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Research article
Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters
Zhuoyue Tang
Chongqing General Hospital,University of Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1433-5966
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background : Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.
Methods: The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).
Results: Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P＜0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P＜0.001).
Conclusions: Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.
Keywords
parotid tumors, image biomarkers, clinical parameters
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On 19 Mar, 2020
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Received 19 Mar, 2020
Reviewer #1 agreed
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Received 21 Feb, 2020
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On 09 Feb, 2020
Editor invited
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Review #2 received
Received 06 Feb, 2020
Editorial decision: Major revision
On 06 Feb, 2020
Reviewer #2 agreed
On 21 Jan, 2020
Reviewer #1 agreed
On 03 Jan, 2020
Review #1 received
Received 03 Jan, 2020
Reviewers invited
Invitations sent on 31 Dec, 2019
Editor invited
On 30 Dec, 2019
Editor assigned
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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
Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters
Zhuoyue Tang
Chongqing General Hospital,University of Chinese Academy of Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-1433-5966
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 4
Posted 13 Apr, 2020
No community comments so far
Editor assigned
On 06 Apr, 2020
Submission checks complete
On 05 Apr, 2020
Editor invited
On 05 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 01 Apr, 2020
Editor assigned
On 31 Mar, 2020
Submission checks complete
On 30 Mar, 2020
Editor invited
On 30 Mar, 2020
No comments provided
Editorial decision: Minor revision
On 20 Mar, 2020
Reviewer #2 agreed
On 19 Mar, 2020
Review #2 received
Received 19 Mar, 2020
Reviewer #1 agreed
On 21 Feb, 2020
Review #1 received
Received 21 Feb, 2020
Reviewers invited
Invitations sent on 11 Feb, 2020
Editor assigned
On 10 Feb, 2020
Submission checks complete
On 09 Feb, 2020
Editor invited
On 09 Feb, 2020
No comments provided
Review #2 received
Received 06 Feb, 2020
Editorial decision: Major revision
On 06 Feb, 2020
Reviewer #2 agreed
On 21 Jan, 2020
Reviewer #1 agreed
On 03 Jan, 2020
Review #1 received
Received 03 Jan, 2020
Reviewers invited
Invitations sent on 31 Dec, 2019
Editor invited
On 30 Dec, 2019
Editor assigned
On 18 Dec, 2019
Submission checks complete
On 17 Dec, 2019
First submitted
On 16 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background : Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.
Methods: The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).
Results: Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P＜0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P＜0.001).
Conclusions: Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5