ArticleBMC cancer2026
Exploratory clinical-CT machine learning characterization of CK7 expression in clear cell renal cell carcinoma.
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
8 authors.
Funding
Abstract
backgroundCytokeratin 7 (CK7) expression in clear cell renal cell carcinoma (ccRCC) may reflect tumor phenotype and biological heterogeneity, but its clinical role remains exploratory. This study aimed to investigate associations between preoperatively available clinical and CT imaging features and CK7 expression and to develop an exploratory machine learning model for CK7 characterization.
methodsThis multicenter retrospective study included 230 patients with pathologically confirmed ccRCC from three institutions, including 139 patients in the training cohort and 91 patients in the validation cohort. Clinical variables and qualitative and quantitative CT features were assessed by two radiologists. CK7 positivity was defined as cytoplasmic staining in at least 10% of tumor cells. After least absolute shrinkage and selection operator regression, four machine learning models using prespecified configurations were developed in the training cohort and evaluated in the validation cohort. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, calibration analysis, and decision curve analysis. Classification thresholds were determined in the training cohort using the maximum Youden index and then applied to the validation cohort. Model stability was assessed using 1000 bootstrap resampling iterations.
resultsAmong 230 patients, 67 had CK7-positive tumors. The final predictors included age, cystic component, corticomedullary attenuation, corticomedullary enhancement ratio, nephrographic attenuation, and tumor size. The logistic regression model was selected as the final model based on its discrimination, calibration performance, interpretability, and overall generalizability. In the validation cohort, the logistic regression model achieved an AUC of 0.714 (95% CI: 0.589, 0.828), with a sensitivity of 0.625 and a specificity of 0.627. Bootstrap resampling supported the stability of performance estimates.
conclusionThis exploratory machine learning model-development study suggests that preoperatively available clinical and CT imaging features may be associated with CK7 expression in ccRCC. Further prospective validation using larger cohorts is required before clinical translation.
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.