A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas

Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma.Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT...

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Main Authors: Shushu Pan, Zhongxiang Ding, Lexing Zhang, Mei Ruan, Yanna Shan, Meixiang Deng, Peipei Pang, Qijun Shen
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Oncology
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.00895/full
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spelling doaj-16ac445210c7450389de2310ac564b242020-11-25T03:18:09ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-06-011010.3389/fonc.2020.00895543697A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian CystadenomasShushu Pan0Zhongxiang Ding1Lexing Zhang2Mei Ruan3Yanna Shan4Meixiang Deng5Peipei Pang6Qijun Shen7Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Radiology, Women's Hospital School of Medicine Zhejiang University, Hangzhou, ChinaDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, ChinaDepartment of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaObjective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma.Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA).Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86–0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76–0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone.Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.https://www.frontiersin.org/article/10.3389/fonc.2020.00895/fullovarian neoplasmscystadenomaalgorithmclassificationtomographyx-ray computed
collection DOAJ
language English
format Article
sources DOAJ
author Shushu Pan
Zhongxiang Ding
Lexing Zhang
Mei Ruan
Yanna Shan
Meixiang Deng
Peipei Pang
Qijun Shen
spellingShingle Shushu Pan
Zhongxiang Ding
Lexing Zhang
Mei Ruan
Yanna Shan
Meixiang Deng
Peipei Pang
Qijun Shen
A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
Frontiers in Oncology
ovarian neoplasms
cystadenoma
algorithm
classification
tomography
x-ray computed
author_facet Shushu Pan
Zhongxiang Ding
Lexing Zhang
Mei Ruan
Yanna Shan
Meixiang Deng
Peipei Pang
Qijun Shen
author_sort Shushu Pan
title A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
title_short A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
title_full A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
title_fullStr A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
title_full_unstemmed A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas
title_sort nomogram combined radiomic and semantic features as imaging biomarker for classification of ovarian cystadenomas
publisher Frontiers Media S.A.
series Frontiers in Oncology
issn 2234-943X
publishDate 2020-06-01
description Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma.Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA).Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86–0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76–0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone.Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.
topic ovarian neoplasms
cystadenoma
algorithm
classification
tomography
x-ray computed
url https://www.frontiersin.org/article/10.3389/fonc.2020.00895/full
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