ArticleInternational journal of medical sciences2020
Machine Learning in Prediction of Second Primary Cancer and Recurrence in Colorectal Cancer.
Article in International journal of medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 30 citations in OpenAlex.
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- Abnormal functional network connectivity mediates the relationship between depressive symptoms and cognitive decline in late-onset depression.Psychological medicine · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
- Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025Article
- Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.Frontiers in oncology · 2025Article
- Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.International journal of medical sciences · 2025Article
- Applying Machine Learning Techniques to Implementation Science.Online journal of public health informatics · 2024Article
- Machine learning based prediction of recurrence after curative resection for rectal cancer.PloS one · 2023Article
- Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women.Asia-Pacific journal of oncology nursing · 2022Article
- Article
- Review
- A Prediction Model for Tumor Recurrence in Stage II-III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling.Biomedicines · 2022Article
- Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.Frontiers in public health · 2021Article
- Dissociation Pattern in Default-Mode Network Homogeneity in Drug-Naive Bipolar Disorder.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is the third commonly diagnosed cancer worldwide. Recurrence of CRC (Re) and onset of a second primary malignancy (SPM) are important indicators in treating CRC, but it is often difficult to predict the onset of a SPM. Therefore, we used mechanical learning to identify risk factors that affect Re and SPM. PATIENT AND
methodsCRC patients with cancer registry database at three medical centers were identified. All patients were classified based on Re or no recurrence (NRe) as well as SPM or no SPM (NSPM). Two classifiers, namely A Library for Support Vector Machines (LIBSVM) and Reduced Error Pruning Tree (REPTree), were applied to analyze the relationship between clinical features and Re and/or SPM category by constructing optimized models.
resultsWhen Re and SPM were evaluated separately, the accuracy of LIBSVM was 0.878 and that of REPTree was 0.622. When Re and SPM were evaluated in combination, the precision of models for SPM+Re, NSPM+Re, SPM+NRe, and NSPM+NRe was 0.878, 0.662, 0.774, and 0.778, respectively.
conclusionsMachine learning can be used to rank factors affecting tumor Re and SPM. In clinical practice, routine checkups are necessary to ensure early detection of new tumors. The success of prediction and early detection may be enhanced in the future by applying "big data" analysis methods such as machine learning.
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