ArticleJournal of imaging informatics in medicine2026
A Multimodal, Multitask Prediction Framework for Diagnosis and Prognosis of Clear Cell Renal Cell Carcinoma.
Article in Journal of imaging informatics in medicine, 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
Abstract
The purpose of this study is to develop and validate a multimodal, multitask prediction framework for clear cell renal cell carcinoma (ccRCC) by integrating preoperative CT radiomics, pathology-derived biomarker data from preoperative biopsy specimens, and clinical variables. The model was built for pathologically confirmed ccRCC and excluded other RCC histologic subtypes (e.g., papillary and chromophobe). In this multicenter retrospective study, ccRCC patients were enrolled and data were collected from preoperative CT scans, AMACR/P504S immunohistochemistry results, pathology reports, and follow-up records. Patients were assigned by hospital site into a training cohort and an independent external test cohort. Radiomic features were extracted from CT tumor regions of interest (ROIs), while deep learning features were derived from pathology images. Clinical variables were incorporated as additional inputs. A post-feature fusion strategy enabled simultaneous prediction of tumor classification and postoperative survival. Model performance was assessed using AUC for diagnosis and C-index for survival, together with calibration curves, decision curve analysis, and bootstrap confidence intervals. SHAP analysis was applied to quantify feature contributions. In external validation, the integrated multimodal model achieved strong diagnostic discrimination for P504S/AMACR (AUC = 0.983) and demonstrated improved prognostic performance for postoperative outcomes (C-index = 0.804). Subgroup analyses by grade and stage further supported model robustness, while SHAP-based interpretation indicated complementary contributions from imaging, pathology, and clinical variables. Overall, the proposed multimodal, multitask fusion framework enables reliable preoperative P504S/AMACR-based classification and postoperative prognostic prediction in ccRCC, supporting more refined risk stratification and individualized postoperative management using noninvasive imaging and clinical information. Calibration and decision curve analyses further support its potential clinical utility. Larger prospective and external multicenter validations are still needed to confirm generalizability.
Indexed as
Identifiers
42443645What 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.