SynthesisEuropean radiology2020
Current status and quality of radiomics studies in lymphoma: a systematic review.
Synthesis in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 7 of them syntheses 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.
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Who cites it
35 citing papers in PubMed, 7 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.European radiology · 2023Pooled it
- Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.European journal of nuclear medicine and molecular imaging · 2022Pooled it
- Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.European radiology · 2022Pooled it
- Quality assessment of radiomics research in cardiac CT: a systematic review.European radiology · 2022Pooled it
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.The British journal of radiology · 2021Pooled it
- A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.European radiology · 2021Pooled it
- Reproducibility of radiomics quality score: an intra- and inter-rater reliability study.European radiology · 2024Trial
- Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis.BMC medical imaging · 2026Article
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Radiomic prediction for durable response to high-dose methotrexate-based chemotherapy in primary central nervous system lymphoma.Cancer medicine · 2024Article
- Utility of contrast-enhanced MRI radiomics features combined with clinical indicators for predicting induction chemotherapy response in primary central nervous system lymphoma.Journal of neuro-oncology · 2024Article
- Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score.World journal of gastroenterology · 2024Review
- Radiomics predictive modeling from dual-time-point FDG PET KEJNMMI research · 2023Article
- BaselineDiagnostics (Basel, Switzerland) · 2023Article
- Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.Japanese journal of radiology · 2023Review
- Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
- Article
- Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review).Oncology reports · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo perform a systematic review regarding the developments in the field of radiomics in lymphoma. To evaluate the quality of included articles by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), the phases classification criteria for image mining studies, and the radiomics quality scoring (RQS) tool.
methodsWe searched for eligible articles in the MEDLINE/PubMed and EMBASE databases using the terms "radiomics", "texture" and "lymphoma". The included studies were divided into two categories: diagnosis-, therapy response- and outcome-related studies. The diagnosis-related studies were evaluated using the QUADAS-2; all studies were evaluated using the phases classification criteria for image mining studies and the RQS tool by two reviewers.
resultsForty-five studies were included; thirteen papers (28.9%) focused on the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Thirty-two (71.1%) studies were classified as discovery science according to the phase classification criteria for image mining studies. The mean RQS score of all studies was 14.2% (ranging from 0.0 to 40.3%), and 23 studies (51.1%) were given a score of < 10%.
conclusionThe radiomics features could serve as diagnostic and prognostic indicators in lymphoma. However, the current conclusions should be interpreted with caution due to the suboptimal quality of the studies. In order to introduce radiomics into lymphoma clinical settings, the lesion segmentation and selection, the influence of the pathological pattern and the extraction of multiple modalities and multiple time points features need to be further studied. KEY POINTS: • The radiomics approach may provide useful information for diagnosis, prediction of the therapy response, and outcome of lymphoma. • The quality of published radiomics studies in lymphoma has been suboptimal to date. • More studies are needed to examine lesion selection and segmentation, the influence of pathological patterns, and the extraction of multiple modalities and multiple time point features.
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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.