546P - An artificial intelligence system integrating deep learning-proteomics, pathomics and clinicopathological features to determine risk of T1 colorectal cancer metastasis to lymph node

Background

More than 70% of patients with T1 colorectal cancer (CRC) undergo a radical surgery involving lymph node dissection, despite the presence of lymph node metastasis (LNM) being observed in only ∼10% of cases. To reduce unnecessary radical surgery, we developed a multimodal artificial intelligence (AI) system to identify T1 CRC at risk for LNM.

Methods

We analyzed 402 CRC patients from two independent multicenter cohorts, including a training (n = 219) and a validation cohort (n = 183). Proteomics dataset from tumor tissue were identified and quantified using LC-MS/MS analysis. Multiple pathomics features were extracted from whole H&E slides with patch-level convolutional neural network training in weakly supervised manner. Clinicopathological characteristics included age, tumor size, location, lymphatic and vascular invasion, perineural invasion, histologic grade, and CEA. A machine-learning artificial neural network using proteomics, pathomics and clinicopathological features was developed.

Results

LNM were detected in 78 out of 219 patients (35.6%) within the training cohort and in 29 out of 183 patients (15.8%) within the validation cohort. A panel of six protein biomarkers (OSBPL5, ATAD2, BAIAP2, MANBA, ITPR2, ARHGAP5) screened by proteomics was quantified by immunohistochemistry and subsequently integrated into the AI system. In the validation cohort, the multimodal AI system achieved robust classification of LNM status, yielding an AUC of 0.978. In comparison, the guideline-based model exhibited an AUC of 0.717 for identifying LNM (P < 0.001).

Conclusions

The AI system integrating deep learning-proteomics, pathomics and clinicopathological features robustly identifies T1 CRC patients at risk of LNM in a preoperative setting. The multimodal AI system would improve clinical practice by alleviating the burden of unnecessary overtreatment for T1 CRC.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Jianmin Xu.

Funding

The National Natural Science Foundation of China.

Disclosure

All authors have declared no conflicts of interest.