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Wen J, Rong Y, Kang Y, Lv D, Cui F, Zhou H, Jia M, Wang Q, Shuang W. Predictive nomogram for ischemic stroke risk in clear cell renal cell carcinoma patients. Sci Rep 2024; 14:30162. [PMID: 39627344 PMCID: PMC11615042 DOI: 10.1038/s41598-024-82072-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Accepted: 12/02/2024] [Indexed: 12/06/2024] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) and ischemic stroke are critical global health challenges with a notable association. This study explores the correlation between tumor-related factors and ischemic stroke risk, aiming to construct a predictive nomogram model for ischemic stroke in ccRCC patients. We retrospectively analyzed data from ccRCC patients who underwent nephrectomy at the First Hospital of Shanxi Medical University between January 1, 2013, and May 31, 2022. The data were randomly divided into a training cohort (70%) and a validation cohort (30%). Predictive factors were identified using univariate logistic regression, least absolute shrinkage and selection operator regression, and multivariate logistic regression. A nomogram and a Shiny local calculator were developed using these predictors. We identified six predictors for the nomogram: WHO/ISUP grade, diabetes, hypertension, LDL-C, age, and D-dimer. The nomogram showed good discrimination, with an area under the ROC curve of 0.816 in the training cohort and 0.775 in the validation cohort. The optimal cutoff value was 53.7%. The model demonstrated excellent calibration and clinical applicability. WHO/ISUP grade correlates with ischemic stroke risk, offering insights into cancer-related ischemic stroke mechanisms. This nomogram aids in identifying high-risk individuals among ccRCC patients, facilitating early management and improved outcomes.
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Affiliation(s)
- Jie Wen
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Yi Rong
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Yinbo Kang
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Dingyang Lv
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Fan Cui
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Huiyu Zhou
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Mohan Jia
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Qiwei Wang
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China
| | - Weibing Shuang
- Department of Urology, First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Yingze District, Taiyuan, Shanxi Province, China.
- Department of First Clinical Medical College, Shanxi Medical University, No.56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China.
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Han Y, Wang G, Zhang J, Pan Y, Cui J, Li C, Wang Y, Xu X, Xu B. The value of radiomics based on 2-[18 F]FDG PET/CT in predicting WHO/ISUP grade of clear cell renal cell carcinoma. EJNMMI Res 2024; 14:115. [PMID: 39570474 PMCID: PMC11582283 DOI: 10.1186/s13550-024-01182-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Accepted: 11/18/2024] [Indexed: 11/22/2024] Open
Abstract
BACKGROUND The aim is to develop and validate radiomics based on 2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) parameters for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of clear cell renal cell carcinoma (ccRCC). METHODS A total of 209 patients with 214 lesions, who underwent 2-[18F]FDG PET/CT scans between December 2016 to December 2023, were included in our study. All ccRCC lesions were categorized into low grade (WHO/ISUP grade I-II) and high grade (WHO/ISUP grade III-IV). The lesions were allocated into a training group and a testing group in a ratio of 7:3. The radiomics features were extracted by a serious of maximum standardized uptake value (SUVmax) thresholds (0,2.5%,25%,40%) with the utilization of the minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression algorithm. The clinical, radiomics and combined models were constructed. The receiver operating characteristic (ROC) curve, decision curve and calibration curves were plotted to assess the predicting performance. RESULTS The area under curve (AUC) of PET-0, PET-2.5%, PET-25%, PET-40% model in the training group were 0.881(95% CI: 0.822-0.940),0.883(95% CI: 0.825-0.942),0.889(95% CI: 0.831-0.946),0.887(95% CI: 0.826-0.948); and 0.878(95% CI: 0.777-0.978),0.876(95% CI: 0.776-0.977),0.871(95% CI: 0.769-0.972),0.882(95% CI: 0.786-0.979) in the testing group. Due to perfect prediction and verification performance, the volume of interest (VOI) from PET images with SUVmax threshold of 40% were selected to construct the radiomics model and combined model. The AUC of the clinical model and radiomics model was 0.859 (sensitivity = 0.846, specificity = 0.747) and 0.909 (sensitivity = 0.808, specificity = 0.751) in the training group, respectively; 0.882 (sensitivity = 0.857, specificity = 0.857) and 0.901 (sensitivity = 0.905, specificity = 0.833) in the testing group, respectively. In combined models, the AUC was 0.916, the sensitivity was 0.923 and the specificity was 0.808 in the training group; the AUC was 0.916, the sensitivity was 0.881 and the specificity was 0.792 in the training group. CONCLUSION Radiomics based on 2-[18F]FDG PET/CT can be helpful to predict WHO/ISUP grade of ccRCC.
