC patients for validation, 13 patients did not report a Fuhrman grade on renal biopsy. There was an upgrade of the tumor in 19 patients (57.6%). Pathological Fuhrman grade 3 or higher (≥III) and mean HU was the only variable with no significant difference when compared to latent variables (p = 0.103).

In conclusion, mean HU for clear cell RCC may be useful for predicting Fuhrman grade 3 or higher (≥III) in surgical specimens. The present invention is useful for screening and monitoring active surveillance for RCC.

When implementing the embodiment of the present invention using hardware, ASICs (application specific integrated circuits) or DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices) configured to perform the present invention , FPGAs (field programmable gate arrays), etc. may be provided in the processor 350 of the present invention.

Meanwhile, the above-described method can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable medium. In addition, the structure of the data used in the above-described method may be recorded in a computer-readable storage medium through various means. Program storage devices that may be used to describe a storage device comprising executable computer code for performing the various methods of the present invention should not be construed as including transitory objects such as carrier waves or signals. do. The computer-readable storage medium includes a storage medium such as a magnetic storage medium (eg, a ROM, a floppy disk, a hard disk, etc.) and an optically readable medium (eg, a CD-ROM, a DVD, etc.).

The embodiments described above are those in which elements and features of the present invention are combined in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, it is also possible to configure an embodiment of the present invention by combining some elements and/or features. The order of operations described in embodiments of the present invention may be changed. Some configurations or features of one embodiment may be included in other embodiments, or may be replaced with corresponding configurations or features of other embodiments. It is obvious that claims that are not explicitly cited in the claims can be combined to form an embodiment or included as a new claim by amendment after filing.

It will be apparent to those skilled in the art to which the present invention pertains that the present invention can be embodied in other forms without departing from the spirit and essential characteristics of the present invention. Accordingly, the above embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the present invention should be determined by a reasonable interpretation of the appended claims and all possible changes within the equivalent scope of the present invention.

Claims (15)

A method for providing information on the pathological grade of a small renal mass (SRM) of clear cell renal cell carcinoma (CCRCC), the method comprising:
acquiring a contrast enhanced computed tomography (CECT) image for the SRM of CCRCC;
acquiring a non-contrast enhanced computed tomography (NCCT) image for the SRM of the CCRCC;
identifying the location of the SRM in the NCCT image using the location of the SRM identified in the CECT image;
calculating a value of at least one image parameter including an average Hounsfield unit (HU) value of the region of the SRM in the NCCT image;
determining the pathological grade of the SRM based on a comparison of the value of the at least one image parameter with a predetermined threshold value; and
outputting the determined pathological grade.
delete delete According to claim 1,
The at least one image parameter further comprises at least one of a size of the SRM in the NCCT image, or a standard deviation of a HU value of a region of the SRM in the NCCT image.
According to claim 1,
Determining the pathological grade of the SRM comprises:
and determining that the Fuhrman grade of the SRM is grade 3 or greater when the average HU value of the region of the SRM in the NCCT image is 29.1 or greater.
5. The method of claim 4,
Determining the pathological grade of the SRM comprises:
When the size of the SRM in the NCCT image is 3.8 cm or more, determining that the Fuhrman grade of the SRM is grade 3 or more.
5. The method of claim 4,
Determining the pathological grade of the SRM comprises:
and determining that the Fuhrman grade of the SRM is grade 3 or greater when the standard deviation of the HU value of the region of the SRM in the NCCT image is 14.2 or greater.
An apparatus for providing information on the pathological grade of a small renal mass (SRM) of clear cell renal cell carcinoma (CCRCC), the apparatus comprising:
memory in which images are stored;
input/output unit; and
at least one processor functionally connected to the memory and the input/output unit;
the at least one processor,
Obtaining a contrast enhanced computed tomography (CECT) image for the SRM of CCRCC,
Obtaining a non-contrast enhanced computed tomography (NCCT) image for the SRM of the CCRCC,
Identify the location of the SRM in the NCCT image using the location of the SRM identified in the CECT image,
calculating a value of at least one image parameter including an average Hounsfield unit (HU) value of the region of the SRM in the NCCT image,
determine the pathological grade of the SRM based on a comparison of the value of the at least one image parameter with a predetermined threshold,
an apparatus configured to output the determined pathological grade.
delete delete 9. The method of claim 8,
The at least one image parameter further comprises at least one of a size of the SRM in the NCCT image, or a standard deviation of a HU value of a region of the SRM in the NCCT image.
9. The method of claim 8,
the at least one processor,
In determining the pathological grade of the SRM,
and determine that the Fuhrman grade of the SRM is grade 3 or greater if the average HU value of the region of the SRM in the NCCT image is 29.1 or greater.
12. The method of claim 11,
the at least one processor,
In determining the pathological grade of the SRM,
and determine that a Fuhrman rating of the SRM is greater than or equal to three when the size of the SRM in the NCCT image is greater than or equal to 3.8 cm.
12. The method of claim 11,
the at least one processor,
In determining the pathological grade of the SRM,
and determine that the Fuhrman grade of the SRM is grade 3 or greater if the standard deviation of the HU value of the region of the SRM in the NCCT image is greater than or equal to 14.2.
A computer program configured to perform the method according to any one of claims 1 to 7, recorded on a computer-readable storage medium.

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