ArticleAnnals of translational medicine2022
Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- An interpretable radiomics-machine learning model for early risk stratification of invasive fungal infections in community-acquired pneumonia: a dual-center study.Scientific reports · 2026Article
- A machine learning-based risk prediction tool for early identification of invasive pulmonary aspergillosis in immunocompetent patients: a systematic review and meta-analysis.Journal of thoracic disease · 2026Article
- Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection.Scientific reports · 2025Article
- Predictive nomogram for early detection of invasive fungal disease deterioration --- a 10-year retrospective cohort study.BMC infectious diseases · 2025Article
- Leveraging innovative diagnostics as a tool to contain superbugs.Antonie van Leeuwenhoek · 2025Review
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
- Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte.Journal of translational medicine · 2023Article
- Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders.Open life sciences · 2023Article
- A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia.BMC medical imaging · 2022Article
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early and accurate diagnosis of invasive fungal infection (IFI) is pivotal for the initiation of effective antifungal therapy for patients with hematologic malignancies. Methods: This retrospective study involved 235 patients with hematologic malignancies and pulmonary infections diagnosed as IFIs (n=118) or bacterial pneumonia (n=117). Patients were randomly divided into training (n=188) and validation (n=47) datasets. Four feature selection methods with nine classifiers were implemented to select the optimal machine learning (ML) model using five-fold cross-validation. A radiomic signature was constructed using a linear ML algorithm, and a radiomic score (Radscore) was calculated. The combined model was developed with the Radscore, the significant clinical and radiologic factors were selected using multivariable logistic regression, and the results were presented as a clinical radiomic nomogram. A prospective pilot study was also conducted to compare the classification performance of the combined nomogram with practicing radiologists. Results: Significant differences were found in the Radscore between IFI and bacterial pneumonia patients in the training (0.683 Conclusions: The clinical radiomic nomogram can serve as a promising predictive tool for IFI in patients with hematologic malignancies.
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