International Medical News

No.518ISSN 0535-1405

INTERNATIONAL MEDICAL NEWSInternational Medical Society of Japan
Since 1925
July 31, 2023
Published by International Medical Society of Japan,Chairman,Board of Directors:Kenichi Ishibashi, MD, PhD
Editors: K. Ito, MD, PhD, T. Kondo, MD, PhD,K. Ichihashi, MD, PhD, T.Murakami, PhD,R.Nagai, MD, PhD,T. Sumiyama, MD, PhD, I.Taniguchi, MD, PhD,andT.Yamazaki, MD,PhD

1-11-9-3F Kamiuma,Setagaya-ku,
Tokyo154-0011,Japan.
TEL03(5486)0601 FAX03(5486)0599 E-mail:imsj@imsj.or.jp https://www.imsj.or.jp/The 459th International Symposium on Therapy
The 459th International Symposium on Therapy was held by the Zoom Webinar onMay 18, 2023. Dr. Hirohisa Murakami, Director of the International Medical Society ofJapan (IMSJ), presided over the meeting.

AI in diagnostic imaging
Introductory Message from the Chair
Tsutomu Yamazaki, MD, PhD
Director, IMSJ【Discourse】
Soundscapes and Exhibitions - Techniques and Prospects

International Medical News

Hirohisa Mori Ph.D
Associate Professor
The University Museum, the University of Tokyo

LectureⅠ
Use of AI and radiomics in CT and MRI
Shigeru Kiryu
Chief and professor of Department of Radiology
Department of Radiology,
International University of Health and Welfare Narita Hospital

LectureⅡ
The role of AI in nuclear medicine diagnosis and theraphy
Kenji Hirata, MD, PhD
Associate Professor
Department of Diagnostic Imaging, Graduate School of Medicine,
Hokkaido University, Sapporo, JapanI gave a talk titled "The Role of AI in Nuclear Medicine Diagnosis and Therapy." In myself-introduction at the beginning of my talk, I mentioned that I started programmingwhen I was around 8 years old. I was fascinated by games such as Super Mario Bros.
(released in 1985) and Dragon Quest (released in 1986), which were popular at the time,and started studying programming to create something that would surpass these games.
At the time, there was no Internet search, and bookstores were all the learning resources
I could find. I read the same books until they became dirty. This method of study is not
so common today, but it seems to have been more effective than today's study methods.

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After graduating from Hokkaido University School of Medicine in 2002, I immediately
joined the Department of Nuclear Medicine at the same university, where I created PETviewer and analysis software using C++ and C#. Among them, Metavol, which I released
in 2014, can easily used to measure SUV, MTV, and TLG of PET/CT, and can also performtexture analysis. As of July 14, 2023, 66 papers citing Metavol have been published invarious diseases such as malignant lymphoma, prostate cancer, brain tumors, andcardiac sarcoidosis. I hope that Metavol has been helpful to many researchers in thepreparation of their papers.
Around 2017, seeing the third AI boom coming into full swing, I started my own AI
researches and gradually became capable to publish scientific papers. I started to use
Python instead of C/C++. Although C/C++ is faster, Python is superior in terms of therichness of machine learning libraries. I introduced several research results: an AI thathighlights pathological accumulation by extracting only physiological accumulation frommaximum intensity projection (MIP) images of FDG-PET/CT images and converselyexcluding it from the original images¹, an AI that diagnoses pathological accumulationin FDG-PET/CT images of breast cancer², a model that accurately predicts prognosis in
thyroid cancer cases by measuring tumor volume by metastatic site in FDG-PET/CT
before I-131 treatment and calculating "corrected tumor volume" by weighting eachorgan ³, an AI that applied texture analysis to the cardiac portion of FDG-PET/CT
performed for the diagnosis of tumors to aid in the diagnosis of cardiac sarcoidosis⁴, apaper showing that the same technique can predict the prognosis of cardiac sarcoidosis
⁵, and preliminary results of a study on improving image quality.
When trying to understand AI in nuclear medicine, a classification into three types of
AI is helpful ⁶ . AI usually stands for artificial intelligence, but we introduce threebackronyms (full-length words made from abbreviations in reverse): assisted
interpretation, additional insights, and augmented image. Assisted interpretation is AI
that uses human diagnosis as the gold standard (ground truth), with the goal ofapproaching human diagnosis. The goal of the AI is to approximate the human diagnosis.
Specialists can use AI to make the current diagnostic process faster, more efficient, andmore accurate, while non-specialists can use AI to provide a level of care similar to thatof specialists. BONENAVI in bone scintigraphy would be an example. The concept ofadditional insights, on the other hand, places the ground truth at markers that are morerigid than human diagnosis. For example, genetic mutations or prognosis. In this way,
the goal is to create an AI that can see what the human eye cannot⁷. Our paper²
introduced above is also a study of additional insights, but the drawback is that since AI
is beyond the human eye, humans cannot logically judge the results of AI output as
correct or wrong. Even if AI performance deteriorates due to unnoticed dataset shifts

