Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning

Abstract

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.

Keywords: Colorectal cancer; Lymph node metastasis; Deep learning; Multiple instance learning; Histopathology

Core Tip: To better predict lymph node metastasis (LNM) in advanced colorectal cancer, this pilot study developed a case-level deep learning framework. By analysing the pathology slides of all patients and emulating a pathologist's workflow, the model achieved a high area under the curve of 0.899, outperforming traditional methods. Integrating the clinical data further increased the accuracy to 0.904. This interpretable approach is a promising tool for refining LNM risk assessments and guiding adjuvant therapy decisions.