整合ConvNeXt Tiny与影像组学的列线图鉴别胶质瘤真性进展与假性进展

PURPOSE: Differentiating true progression (TP) from pseudoprogression (PsP) in glioma is challenging due to overlapping enhancement patterns on conventional MRI. Therefore, a reliable noninvasive approach integrating imaging heterogeneity is needed to improve TP/PsP discrimination.

METHODS: This multicenter retrospective study included 293 patients with true progression (TP, n = 208) or pseudoprogression (PsP, n = 85). Baseline multiparametric MRI was analyzed. Traditional radiomics and deep learning features extracted using a pre-trained ConvNeXt Tiny network were selected through reproducibility, redundancy, and LASSO analyses to construct imaging signatures, which were combined with clinical factors to develop a deep learning radiomics nomogram (DLRN). Model performance was evaluated using ROC analysis, calibration curves, and decision curve analysis, and compared with radiologists' assessments.

RESULTS: The DLRN demonstrated excellent predictive efficacy, achieving an area under the curve (AUC) of 0.908 in the test set. Its performance significantly surpassed that of any individual signature (DeLong test, P < 0.001) and the independent assessments of two senior radiologists. The model exhibited good calibration, and decision curve analysis confirmed its superior clinical net benefit across a wide range of threshold probabilities. When used as a decision-support tool, the nomogram significantly and consistently improved both radiologists' diagnostic performance, yielding a net reclassification improvement greater than 1.1 in both the training and test sets (all P < 0.01).

CONCLUSION: The deep learning imaging biomarker nomogram demonstrated excellent performance in differentiating TP from PsP in gliomas, outperforming traditional methods and radiologists, and effectively assisting clinical decision-making.