ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2024
Preoperative CT radiomics
Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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
2 citing papers in PubMed.
- Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.NPJ precision oncology · 2025Article
- [CT radiomicsZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), and developing personalized treatment strategies is crucial for improving patient prognosis. This study aims to develop and validate a preoperative computer tomography (CT) radiomics-based predictive model to estimate Ki-67 expression in ccRCC patients, thereby assisting in clinical treatment decisions and prognosis prediction.
methodsA retrospective analysis was conducted on 214 ccRCC patients who underwent surgical treatment at Gansu Provincial Hospital between January 2018 and November 2023. Patients were classified into high Ki-67 expression (
resultsA total of 107 radiomic features were extracted from 5 mm arterial-phase CT images, and twenty-one features significantly associated with Ki-67 expression were selected using the LASSO algorithm. Predictive models were developed using LR, MLP, and SVM classifiers. In the training and validation sets, the AUC values for each model were 0.904 (95%
conclusionsA CT radiomics-based model for predicting Ki-67 expression in ccRCC was successfully developed. This model provides valuable guidance for treatment planning and prognostic assessment in ccRCC patients.
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What Socratic 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 Socratic graph.