Predicting disease progression and mortality in metastatic colorectal cancer patients (mCRC) through an artificial intelligence-based analytical tool.

1549 Background: Predicting the clinical course of metastatic disease remains a key challenge in CRC. Estimating prognosis of these late-stage patients can avoid undertreatment or overtreatment and also guide the follow-up intensity. This study has investigated the ability of an artificial intelligence-based analytical tool to identify those mCRC patients with high risk of disease progression and mortality based on their clinical parameters. Methods: Through www.projectdatasphere.org we accessed datasets of two randomised phase III trials including chemo-naïve (NCT00364013, n = 1183 patients) and chemo-refractory (NCT00113763, N = 483) mCRC patients. We generated synthetic fingerprints (SF) for each patient through the integration of 44 clinical features (demographics, anthropometrics, medical history, blood tests and treatment characteristics) collected, respectively, during the screening phase and the first month of inclusion in each trial. These SF were then input into a deep learning framework (DLF) to identify subgroup of patients based on their similarities. The resultant clusters were correlated with progression-free survival (PFS) and overall survival (OS). Results: After discarding missing data, 861 chemo-naïve and 341 chemo-resistant mCRC patients were eligible for the study. In the chemo-naïve cohort, the SF/DLF system was able to detect two different clusters: C0 (n = 31) and C1 (n = 830). Patients in C0 had a higher risk of progression (median PFS 6.2 months vs. 9.1 months; hazard ratio 1.83, 95% CI 1.16-2.88; p = 0.008) and death (median OS 13.2 months vs. 20.1 months; hazard ratio 2.84, 95% CI 1.68-4.80; p < 0.001) compared to patients in C1. When applied to the chemo-resistant cohort, the SF/DLF system was again able to identify two different clusters: P0 (n = 159) and P1 (n = 182). Patients in P0 had a higher risk of progression (median PFS 1.7 months vs. 1.8 months; hazard ratio 1.32, 95% CI 1.05-1.67; p < 0.001) and death (median OS 6.1 months vs. 6.8 months; hazard ratio 1.34, 95% CI 1.07-1.68; p = 0.01) compared to patients in P1. In both cases, feature contribution analysis showed that major differences between clusters were related to clinical status, anthropometrics and haematological and biochemistry tests. Conclusions: Our SF/DLF system can identify mCRC subtypes based on distinct clinical features that correlate with higher risk of progression and death. Further work is required to validate this approach as a novel prognostic biomarker tool for monitoring mCRC patients.