Cross-cancer knowledge transfer for lymph node metastasis prediction: Applicability of an axillary lymph node-based deep learning model
Evaluation of lymph node (LN) metastasis is critical for cancer staging and treatment planning. Since radiologic hallmarks of metastatic LNs are shared across primary cancers, a computed tomography (CT)-based deep learning (DL) classifier developed from one anatomy may transfer to others. This study built a DenseNet-121 ensemble model using clinical-implication-applied preprocessing and evaluated its cross-anatomical generalizability to LNs from diverse primary cancers. A DenseNet-121 ensemble model combining fixed-size cropping (FSC) and adjustable square cropping (ASC) was trained from ImageNet initialization on an axillary lymph node (ALN) dataset (n = 1,128 from 523 breast cancer patients). The model utilized clinical-implication-applied perinodal cropping with bicubic filtering and standardized resizing. To test cross-anatomical generalizability, an external dataset of 256 CT images across four anatomical regions was evaluated: cervical, mediastinal, upper-abdominal, and lower-abdominal LNs. Perinodal free margins were systematically varied from 10% to 40% to optimize the balance between context preservation and surround interference. Bicubic filtering and standardized resizin
Evaluation of lymph node (LN) metastasis is critical for cancer staging and treatment planning. Since radiologic hallmarks of metastatic LNs are shared across primary cancers, a computed tomography (CT)-based deep learning (DL) classifier developed from one anatomy may transfer to others. This study built a DenseNet-121 ensemble model using clinical-implication-applied preprocessing and evaluated its cross-anatomical generalizability to LNs from diverse primary cancers. A DenseNet-121 ensemble model combining fixed-size cropping (FSC) and adjustable square cropping (ASC) was trained from ImageNet initialization on an axillary lymph node (ALN) dataset (n = 1,128 from 523 breast cancer patients). The model utilized clinical-implication-applied perinodal cropping with bicubic filtering and standardized resizing. To test cross-anatomical generalizability, an external dataset of 256 CT images across four anatomical regions was evaluated: cervical, mediastinal, upper-abdominal, and lower-abdominal LNs. Perinodal free margins were systematically varied from 10% to 40% to optimize the balance between context preservation and surround interference. Bicubic filtering and standardized resizin