Evidence map›Paper›PMID 39311948›Full record

ArticleAbdominal radiology (New York)2025

Predicting postoperative prognosis in clear cell renal cell carcinoma using a multiphase CT-based deep learning model.

Changyin Yao, Bao Feng, Shurong Li, Fan Lin, Changyi Ma, Jin Cui, Yu Liu, Ximiao Wang, Enming Cui

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Changyin Yao *Department of Radiology, Jiangmen Central Hospital, Jiangmen, China.
Bao Feng *School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
Shurong LiDepartment of Radiology, The First Affiliated Hospital of SUN YAT-SEN University, Guangzhou, China.
Fan LinDepartment of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
Changyi MaDepartment of Radiology, Jiangmen Central Hospital, Jiangmen, China.
Jin CuiDepartment of Radiology, Jiangmen Central Hospital, Jiangmen, China.
Yu LiuSchool of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
Ximiao WangSchool of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China.
Enming CuiDepartment of Radiology, Jiangmen Central Hospital, Jiangmen, China. cem2008@163.com.

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515220080
6 · The paper itself

Abstract

backgroundSome clinicopathological risk stratification systems (CRSSs) such as the leibovich score have been used to predict the postoperative prognosis of patients with clear cell renal cell carcinoma (ccRCC), but there are no reliable noninvasive preoperative indicators for predicting postoperative prognosis in clinical practice. PURPOSE: To assess the value of a deep learning (DL) model based on CT images in predicting the postoperative prognosis of patients with ccRCC. MATERIALS AND

methodsA total of 382 patients with ccRCC were retrospectively enrolled andallocated to training (n = 229) or testing (n = 153) cohorts at a 6:4 ratio. The features were extracted from precontrast-phase (PCP), corticomedullary-phase (CMP) and nephrographic-phase (NP) CT images with ResNet50, and then extreme learning machines (ELMs) were used to construct classification models. The DL model and Leibovich score were compared and combined. A receiver operating characteristic (ROC) curve and integrated discrimination improvement (IDI) were used to evaluate model performance.

resultsCompared with other single-phase DL models, the three-phase CT-based DL model achieved the best performance, with an area under the curve (AUC) of 0.839. Combining the three-phase DL model and the Leibovich score (AUC = 0.823) into a nomogram (AUC = 0.888) statistically improved performance (IDI

conclusionThe CT-based DL model could be valuable for preoperatively predicting the prognosis of patients with ccRCC, and combining it with the Leibovich score can further improve its predictive performance.

Indexed as

Carcinoma, Renal CellDeep LearningKidney NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansKidneyMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective StudiesClear cell renal cell carcinomaClinical decision-makingDeep learningPrognosis

Identifiers

PMID39311948

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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.