Authors
Gang Yan, Na Hu, Mengjuan Ran, Shuzhen Liu, Guijuan Han, Luying Xu, Rongmei Li, Qinghong Duan
Published in
Nuclear medicine communications. Sep 24, 2026. Epub Sep 24, 2026.
Abstract
This study aimed to evaluate 18F-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT)-based deep learning for differentiating squamous cell carcinoma (SqCC) from adenocarcinoma (ADC) in non-small cell lung cancer (NSCLC) and to compare single-modality and multimodal fusion strategies.
This retrospective study included 220 patients with pathologically confirmed NSCLC (86 SqCC and 134 ADC) who underwent pretreatment 18F-FDG PET/CT. A 2.5D input strategy used the axial tumor slice with the largest mask area and its two adjacent slices. Four models were developed: PET-only, CT-only, PET/CT dual-branch fusion, and PET/CT six-channel early fusion. Performance was evaluated using five-fold stratified cross-validation with area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, sensitivity, specificity, F1-score, and Matthews correlation coefficient (MCC).
The PET-only model achieved higher mean AUC and MCC than the CT-only model. The PET/CT six-channel early fusion model achieved the highest mean AUC (0.766 ± 0.082), balanced accuracy (0.765 ± 0.071), specificity (0.882 ± 0.093), and MCC (0.530 ± 0.132). The dual-branch fusion model achieved the highest mean sensitivity (0.755 ± 0.144), although its mean AUC was lower than that of the PET-only model.
PET/CT six-channel early fusion showed potential for differentiating ADC from SqCC and may provide complementary information for noninvasive histological assessment. External validation is required before clinical application.
PMID:
42802911
Bibliographic data and abstract were imported from PubMed on 28 Sep 2026.
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