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[Attention-based multi-task deep learning model for predicting the primary site of cervical metastatic squamous cell
1Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan 250012, China.
Abstract:
Objective: This study aimed to construct an attention-based multi-task deep learning model that utilizes readily available CT images and clinical features to predict the primary site of cervical metastatic squamous cell carcinoma of unknown primary (CMSCCUP). Methods: In a single-center retrospective design, we enrolled 2 286 patients with cervical lymph node metastases from known primary sites and 1 264 normal controls (individuals with benign diseases, contrast-enhanced neck CT, and no malignancy history) from Qilu Hospital of Shandong University; CT images (2D maximum cross-sections of lymph nodes in levels Ⅰ-Ⅵ, with multi-channel fusion) were enhanced via super-resolution reconstruction, and a deep learning model based on ResNet with a CBAM attention module was trained. Clinical features were further integrated via a deep neural network combined with a Transformer module under a multi-task learning framework to simultaneously predict malignancy and the primary site, and gradient-weighted class activation heatmaps were used to visualize the model's focus areas. Results: The model showed good performance on the training (n=1 829) and test (n=457) sets: in the test set, the AUC for benign/malignant prediction was 0.851, the Micro-AUC for primary site prediction was 0.819, and Top-1 and Top-3 accuracy reached 79% and 93%, respectively; heatmaps clearly delineated the lymph node levels and imaging features of interest. In a real-world CMSCCUP cohort (172 cases, including 86 CMSCCUP patients and 86 normal controls), the model achieved a Micro-AUC of 0.809 against the gold standard and a Top-3 accuracy of 88%, significantly improving the predictive accuracy of junior physicians (all P<0.001). Conclusions: Collectively, the attention-based multi-task deep learning model can effectively leverage CT imaging and clinical features to predict the primary site in CMSCCUP, demonstrating considerable potential for clinical decision support.