Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study

Siegel RL, Miller KD, Wagle NS, Jemal A: Cancer statistics, 2023. CA: a cancer journal for clinicians 2023, 73(1):17-48.

PubMed  Google Scholar 

Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM: The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. Eur Urol 2016, 70(1):93-105.

Article  PubMed  Google Scholar 

Ljungberg B, Albiges L, Abu-Ghanem Y, Bedke J, Capitanio U, Dabestani S, Fernández-Pello S, Giles RH, Hofmann F, Hora M et al: European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol 2022, 82(4):399-410.

Article  PubMed  Google Scholar 

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(2):444-453.

Article  PubMed  Google Scholar 

Hodgdon T, McInnes MDF, Schieda N, Flood TA, Lamb L, Thornhill RE: Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images? Radiology 2015, 276(3):787-796.

Article  PubMed  Google Scholar 

Vogel C, Ziegelmüller B, Ljungberg B, Bensalah K, Bex A, Canfield S, Giles RH, Hora M, Kuczyk MA, Merseburger AS et al: Imaging in Suspected Renal-Cell Carcinoma: Systematic Review. Clinical genitourinary cancer 2019, 17(2):e345-e355.

Article  PubMed  Google Scholar 

Rossi SH, Prezzi D, Kelly-Morland C, Goh V: Imaging for the diagnosis and response assessment of renal tumours. World journal of urology 2018, 36(12):1927-1942.

Article  PubMed  PubMed Central  Google Scholar 

Zhang J, Tehrani YM, Wang L, Ishill NM, Schwartz LH, Hricak H: Renal masses: characterization with diffusion-weighted MR imaging--a preliminary experience. Radiology 2008, 247(2):458-464.

Article  PubMed  Google Scholar 

Hecht EM, Israel GM, Krinsky GA, Hahn WY, Kim DC, Belitskaya-Levy I, Lee VS: Renal masses: quantitative analysis of enhancement with signal intensity measurements versus qualitative analysis of enhancement with image subtraction for diagnosing malignancy at MR imaging. Radiology 2004, 232(2):373-378.

Article  PubMed  Google Scholar 

Zhang J, Lefkowitz RA, Ishill NM, Wang L, Moskowitz CS, Russo P, Eisenberg H, Hricak H: Solid renal cortical tumors: differentiation with CT. Radiology 2007, 244(2):494-504.

Article  PubMed  Google Scholar 

Dyer R, DiSantis DJ, McClennan BL: Simplified imaging approach for evaluation of the solid renal mass in adults. Radiology 2008, 247(2):331-343.

Article  PubMed  Google Scholar 

Marconi L, Dabestani S, Lam TB, Hofmann F, Stewart F, Norrie J, Bex A, Bensalah K, Canfield SE, Hora M et al: Systematic Review and Meta-analysis of Diagnostic Accuracy of Percutaneous Renal Tumour Biopsy. Eur Urol 2016, 69(4):660-673.

Article  PubMed  Google Scholar 

Leveridge MJ, Finelli A, Kachura JR, Evans A, Chung H, Shiff DA, Fernandes K, Jewett MA: Outcomes of small renal mass needle core biopsy, nondiagnostic percutaneous biopsy, and the role of repeat biopsy. Eur Urol 2011, 60(3):578-584.

Article  PubMed  Google Scholar 

van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B: Radiomics in medical imaging-“how-to” guide and critical reflection. Insights into imaging 2020, 11(1):91.

Article  PubMed  Google Scholar 

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A et al: Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012, 48(4):441-446.

Article  PubMed  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H: Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016, 278(2):563-577.

Article  PubMed  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A et al: Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017, 14(12):749-762.

Article  PubMed  Google Scholar 

Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A: Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nature Reviews Clinical Oncology 2021, 19(2):132-146.

Article  PubMed  Google Scholar 

Zhang X, Ruan S, Xiao W, Shao J, Tian W, Liu W, Zhang Z, Wan D, Huang J, Huang Q et al: Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study. Clinical and translational medicine 2020, 10(2):e111.

