Predictive Artificial Intelligence for Precision Oncology: Evaluating Lymph Node Metastasis in T1 and T2 Colorectal Cancer
Code: G-1928
Authors: Mohsen Nooroulahi ℗, Arash Dadvand, Somayeh Matin *
Schedule: Not Scheduled!
Tag: Cancer Diagnosis & Treatment
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Abstract:
Abstract
Background and Aim: Colorectal cancer (CRC) remains a major cause of cancer-related mortality worldwide. Early-stage detection and accurate prediction of lymph node metastasis (LNM), particularly in T1 and T2 tumors, are crucial for determining optimal treatment strategies. Artificial intelligence (AI) and deep learning have emerged as powerful tools for histopathological image analysis, evaluation of diverse risk factors for metastasis and micro metastasis, predictive modeling of metastatic sites, and the potential to enhance diagnostic accuracy and assist in risk stratification. This review aims to evaluate the current applications of AI in the prediction of the probability of LNM in patients with T1 and T2 CRC. Methods: In this review, we comprehensively examined studies that employed various AI tools to predict the probability of LNM in patients with stage T1 and T2 CRC. We analyzed the performance of these AI models and conducted a comparative evaluation of their effectiveness. Results: These studies investigated the possibility of LNM from various perspectives, including histopathological image analysis, tumor microenvironment assessment, patient-specific clinical features, and tumor-related morphological and immunological characteristics. The primary focus of these studies was on the application of advanced deep learning techniques, particularly convolutional neural networks and random forest algorithms. Compared to traditional methods such as CT scans, MRIs, and conventional pathology, the use of these AI-based approaches has significantly improved the accuracy of LNM prediction and diagnosis, leading to a reduction in unnecessary surgical procedures, which cause high mortality and morbidity, by up to 70%. Conclusion: AI and deep learning offer significant potential in improving diagnostic precision and predicting LNM in CRC, particularly in early-stage disease. These technologies can support personalized treatment decisions and reduce overtreatment. However, further validation through multicenter prospective studies is essential to ensure clinical reliability and generalizability before widespread adoption in routine practice.
Keywords
Artificial Intelligence, Colorectal Cancer, LNM
