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Molecular Imaging and Biology

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01-04-2021 | Tuberculosis | Research Article

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

Authors: Dongyang Du, Jiamei Gu, Xiaohui Chen, Wenbing Lv, Qianjin Feng, Arman Rahmim, Hubing Wu, Lijun Lu

Published in: Molecular Imaging and Biology | Issue 2/2021

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.

Available only for authorised users

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Title: Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer
Authors: Dongyang Du
Jiamei Gu
Xiaohui Chen
Wenbing Lv
Qianjin Feng
Arman Rahmim
Hubing Wu
Lijun Lu
Publication date: 01-04-2021
Publisher: Springer International Publishing
Keywords: Tuberculosis
Lung Cancer
Lung Cancer
Tuberculosis
Lung Cancer
Published in: Molecular Imaging and Biology / Issue 2/2021
Print ISSN: 1536-1632
Electronic ISSN: 1860-2002
DOI: https://doi.org/10.1007/s11307-020-01550-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Procedures

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Procedures

Results

Conclusion

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