학술논문
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Document Type
article
Author
Ahmet Said Kucukkaya; Tal Zeevi; Nathan Xianming Chai; Rajiv Raju; Stefan Philipp Haider; Mohamed Elbanan; Alexandra Petukhova-Greenstein; MingDe Lin; John Onofrey; Michal Nowak; Kirsten Cooper; Elizabeth Thomas; Jessica Santana; Bernhard Gebauer; David Mulligan; Lawrence Staib; Ramesh Batra; Julius Chapiro
Source
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Subject
Language
English
ISSN
2045-2322
Abstract
Abstract Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1–6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC–ROC). After prediction, the model’s clinical relevance was evaluated using Kaplan–Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan–Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p