Integrating ConvNeXt tiny and radiomics in a nomogram to differentiate true progression from pseudoprogression in glioma.

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Abstract

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. 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. 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). 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.

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