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Affiliation(s)
- Yun Han
- Graduate School, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Guanyun Wang
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Nuclear Medicine Department, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, China
| | - Jingfeng Zhang
- Graduate School, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yue Pan
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jianbo Cui
- Graduate School, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Can Li
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yanmei Wang
- GE Healthcare China, Pudong New Town, Shanghai, China
| | - Xiaodan Xu
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Baixuan Xu
- Graduate School, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
- Department of Nuclear Medicine, The First Medical Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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Wang S, Zhu C, Jin Y, Yu H, Wu L, Zhang A, Wang B, Zhai J. A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma. Insights Imaging 2023; 14:207. [PMID: 38010567 PMCID: PMC10682311 DOI: 10.1186/s13244-023-01557-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/01/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVES This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC). METHODS In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell's concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration. RESULTS The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912-0.980) and 0.864 (95% CI: 0.734-0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group. CONCLUSIONS The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC. CRITICAL RELEVANCE STATEMENT We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades. KEY POINTS 1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells.
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Affiliation(s)
- Shihui Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Yidong Jin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, People's Republic of China
| | - Hongqing Yu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Lili Wu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Aijuan Zhang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Beibei Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China
| | - Jian Zhai
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, People's Republic of China.
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Komiyama T, Kim H, Tanaka M, Isaki S, Yokoyama K, Miyajima A, Kobayashi H. RNA-seq and Mitochondrial DNA Analysis of Adrenal Gland Metastatic Tissue in a Patient with Renal Cell Carcinoma. BIOLOGY 2022; 11:biology11040589. [PMID: 35453788 PMCID: PMC9030821 DOI: 10.3390/biology11040589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/02/2022] [Accepted: 04/11/2022] [Indexed: 01/27/2023]
Abstract
This study aimed to clarify whether genetic mutations participate in renal cell carcinoma (RCC) metastasis to the adrenal gland (AG). Our study analyzed whole mitochondrial gene and ribonucleic acid sequencing (RNA-seq) data from a male patient in his 60s with metastatic RCC. We confirmed common mutation sites in the mitochondrial gene and carried out Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using RNA-seq data for RCC and adrenal carcinoma. Furthermore, we confirmed the common mutation sites of mitochondrial genes in which the T3394Y (p.H30Y) site transitioned from histidine (His.; H) to tyrosine (Tyr.; Y) in the NADH dehydrogenase subunit 1 (ND1) gene. The R11,807G (p.T350A) site transitioned from threonine (Thr.; T) to alanine (Ala.; A). Additionally, the G15,438R or A (p.G231D) site transitioned from glycine (Gly.; G) to aspartic acid (Asp.; D) in cytochrome b (CYTB). Furthermore, pathway analysis, using RNA-seq, confirmed the common mutant pathway between RCC and adrenal carcinoma as cytokine–cytokine receptor (CCR) interaction. Confirmation of the original mutation sites suggests that transfer to AG may be related to the CCR interaction. Thus, during metastasis to the AG, mitochondria DNA mutation may represent the initial origin of the metastasis, followed by the likely mutation of the nuclear genes.
