Development of a Novel Prognostic Model for Predicting Lymph Node Metastasis in Early Colorectal Cancer: Analysis Based on the Surveillance, Epidemiology, and End Results Database

Abstract

BackgroundIdentification of a simplified prediction model for lymph node metastasis (LNM) for patients with early colorectal cancer (CRC) is urgently needed to determine treatment and follow-up strategies. Therefore, in this study, we aimed to develop an accurate predictive model for LNM in early CRC.MethodsWe analyzed data from the 2004-2016 Surveillance Epidemiology and End Results database to develop and validate prediction models for LNM. Seven models, namely, logistic regression, XGBoost, k-nearest neighbors, classification and regression trees model, support vector machines, neural network, and random forest (RF) models, were used.ResultsA total of 26,733 patients with a diagnosis of early CRC (T1) were analyzed. The models included 8 independent prognostic variables; age at diagnosis, sex, race, primary site, histologic type, tumor grade, and, tumor size. LNM was significantly more frequent in patients with larger tumors, women, younger patients, and patients with more poorly differentiated tumor. The RF model showed the best predictive performance in comparison to the other method, achieving an accuracy of 96.0%, a sensitivity of 99.7%, a specificity of 92.9%, and an area under the curve of 0.991. Tumor size is the most important features in predicting LNM in early CRC.ConclusionWe established a simplified reproducible predictive model for LNM in early CRC that could be used to guide treatment decisions. These findings warrant further confirmation in large prospective clinical trials.

Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

“…Numerous novel ML algorithms have remedied deficiencies of canonical statistical methods, such as overfitting, unbalanced data distribution and so on. Ji Hyun Ahn et al [ 19 ] developed an innovative model (AUC = 0.96) to predict LNM in the early stage of CRC patients via utilizing the SEER database and adopting seven AI methods. Nevertheless, these studies were retrospective, single-center, and with small quantities of patients.…”

Application of Artificial Intelligence in a Real-World Research for Predicting the Risk of Liver Metastasis in T1 Colorectal Cancer

“…They also found that synchronous LM was an independent prognostic factor for CRC patients [12]. Likewise, Ji Hyun Ahn [17] et al have developed an innovative model to predict LNM in the early stage of CRC patients via utilizing the SEER database and adopting seven AI methods. Nevertheless, these studies were retrospective, single-center, and with small quantities of patients.…”

“…Besides, the depth and breadth of the discipline integration have signi cantly enhanced [14,15]. Researchers have employed machine learning (ML) as the breakpoint to better solve the complicated problem for clinical prediction and acquired several signi cant breakthroughs in CRC [16][17][18]. Given that most of the present studies merely focused on the public database when studying the apparent discrepancy among different populations, limitations ineluctably appeared.…”

Machine learning in handling disease outbreaks: a comprehensive review

“…Most of the articles on this topic use machine learning and deep learning approaches for prediction (81 articles), and diagnosis (16 articles). In particular, regarding the prediction approaches, some previous research elaborated the following predictions: patient survival likelihood [86], cancer prognosis [87], treatment plans [88], and metastasis (cancer spreads to a different body part) [89]. And the rest were the predictions of radiation risk in breast cancer [90], mortality risk [91], malignancy (local spreading) [92], and recurrences [93].…”