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Open Access

Peer-reviewed

Research Article

Diagnostic performance of machine learning models based on dual-phase ⁹⁹mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules

Kun Zhang ,

Roles Conceptualization

¶☯ These authors contributed equally as co–first authors.

Affiliation Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang, China
⨯
Xin Zhou ,

Roles Data curation

¶☯ These authors contributed equally as co–first authors.

Affiliation Department of General Surgery II, Heilongjiang Hospital of Beijing Children’s Hospital, Daoli District, Harbin, Heilongjiang, China
⨯
Yuhang Zhang,

Roles Writing – review & editing

Affiliation Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang, China
⨯
Gang Jin,

Roles Funding acquisition

Affiliation Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang, China
⨯
Ping Li,

Roles Formal analysis

Affiliation Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang, China
⨯
Yuzhuo Xing

Roles Investigation

* E-mail: xyzmahw@163.com

Affiliation Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, Heilongjiang, China

https://orcid.org/0000-0002-7945-3066

⨯

Diagnostic performance of machine learning models based on dual-phase ⁹⁹mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules

Kun Zhang,
Xin Zhou,
Yuhang Zhang,
Gang Jin,
Ping Li,
Yuzhuo Xing

x

Published: July 6, 2026
https://doi.org/10.1371/journal.pone.0353271

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Lung and intrathoracic tumors
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Machine learning
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Artificial neural networks
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Cancer detection and diagnosis
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Pulmonary imaging
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Malignant tumors
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Support vector machines
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Squamous cell carcinoma
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