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Affiliation(s)
- Tomoyoshi Komiyama
- Department of Clinical Pharmacology, Tokai University School of Medicine, Isehara 259-1193, Kanagawa, Japan;
- Correspondence: (T.K.); (H.K.); Tel.: +81-463-93-1121 (T.K.)
| | - Hakushi Kim
- Department of Urology, Tokai University Hachioji Hospital, Tokyo 192-0032, Japan
- Correspondence: (T.K.); (H.K.); Tel.: +81-463-93-1121 (T.K.)
| | - Masayuki Tanaka
- Medical Science College Office, Tokai University, Isehara 259-1193, Kanagawa, Japan; (M.T.); (S.I.); (K.Y.)
| | - Sanae Isaki
- Medical Science College Office, Tokai University, Isehara 259-1193, Kanagawa, Japan; (M.T.); (S.I.); (K.Y.)
| | - Keiko Yokoyama
- Medical Science College Office, Tokai University, Isehara 259-1193, Kanagawa, Japan; (M.T.); (S.I.); (K.Y.)
| | - Akira Miyajima
- Department of Urology, Tokai University School of Medicine, Isehara 259-1193, Kanagawa, Japan;
| | - Hiroyuki Kobayashi
- Department of Clinical Pharmacology, Tokai University School of Medicine, Isehara 259-1193, Kanagawa, Japan;
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Zhang L, Zhao H, Jiang H, Zhao H, Han W, Wang M, Fu P. 18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (NY) 2021; 46:5618-5628. [PMID: 34455450 PMCID: PMC8590655 DOI: 10.1007/s00261-021-03246-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE This article analyzes the image heterogeneity of clear cell renal cell carcinoma (ccRCC) based on positron emission tomography (PET) and positron emission tomography-computed tomography (PET/CT) texture parameters, and provides a new objective quantitative parameter for predicting pathological Fuhrman nuclear grading before surgery. METHODS A retrospective analysis was performed on preoperative PET/CT images of 49 patients whose surgical pathology was ccRCC, 27 of whom were low grade (Fuhrman I/II) and 22 of whom were high grade (Fuhrman III/IV). Radiological parameters and standard uptake value (SUV) indicators on PET and computed tomography (CT) images were extracted by using the LIFEx software package. The discriminative ability of each texture parameter was evaluated through receiver operating curve (ROC). Binary logistic regression analysis was used to screen the texture parameters with distinguishing and diagnostic capabilities and whose area under curve (AUC) > 0.5. DeLong's test was used to compare the AUCs of PET texture parameter model and PET/CT texture parameter model with traditional maximum standardized uptake value (SUVmax) model and the ratio of tumor SUVmax to liver SUVmean (SUL)model. In addition, the models with the larger AUCs among the SUV models and texture models were prospectively internally verified. RESULTS In the ROC curve analysis, the AUCs of SUVmax model, SUL model, PET texture parameter model, and PET/CT texture parameter model were 0.803, 0.819, 0.873, and 0.926, respectively. The prediction ability of PET texture parameter model or PET/CT texture parameter model was significantly better than SUVmax model (P = 0.017, P = 0.02), but it was not better than SUL model (P = 0.269, P = 0.053). In the prospective validation cohort, both the SUL model and the PET/CT texture parameter model had good predictive ability, and the AUCs of them were 0.727 and 0.792, respectively. CONCLUSION PET and PET/CT texture parameter models can improve the prediction ability of ccRCC Fuhrman nuclear grade; SUL model may be the more accurate and easiest way to predict ccRCC Fuhrman nuclear grade.
