Toward Precision Diagnosis

doi:10.1097/RLI.0000000000001076

Toward Precision Diagnosis

2024-04-11

1

PRE

AI

DOI：10.1097/RLI.0000000000001076

原文链接

原文求助

摘要

En 中文

A machine learning methodology that integrates multiparametric magnetic resonance imaging data-including dynamic contrast-enhanced, diffusion-weighted imaging, intravoxel incoherent motion, and morphological imaging-provides robust models to distinguish between malignant and benign orbital tumors. BackgroundOrbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artificial intelligence in radiology and ophthalmology has demonstrated promising outcomes.PurposeThis study aimed to evaluate the performance of machine learning models in accurately distinguishing malignant orbital tumors from benign ones using multiparametric 3 T magnetic resonance imaging (MRI) data.Materials and MethodsIn this single-center prospective study, patients with orbital masses underwent presurgery 3 T MRI scans between December 2015 and May 2021. The MRI protocol comprised multiparametric imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), as well as morphological imaging acquisitions. A repeated nested cross-validation strategy using random forest classifiers was used for model training and evaluation, considering 8 combinations of explanatory features. Shapley additive explanations (SHAP) values were used to assess feature contributions, and the model performance was evaluated using multiple metrics.ResultsOne hundred thirteen patients were analyzed (57/113 [50.4%] were women; average age was 51.5 +/- 17.5 years, range: 19-88 years). Among the 8 combinations of explanatory features assessed, the performance on predicting malignancy when using the most comprehensive model, which is the most exhaustive one incorporating all 46 explanatory features-including morphology, DWI, DCE, and IVIM, achieved an area under the curve of 0.9 [0.73-0.99]. When using the streamlined 10-feature signature model, performance reached an area under the curve of 0.88 [0.71-0.99]. Random forest feature importance graphs measured by the mean of SHAP values pinpointed the 10 most impactful features, which comprised 3 quantitative IVIM features, 4 quantitative DCE features, 1 quantitative DWI feature, 1 qualitative DWI feature, and age.ConclusionsOur findings demonstrate that a machine learning approach, integrating multiparametric MRI data such as DCE, DWI, IVIM, and morphological imaging, offers high-performing models for differentiating malignant from benign orbital tumors. The streamlined 10-feature signature, with a performance close to the comprehensive model, may be more suitable for clinical application.

Keyword:

machine learning

magnetic resonance imaging

orbital neoplasms

Toward Precision Diagnosis

摘要

期刊

机构

引用论文