ArticleBMC cancer2026
Comparison of radiomics handcrafted features and deep features in
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe differentiation between benign and malignant persistent pulmonary ground-glass nodules (GGNs) remains challenging, and the relative value of radiomics handcrafted features and deep features derived from
methodsData from 173 patients (184 GGNs) across three PET/CT centers were retrospectively analyzed. Patients underwent
resultsIn the internal validation set, the CT_HF and PET_HF models achieved AUCs of 0.889 (95% CI: 0.756-0.964) and 0.903 (95% CI: 0.774-0.972), respectively, which were numerically higher than that of the reference model (AUC = 0.864, 95% CI: 0.725-0.949). In the external test set, the PET_ResNet50/CT_VGG19 combined model achieved the highest AUC of 0.927 (95% CI: 0.802-0.984), compared with 0.824 (95% CI: 0.676-0.924) for the reference model and 0.884 (95% CI: 0.747-0.962) for PET_ResNet50; however, the differences among the three models were not statistically significant (P = 0.101-0.464). DCA showed that this combined model had a relatively high net benefit when the threshold probability exceeded 0.5, and its Brier score in the external test set was 0.122, which was comparable to that of the reference model.
conclusionThe integrated ¹⁸F-FDG PET/CT model with PET_ResNet50 and CT_VGG19 deep features achieved favorable discrimination and modest net clinical benefit in the external test set. However, its probability calibration and generalizability await validation on larger multicenter datasets due to insufficient benign nodules in the external test set.
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