Evidence map›Paper›PMID 40285894›Full record

ArticleEuropean radiology experimental2025

Early treatment monitoring of multidrug-resistant tuberculosis based on CT radiomics of cavity and cavity periphery.

Xinna Lv, Ye Li, Chenyu Ding, Lixin Qin, Xiaoyue Xu, Ziwei Zheng, Dailun Hou

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Article in European radiology experimental, 2025. 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

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xinna Lv *Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Ye Li *Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Chenyu DingDepartment of Radiology, Wuhan Pulmonary Hospital, Wuhan, China.
Lixin QinDepartment of Radiology, Wuhan Pulmonary Hospital, Wuhan, China.
Xiaoyue XuDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Ziwei ZhengDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Dailun HouDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China. hou.dl@mail.ccmu.edu.cn.ORCID http://orcid.org/0000-0003-0773-3103

Funding

Beijing Hospitals Authority Clinical Medicine Development of Special Funding XMLX202146
6 · The paper itself

Abstract

backgroundEarly identification of treatment failure can effectively improve the success rate of antituberculosis treatment. This study aimed to construct a predictive model using radiomics based on cavity and cavity periphery to monitor the early treatment efficacy in multidrug-resistant tuberculosis (MDR-TB).

methodsWe retrospectively collected data on 350 MDR-TB patients who underwent pretreatment chest computed tomography (CT) and received longer regimens from two hospitals. They were subdivided into training (252 patients from hospital 1) and testing (98 patients from hospital 2) cohorts. According to at least two consecutive sputum culture results within the early sixth months of treatment, patients were divided into high-risk and low-risk groups. Radiomics models were established based on cavity and periphery with a range of 2, 4, 6, 8, and 10 mm. A combined model fused radiomics features of cavity with the best-performing peripheral regions. The performance of these models was evaluated by the receiver operating characteristic area under the curve (AUC) and clinical decision curve analysis.

resultsThe cavity model achieved AUCs of 0.858 and 0.809 in the training and testing cohort, respectively. The radiomics model based on 4 mm peripheral region showed superior performance compared to other surrounding areas with AUCs of 0.884 and 0.869 in the two cohorts. The AUCs of the combined model were 0.936 and 0.885 in the two cohorts.

conclusionCT radiomics analysis integrating cavity and cavity periphery provided value in identifying MDR-TB patients at high risk of treatment failure. The optimal periphery extent was 4 mm. RELEVANCE STATEMENT: The cavity periphery also contains therapy-related information. Radiomics model based on cavity and 4 mm periphery is an effective adjunct to monitor early treatment efficacy for MDR-TB patients that can guide clinical decision. KEY POINTS: A combined CT radiomics model integrating cavity with periphery can effectively monitor treatment response. A periphery of 4 mm showed superior performance compared to other peripheral smaller or greater extent. This study provided a surrogate for identifying the high risk of treatment failure in multidrug-resistant tuberculosis patients.

Indexed as

Tomography, X-Ray ComputedTuberculosis, Multidrug-ResistantAdultAntitubercular AgentsFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesAntitubercular AgentsRadiomicsSputumTomography (x-ray computed)Treatment failureTuberculosis (multidrug-resistant)

Identifiers

PMID40285894
PMCPMC12033146

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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.