ArticleIEEE transactions on radiation and plasma medical sciences2019
Treatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory.
Article in IEEE transactions on radiation and plasma medical sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current status and quality of radiomics studies in lymphoma: a systematic review.European radiology · 2020Pooled it
- Semiquantitative 2-[Frontiers in medicine · 2024Review
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).PET clinics · 2022Article
- A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers.Bioinformatics (Oxford, England) · 2021Article
- Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers.Sensors (Basel, Switzerland) · 2021Article
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
- Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice.Journal of applied clinical medical physics · 2020Article
- Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise.ACM computing surveys · 2020Article
- Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.European journal of nuclear medicine and molecular imaging · 2019Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
In this study, we proposed a new radiomics-based treatment outcome prediction model for cancer patients. The prediction model is developed based on belief function theory (BFT) and sparsity learning to address the challenges of redundancy, heterogeneity, and uncertainty of radiomic features, and relatively small-sized and unbalanced training samples. The model first selects the most predictive feature subsets from relatively large amounts of radiomic features extracted from pre- and/or in-treatment positron emission tomography (PET) images and available clinical and demographic features. Then an evidential k-nearest neighbor (EK-NN) classifier is proposed to utilize the selected features for treatment outcome prediction. Twenty-five stage II-III lung, 36 esophagus, 63 stage II-III cervix, and 45 lymphoma cancer patient cases were included in this retrospective study. Performance and robustness of the proposed model were assessed with measures of feature selection stability, outcome prediction accuracy, and receiver operating characteristics (ROC) analysis. Comparison with other methods were conducted to demonstrate the feasibility and superior performance of the proposed model.
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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.