Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer

Mio Mori; Tomoyuki Fujioka; Mayumi Hara; Leona Katsuta; Yuka Yashima; Emi Yamaga; Ken Yamagiwa; Junichi Tsuchiya; Kumiko Hayashi; Yuichi Kumaki; Goshi Oda; Tsuyoshi Nakagawa; Iichiroh Onishi; Kazunori Kubota; Ukihide Tateishi

doi:10.3390/diagnostics13040794

Diagnostics (Feb 2023)

Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer

Mio Mori,
Tomoyuki Fujioka,
Mayumi Hara,
Leona Katsuta,
Yuka Yashima,
Emi Yamaga,
Ken Yamagiwa,
Junichi Tsuchiya,
Kumiko Hayashi,
Yuichi Kumaki,
Goshi Oda,
Tsuyoshi Nakagawa,
Iichiroh Onishi,
Kazunori Kubota,
Ukihide Tateishi

Affiliations

Mio Mori: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Tomoyuki Fujioka: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Mayumi Hara: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Leona Katsuta: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Yuka Yashima: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Emi Yamaga: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Ken Yamagiwa: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Junichi Tsuchiya: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Kumiko Hayashi: Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Yuichi Kumaki: Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Goshi Oda: Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Tsuyoshi Nakagawa: Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Iichiroh Onishi: Department of Comprehensive Pathology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Kazunori Kubota: Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minamiko-shigaya, Koshigaya 343-8555, Japan
Ukihide Tateishi: Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan

DOI: https://doi.org/10.3390/diagnostics13040794
Journal volume & issue: Vol. 13, no. 4
p. 794

Abstract

Read online

We investigated whether 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography images restored via deep learning (DL) improved image quality and affected axillary lymph node (ALN) metastasis diagnosis in patients with breast cancer. Using a five-point scale, two readers compared the image quality of DL-PET and conventional PET (cPET) in 53 consecutive patients from September 2020 to October 2021. Visually analyzed ipsilateral ALNs were rated on a three-point scale. The standard uptake values SUVmax and SUVpeak were calculated for breast cancer regions of interest. For “depiction of primary lesion”, reader 2 scored DL-PET significantly higher than cPET. For “noise”, “clarity of mammary gland”, and “overall image quality”, both readers scored DL-PET significantly higher than cPET. The SUVmax and SUVpeak for primary lesions and normal breasts were significantly higher in DL-PET than in cPET (p p = 0.250, 0.625). DL-PET improved visual image quality for breast cancer compared with cPET. SUVmax and SUVpeak were significantly higher in DL-PET than in cPET. DL-PET and cPET exhibited comparable diagnostic abilities for ALN metastasis.

Published in Diagnostics

ISSN: 2075-4418 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Medicine: Medicine (General)
Website: http://www.mdpi.com/journal/diagnostics

About the journal

Abstract

Keywords