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Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer

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Abstract

Purpose

We aim to accurately differentiate between active pulmonary tuberculosis (TB) and lung cancer (LC) based on radiomics and semantic features as extracted from pre-treatment positron emission tomography/X-ray computed tomography (PET/CT) images.

Procedures

A total of 174 patients (77/97 pulmonary TB/LC as confirmed by pathology) were retrospectively selected, with 122 in the training cohort and 52 in the validation cohort. Four hundred eighty-seven radiomics features were initially extracted to quantify phenotypic characteristics of the lesion region in both PET and CT images. Eleven semantic features were additionally defined by two experienced nuclear medicine physicians. Feature selection was performed in 5 steps to enable derivation of robust and effective signatures. Multivariable logistic regression analysis was subsequently used to develop a radiomics nomogram. The calibration, discrimination, and clinical usefulness of the nomogram were evaluated in both the training and independent validation cohorts.

Results

The individualized radiomics nomogram, which combined PET/CT radiomics signature with semantic features, demonstrated good calibration and significantly improved the diagnostic performance with respect to the semantic model alone or PET/CT signature alone in training cohort (AUC 0.97 vs. 0.94 or 0.91, p = 0.0392 or 0.0056), whereas did not significantly improve the performance in validation cohort (AUC 0.93 vs. 0.89 or 0.91, p = 0.3098 or 0.3323).

Conclusion

The radiomics nomogram showed potential for individualized differential diagnosis between solid active pulmonary TB and solid LC, although the improvement of performance was not significant relative to semantic model.

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Funding

This study was supported by the National Natural Science Foundation of China under grant 81871437, the Natural Science Foundation of Guangdong Province under grants 2019A1515011104, and the Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme (Lijun Lu 2018).

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Authors and Affiliations

  1. School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Dongyang Du, Wenbing Lv, Qianjin Feng & Lijun Lu

  2. Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Jiamei Gu, Xiaohui Chen & Hubing Wu

  3. Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada

    Arman Rahmim

  4. Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, V5Z 1L3, Canada

    Arman Rahmim

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  1. Dongyang Du
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  2. Jiamei Gu
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  3. Xiaohui Chen
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  4. Wenbing Lv
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Correspondence to Hubing Wu or Lijun Lu.

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Du, D., Gu, J., Chen, X. et al. Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer. Mol Imaging Biol 23, 287–298 (2021). https://doi.org/10.1007/s11307-020-01550-4

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