CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions

GLOBAL CANCER OBSERVATORY. International Agency for Research on Cancer. (2022). http://gco.iarc.fr/. Accessed 20 Apr 2024.

Suarez-Ibarrola R, Basulto-Martinez M, Heinze A, Gratzke C, Miernik A. Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature. Cancers. 2020;12(6). https://doi.org/10.3390/cancers12061387.

Welch HG, Skinner JS, Schroeck FR, Zhou W, Black WC. Regional Variation of Computed Tomographic Imaging in the United States and the Risk of Nephrectomy. JAMA Intern Med. 2018;178(2):221–7. https://doi.org/10.1001/jamainternmed.2017.7508.

Article  PubMed  Google Scholar 

European Association of Urology. EAU guidelines on renal cell carcinoma. (2022). https://uroweb.org/guidelines/renal-cell-carcinoma. Accessed 16 July 2023.

Sanchez A, Feldman AS, Hakimi AA. Current Management of Small Renal Masses, Including Patient Selection, Renal Tumor Biopsy, Active Surveillance, and Thermal Ablation. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2018;36(36):3591–600. https://doi.org/10.1200/jco.2018.79.2341.

Article  PubMed  Google Scholar 

Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. Solid renal tumors: an analysis of pathological features related to tumor size. J Urol. 2003;170(6 Pt 1):2217–20. https://doi.org/10.1097/01.ju.0000095475.12515.5e.

Article  PubMed  Google Scholar 

Tan H-J, Norton EC, Ye Z, Hafez KS, Gore JL, Miller DC. Long-term survival following partial vs radical nephrectomy among older patients with early-stage kidney cancer. JAMA. 2012;307(15):1629–35. https://doi.org/10.1001/jama.2012.475.

Article  CAS  PubMed  Google Scholar 

Rossi SH, Blick C, Handforth C, Brown JE, Stewart GD. Essential Research Priorities in Renal Cancer: A Modified Delphi Consensus Statement. Eur Urol Focus. 2020;6(5):991–8. https://doi.org/10.1016/j.euf.2019.01.014.

Article  PubMed  Google Scholar 

Choudhary S, Rajesh A, Mayer NJ, Mulcahy KA, Haroon A. Renal oncocytoma: CT features cannot reliably distinguish oncocytoma from other renal neoplasms. Clin Radiol. 2009;64(5):517–22. https://doi.org/10.1016/j.crad.2008.12.011.

Article  CAS  PubMed  Google Scholar 

Pedrosa I, Sun MR, Spencer M, Genega EM, Olumi AF, Dewolf WC, Rofsky NM. MR imaging of renal masses: correlation with findings at surgery and pathologic analysis. Radiographics. 2008;28(4):985–1003. https://doi.org/10.1148/rg.284065018.

Article  PubMed  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures. They Are Data Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.

Article  PubMed  Google Scholar 

Massa’a RN, Stoeckl EM, Lubner MG, Smith D, Mao L, Shapiro DD, Abel EJ, Wentland AL. Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning. Abdominal radiology (New York). 2022;47(8):2896–904. https://doi.org/10.1007/s00261-022-03577-3.

Article  PubMed  Google Scholar 

Wentland AL, Yamashita R, Kino A, Pandit P, Shen L, Brooke Jeffrey R, Rubin D, Kamaya A. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation. Abdominal radiology (New York). 2023;48(2):642–8. https://doi.org/10.1007/s00261-022-03735-7.

Article  PubMed  Google Scholar 

Erdim C, Yardimci AH, Bektas CT, Kocak B, Koca SB, Demir H, Kilickesmez O. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. Acad Radiol. 2020;27(10):1422–9. https://doi.org/10.1016/j.acra.2019.12.015.

Article  PubMed  Google Scholar 

Sun XY, Feng QX, Xu X, Zhang J, Zhu FP, Yang YH, Zhang YD. Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists. AJR Am J Roentgenol. 2020;214(1):W44-w54. https://doi.org/10.2214/ajr.19.21617.

