ReviewCancers2026
CT-Based Radiomics in Renal Tumors: Current Evidence, Methodological Challenges, and Future Perspectives for Precision Oncology.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCT-based radiomics may provide non-invasive imaging biomarkers for renal-tumor characterization and risk stratification. This systematic review evaluates the clinical evidence and translational readiness of radiomics in renal oncology and reports an exploratory methodological appraisal of a clearly delimited subgroup.
methodsPubMed/MEDLINE and Embase were searched from database inception through 15 August 2026 using controlled vocabulary and free-text terms for renal tumors, computed tomography, and radiomics or quantitative image analysis. Eligibility was restricted to English-language original studies published from 1 January 2020 to 15 August 2026 that were available in full text. Engineered-feature radiomics constituted the primary evidence base; end-to-end deep-learning studies were considered separately. Owing to clinical and methodological heterogeneity, findings were synthesized narratively.
resultsThe searches retrieved 1521 records (PubMed/MEDLINE, n = 357; Embase, n = 1164). The PubMed/MEDLINE stream yielded 242 unique records after removal of 115 duplicates; 72 full-text reports were assessed and included. Cross-deduplication and screening of the Embase records identified no additional eligible study. Contemporary evidence supports potential applications in benign-malignant differentiation, histological subtype classification, WHO/ISUP grade and pathological-stage prediction, and postoperative outcome assessment. Reported discrimination was frequently high, but performance estimates were not directly comparable and often declined in independent testing. Within the illustrative, non-representative 28-report detailed appraisal subset, 25 engineered-radiomics studies had an available numerical RQS (median, 16; interquartile range, 15-20; range, 12-24; 44.4% of the maximum).
conclusionsCT radiomics is technically promising but not yet ready for routine clinical use. Quality-related findings apply only to the assessed subgroup and cannot characterize the entire evidence base. Standardized acquisition and feature definitions, transparent analysis, clinically relevant comparators, prospective multicenter validation, formal risk-of-bias assessment in future reviews, and impact studies are needed.
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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.