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Affiliation(s)
- Linhan Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Hong Zhao
- Department of Nuclear Medicine, ShenZhen People's Hospital, ShenZhen, China
| | - Wei Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Mengjiao Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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Li X, Xu Z, Xu T, Qi F, Song N. Basic Characteristics and Survival Outcomes of Asian-American Patients with Clear Cell Renal Cell Carcinoma and Comparisons with White Patients: A Population-Based Analysis. Int J Gen Med 2021; 14:7869-7883. [PMID: 34795508 PMCID: PMC8593352 DOI: 10.2147/ijgm.s340284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/21/2021] [Indexed: 12/16/2022] Open
Abstract
Background To explore the baseline characteristics, pathological and survival outcomes of Asian-American patients with clear cell renal cell carcinoma (ccRCC), and make comparisons with White patients. Materials and Methods In this study, patients diagnosed with ccRCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Basic characteristics of Asian-American patients were analysed and compared with White patients. Then, proportional mortality ratio (PMR) analyses were performed in Asian population to investigate the proportions of different cause of deaths (CODs), and make comparisons with White patients. Moreover, Kaplan-Meier (KM) analyses were developed to investigate the survival disparities of ccRCC patients between Asian-Americans and White patients. Finally, a competing risk regression model was constructed to identify potential prognostic factors for ccRCC patients in the whole population. Results A total of 1586 Asian-American patients were eventually identified, and the median age at diagnosis was 61 years old. In Asian patients, those from South Asian had the youngest age at diagnosis (P<0.001) and the earliest stage of diseases (localized: 76.83%, T1: 70.73%, all P<0.05) when compared with other ethnicities. No significant differences were detected in tumor characteristics between Asian-Americans and White patients. Older age (P<0.001), earlier stage (P<0.001) and the administration of surgery (P=0.050) were tightly associated with a lower risk of dying of RCC in Asian-American patients. Additionally, Asian-American patients had comparable survival outcomes when compared with White patients. Lastly, competing risk regression model revealed that age at diagnosis (P<0.001), tumor grade (P<0.001), histological stage (P<0.001), median household income (P<0.001) and the administration of surgery (P<0.001) were prognostic factors for cancer-specific survival (CSS) in ccRCC patients, while died of other causes was regarded as a competing event. Conclusion Asian-American patients had similar tumor characteristics and survival outcomes with White patients. In Asian patients, those from South Asian had the youngest age at diagnosis and the earliest stage of diseases. Age, grade, histological stage, household income and surgery were identified to be closely related to CSS in ccRCC patients. In the future, prospective and well-designed studies are needed to verify our findings.
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Affiliation(s)
- Xiao Li
- Department of Urology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, People's Republic of China
| | - Zicheng Xu
- Department of Urology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, People's Republic of China
| | - Ting Xu
- Department of Urology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, People's Republic of China
| | - Feng Qi
- Department of Urology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210009, People's Republic of China
| | - Ninghong Song
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, People's Republic of China
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Demirjian NL, Varghese BA, Cen SY, Hwang DH, Aron M, Siddiqui I, Fields BKK, Lei X, Yap FY, Rivas M, Reddy SS, Zahoor H, Liu DH, Desai M, Rhie SK, Gill IS, Duddalwar V. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur Radiol 2021; 32:2552-2563. [PMID: 34757449 DOI: 10.1007/s00330-021-08344-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/26/2021] [Accepted: 09/24/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.
