MR findings for differentiating decidualized endometriomas from seromucinous borderline tumors of the ovary

Ando, Tomohiro; Kato, Hiroki; Kawaguchi, Masaya; Furui, Tatsuro; Morishige, Ken‐ichirou; Hyodo, Fuminori

doi:10.1007/s00261-020-02412-x

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…One of the purposes of MRI is to distinguish malignant tumors from benign lesions and infer their histology; however, the imaging findings of ovarian tumors differ greatly depending on their histological type. For example, high-grade serous carcinoma typically tends to appear as small, bilateral masses often accompanied by peritoneal dissemination [ 23 ]; mucinous tumors can be very large, and the signal intensity of the mucinous content is variable [ 24 ]; endometrioid, clear cell, and seromucinous tumors are associated with endometriosis [ 23 , 24 , 25 ]. Moreover, distinguishing between borderline tumors and adenomas is often difficult, even for experienced radiologists [ 5 , 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

Mori

Hoshiai

Sakai

et al. 2022

Cancers

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Background: This study aimed to compare deep learning with radiologists’ assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. Results: The CNN of each sequence had a sensitivity of 0.77–0.85, specificity of 0.77–0.92, accuracy of 0.81–0.87, and an AUC of 0.83–0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). Conclusion: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI.

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