World journal of gastroenterology · 2026
Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Zou Ling-Feng, Wang Xuan-Bing, Li Jing-Wen, Ouyang Xin, Luo Yi-Ying, Luo Yan, Wang Cheng-Long
Аннотация
BACKGROUND: The accurate prediction of lymph node metastasis (LNM) is crucial for managing locally advanced (T3/T4) colorectal cancer (CRC). However, both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.
AIM: To develop and validate a case-level multiple-instance learning (MIL) framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.
METHODS: The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected. A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosin-stained primary tumour slides for each patient. These pathological features were subsequently integrated with clinical data, and model performance was evaluated using the area under the curve (AUC).
RESULTS: The case-level framework demonstrated superior LNM prediction over slide-level training, with the CONCH v1.5 model achieving a mean AUC (± SD) of 0.899 ± 0.033 vs 0.814 ± 0.083, respectively. Integrating pathology features with clinical data further enhanced performance, yielding a top model with a mean AUC of 0.904 ± 0.047, in sharp contrast to a clinical-only model (mean AUC 0.584 ± 0.084). Crucially, a pathologist's review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.
CONCLUSION: A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC. This method shows promise for risk stratification and therapy decisions, requiring further validation.
AIM: To develop and validate a case-level multiple-instance learning (MIL) framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.
METHODS: The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected. A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosin-stained primary tumour slides for each patient. These pathological features were subsequently integrated with clinical data, and model performance was evaluated using the area under the curve (AUC).
RESULTS: The case-level framework demonstrated superior LNM prediction over slide-level training, with the CONCH v1.5 model achieving a mean AUC (± SD) of 0.899 ± 0.033 vs 0.814 ± 0.083, respectively. Integrating pathology features with clinical data further enhanced performance, yielding a top model with a mean AUC of 0.904 ± 0.047, in sharp contrast to a clinical-only model (mean AUC 0.584 ± 0.084). Crucially, a pathologist's review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.
CONCLUSION: A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC. This method shows promise for risk stratification and therapy decisions, requiring further validation.