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

  • Author Footnotes
    1 Both Song-Qi Cai and Zhen-Yu Song were contributed equally to this work
    Song-Qi Cai
    Footnotes
    1 Both Song-Qi Cai and Zhen-Yu Song were contributed equally to this work
    Affiliations
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Author Footnotes
    1 Both Song-Qi Cai and Zhen-Yu Song were contributed equally to this work
    Zhen-Yu Song
    Footnotes
    1 Both Song-Qi Cai and Zhen-Yu Song were contributed equally to this work
    Affiliations
    Ovarian Cancer Program, Department of Gynecologic Oncology, Zhongshan Hospital, Shanghai, China
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  • Min-Rong Wu
    Affiliations
    Department of Radiology, Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, Fujian, China
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  • Jing-Jing Lu
    Affiliations
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Wen-Wen Sun
    Affiliations
    Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Feng Wei
    Affiliations
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Hai-Ming Li
    Affiliations
    Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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  • Jin-Wei Qiang
    Affiliations
    Department of Radiology, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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  • Yong-Ai Li
    Affiliations
    Department of Radiology, Jinshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
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  • Author Footnotes
    2 Both Jian Zhu and Jian-Jun Zhou were contributed equally to this work
    Jian Zhu
    Correspondence
    Address correspondence to: Jian Zhu, MD, Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Tel: 86-13456002322.
    Footnotes
    2 Both Jian Zhu and Jian-Jun Zhou were contributed equally to this work
    Affiliations
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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  • Author Footnotes
    2 Both Jian Zhu and Jian-Jun Zhou were contributed equally to this work
    Jian-Jun Zhou
    Footnotes
    2 Both Jian Zhu and Jian-Jun Zhou were contributed equally to this work
    Affiliations
    Department of Radiology, Zhongshan Hospital Fudan University Xiamen Branch, Xiamen, Fujian, China
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  • Meng-Su Zeng
    Affiliations
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Author Footnotes
    1 Both Song-Qi Cai and Zhen-Yu Song were contributed equally to this work
    2 Both Jian Zhu and Jian-Jun Zhou were contributed equally to this work

      Rationale and Objectives

      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).

      Materials and Methods

      Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm2) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC – these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance.

      Results

      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.

      Conclusion

      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.

      Key Words

      Abbreviations:

      ADC (Apparent diffusion coefficient), AUC (Areas under the receiver operating characteristics curve), CI (Confidence interval), DWI (Diffusion weighted imaging), FIGO (International of Gynecology and Obstetrics), HGSOC (High-grade serous ovarian cancer), ICC (Interclass correlation coefficient), MT (Mesenchymal transition), NACT/IDS (neoadjuvant chemotherapy followed by interval debulking surgery), ROC (Receiver operating characteristics), SD (Standard deviation), VOI (Volumes of interest)
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