ArticleAnnals of translational medicine2020
Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 29 citations in OpenAlex.
- MRI Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy.Cancer reports (Hoboken, N.J.) · 2026Article
- Clinical utility and future directions of FDG-PET in rectal cancer management.Annals of nuclear medicine · 2026Review
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Uncover This Tech Term: Random Forest.Korean journal of radiology · 2025Article
- Predictive value ofReports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 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
- Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Predictive Value ofCancers · 2022Article
- Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.Journal of oncology · 2022Review
- Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer.European radiology · 2021Article
- Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.World journal of gastroenterology · 2021Review
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer.Annals of translational medicine · 2021Article
- Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNeoadjuvant chemoradiotherapy (NCRT) followed by surgery is the standard treatment for patients with locally advanced rectal cancer. This study developed a random forest (RF) model to predict pathological complete response (pCR) based on radiomics derived from baseline
methodsThis study included 169 patients with newly diagnosed rectal cancer. All patients received
resultsAfter NCRT, 22 patients (13%) achieved pCR, and 42 features that could differentiate tumors with pCR were used to construct the RF model. Six decision trees and seven splits were suitable. Accordingly, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 81.8%, 97.3%, 81.8%, 97.3%, and 95.3%, respectively.
conclusionsBy using an RF, we determined that radiomics derived from baseline
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Registered trials
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.