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Affiliation(s)
| | - Bino A Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Y Cen
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darryl H Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Imran Siddiqui
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaomeng Lei
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Felix Y Yap
- Radiology Associates of San Luis Obispo, Atascadero, CA, USA
| | - Marielena Rivas
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sharath S Reddy
- Department of Urology, Yale New Haven Hospital, New Haven, CT, USA
| | - Haris Zahoor
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Derek H Liu
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mihir Desai
- Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Inderbir S Gill
- Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. .,Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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Wang X, Song G, Jiang H, Zheng L, Pang P, Xu J. Can texture analysis based on single unenhanced CT accurately predict the WHO/ISUP grading of localized clear cell renal cell carcinoma? Abdom Radiol (NY) 2021; 46:4289-4300. [PMID: 33909090 DOI: 10.1007/s00261-021-03090-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/08/2021] [Accepted: 04/10/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The purpose was to investigate the value of texture analysis in predicting the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of localized clear cell renal cell carcinoma (ccRCC) based on unenhanced CT (UECT). MATERIALS AND METHODS Pathologically confirmed subjects (n = 104) with localized ccRCC who received UECT scanning were collected retrospectively for this study. All cases were classified into low grade (n = 53) and high grade (n = 51) according to the WHO/ISUP grading and were randomly divided into training set and test set as a ratio of 7:3. Using 3D-ROI segmentation on UECT images and extracted ninety-three texture features (first-order, gray-level co-occurrence matrix [GLCM], gray-level run length matrix [GLRLM], gray-level size zone matrix [GLSZM], neighboring gray tone difference matrix [NGTDM] and gray-level dependence matrix [GLDM] features). Univariate analysis and the least absolute shrinkage selection operator (LASSO) regression were used for feature dimension reduction, and logistic regression classifier was used to develop the prediction model. Using receiver operating characteristic (ROC) curve, bar chart and calibration curve to evaluate the performance of the prediction model. RESULTS Dimension reduction screened out eight optimal texture features (maximum, median, dependence variance [DV], long run emphasis [LRE], run entropy [RE], gray-level non-uniformity [GLN], gray-level variance [GLV] and large area low gray-level emphasis [LALGLE]), and then the prediction model was developed according to the linear combination of these features. The accuracy, sensitivity, specificity, and AUC of the model in training set were 86.1%, 91.4%, 81.1%, and 0.937, respectively. The accuracy, sensitivity, specificity, and AUC of the model in test set were 81.2%, 81.2%, 81.2%, and 0.844, respectively. The calibration curves showed good calibration both in training set and test set (P > 0.05). CONCLUSION This study has demonstrated that the radiomics model based on UECT texture analysis could accurately evaluate the WHO/ISUP grading of localized ccRCC.
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Affiliation(s)
- Xu Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | - Ge Song
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China.
| | - Linfeng Zheng
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
| | | | - Jingjing Xu
- Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
- Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), No 1, Banshan East Road, Hangzhou, 310022, Zhejiang, China
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9
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Browning L, Colling R, Verrill C. WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade. Diagn Pathol 2021; 16:75. [PMID: 34419085 PMCID: PMC8380382 DOI: 10.1186/s13000-021-01130-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 07/12/2021] [Indexed: 11/10/2022] Open
Abstract
Background There are recognised potential pitfalls in digital diagnosis in urological pathology, including the grading of dysplasia. The World Health Organisation/International Society of Urological Pathology (WHO/ISUP) grading system for renal cell carcinoma (RCC) is prognostically important in clear cell RCC (CCRCC) and papillary RCC (PRCC), and is included in risk stratification scores for CCRCC, thus impacting on patient management. To date there are no systematic studies examining the concordance of WHO/ISUP grading between digital pathology (DP) and glass slide (GS) images. We present a validation study examining intraobserver agreement in WHO/ISUP grade of CCRCC and PRCC. Methods Fifty CCRCCs and 10 PRCCs were graded (WHO/ISUP system) by three specialist uropathologists on three separate occasions (DP once then two GS assessments; GS1 and GS2) separated by wash-out periods of at least two-weeks. The grade was recorded for each assessment, and compared using Cohen’s and Fleiss’s kappa. Results There was 65 to 78% concordance of WHO/ISUP grading on DP and GS1. Furthermore, for the individual pathologists, the comparative kappa scores for DP versus GS1, and GS1 versus GS2, were 0.70 and 0.70, 0.57 and 0.73, and 0.71 and 0.74, and with no apparent tendency to upgrade or downgrade on DP versus GS. The interobserver kappa agreement was less, at 0.58 on DP and 0.45 on GS. Conclusion Our results demonstrate that the assessment of WHO/ISUP grade on DP is noninferior to that on GS. There is an apparent slight improvement in agreement between pathologists on RCC grade when assessed on DP, which may warrant further study.
