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Cancer Management and Research Volume 13, 2021 - Issue

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Original Research

MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

Xin-ping Yu1 Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Lei Wang1 Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Hai-yang Yu2 Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Yu-wei Zou3 Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Chang Wang1 Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Jin-wen Jiao1 Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of China

Hao Hong4 Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing, People’s Republic of China

https://orcid.org/0000-0003-1653-4026

Shuai Zhang2 Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People’s Republic of ChinaCorrespondence[email protected]

https://orcid.org/0000-0002-3580-6034

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Pages 329-336 | Published online: 12 Jan 2021

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Xinping Yu, Yuwei Zou, Lei Wang, Hongjuan Yang, Jinwen Jiao, Haiyang Yu & Shuai Zhang. (2024) Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors. Frontiers in Oncology 13.
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Arianna Defeudis, Jovana Panic, Giulia Nicoletti, Simone Mazzetti, Valentina Giannini & Daniele Regge. (2023) Virtual biopsy in abdominal pathology: where do we stand?. BJR|Open 5:1.
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Pratik Adusumilli, Nishant Ravikumar, Geoff Hall, Sarah Swift, Nicolas Orsi & Andrew Scarsbrook. (2023) Radiomics in the evaluation of ovarian masses — a systematic review. Insights into Imaging 14:1.
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J. Li, F. Wang, J. Ma, Z. Zhang, N. Zhang, S. Cui & Z. Ye. (2023) A CT-based radiomics nomogram for differentiating ovarian cystadenomas and endometriotic cysts. Clinical Radiology 78:9, pages e635-e643.
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Meng-Lin Huang, Jing Ren, Zheng-Yu Jin, Xin-Yu Liu, Yong-Lan He, Yuan Li & Hua-Dan Xue. (2023) A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights into Imaging 14:1.
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Yuemei Zheng, Hong Wang, Qiong Li, Haoran Sun & Li Guo. (2023) Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI. Academic Radiology 30:5, pages 814-822.
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Ya-Ting Jan, Pei-Shan Tsai, Wen-Hui Huang, Ling-Ying Chou, Shih-Chieh Huang, Jing-Zhe Wang, Pei-Hsuan Lu, Dao-Chen Lin, Chun-Sheng Yen, Ju-Ping Teng, Greta S. P. Mok, Cheng-Ting Shih & Tung-Hsin Wu. (2023) Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors. Insights into Imaging 14:1.
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Lin Ma, Liqiong Huang, Yan Chen, Lei Zhang, Dunli Nie, Wenjing He & Xiaoxue Qi. (2023) AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis. Frontiers in Oncology 13.
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Jiaojiao Li, Xubin Li, Juanwei Ma, Fang Wang, Shujun Cui & Zhaoxiang Ye. (2022) Computed tomography–based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. European Radiology 33:7, pages 5193-5204.
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Mandi Wang, Jose A. U. Perucho, Yangling Hu, Moon Hyung Choi, Lujun Han, Esther M. F. Wong, Grace Ho, Xiaoling Zhang, Philip Ip & Elaine Y. P. Lee. (2022) Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma. JAMA Network Open 5:12, pages e2245141.
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Aining Zhang, Qiming Hu, Zhanlong Ma, Jiacheng Song & Ting Chen. (2022) Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors. Journal of X-Ray Science and Technology 30:6, pages 1185-1199.
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He-Li Xu, Ting-Ting Gong, Fang-Hua Liu, Hong-Yu Chen, Qian Xiao, Yang Hou, Ying Huang, Hong-Zan Sun, Yu Shi, Song Gao, Yan Lou, Qing Chang, Yu-Hong Zhao, Qing-Lei Gao & Qi-Jun Wu. (2022) Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. eClinicalMedicine 53, pages 101662.
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Pallabi Shrestha, Bhavya Poudyal, Sepideh Yadollahi, Darryl E. Wright, Adriana V. Gregory, Joshua D. Warner, Panagiotis Korfiatis, Isabel C. Green, Sarah L. Rassier, Andrea Mariani, Bohyun Kim, Shannon K. Laughlin-Tommaso & Timothy L. Kline. (2022) A systematic review on the use of artificial intelligence in gynecologic imaging – Background, state of the art, and future directions. Gynecologic Oncology 166:3, pages 596-605.
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Jiaojiao Li, Tianzhu Zhang, Juanwei Ma, Ningnannan Zhang, Zhang Zhang & Zhaoxiang Ye. (2022) Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Frontiers in Oncology 12.
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He-Li Xu, Fang-Hua Liu, Hong-Yu Chen, Qian Xiao, Hong-Zan Sun, Yu Shi, Song Gao, Yu-Hong Zhao, Ting-Ting Gong & Qi-Jun Wu. (2022) Artificial Intelligence Performance in Image-Based Ovarian Cancer Identification: A Systematic Review and Meta-Analysis. SSRN Electronic Journal.
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MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

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MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

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