ArticleScientific reports2020
Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- MRI-based radiomics for predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Accuracy of deep neural learning models in the imaging prediction of pathological complete response after neoadjuvant chemoradiotherapy for locally advanced rectal cancer: a systematic review.Langenbeck's archives of surgery · 2023Pooled it
- The Auxiliary Effects of Low-Molecular-Weight Fucoidan in Locally Advanced Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy Before Surgery: A Double-Blind, Randomized, Placebo-Controlled Study.Integrative cancer therapiesTrial
- Prediction of Pathological Complete Response after Chemoradiation for Locally Advanced Rectal Cancer Using Machine Learning with Clinical Features, the INTERCEPTOR Study Preliminary Model.Journal of the anus, rectum and colon · 2026Article
- Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.International journal of colorectal disease · 2025Article
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- Smoking and Elevated Preneoadjuvant Chemoradiotherapy Serum Carcinoembryonic Antigen Levels Are Associated With High Tumor Regression Grade and Poor Survival in Patients With Locally Advanced Rectal Cancer.The Kaohsiung journal of medical sciences · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Prediction of the Therapeutic Response to Neoadjuvant Chemotherapy for Rectal Cancer Using a Deep Learning Model.Journal of the anus, rectum and colon · 2025Article
- Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?Journal of gastrointestinal cancer · 2024Article
- Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer.Scientific reports · 2024Article
- A dynamic nomogram for predicting pathologic complete response to neoadjuvant chemotherapy in locally advanced rectal cancer.Cancer medicine · 2024Article
- Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach.Journal of medical signals and sensors · 2024Article
- A management of patients achieving clinical complete response after neoadjuvant therapy and perspectives: on locally advanced rectal cancer.Frontiers in oncology · 2024Review
- Is ypTNM staging a comparable predictor as pTNM staging for survival in non-metastatic rectal cancer after preoperative chemoradiation therapy?Oncology research · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.European radiology · 2023Article
- Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.Exploration of targeted anti-tumor therapy · 2023Review
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis. However, no reliable prediction model is presently available. We evaluated the performance of an artificial neural network (ANN) model in pCR prediction in patients with LARC. Predictive accuracy was compared between the ANN, k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes classifier (NBC), and multiple logistic regression (MLR) models. Data from two hundred seventy patients with LARC were used to compare the efficacy of the forecasting models. We trained the model with an estimation data set and evaluated model performance with a validation data set. The ANN model significantly outperformed the KNN, SVM, NBC, and MLR models in pCR prediction. Our results revealed that the post-CRT carcinoembryonic antigen is the most influential pCR predictor, followed by intervals between CRT and surgery, chemotherapy regimens, clinical nodal stage, and clinical tumor stage. The ANN model was a more accurate pCR predictor than other conventional prediction models. The predictors of pCR can be used to identify which patients with LARC can benefit from watch-and-wait approaches.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.