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Affiliation(s)
- Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK. .,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, OX3 9DU, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headley Way, OX3 9DU, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, OX3 9DU, Oxford, UK
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10
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Shao Y, Zhang YX, Chen HH, Lu SS, Zhang SC, Zhang JX. Advances in the application of artificial intelligence in solid tumor imaging. Artif Intell Cancer 2021; 2:12-24. [DOI: 10.35713/aic.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/02/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Ying Shao
- Department of Laboratory Medicine, People Hospital of Jiangying, Jiangying 214400, Jiangsu Province, China
| | - Yu-Xuan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Huan-Huan Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shan-Shan Lu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Shi-Chang Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
| | - Jie-Xin Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
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11
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Yi X, Xiao Q, Zeng F, Yin H, Li Z, Qian C, Wang C, Lei G, Xu Q, Li C, Li M, Gong G, Zee C, Guan X, Liu L, Chen BT. Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma. Front Oncol 2021; 10:570396. [PMID: 33585193 PMCID: PMC7873602 DOI: 10.3389/fonc.2020.570396] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 12/08/2020] [Indexed: 12/16/2022] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery. Methods Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis. Results A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively. Conclusion We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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Affiliation(s)
- Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qiao Xiao
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Feiyue Zeng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongling Yin
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zan Li
- Xiangya School of Medicine, Central-South University, Changsha, China
| | - Cheng Qian
- Xiangya School of Medicine, Central-South University, Changsha, China
| | - Cikui Wang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Guangwu Lei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Qingsong Xu
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Chuanquan Li
- School of Mathematics and Statistics, Central South University, Changsha, China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Guanghui Gong
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Chishing Zee
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Xiao Guan
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Longfei Liu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
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12
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Xiao Q, Yi X, Guan X, Yin H, Wang C, Zhang L, Pang Y, Li M, Gong G, Chen D, Liu L. Validation of the World Health Organization/International Society of Urological Pathology grading for Chinese patients with clear cell renal cell carcinoma. Transl Androl Urol 2020; 9:2665-2674. [PMID: 33457238 PMCID: PMC7807344 DOI: 10.21037/tau-20-799] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Background This study aimed to compare the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system and the Fuhrman grading system and to verify the WHO/ISUP grade as a prognostic parameter of clear cell renal cell carcinoma (ccRCC) in a Chinese population. Methods The study consisted of 753 ccRCC patients treated with curative surgery between 2010 and 2018 at Xiangya Hospital Central South University (Changsha, China). All pathologic data were retrospectively reviewed by two pathologists. Cancer-specific survival (CSS) and recurrence-free survival (RFS) were examined as clinical outcomes. Results According to the WHO/ISUP grading system (ISUP group), nephrectomy type, pT stage and WHO/ISUP grade were independent risk factors for CSS (P<0.0001, P=0.0127 and P<0.0001, respectively) and RFS (P<0.0001, P=0.0077, and P<0.0001, respectively). In the Fuhrman group, nephrectomy type, pT stage and Fuhrman grade were independent risk factors for CSS (P<0.0001, P=0.0004, and P<0.0001, respectively) and RFS (P<0.0001, P=0.0001, and P<0.0001, respectively). The C-index for CSS and RFS using the Fuhrman grading system was 0.6323 and 0.6342, respectively, and that using the WHO/ISUP grading system was 0.6983 and 0.7005, respectively, both higher than the former (P=0.0185, and P=0.0172, respectively). In addition, upgrading from Fuhrman grade 2 to ISUP grade 3 resulted in worse CSS and RFS for ccRCC patients (P=0.0033 and P =0.0003, respectively). Conclusions We first verified correlations between the postoperative prognosis and WHO/ISUP grade of ccRCC in a Chinese population and confirmed that the ability to predict clinical outcomes with the WHO/ISUP grading system was superior to that with the Fuhrman grading system.
