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Research article
Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor
Yong Yin
Shandong Tumor Hospital and Institute
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Imaging.
Background: This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.
Methods: In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.
Results: A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC³0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves(AUC) of these features were 0.731 0.075, 0.717, 0.748 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features(Child features).
Conclusion: Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.
Keywords
radiomics features, peripheral lung cancer, PET/CT, pulmonary inflammatory pseudotumor
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CURRENT STATUS: Published
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Published
On 06 Jul, 2020
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Editor assigned
On 25 May, 2020
Reviewers invited
Invitations sent on 25 May, 2020
Submission checks complete
On 24 May, 2020
Editor invited
On 24 May, 2020
No comments provided
Editorial decision: Major revision
On 30 Apr, 2020
Review #2 received
Received 26 Apr, 2020
Review #1 received
Received 26 Apr, 2020
Reviewer #3 agreed
On 11 Apr, 2020
Reviewer #2 agreed
On 08 Apr, 2020
Reviewer #1 agreed
On 01 Apr, 2020
Reviewers invited
Invitations sent on 31 Mar, 2020
Editor assigned
On 15 Mar, 2020
First submitted
On 14 Mar, 2020
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This preprint is under consideration at BMC Medical Imaging. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Using CT texture analysis to differentiate between peripheral lung cancer and pulmonary inflammatory pseudotumor
Yong Yin
Shandong Tumor Hospital and Institute
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 01 Jun, 2020
Published
On 06 Jul, 2020
No community comments so far
Editor assigned
On 25 May, 2020
Reviewers invited
Invitations sent on 25 May, 2020
Submission checks complete
On 24 May, 2020
Editor invited
On 24 May, 2020
No comments provided
Editorial decision: Major revision
On 30 Apr, 2020
Review #2 received
Received 26 Apr, 2020
Review #1 received
Received 26 Apr, 2020
Reviewer #3 agreed
On 11 Apr, 2020
Reviewer #2 agreed
On 08 Apr, 2020
Reviewer #1 agreed
On 01 Apr, 2020
Reviewers invited
Invitations sent on 31 Mar, 2020
Editor assigned
On 15 Mar, 2020
First submitted
On 14 Mar, 2020
Submission checks complete
On 14 Mar, 2020
Editor invited
On 14 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Imaging.
Background: This study is to distinguish peripheral lung cancer and pulmonary inflammatory pseudotumor using CT-radiomics features extracted from PET/CT images.
Methods: In this study, the standard 18F-fluorodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 21 patients with pulmonary inflammatory pseudotumor (PIPT) and 21 patients with peripheral lung cancer were retrospectively collected. The dataset was used to extract CT-radiomics features from regions of interest (ROI), The intra-class correlation coefficient (ICC) was used to screen the robust feature from all the radiomic features. Using, then, statistical methods to screen CT-radiomics features, which could distinguish peripheral lung cancer and PIPT. And the ability of radiomics features distinguished peripheral lung cancer and PIPT was estimated by receiver operating characteristic (ROC) curve and compared by the Delong test.
Results: A total of 435 radiomics features were extracted, of which 361 features showed relatively good repeatability (ICC³0.6). 20 features showed the ability to distinguish peripheral lung cancer from PIPT. these features were seen in 14 of 330 Gray-Level Co-occurrence Matrix features, 1 of 49 Intensity Histogram features, 5 of 18 Shape features. The area under the curves(AUC) of these features were 0.731 0.075, 0.717, 0.748 0.038, respectively. The P values of statistical differences among ROC were 0.0499 (F9, F20), 0.0472 (F10, F11) and 0.0145 (F11, Mean4). The discrimination ability of forming new features (Parent Features) after averaging the features extracted at different angles and distances was moderate compared to the previous features(Child features).
Conclusion: Radiomics features extracted from non-contrast CT based on PET/CT images can help distinguish peripheral lung cancer and PIPT.
Figure 1
Figure 2
Figure 3
Figure 4