摘要
Abstract
Objective:To investigate the feasibility of using a deep learning-based image classification model for distinguishing liver multi-parameter magnetic resonance imaging(mpMRI)sequences.Materials and Methods:A retrospective dataset of 1744 liver mpMRI examinations from 1676 patients(November 16,2022 to June 29,2023)was collected as model development set,yielding 25 365 independent sequences.These were randomly divided into training[number of series(ns)=20 207],validation(ns=2664),and test sets(ns=2494)at an 8∶1∶1 ratio.A 3D-ResNet model was trained to classify liver mpMRI sequences,with input as image and output categories including:T1-weighted in-phase(T1WI_In),T1-weighted opposed-phase(T1WI_Opp),T2-weighted imaging with fat-suppression(T2WI_Fs),high b-value DWI,ADC maps,and dynamic contrast-enhanced MRI(pre-contrast,arterial,portal venous,delayed).The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA_LIHC)dataset was used as the external validation set for the model,comprising a total of 59 mpMRI examinations involving 38 patients.Radiologists'classifications served as the gold standard.Model performance was evaluated using confusion matrices.Results:At the overall classification level,the training,validation and test sets achieved average accuracy,macro-F1,and micro-F1 scores of 97.2%to 99.0%,0.949 to 0.982 and 0.960 to 0.985,respectively.For individual sequences,the training,validation and test sets demonstrated per-class accuracy(89.6%to 100.0%),sensitivity(81.0%to 100.0%),specificity(98.2%to 100.0%),and F1 scores(0.797 to 1.000).On the external validation set,the model achieved macro-accuracy,macro-F1,and micro-F1 scores of 91.6%,0.819,and 0.816,respectively.Per-sequence metrics included accuracy(74.1%to 99.4%),sensitivity(55.4%to 100.0%),specificity(92.8%to 100.0%),and F1 score(0.579 to 0.968).Conclusions:The deep learning-based model demonstrated high accuracy in classifying liver mpMRI sequences,supporting its potential for automated sequence classification in clinical practice.关键词
肝脏/磁共振成像/图像自动分类/人工智能/深度学习Key words
liver/magnetic resonance imaging/automated classification of images/artificial intelligence/deep learning分类
医药卫生