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Affiliation(s)
- Qiao Xiao
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiao Guan
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Hongling Yin
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Cikui Wang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Liang Zhang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Yingxian Pang
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Guanghui Gong
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Danlei Chen
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Longfei Liu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
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2-[ 18F]FDG PET/CT parameters associated with WHO/ISUP grade in clear cell renal cell carcinoma. Eur J Nucl Med Mol Imaging 2020; 48:570-579. [PMID: 32814979 DOI: 10.1007/s00259-020-04996-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/10/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE To explore the potential parameters from preoperative 2-[18F]FDG PET/CT that might associate with the World Health Organization/the International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC). METHODS One hundred twenty-five patients with newly diagnosed ccRCC who underwent 2-[18F]FDG PET/CT prior to surgery or biopsy were retrospectively reviewed. The metabolic parameters and imaging features obtained from 2-[18F]FDG PET/CT examinations were analyzed in combination with clinical characteristics. Univariate and multivariate logistic regression analyses were performed to identify the predictive factors of WHO/ISUP grade. RESULTS Metabolic parameters of primary tumor maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), and tumor-to-kidney SUV ratio (TKR) were significantly different between any two of the four different WHO/ISUP grades, except those between the WHO/ISUP grade 3 and grade 4. The optimal cutoff values to predict high WHO/ISUP grade for SUVmax, TLR, and TKR were 4.15, 1.63, and 1.59, respectively. TLR (AUC: 0.841) was superior to TKR (AUC: 0.810) in distinguishing high and low WHO/ISUP grades (P = 0.0042). In univariate analysis, SUVmax, TLR, TKR, primary tumor size, tumor thrombus, distant metastases, and clinical symptoms could discriminate between the high and low WHO/ISUP grades (P < 0.05). In multivariate analysis, TLR (P < 0.001; OR: 1.732; 95%CI: 1.289-2.328) and tumor thrombus (P < 0.001; OR: 6.199; 95%CI: 2.499-15.375) were significant factors for differentiating WHO/ISUP grades. CONCLUSION Elevated TLR (> 1.63) and presence of tumor thrombus from preoperative 2-[18F]FDG PET/CT can distinguish high WHO/ISUP grade ccRCC effectively. 2-[18F]FDG PET/CT may be a feasible method for noninvasive assessment of WHO/ISUP grade.
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Liu L, Yi X, Lu C, Qi L, Zhang Y, Li M, Xiao Q, Wang C, Zhang L, Pang Y, Wang Y, Guan X. Applications of radiomics in genitourinary tumors. Am J Cancer Res 2020; 10:2293-2308. [PMID: 32905456 PMCID: PMC7471369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023] Open
Abstract
Genitourinary tumors are heterogeneous groups of tumors with high morbidity and mortality rates. Confronted with existing problems in the management of genitourinary tumors, a personalized imaging method called radiomics shows great potential in areas including detection, grading, and treatment response assessment. Radiomics is characterized by extraction of quantitative imaging features which are not visible to the naked eye from conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT), followed by data analysis and model building. It outperforms other invasive methods in terms of non-invasiveness, low cost and high efficiency. Recently, a number of studies have evaluated the application of radiomics in patients with genitourinary tumors with promising data. The combination of radiomics and clinical/laboratory factors provides added value in many studies. Despite this, there are limitations and challenges to be overcome before a more extensive clinical application in the future. In this article, we will introduce the concept, significance and workflow of radiomics, review their current applications in patients with genitourinary tumors and discuss limitations and future directions of radiomics. It would help multidisciplinary team involved in the treatment of patients with genitourinary tumors to achieve a better understanding of the results of radiomics study toward a personalized medicine.
