Magnetic Resonance Imaging and Diffusion Weighted Imaging-Based Histogram in Predicting Mesenchymal Transition High-Grade Serous Ovarian Cancer.


Journal

Academic radiology
ISSN: 1878-4046
Titre abrégé: Acad Radiol
Pays: United States
ID NLM: 9440159

Informations de publication

Date de publication:
06 2023
Historique:
received: 30 03 2022
revised: 24 06 2022
accepted: 26 06 2022
medline: 19 5 2023
pubmed: 1 8 2022
entrez: 31 7 2022
Statut: ppublish

Résumé

To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC). Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82. The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.

Identifiants

pubmed: 35909051
pii: S1076-6332(22)00368-3
doi: 10.1016/j.acra.2022.06.021
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1118-1128

Informations de copyright

Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Auteurs

Song-Qi Cai (SQ)

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Zhen-Yu Song (ZY)

Ovarian Cancer Program, Department of Gynecologic Oncology, Zhongshan Hospital, Shanghai, China.

Min-Rong Wu (MR)

Department of Radiology, Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, Fujian, China.

Jing-Jing Lu (JJ)

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Wen-Wen Sun (WW)

Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China.

Feng Wei (F)

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Hai-Ming Li (HM)

Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

Jin-Wei Qiang (JW)

Department of Radiology, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.

Yong-Ai Li (YA)

Department of Radiology, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.

Jian Zhu (J)

Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: wzzhujian@163.com.

Jian-Jun Zhou (JJ)

Department of Radiology, Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, Fujian, China.

Meng-Su Zeng (MS)

Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

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