Development and validation of a machine learning-based diagnostic model for identifying nonneutropenic invasive pulmonary aspergillosis in suspected patients: a multicenter cohort study

Although clinicians can screen out suspected cases through medical history inquiries, the diagnosis of nonneutropenic invasive pulmonary aspergillosis (IPA) from suspected cases remains a significant challenge. The study developed a novel diagnostic framework by integrating clinical parameters, imaging features, and laboratory biomarkers using machine learning techniques. The risk score, derived from SHapley Additive explanation values, exhibited a highly significant correlation with the predicted probability of the weighted ensemble model, demonstrating robust discrimination capacity and generalizability. The diagnostic model and risk score could assist in identifying nonneutropenic IPA from suspected cases independently of invasive procedures, thereby enhancing clinical applicability.

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Publication Details

Journal
Microbiology Spectrum
Published
2025-05-22
DOI
https://doi.org/10.1128/spectrum.00607-25
Citations
3
Primary Topic
Antifungal resistance and susceptibility
Type
article
Field-Weighted Citation Impact
4.19

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article

Development and validation of a machine learning-based diagnostic model for identifying nonneutropenic invasive pulmonary aspergillosis in suspected patients: a multicenter cohort study

Yujian Tao, Min Cao, Guoer Ma, Yuchen Cai et al.
3 citations
Microbiology Spectrum
Antifungal resistance and susceptibility
4.19
article

Development and validation of a machine learning-based diagnostic model for identifying nonneutropenic invasive pulmonary aspergillosis in suspected patients: a multicenter cohort study

Yujian Tao, Min Cao, Guoer Ma, Yuchen Cai, Chao Sun, Jiaxin Shi, Yuanqin Li, Yajie Lu, Xinyu Wang, Cheng Chen, Wenkui Sun, Chunlai Feng, Yanbin Chen, Jun Zhou, Huanhuan Zhong, Xin Su, Xin Lu, Li Wang, Xuefan Cui
article en
3 citations

Abstract

Although clinicians can screen out suspected cases through medical history inquiries, the diagnosis of nonneutropenic invasive pulmonary aspergillosis (IPA) from suspected cases remains a significant challenge. The study developed a novel diagnostic framework by integrating clinical parameters, imaging features, and laboratory biomarkers using machine learning techniques. The risk score, derived from SHapley Additive explanation values, exhibited a highly significant correlation with the predicted probability of the weighted ensemble model, demonstrating robust discrimination capacity and generalizability. The diagnostic model and risk score could assist in identifying nonneutropenic IPA from suspected cases independently of invasive procedures, thereby enhancing clinical applicability.

Microbiology SpectrumVol. 13(7)
Jiangsu University (CN), Nanjing University of Chinese Medicine (CN), Xuzhou Medical College (CN), Nanjing General Hospital of Nanjing Military Command (CN), Soochow University (CN), The First People's Hospital of Changzhou (CN), Nanjing Drum Tower Hospital (CN), Affiliated Hospital of Jiangsu University (CN), Second Affiliated Hospital of Soochow University (CN), The First People’s Hospital of Lianyungang (CN), Jiangsu Province Hospital (CN), First Affiliated Hospital of Soochow University (CN), Nanjing Jiangning Hospital (CN), Yangzhou University (CN), Nanjing Medical University (CN), Nanjing University (CN)
National Science and Technology Major Project, Nanjing Drum Tower Hospital
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 6%
Antifungal resistance and susceptibility
4.19
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