To develop and validate an interpretable machine learning (ML) framework that differentiates adrenal lipid-poor adenomas (LPAs) from pheochromocytomas (PHEOs) using radiomic features derived from multi-phase contrast-enhanced computed tomography (CECT).
This retrospective study included 229 patients with pathologically confirmed adrenal tumours (132 LPAs, 97 PHEOs). Lesions were stratified into washout (absolute percentage washout [APW] > 60%) and non-washout (APW ≤ 60%) cohorts according to established criteria. We trained decision tree (DT), support vector machine (SVM), and logistic regression (LR) models using a predefined set of clinically relevant CT features. The inherently interpretable DT model was further dissected to revealr key discriminative features and its underlying decision logic. Model performance was evaluated using the area under the curve (AUC) with 95% confidence intervals.
The DT model achieved excellent diagnostic performance, with an AUC of 0.977 (95% CI: 0.934–1.000) in the washout group and 0.983 (95% CI: 0.962–1.000) in the non-washout group. Feature importance analysis identified cystic degeneration as the most influential discriminator, followed by enhancement potential (EP) and baseline attenuation (CTu). The DT’s hierarchical decision rules provided clear and clinically transparent pathways.
This interpretable ML framework accurately distinguishes LPAs from PHEOs. The DT model strikes an optimal balance between high diagnostic accuracy and inherent interpretability, offering a practical tool that may enhance clinical confidence when managing indeterminate adrenal lesions.
Pan-Liang Zhao, F. Zhu, C. Yan et al.· Frontiers in Artificial Inte...· 0 citations
Background The preoperative differentiation of lung adenocarcinoma subtypes is critical for implementing personalized treatment but is difficult to accomplish with conventional imaging. This study aimed to develop an interpretable multimodal model integrating clinical, peritumoral, radiomic, and deep learning features to improve diagnostic accuracy. Methods A total of 3,038 patients from four hospitals were divided into training (n=1,822), test (n=608), and validation (n=608) sets. Two radiologists manually segmented two-dimensional tumor regions on computed tomography using ITK-SNAP software. After Pearson correlation analysis and least absolute shrinkage and selection operator regression, the radiomic score and deep learning score were generated. Clinical features were selected via univariate analysis, the Boruta algorithm, and recursive feature elimination (RFE). Individual logistic models were built and fused with the optimal combination selected via support vector machine-synthetic minority oversampling technique and extreme gradient boosting. Performance was evaluated in terms of the Obuchowski index, accuracy, F1-score, calibration, and decision curves, while interpretability was assessed via Shapley additive explanations (SHAP) and individual conditional expectation (ICE). Results The fused model achieved Obuchowski indices of 0.85 [95% confidence interval (CI): 0.84–0.87], 0.81 (95% CI: 0.78–0.83), and 0.79 (95% CI: 0.76–0.81) in the training, test, and validation sets, respectively outperforming the single-modality models. The F1-scores for the lepidic, acinar/papillary, and solid/micropapillary subtypes, respectively, were 0.77, 0.61, and 0.63 in the training set; 0.72, 0.57, and 0.59 in the test set; and 0.74, 0.54, and 0.52 in the validation set. Calibration and decision curve analysis confirmed the robustness and clinical utility of the model. SHAP analysis identified ResNet-101 feature as the best predictor, followed by peritumoral radiomic score, and lobulation. ICE plots revealed the linear and monotonic relationships between key features and predicted probabilities across subtypes. Conclusions The radiomics model developed in this study facilitates the accurate and interpretable preoperative classification of lung adenocarcinoma subtypes. Fusion of clinical, peritumoral, and deep learning features enhances diagnostic performance and supports clinical decision-making.
Feng-Juan Tian, Jing Ding, Zhen-Yu Cao et al.· Quantitative Imaging in Medi...· 0 citations
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