ReviewAbdominal radiology (New York)2019
Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.
Review in Abdominal radiology (New York), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06108310 (ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors), which is not on this map. Cited by 48 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors
Who cites it
48 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.Frontiers in oncology · 2022Pooled it
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Current status and quality of radiomics studies in lymphoma: a systematic review.European radiology · 2020Pooled it
- Prognostic value of quantitative multiparametric magnetic resonance imaging parameters and histopathologic features for disease-free survival in locally advanced non-mucinous rectal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Computed tomography radiomics-based cross-sectional detection of mandibular osteoradionecrosis in head and neck cancer survivors.Oral oncology · 2025Article
- A novel structural modeling magnitude and orientation radiomic descriptor for evaluating response to neoadjuvant therapy in rectal cancers via MRI.NPJ precision oncology · 2025Article
- Development of a CT radiomics model for detection of bladder invasion by colorectal carcinoma.Scientific reports · 2025Article
- MRI management of focal liver lesions: what a beginner cannot fail to know.Frontiers in oncology · 2025Review
- Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to Neoadjuvant Therapy for Rectal Cancer.Cancer management and research · 2025Article
- Radiomics Analysis for Clinical Decision Support in 177Lu-DOTATATE Therapy of Metastatic Neuroendocrine Tumors using CT Images.Journal of biomedical physics & engineering · 2024Article
- Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics.Insights into imaging · 2024Article
- The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.European radiology · 2024Article
- Predicting the Response of Patients Treated withJournal of medical signals and sensors · 2024Article
- The Crying Need for a Better Response Assessment in Rectal Cancer.Current treatment options in oncology · 2023Review
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- MRI-based radiomic score increased mrTRG accuracy in predicting rectal cancer response to neoadjuvant therapy.Abdominal radiology (New York) · 2023Article
- Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions.Current oncology (Toronto, Ont.) · 2023Review
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
introductionAs computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set. MATERIAL AND
methodsIn this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer.
conclusionRadiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.
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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.