The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs).
CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3–4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change.
The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67.
Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis
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Moreno CC, Hemingway J, Johnson AC, Hughes DR, Mittal PK, Duszak R, Jr. Changing Abdominal Imaging Utilization Patterns: Perspectives From Medicare Beneficiaries Over Two Decades. J Am Coll Radiol 2016; 13:894-903
Chow WH, Devesa SS, Warren JL, Fraumeni JF, Jr. Rising incidence of renal cell cancer in the United States. JAMA 1999; 281:1628-1631
Cho E, Adami HO, Lindblad P. Epidemiology of renal cell cancer. Hematol Oncol Clin North Am 2011; 25:651-665
Gandaglia G, Ravi P, Abdollah F, et al. Contemporary incidence and mortality rates of kidney cancer in the United States. Can Urol Assoc J 2014; 8:247-252
Nguyen MM, Gill IS, Ellison LM. The evolving presentation of renal carcinoma in the United States: trends from the Surveillance, Epidemiology, and End Results program. J Urol 2006; 176:2397–2400; discussion 2400
Smith-Bindman R, Kwan ML, Marlow EC, et al. Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA 2019; 322:843-856
Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366:883-892
Ball MW, Bezerra SM, Gorin MA, et al. Grade Heterogeneity in Small Renal Masses: Potential Implications for Renal Mass Biopsy. J Urol 2014;
Halverson SJ, Kunju LP, Bhalla R, et al. Accuracy of determining small renal mass management with risk stratified biopsies: confirmation by final pathology. J Urol 2013; 189:441-446
Silverman SG, Israel GM, Herts BR, Richie JP. Management of the incidental renal mass. Radiology 2008; 249:16-31
Volpe A, Cadeddu JA, Cestari A, et al. Contemporary management of small renal masses. Eur Urol 2011; 60:501-515
Volpe A, Finelli A, Gill IS, et al. Rationale for percutaneous biopsy and histologic characterisation of renal tumours. Eur Urol 2012; 62:491-504
Kapur P, Pena-Llopis S, Christie A, et al. Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. Lancet Oncol 2013; 14:159-167
Shuch B, Bratslavsky G, Linehan WM, Srinivasan R. Sarcomatoid renal cell carcinoma: a comprehensive review of the biology and current treatment strategies. Oncologist 2012; 17:46-54
Shuch B, Bratslavsky G, Shih J, et al. Impact of pathological tumour characteristics in patients with sarcomatoid renal cell carcinoma. BJU Int 2012; 109:1600-1606
Karlo CA, Di Paolo PL, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 2014; 270:464-471
Lebret T, Poulain JE, Molinie V, et al. Percutaneous core biopsy for renal masses: indications, accuracy and results. J Urol 2007; 178:1184–1188; discussion 1188
Leng S, Takahashi N, Gomez Cardona D, et al. Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT. Abdom Radiol (NY) 2016;
Lubner MG, Stabo N, Abel EJ, Del Rio AM, Pickhardt PJ. CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes. AJR Am J Roentgenol 2016; 207:96-105
Millet I, Curros F, Serre I, Taourel P, Thuret R. Can renal biopsy accurately predict histological subtype and Fuhrman grade of renal cell carcinoma? J Urol 2012; 188:1690-1694
Pierorazio PM, Hyams ES, Tsai S, et al. Multiphasic enhancement patterns of small renal masses (</=4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma. Urology 2013; 81:1265-1271
Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT Texture Analysis of Renal Masses: Pilot Study Using Random Forest Classification for Prediction of Pathology. Acad Radiol 2014; 21:1587-1596
Raman SP, Johnson PT, Allaf ME, Netto G, Fishman EK. Chromophobe renal cell carcinoma: multiphase MDCT enhancement patterns and morphologic features. AJR Am J Roentgenol 2013; 201:1268-1276
Schieda N, Thornhill RE, Al-Subhi M, et al. Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis. AJR Am J Roentgenol 2015; 204:1013-1023
Scrima AT, Lubner MG, Abel EJ, et al. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers. Abdom Radiol (NY) 2018;
