ArticleInternational journal of colorectal disease2020
Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
Article in International journal of colorectal disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Repeatability of radiomics studies in colorectal cancer: a systematic review.BMC gastroenterology · 2023Pooled it
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer.Radiation oncology (London, England) · 2023Article
- MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.Frontiers in oncology · 2023Article
- Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods.Heliyon · 2022Article
- Rectal MRI radiomics inter- and intra-reader reliability: should we worry about that?Abdominal radiology (New York) · 2022Review
- An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy.Frontiers in genetics · 2022Article
- Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.World journal of gastroenterology · 2021Review
- Emerging applications of radiomics in rectal cancer: State of the art and future perspectives.World journal of gastroenterology · 2021Review
- Research progress of omics technology in the field of tumor resistance: From singleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2021Article
- Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis.Journal of oncology · 2021Article
- Article
- Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.World journal of gastroenterology · 2020Observational
- A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer.Frontiers in oncology · 2020Article
- Magnetic resonance radiomics-based deep learning model for diagnosis of Alzheimer's disease.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo develop a predicting model for tumor resistance to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) by using pre-treatment apparent diffusion coefficient (ADC) image-derived radiomics features.
methodA total of 89 patients with LARC were randomly assigned into training (N = 66) and testing cohorts (N = 23) at the ratio of 3:1. Radiomics features were derived from manually determined tumor region of pre-treatment ADC images. Random forest algorithm was used to determine the most relevant features and then to construct a predicting model for identifying resistant tumor. Stability and diagnostic performance of the random forest model was evaluated with the testing cohort.
resultsThe top 10 most relevant features (entropy
conclusionThe random forest classifier based on radiomics features derived from pre-treatment ADC images have the potential to predict tumor resistance to NCRT in patients with LARC, and the use of predicting model may facilitate individualized management of rectal cancer.
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