ArticleBMC cancer2026
Radiomics analysis of CT imaging for predicting MIT family translocation renal cell carcinoma: a multicenter retrospective clinical study.
Article in BMC cancer, 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
11 authors.
Funding
Abstract
objectiveThis study aimed to investigate the imaging characteristics of Microphthalmia Transcription Factor (MIT) Family Translocation Renal Cell Carcinoma (MIT-RCC), assess the relationship between CT-based radiomics and MIT-RCC, and develop a predictive model to improve preoperative non-invasive diagnosis. MATERIALS AND
methodsA retrospective analysis was conducted using preoperative computed tomography (CT) images and clinical data from 746 RCC patients (77 MIT-RCC and 669 other RCC subtypes) across multiple centers. Regions of interest (ROIs) in both tumor and normal renal parenchymal during non-contrast and arterial phases were manually segmented using ITK-SNAP. Radiomics features were extracted via Python, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was applied for feature selection. A combined model incorporating radiomics scores and clinical predictors (age, stage) was developed using logistic regression and visualized as a nomogram. Patients from two centers formed the training cohort (n = 539), and three other centers served as the validation cohort (n = 207).
resultsFrom 1316 features per ROI, 22 tumor and 24 normal renal parenchymal features were selected. The nomogram integrating Tumor_Radscores, Kidney_Radscores, age, and clinical stage showed strong predictive performance in the training cohort (area under the curve [AUC] = 0.952, 95% confidence interval [CI]: 0.927-0.971) and validation cohort (AUC = 0.914, 95% CI: 0.879-0.942), with good calibration and clinical utility.
conclusionBy integrating radiomics and clinical data, the proposed nomogram achieved accurate, non-invasive prediction of MIT-RCC, offering valuable support for preoperative diagnosis and personalized treatment planning.
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.