Takeuchi M, Kawai T, Suzuki T, et al. MRI for differentiation of renal cell carcinoma with sarcomatoid component from other renal tumor types. Abdom Imaging 2014;
Wang W, Ding J, Li Y, et al. Magnetic Resonance Imaging and Computed Tomography Characteristics of Renal Cell Carcinoma Associated with Xp11.2 Translocation/TFE3 Gene Fusion. PLoS One 2014; 9:e99990
Young JR, Margolis D, Sauk S, Pantuck AJ, Sayre J, Raman SS. Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT. Radiology 2013; 267:444-453
Abel EJ, Culp SH, Matin SF, et al. Percutaneous biopsy of primary tumor in metastatic renal cell carcinoma to predict high risk pathological features: comparison with nephrectomy assessment. J Urol 2010; 184:1877-1881
Bektas CT, Kocak B, Yardimci AH, et al. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade. Eur Radiol 2019; 29:1153-1163
He X, Wei Y, Zhang H, et al. Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images. Acad Radiol 2020; 27:157-168
Sun X, Liu L, Xu K, et al. Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images. Medicine (Baltimore) 2019; 98:e15022
Shu J, Wen D, Xi Y, et al. Clear cell renal cell carcinoma: Machine learning-based computed tomography radiomics analysis for the prediction of WHO/ISUP grade. Eur J Radiol 2019; 121:108738
Haji-Momenian S, Lin Z, Patel B, et al. Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study. Abdom Radiol (NY) 2020; 45:789-798
Lin F, Cui EM, Lei Y, Luo LP. CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (NY) 2019; 44:2528-2534
Kocak B, Durmaz ES, Ates E, Kaya OK, Kilickesmez O. Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning-Based Study for Predicting Histopathologic Nuclear Grade. AJR Am J Roentgenol 2019:W1-W8
Dreyfuss LD LM, Nystrom J, Stabo N, Pickhart PJ, Abel EJ. Texture Analysis of Large Renal Cell Carcinoma: Comparing the Performance of Texture Analysis Platforms in Predicting Histologic Findings and Clinical Outcomes. AJR Am J Roentgenol 2021; in press
Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 2017; 37:1483-1503
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res 2011; 12:2825-2830
Abel EJ, Carrasco A, Culp SH, et al. Limitations of preoperative biopsy in patients with metastatic renal cell carcinoma: comparison to surgical pathology in 405 cases. BJU Int 2012; 110:1742-1746
Yan L, Chai N, Bao Y, Ge Y, Cheng Q. Enhanced Computed Tomography-Based Radiomics Signature Combined With Clinical Features in Evaluating Nuclear Grading of Renal Clear Cell Carcinoma. J Comput Assist Tomogr 2020; 44:730-736
Han D, Yu Y, Yu N, et al. Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT. Br J Radiol 2020; 93:20200131
Meng X, Shu J, Xia Y, Yang R. A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma. Biomed Res Int 2020; 2020:7103647
Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings. PLoS One 2016; 11:e0166550
Mackin D, Fave X, Zhang L, et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol 2015; 50:757-765
Shafiq-Ul-Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 2017;
Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E. Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm. Radiology 2016; 279:185-194
van Timmeren JE, Leijenaar RTH, van Elmpt W, et al. Test-Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific? Tomography 2016; 2:361-365
Meyer M, Ronald J, Vernuccio F, et al. Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings. Radiology 2019; 293:583-591
Zwanenburg A, Vallieres M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020; 295:328-338
This work was supported by internal funding from the ML4MI program and Shapiro research program at University of Wisconsin.
SG, DM, VJ, LD, MS, AD, EJA have no relevant disclosures. GL: Prior Grant funding Philips, Ethicon.
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Gurbani, S., Morgan, D., Jog, V. et al. Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC). Abdom Radiol 46, 4278–4288 (2021). https://doi.org/10.1007/s00261-021-03083-y