ArticleAnnals of medicine2024
Development and validation of a clinical-radiomics nomogram for the early prediction of Klebsiella pneumoniae liver abscess.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and validation of a predictive model for assessing the risk of recurrence in patients with Klebsiella pneumoniae-induced pyogenic liver abscess.BMC infectious diseases · 2025Article
- Risk factors analysis for poor prognosis after percutaneous drainage in liver abscess patients with fatty liver.World journal of gastrointestinal surgery · 2025Article
- Application of metagenomic next-generation sequencing in treatment guidance for deep neck space abscess.BMC microbiology · 2025Article
- Constructing a risk screen for attention difficulty in U.S. adults using six machine learning methods.Frontiers in artificial intelligence · 2025Article
- Establishment of a Machine Learning-Based Predictive Model forInfection and drug resistance · 2025Article
- Article
- Using baseline MRI radiomics to predict the tumor shrinkage patterns in HR-Positive, HER2-Negative Breast Cancer.Frontiers in oncology · 2025Article
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Authors and funding
6 authors.
Funding
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Abstract
BACKGROUND AND
aimPyogenic liver abscess (PLA) is a devastating and potentially life-threatening disease globally, with Klebsiella pneumoniae liver abscess (KPLA) being the most prevalent in Asia. This study aims to develop an effective and comprehensive nomogram combining clinical and radiomics features for early prediction of KPLA.
methods255 patients with PLA from 2013 to 2023 were enrolled and randomly divided into the training and validation cohorts at a 7:3 ratio. The differences between the two cohorts of patients were assessed
resultsThe nomogram incorporated four clinical features of diabetes mellitus, cryptogenic liver abscess, C-reactive protein level, and splenomegaly, and the Rad-score that was constructed based on seven optimal radiomics features. It had an AUC of 0.929 (95% CI, 0.894-0.964) and 0.923 (95% CI, 0.864-0.981) in the training and validation cohorts, respectively. The calibration and decision curves showed that the nomogram had good agreement and clinical applicability.
conclusionsThe clinical-radiomics nomogram performed well in predicting KPLA, hopefully serving as a reference for early diagnosis of KPLA.
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