Article  PubMed  Google Scholar 

Tanaka T, Huang Y, Marukawa Y, Tsuboi Y, Masaoka Y, Kojima K, Iguchi T, Hiraki T, Gobara H, Yanai H, Nasu Y, Kanazawa S. Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning. AJR Am J Roentgenol. 2020;214(3):605–12. https://doi.org/10.2214/ajr.19.22074.

Article  PubMed  Google Scholar 

Zhou L, Zhang Z, Chen YC, Zhao ZY, Yin XD, Jiang HB. A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors. Translational oncology. 2019;12(2):292–300. https://doi.org/10.1016/j.tranon.2018.10.012.

Article  PubMed  Google Scholar 

Kunapuli G, Varghese BA, Ganapathy P, Desai B, Cen S, Aron M, Gill I, Duddalwar V. A Decision-Support Tool for Renal Mass Classification. J Digit Imaging. 2018;31(6):929–39. https://doi.org/10.1007/s10278-018-0100-0.

Article  PubMed  PubMed Central  Google Scholar 

Zhang S, Shao H, Li W, Zhang H, Lin F, Zhang Q, Zhang H, Wang Z, Gao J, Zhang R, Gu Y, Wang Y, Mao N, Xie H. Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study. Eur Radiol. 2023;33(8):5411–22. https://doi.org/10.1007/s00330-023-09513-3.

Article  PubMed  Google Scholar 

Shi Z, Huang X, Cheng Z, Xu Z, Lin H, Liu C, Chen X, Liu C, Liang C, Lu C, Cui Y, Han C, Qu J, Shen J, Liu Z. MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology. 2023;308(1): e222830. https://doi.org/10.1148/radiol.222830.

Article  PubMed  Google Scholar 

Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallières M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res. 2020;26(8):1944–52. https://doi.org/10.1158/1078-0432.Ccr-19-0374.

Article  PubMed  Google Scholar 

Nassiri N, Maas M, Cacciamani G, Varghese B, Hwang D, Lei X, Aron M, Desai M, Oberai AA, Cen SY, Gill IS, Duddalwar VA. A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma. Eur Urol Focus. 2022;8(4):988–94. https://doi.org/10.1016/j.euf.2021.09.004.

Article  PubMed  Google Scholar 

Zhou T, Guan J, Feng B, Xue H, Cui J, Kuang Q, Chen Y, Xu K, Lin F, Cui E, Long W. Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies. Eur Radiol. 2023;33(6):4323–32. https://doi.org/10.1007/s00330-022-09384-0.

Article  CAS  PubMed  Google Scholar 

van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H. Computational Radiomics System to Decode the Radiographic Phenotype. Can Res. 2017;77(21):e104–7. https://doi.org/10.1158/0008-5472.Can-17-0339.

Article  Google Scholar 

Zwanenburg A, Vallières M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers R, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Löck S. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328–38. https://doi.org/10.1148/radiol.2020191145.

Article  PubMed  Google Scholar 

Zwanenburg A, Leger S, Vallières M, Löck S, Initiative f. Image biomarker standardisation initiative - feature definitions. 2016.

Google Scholar 

Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Trans Pattern Anal Mach Intell. 2012;34(11):2274–82. https://doi.org/10.1109/TPAMI.2012.120.

Article  PubMed  Google Scholar 

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.

Article  CAS  PubMed  Google Scholar 

Yu S, Tao J, Dong B, Fan Y, Du H, Deng H, Cui J, Hong G, Zhang X. Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy. BMC Urol. 2021;21(1):80. https://doi.org/10.1186/s12894-021-00849-w.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Uhlig J, Biggemann L, Nietert MM, Beißbarth T, Lotz J, Kim HS, Trojan L, Uhlig A. Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach. Medicine. 2020;99(16): e19725. https://doi.org/10.1097/md.0000000000019725.

Article  PubMed  PubMed Central  Google Scholar 

Toda N, Hashimoto M, Arita Y, Haque H, Akita H, Akashi T, Gobara H, Nishie A, Yakami M, Nakamoto A, Watadani T, Oya M, Jinzaki M. Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database. Invest Radiol. 2022;57(5):327–33.

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