PET/CT-based deep learning for differentiating squamous cell carcinoma from adenocarcinoma in non-small cell lung cancer: comparison of multimodal fusion strategies
Gang Yan, Na Hu, Mengjuan Ran, Shuzhen Liu, Guijuan Han, Luying Xu, Rongmei Li, Qinghong DuanObjective
This study aimed to evaluate 18 F-fluorodeoxyglucose ( 18 F-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.
Methods
This retrospective study included 220 patients with pathologically confirmed NSCLC (86 SqCC and 134 ADC) who underwent pretreatment 18 F-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).
Results
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.
Conclusion
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.