• Shu Zama, Tomoyuki Fujioka, Emi Yamaga, Kazunori Kubota, Mio Mori, Leona Katsuta, Yuka Yashima, Arisa Sato, Miho Kawauchi, Subaru Higuchi, Masaaki Kawanishi, Toshiyuki Ishiba, Goshi Oda, Tsuyoshi Nakagawa, and Ukihide Tateishi.
• Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan.
• Medicina (Kaunas). 2023 Dec 21; 60 (1).

Background And ObjectivesThis study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images.Materials And MethodsWe retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient.ResultsThe synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar.ConclusionThe DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

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