ReviewCancers2021
Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Radiomic analysis of postmortem lung changes: a PMCT-based approach for estimating the postmortem interval.Forensic science, medicine, and pathology · 2026Article
- Analyzing histopathological images using fused CNN features based on the geometric active contour method for early diagnosis of lung and colon cancer.Discover oncology · 2025Article
- Review
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- 2.5D peritumoural radiomics predicts postoperative recurrence in stage I lung adenocarcinoma.Frontiers in oncology · 2024Article
- Role of Artificial Intelligence in Surgical Decision-Making: A Comprehensive Review.Galen medical journal · 2024Review
- Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.Molecular imaging and biology · 2023Article
- CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial.Diagnostics (Basel, Switzerland) · 2023Article
- The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy.Frontiers in oncology · 2023Review
- Artificial intelligence fusion for predicting survival of rectal cancer patients using immunohistochemical expression of Ras homolog family member B in biopsy.Exploration of targeted anti-tumor therapy · 2023Article
- Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer.Journal of personalized medicine · 2022Article
- Radiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapy.La Radiologia medica · 2022Review
- Article
- Clinical Perspectives forMetabolites · 2022Review
- Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review.Exploration of targeted anti-tumor therapy · 2022Review
- Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features.La Radiologia medica · 2021Article
- Quantitative Analysis of Residual COVID-19 Lung CT Features: Consistency among Two Commercial Software.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNeoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. MATERIALS AND
methodsKey references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used.
resultsThis paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer.
conclusionsRadiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
Indexed as
Identifiers
What OpenQuestion holds
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.