Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer

Background The treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specimens used for the definitive diagnosis of T1 CRC were analyzed using artificial intelligence (AI) to construct a risk index for lymph node metastasis. Methods A total of 146 T1 CRC cases were analyzed. The specimens for analysis were mainly biopsy specimens, and in the absence of biopsy specimens, the mucosal layer of the surgical specimens was analyzed. The pathology slides for each case were digitally imaged, and the morphological features of cancer cell nuclei were extracted from the tissue images. First, statistical methods were used to analyze how well these features could predict lymph node metastasis risk. A lymph node metastasis risk model using AI was created based on these morphological features, and accuracy in test cases was verified. Results Each developed model could predict lymph node metastasis risk with a > 90% accuracy in each region of interest of the training cases. Lymph node metastasis risk was predicted with 81.8-86.3% accuracy for randomly validated cases, using a learning model with biopsy data. Moreover, no case with lymph node metastasis or lymph node risk was judged to have no risk using the same model. Conclusions AI models suggest an association between biopsy specimens and lymph node metastases in T1 CRC and may contribute to increased accuracy of preoperative diagnosis.