Published online Oct 27, 2023. doi: 10.4240/wjgs.v15.i10.2234
Peer-review started: August 16, 2023
First decision: August 31, 2023
Revised: September 7, 2023
Accepted: September 14, 2023
Article in press: September 14, 2023
Published online: October 27, 2023
Colorectal cancer (CRC) is a significant global health issue, and accurate prediction of lymph node metastasis (LNM) is crucial for individualized treatment strategies. However, predicting LNM is challenging due to various factors and limitations in the histopathological examination method. This study aimed to develop a risk prediction model for LNM in CRC by incorporating machine learning and clinicopathological factors. The model demonstrated high accuracy, sensitivity, and specificity, providing valuable insights into the potential of deep learning in improving LNM prediction and guiding clinical decision-making for CRC patients.
Accurate prediction of LNM in CRC is crucial for improving patient outcomes and developing personalized treatment strategies. However, existing methods are invasive, time-consuming, and prone to errors. This study aimed to address these limitations by developing a risk prediction model using machine learning and clinicopathological factors. The motivation was to provide a more accurate and efficient approach for predicting LNM in CRC, enabling clinicians to make informed decisions regarding treatment and facilitating improved patient care.
The main objectives of this study were to analyze the factors influencing LNM in CRC, and to develop and validate a risk prediction model for LNM based on a large patient cohort. The study aimed to utilize machine learning techniques and clinicopathological factors to construct an accurate prediction model that outperforms existing models. The goal was to improve the prediction of LNM in CRC, enabling personalized treatment strategies and enhancing clinical decision-making. Additionally, the study sought to explore the potential of deep learning in extracting valuable information from tumor histology for improved risk stratification.
In this study, a retrospective analysis was conducted on 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. The main approach involved the development of a risk prediction model for LNM in CRC. A deep learning method was utilized to extract features from primary tumor histological images that could be associated with LNM. Additionally, a logistic regression model was used, incorporating these machine-learning-derived features along with clinicopathological variables, as predictors for LNM in CRC. The performance of the prediction model was evaluated based on accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC), and Brier score to assess calibration and clinical utility.
The developed risk prediction model for LNM in CRC achieved excellent performance. The model demonstrated high accuracy (0.86), sensitivity (0.81), specificity (0.87), PPV (0.66), NPV (0.94), and area under the curve for the ROC (0.91). Additionally, it exhibited a low Brier score of 0.10. The observed and predicted probabilities of LNM showed strong agreement across various risk thresholds, indicating reliable calibration and clinical utility. These findings highlight the effectiveness and clinical applicability of the model, which utilizes machine-learning-derived features from primary tumor histology and clinicopathological variables.
The study successfully developed and validated a powerful risk prediction model for LNM in CRC. The model, incorporating machine-learning-derived features from primary tumor histology and clinicopathological variables, displayed superior performance and clinical applicability compared to existing models. By leveraging deep learning techniques, valuable information was extracted from tumor histology, leading to improved LNM prediction. This development has significant implications for individualized treatment strategies and clinical decision-making in CRC, enabling better risk stratification. The findings highlight the potential of machine learning and deep learning in enhancing LNM prediction and improving patient outcomes in CRC management.
The successful development and validation of a potent risk prediction model for LNM in CRC opens up promising research avenues. Further exploration can focus on refining the model by incorporating additional molecular characteristics and genetic data to enhance its predictive accuracy. Additionally, prospective studies can be conducted to validate the model’s performance in larger and diverse patient populations. Furthermore, the integration of real-time image analysis techniques and artificial intelligence algorithms can streamline the prediction process, enabling faster and more accurate LNM assessment. These advancements have the potential to revolutionize clinical practice and optimize treatment strategies for CRC patients.