Development and external validation of the multichannel deep learning model based on unenhanced CT for differentiating fat-poor angiomyolipoma from renal cell carcinoma: a two-center retrospective study

[1] Anthimopoulos M(2016)Lung pattern classification for interstitial lung diseases using a deep convolutional neural network IEEE Trans Med Imaging 35 1207-1216

[2] Christodoulidis S(2021)Renal mass and localized renal cancer: evaluation, management, and follow-up: AUA guideline: part I J Urol 206 199-208

[3] Ebner L(2022)Classification of clinically significant prostate cancer on multi-parametric MRI: a validation study comparing deep learning and radiomics Cancers 60 1543-1552

[4] Christe A(2019)Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features Acta Radiol 46 101348-1633

[5] Mougiakakou S(2022)A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: a multicenter cohort study Eclin Med 28 1625-602

[6] Campbell SC(2018)Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma Eur Radiol 27 100558-796

[7] Clark PE(2020)A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study Eclin Med 72 598-411

[8] Chang SS(2008)Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients Urology 276 787-604

[9] Karam JA(2015)Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology 51 404-21

[10] Souter L(2022)Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: serum levels of zinc, calcium, and vitamin D Clin Nutr ESPEN 39 588-684

[1] Anthimopoulos M(2016)Lung pattern classification for interstitial lung diseases using a deep convolutional neural network IEEE Trans Med Imaging 35 1207-1216

[2] Christodoulidis S(2021)Renal mass and localized renal cancer: evaluation, management, and follow-up: AUA guideline: part I J Urol 206 199-208

[3] Ebner L(2022)Classification of clinically significant prostate cancer on multi-parametric MRI: a validation study comparing deep learning and radiomics Cancers 60 1543-1552

[4] Christe A(2019)Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features Acta Radiol 46 101348-1633

[5] Mougiakakou S(2022)A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: a multicenter cohort study Eclin Med 28 1625-602

[6] Campbell SC(2018)Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma Eur Radiol 27 100558-796

[7] Clark PE(2020)A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study Eclin Med 72 598-411

[8] Chang SS(2008)Incidence of benign pathologic lesions at partial nephrectomy for presumed RCC renal masses: Japanese dual-center experience with 176 consecutive patients Urology 276 787-604

[9] Karam JA(2015)Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology 51 404-21

[10] Souter L(2022)Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: serum levels of zinc, calcium, and vitamin D Clin Nutr ESPEN 39 588-684