ArticlePloS one2020
Association of visual and quantitative heterogeneity of 18F-FDG PET images with treatment response in locally advanced rectal cancer: A feasibility study.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
6 citing papers in PubMed.
- Clinical utility and future directions of FDG-PET in rectal cancer management.Annals of nuclear medicine · 2026Review
- Predictive value of intratumoral-metabolic heterogeneity derived fromFrontiers in oncology · 2023Article
- Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- The Role of Histogram-Based Textural Analysis ofMolecular imaging and radionuclide therapy · 2022Article
- Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges.Journal of oncology · 2022Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeFew tools are available to predict tumor response to treatment. This retrospective study assesses visual and automatic heterogeneity from 18F-FDG PET images as predictors of response in locally advanced rectal cancer.
methodsThis study included 37 LARC patients who underwent an 18F-FDG PET before their neoadjuvant therapy. One expert segmented the tumor from the PET images. Blinded to the patient´s outcome, two experts established by consensus a visual score for tumor heterogeneity. Metabolic and texture parameters were extracted from the tumor area. Multivariate binary logistic regression with cross-validation was used to estimate the clinical relevance of these features. Area under the ROC Curve (AUC) of each model was evaluated. Histopathological tumor regression grade was the ground-truth.
resultsStandard metabolic parameters could discriminate 50.1% of responders (AUC = 0.685). Visual heterogeneity classification showed correct assessment of the response in 75.4% of the sample (AUC = 0.759). Automatic quantitative evaluation of heterogeneity achieved a similar predictive capacity (73.1%, AUC = 0.815).
conclusionA response prediction model in LARC based on tumor heterogeneity (assessed either visually or with automatic texture measurement) shows that texture features may complement the information provided by the metabolic parameters and increase prediction accuracy.
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