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Affiliation(s)
- Longfei Liu
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Xiaoping Yi
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- Department of Radiology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Can Lu
- Department of Nephrology, The Second Xiangya Hospital of Central South UniversityChangsha 410000, Hunan, P. R. China
| | - Lin Qi
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Minghao Li
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Qiao Xiao
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Cikui Wang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Liang Zhang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Yingxian Pang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Yong Wang
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
| | - Xiao Guan
- Department of Urology, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South UniversityChangsha 410008, Hunan, P. R. China
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Cui E, Li Z, Ma C, Li Q, Lei Y, Lan Y, Yu J, Zhou Z, Li R, Long W, Lin F. Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics. Eur Radiol 2020; 30:2912-2921. [PMID: 32002635 DOI: 10.1007/s00330-019-06601-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/13/2019] [Accepted: 11/26/2019] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation. RESULTS Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC. CONCLUSIONS MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging. KEY POINTS • Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.
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Affiliation(s)
- Enming Cui
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Zhuoyong Li
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Changyi Ma
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Qing Li
- Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Yi Lei
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China
| | - Yong Lan
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Juan Yu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China
| | - Zhipeng Zhou
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China.
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China.
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Histopathological Prognostic Factors in Clear Cell Renal Cell Carcinoma. CURRENT HEALTH SCIENCES JOURNAL 2019; 44:201-205. [PMID: 30647938 PMCID: PMC6311217 DOI: 10.12865/chsj.44.03.01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 06/25/2018] [Indexed: 11/18/2022]
Abstract
Clear cell renal cell carcinoma (CCRCC) are the most frequent type of renal cell
carcinoma. Fuhrman grade and tumor stage are prognostic factors with great importance
in survival rate. This study was performed on 75 cases of CCRCC diagnosed by the
Anatomical Pathology Laboratory of the County Clinical Emergency Hospital of Craiova
between 2014 and 2017. The biological material was represented by pieces of nephrectomy.
The cases were analyzed on two criteria: epidemiology (age, sex) and histopathology
(Fuhrman grade, tumor stage, architectural pattern, sarcomatoid transformation, and
necrosis). Statistical analysis was done using Chi Square tests in IBM SPSS software.
Average diagnosis age of CCRCC was 58.8±10.2 years, predominantly in male patients
(66.7%). Tumor sizes were between 2 and 14cm, with an average of 6.7±2.9cm.
Most cases were determined to be tumor stage III (60%) and Fuhrman grade 2 (56%),
followed, in order of frequency, by tumor stages I and II (28% and 10.7%) and Fuhrman
grades 3 and 1 (21.3% and 20%). High Fuhrman grade CCRCC were significantly associated
with advanced tumor stage (p<0.05, χ2 test). Most cases presented a mixed pattern,
significantly associated with advanced tumor stages (p<0.05, χ2 test). Even though
the presence of sarcomatoid transformation was more frequent in advanced tumor stages,
it wasn’t significantly linked to them (p<0.05, χ2 test). Conclusions:
Analyzed histopathological parameters are useful for determining CCRCC aggressiveness.
CCRCC in advanced tumor stages is associated with high Fuhrman grade and mixed
architectural pattern.
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Borgmann H, Musquera M, May M, Brookman-May SD. Answer to comment on manuscript "Prognostic significance of Fuhrman grade and age for cancer-specific and overall survival in patients with papillary renal cell carcinoma: results of an international multi-institutional study on 2189 patients". World J Urol 2018; 36:2091-2092. [PMID: 30022407 DOI: 10.1007/s00345-018-2383-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 06/16/2018] [Indexed: 10/28/2022] Open
Affiliation(s)
- H Borgmann
- Department of Urology, University Hospital Mainz, Mainz, Germany
| | - M Musquera
- Hospital Clínic, University of Barcelona, Barcelona, Spain.
| | - M May
- Department of Urology, Klinikum St. Elisabeth Straubing, Straubing, Germany
| | - S D Brookman-May
- Department of Urology, Ludwig-Maximilians-University, Campus Grosshadern, Marchionistrasse 15, 81377, Munich, Germany
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