Abstract
BackgroundLiver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically.MethodsWe integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model.ResultsA total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956).ConclusionWe successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.
Highlights
Colorectal cancer (CRC) is universally acknowledged as one of the most prevalent gastrointestinal tract malignancies with considerably high morbidity and mortality, Han et al Cancer Cell International (2022) 22:28Endoscopic therapy is a widely accepted and adopted as a valid therapeutic method for T1 colorectal cancer (CRC) patients
We developed a comprehensive recognition model via adopting artificial intelligence (AI) and machine learning (ML) algorithms, which could remarkably promote the identification of T1 CRC with liver metastasis (LM) and improve the prognosis of these patients in clinical practice
Eleven independent clinical factors were included in our established model, incorporating age at diagnosis, gender, marital status at diagnosis, primary site, tumor size, tumor grade, tumor type, N stage, carcinoembryonic antigen (CEA) level, tumor deposits, and perineural invasion (PNI)
Summary
Colorectal cancer (CRC) is universally acknowledged as one of the most prevalent gastrointestinal tract malignancies with considerably high morbidity and mortality, Han et al Cancer Cell International (2022) 22:28Endoscopic therapy is a widely accepted and adopted as a valid therapeutic method for T1 CRC patients. We aimed at developing an easy-to-use model to predict the risk of LM for patients in the early stage of CRC accurately and robustly. Researchers employed machine learning (ML) as the breaking point in solving the complicated issue of CRC clinical prediction and acquired plentiful significant breakthroughs [18,19,20]. These findings shed light on the intriguing area of T1 CRC with lymph node metastasis which resembles a virgin land to be further explored by utilizing ML. We aim to construct an accurate predictive model and an easy-to-use tool clinically
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