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Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted.
Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.
Keywords
Lung Cancer, Computed Tomography of the Chest, Computer-Aided Diagnosis, Radiomic Features, Vessel Tortuosity
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CURRENT STATUS: Published
View the published article at BMC Research Notes.
Version 1
Posted 13 Jan, 2021
No community comments so far
Review #1 received
Received 22 Jan, 2021
Editorial decision: Minor revision
On 22 Jan, 2021
Reviewer #1 agreed
On 10 Jan, 2021
Editor assigned
On 06 Jan, 2021
Reviewers invited
Invitations sent on 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
First submitted
On 27 Nov, 2020
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Subject Areas
Health Economics & Outcomes Research
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See the published version of this preprint at BMC Research Notes.
This is a preprint, 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 note
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
View the published article at BMC Research Notes.
Version 1
Posted 13 Jan, 2021
No community comments so far
Review #1 received
Received 22 Jan, 2021
Editorial decision: Minor revision
On 22 Jan, 2021
Reviewer #1 agreed
On 10 Jan, 2021
Editor assigned
On 06 Jan, 2021
Reviewers invited
Invitations sent on 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
First submitted
On 27 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Research Notes.
Objective: The most common histopathologic malignant and benign nod- ules are Adenocarcinoma and Granuloma, respectively, which have di_erent standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuos- ity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker pa- tients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted.
Results: We compare our framework with the state-of-the-art feature selec- tion methods for di_erentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.
Figure 1
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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