Evidence map›Paper›PMID 40926363›Full record

ArticleCurrent medical imaging2025

CT-based Radiomics of Intratumoral and Peritumoral Regions to Predict the Recurrence Risk in Patients with Non-muscle-invasive Bladder Cancer within Two Years after TURBT.

Ting Cao, Na Li, Chuanchao Guo, Hepeng Zhang, Lihua Chen, Ke Wu, Lisha Liang, Ximing Wang, Wen Shen

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Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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5 · Who and what money

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9 authors.

Ting CaoDepartment of Radiology, First Central Clinical College, Tianjin Medical University, Tianjin, China.
Na LiDepartment of Radiology, The People's Hospital of Zhangqiu Area, Jinan, China.
Chuanchao GuoDepartment of Radiology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China.
Hepeng ZhangDepartment of Urology Surgery, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China.
Lihua ChenDepartment of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Jinan, China.
Ke WuDepartment of Radiology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China.
Lisha LiangDepartment of Radiology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, China.
Ximing WangDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Wen ShenDepartment of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPredicting the recurrence risk of NMIBC after TURBT is crucial for individualized clinical treatment.

objectiveThe objective of this study is to evaluate the ability of radiomic feature analysis of intratumoral and peritumoral regions based on computed tomography (CT) imaging to predict recurrence in non-muscle-invasive bladder cancer (NMIBC) patients who underwent transurethral resection of bladder tumor (TURBT).

methodsA total of 233 patients with NMIBC who underwent TURBT were retrospectively analyzed. Within the intratumoral and peritumoral regions of the venous phase images, 1316 radiomics features were extracted. Feature selection was used to identify a set of top recurrence-associated features within the training cohort. Three models were constructed to predict recurrence for a given patient using Random Forest (RF): Model 1 was based on the radiomics features set from the intratumoral region, Model 2 was based on a combination of intratumoral and peritumoral regions, and Model 3 combined the radiomics features from Model 2 and clinical factors. The three models were then independently tested on internal and external cohorts, and their performance was evaluated. We also employed the bootstrap method on the internal cohort to further validate the performance of the model.

resultsCombining intratumoral and peritumoral regions, Model 2 yielded a higher area under the receiver operator characteristic curves (AUC) than Model 1, with 0.826 AUCs of the training cohort. After adding clinical factors, the predictive performance of Model 3 for postoperative recurrence of NMIBC was further improved, and the AUCs of the training, internal, and external validation cohorts of Model 3 were 0.860 (95% CI: 0.829-0.954), 0.829 (0.812-0.863), and 0.805 (0.652-0.840), respectively (all p>0.05). The bootstrap value of Model 3 on the internal cohort was 0.852. Model 3 stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) (p<0.001).

conclusionRadiomic features derived from intratumoral regions can predict the 2-year recurrence risk following TURBT in patients with NMIBC. The predictive performance is further enhanced when combined with radiomic features from peritumoral regions and clinical risk factors.

Indexed as

Neoplasm Recurrence, LocalTomography, X-Ray ComputedUrinary Bladder NeoplasmsAgedAged, 80 and overFemaleHumansMaleMiddle AgedNeoplasm InvasivenessNon-Muscle Invasive Bladder NeoplasmsRadiomicsRetrospective StudiesBladder cancerCTMachine learningNon-muscle-invasive bladder cancerRadiomicsRecurrenceTME.Transurethral resection of bladder tumor

Identifiers

PMID40926363
PMCPMC13176758

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