This preprint is under consideration at World Journal of Surgical Oncology. 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.
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CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”
Ayten KAYICANGIR
Ankara Universitesi Tip Fakultesi
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2052-1642
License:
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Introduction
Radiomics has become a hot issue in the medical imaging field, particularly in cancer imaging. Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases.
This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups.
Materials and Methods
In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.
Results
Four machine learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.
Conclusions
The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.
Keywords
Radiomics, machine learning, thymoma, minimally invasive surgery, diagnostic tool
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CURRENT STATUS: Under Review
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Posted 05 Jan, 2021
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Reviewers invited
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On 28 Dec, 2020
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On 28 Dec, 2020
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This preprint is under consideration at World Journal of Surgical Oncology. 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
CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”
Ayten KAYICANGIR
Ankara Universitesi Tip Fakultesi
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2052-1642
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: Under Review
Version 1
Posted 05 Jan, 2021
No community comments so far
Reviewers invited
Invitations sent on 29 Dec, 2020
Editor assigned
On 28 Dec, 2020
Submission checks complete
On 28 Dec, 2020
Editor invited
On 28 Dec, 2020
First submitted
On 27 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Introduction
Radiomics has become a hot issue in the medical imaging field, particularly in cancer imaging. Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases.
This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups.
Materials and Methods
In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.
Results
Four machine learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.
Conclusions
The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.
Figure 1
Figure 2
Figure 3