ArticleEuropean radiology2021
Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 24 citations in OpenAlex.
- Clinical utility and future directions of FDG-PET in rectal cancer management.Annals of nuclear medicine · 2026Review
- Role of 18FDG PET-CT & MRI for Predicting Response to Neo-Adjuvant Therapy in Rectal Cancer.Journal of gastrointestinal cancer · 2025Article
- The Application of Combined PET/MRI in Staging and Response Assessment of Rectal Cancer.Journal of clinical medicine · 2025Review
- Article
- Construction of a prognostic survival model for colorectal cancer patients using CT image texture analysis: a prospective cohort study.Frontiers in oncology · 2025Article
- Predicting response to chemoradiotherapy in rectal cancer via visual morphologic assessment and staging on baseline MRI: a multicenter and multireader study.Abdominal radiology (New York) · 2023Article
- Four-dimensional quantitative analysis using FDG-PET in clinical oncology.Japanese journal of radiology · 2023Review
- Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer.The British journal of radiology · 2023Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveTo investigate whether quantifying local tumour heterogeneity has added benefit compared to global tumour features to predict response to chemoradiotherapy using pre-treatment multiparametric PET and MRI data.
methodsSixty-one locally advanced rectal cancer patients treated with chemoradiotherapy and staged at baseline with MRI and FDG-PET/CT were retrospectively analyzed. Whole-tumour volumes were segmented on the MRI and PET/CT scans from which global tumour features (T2W
resultsWhen using only imaging features, local texture features showed an AUC = 0.81 versus AUC = 0.74 for global tumour features. After internal cross-validation (LOOCV), AUC to predict a good response was the highest for the combination of clinical baseline variables + global tumour features (AUC = 0.83), compared to AUC = 0.79 for baseline + local texture and AUC = 0.76 for all combined (baseline + global + local texture).
conclusionIn imaging-based prediction models, local texture analysis has potential added value compared to global tumour features to predict response. However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture analysis appears to be limited. The overall performance to predict response when combining baseline variables with quantitative imaging parameters is promising and warrants further research. KEY POINTS: • Quantification of local tumour texture on pre-therapy FDG-PET/CT and MRI has potential added value compared to global tumour features to predict response to chemoradiotherapy in rectal cancer. • However, when combined with clinical baseline parameters such as cTN-stage, the added value of local texture over global tumour features is limited. • Predictive performance of our optimal model-combining clinical baseline variables with global quantitative tumour features-was encouraging (AUC 0.83), warranting further research in this direction on a larger scale.
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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.