Differentiating Benign from Malignant Renal Tumors Using T2 ‐ and Diffusion‐Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

(2021) Shotaro Asano et al.   Scientific Reports

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning

(2020) Takashi Tanaka et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings

(2020) Zhan Feng et al.   Frontiers in Oncology

Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning

(2020) Weixiao Liu et al.   ACADEMIC RADIOLOGY

A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma

(2020) Fan Lin et al.   EUROPEAN JOURNAL OF RADIOLOGY

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

(2020) Fatemeh Zabihollahy et al.   EUROPEAN RADIOLOGY

Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

(2020) Daniela Said et al.   Abdominal Radiology

Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation

(2020) Sohi Bae et al.   Scientific Reports

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

(2019) Heidi Coy et al.   Abdominal Radiology

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features

(2019) Gangcai Xie et al.   Genes

Influence of segmentation margin on machine learning–based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

(2019) Burak Kocak et al.   EUROPEAN RADIOLOGY

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method

(2019) Seokmin Han et al.   JOURNAL OF DIGITAL IMAGING

Unenhanced CT Texture Analysis of Clear Cell Renal Cell Carcinomas: A Machine Learning–Based Study for Predicting Histopathologic Nuclear Grade

(2019) Burak Kocak et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images

(2019) Pengyu Yuan et al.   Journal of Clinical Medicine

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation

(2018) Hansang Lee et al.   MEDICAL PHYSICS

Characterization of small (

(2018) N. Schieda et al.   Diagnostic and Interventional Imaging

Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy

(2018) Price Jackson et al.   Frontiers in Oncology

Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade

(2018) Ceyda Turan Bektas et al.   EUROPEAN RADIOLOGY

Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective

(2018) Zhi-Cheng Li et al.   EUROPEAN RADIOLOGY

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

(2017) Zhichao Feng et al.   EUROPEAN RADIOLOGY

Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification

(2017) Han Sang Lee et al.   MEDICAL PHYSICS

Radiomics: the bridge between medical imaging and personalized medicine

(2017) Philippe Lambin et al.   Nature Reviews Clinical Oncology

Does Computed Tomography Still Have Limitations to Distinguish Benign from Malignant Renal Tumors for Radiologists?

(2017) Toshitaka Shin et al.   UROLOGIA INTERNATIONALIS

Texture analysis as a radiomic marker for differentiating renal tumors

(2017) HeiShun Yu et al.   Abdominal Radiology

Diagnostic accuracy of contrast-enhanced computed tomography and contrast-enhanced magnetic resonance imaging of small renal masses in real practice: sensitivity and specificity according to subjective radiologic interpretation

(2016) Jae Heon Kim et al.   World Journal of Surgical Oncology

Angiomyolipoma with Minimal Fat

(2015) Lifen Yan et al.   ACADEMIC RADIOLOGY

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

(2015) Taryn Hodgdon et al.   RADIOLOGY

Outcome Assessment of Patients With Metastatic Renal Cell Carcinoma Under Systemic Therapy Using Artificial Neural Networks

(2011) Alexander Buchner et al.   Clinical Genitourinary Cancer

Incidence of Benign Pathologic Lesions at Partial Nephrectomy for Presumed RCC Renal Masses: Japanese Dual-Center Experience with 176 Consecutive Patients

(2008) Yasuhisa Fujii et al.   UROLOGY