Evidence map›Paper›PMID 41968233›Full record

ArticleAnnals of surgical oncology2026

Integration of Node-RADS and Habitat Radiomics for Predicting Occult Nodal Metastasis in Bladder Cancer.

Chenyu Li, Chunliang Cheng, Sai Li, Dongcui Wang, Weihua Liao

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Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chenyu LiDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Chunliang ChengDepartment of Colorectal Surgery, Department of Oncology, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China.
Sai Li *Department of Radiology, Xiangya Hospital, Central South University, Changsha, China. llisai@csu.edu.cn.
Dongcui Wang *Department of Radiology, Xiangya Hospital, Central South University, Changsha, China. wangdongcui_bme@csu.edu.cn.
Weihua LiaoDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.

Funding

National Natural Science Foundation of China 82071984National Natural Science Foundation of China 82471894National Natural Science Foundation of China 91959117the Science and Technology Innovation Program of Hunan Province 2020RC4007The Scientific Research Program of FuRong Laboratory 2024PT5110
6 · The paper itself

Abstract

backgroundThis study aimed to evaluate the role of tumor habitat analysis in predicting occult nodal metastasis (ONM) in bladder cancer patients.

methodsThis retrospective study enrolled 84 bladder cancer patients who underwent pelvic lymphadenectomy. The tumor area was segmented into sub-regions using a Fuzzy C-Means clustering algorithm based on Hounsfield unit (HU) values. Independent clinical factors related to ONM were screened through univariate logistic regression analysis. Radiomics features were extracted from both the whole tumor and each sub-region to construct predictive models. The predictive performance of different models was evaluated. Furthermore, a nomogram was constructed by integrating habitat radscore with clinical factors, and its predictive efficacy was evaluated using the area under the curve (AUC). Finally, the practical clinical value of the nomogram was systematically evaluated through 1000 bootstrap iterations combined with decision curve analysis.

resultsThe whole-tumor model showed moderate discrimination (AUC: 0.735 training, 0.727 validation). The best single-habitat model (region 3) achieved AUCs of 0.859 and 0.788, whereas the regions 1, 2, and 3 model reached 0.920 and 0.864. The nomogram yielded AUCs of 0.952 (training) and 0.742 (validation).

conclusionHabitat radiomics improves ONM prediction over whole-tumor radiomics, and an integrated nomogram shows promising clinical utility pending external validation.

Indexed as

Lymph NodesNomogramsRadiomicsTomography, X-Ray ComputedUrinary Bladder NeoplasmsAgedFemaleFollow-Up StudiesHumansLymphatic MetastasisLymph Node ExcisionMaleMiddle AgedNeoplasm StagingPrognosisRetrospective StudiesBladder cancerComputed tomographyHabitat radiomicsOccult nodal metastasisUrinary bladder

Identifiers

PMID41968233

What Socratic holds

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Registered trials

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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 Socratic graph.