Article  PubMed  Google Scholar 

Uhlig J, Leha A, Delonge LM, Haack AM, Shuch B, Kim HS, Bremmer F, Trojan L, Lotz J, Uhlig A: Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography. Cancers (Basel) 2020, 12(10):3010.

Article  CAS  PubMed  Google Scholar 

Nazari M, Shiri I, Zaidi H: Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Computers in biology and medicine 2021, 129:104135.

Article  PubMed  Google Scholar 

Nazari M, Shiri I, Hajianfar G, Oveisi N, Abdollahi H, Deevband MR, Oveisi M, Zaidi H: Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning. La Radiologia medica 2020, 125(8):754-762.

Article  PubMed  Google Scholar 

Hung PS, Lin PR, Hsu HH, Huang YC, Wu SH, Kor CT: Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation. Diagnostics (Basel, Switzerland) 2022, 12(6):1496.

CAS  PubMed  Google Scholar 

Li C, He Z, Lv F, Liu Y, Hu Y, Zhang J, Liu H, Ma S, Xiao Z: An interpretable MRI-based radiomics model predicting the prognosis of high-intensity focused ultrasound ablation of uterine fibroids. Insights into imaging 2023, 14(1):129.

Article  PubMed  PubMed Central  Google Scholar 

Bang M, Park YW, Eom J, Ahn SS, Kim J, Lee SK, Lee SH: An interpretable radiomics model for the diagnosis of panic disorder with or without agoraphobia using magnetic resonance imaging. Journal of affective disorders 2022, 305:47-54.

Article  PubMed  Google Scholar 

Yang H, Wu K, Liu H, Wu P, Yuan Y, Wang L, Liu Y, Zeng H, Li J, Liu W et al: An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma. Eur Radiol 2023, 33(11):7532-7541.

Article  PubMed  PubMed Central  Google Scholar 

Ye JY, Fang P, Peng ZP, Huang XT, Xie JZ, Yin XY: A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors. Eur Radiol 2024, 34(3):1994-2005.

Article  CAS  PubMed  Google Scholar 

Hong JH, Jung JY, Jo A, Nam Y, Pak S, Lee SY, Park H, Lee SE, Kim S: Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT. Radiology 2021, 299(3):626-632.

Article  PubMed  Google Scholar 

Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S et al: A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 2018, 19(9):1180-1191.

Article  CAS  PubMed  Google Scholar 

Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D et al: Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 2017, 44(3):1050-1062.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiang YW, Xu XJ, Wang R, Chen CM: Radiomics analysis based on lumbar spine CT to detect osteoporosis. Eur Radiol 2022, 32(11):8019-8026.

Article  PubMed  PubMed Central  Google Scholar 

Yi Z, Hu S, Lin X, Zou Q, Zou M, Zhang Z, Xu L, Jiang N, Zhang Y: Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68 Ga-PSMA-11 PET/CT in patients with primary prostate cancer. Eur J Nucl Med Mol Imaging 2021, 49:1523-1534.

Article  PubMed  Google Scholar 

Zamboglou C, Carles M, Fechter T, Kiefer S, Reichel K, Fassbender TF, Bronsert P, Koeber G, Schilling O, Ruf J et al: Radiomic features from PSMA PET for non-invasive intraprostatic tumor discrimination and characterization in patients with intermediate- and high-risk prostate cancer - a comparison study with histology reference. Theranostics 2019, 9(9):2595-2605.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Gong L, Xu M, Fang M, Zou J, Yang S, Yu X, Xu D, Zhou L, Li H, He B et al: Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics. Journal of magnetic resonance imaging : JMRI 2020, 52(4):1102-1109.

Article  PubMed  Google Scholar 

Chia CS, Wong LCK, Hennedige TP, Ong WS, Zhu HY, Tan GHC, Kwek JW, Seo CJ, Wong JSM, Ong CJ et al: Prospective Comparison of the Performance of MRI Versus CT in the Detection and Evaluation of Peritoneal Surface Malignancies. Cancers (Basel) 2022, 14(13):3179.

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):642-648.

Article  PubMed  Google Scholar 

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