International Medical News(e.g., changes in patient background), humans cannot notice this⁸. The augmented image
is an AI that returns an image when given an image, and is used to reduce radiation
exposure, to correct absorption in PET/MR, and to perform shorter-time imaging.
Attempts

have

also

been

madetoestimate

thedistributionofdifferent

radiopharmaceuticals from PET images taken with one radiopharmaceutical⁹.
I concluded my talk by discussing the potential use of AI in nuclear medicine therapy.
Current nuclear medicine therapy does not make use of dosimetry and many cases areunder-dosed and under-treated¹⁰. Nuclear medicine has a concept called theranostics, aterm coined by combining the words therapy and diagnosis. It would also give useful
information by AI to determine the absorbed dose for each organ and to determine themaximum dose within which side effects are acceptable.

1. Kawakami M, Hirata K, Furuya S, Kobayashi K, Sugimori H, Magota K, Katoh C.
Development of Combination Methods for Detecting Malignant Uptakes Based on
Physiological Uptake Detection Using Object Detection With PET-CT MIP Images.
Front Med (Lausanne). 2020 Dec 23;7:616746. doi: 10.3389/fmed.2020.616746.
2. Li Z, Kitajima K, Hirata K, Togo R, Takenaka J, Miyoshi Y, Kudo K, Ogawa T,Haseyama M. Preliminary study of AI-assisted diagnosis using FDG-PET/CT foraxillary lymph node metastasis in patients with breast cancer. EJNMMI Res. 2021
Jan 25;11(1):10. doi: 10.1186/s13550-021-00751-4.
3. Uchiyama Y, Hirata K, Watanabe S, Okamoto S, Shiga T, Okada K, Ito YM, Kudo K.
Development and validation of a prediction model based on the organ-based
metabolic tumor volume on FDG-PET in patients with differentiated thyroid
carcinoma. Ann Nucl Med. 2021 Nov;35(11):1223-1231. doi: 10.1007/s12149-02101664-x. Epub 2021 Aug 11.
4. Manabe O, Ohira H, Hirata K, Hayashi S, Naya M, Tsujino I, Aikawa T, Koyanagawa
K, Oyama-Manabe N, Tomiyama Y, Magota K, Yoshinaga K, Tamaki N. Use of 18FFDG PET/CT texture analysis to diagnose cardiac sarcoidosis. Eur J Nucl Med Mol
Imaging. 2019 Jun;46(6):1240-1247. doi: 10.1007/s00259-018-4195-9. Epub 2018 Oct
16.
5. Manabe O, Koyanagawa K, Hirata K, Oyama-Manabe N, Ohira H, Aikawa T, FuruyaS, Naya M, Tsujino I, Tomiyama Y, Otaki Y, Anzai T, Tamaki N. Prognostic Value of18F-FDG PET Using Texture Analysis in Cardiac Sarcoidosis. JACC Cardiovasc
Imaging. 2020 Apr;13(4):1096-1097. doi: 10.1016/j.jcmg.2019.11.021. Epub 2020 Jan
15.

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6. Hirata K, Sugimori H, Fujima N, Toyonaga T, Kudo K. Artificial intelligence fornuclear medicine in oncology. Ann Nucl Med. 2022 Feb;36(2):123-132. doi:
10.1007/s12149-021-01693-6. Epub 2022 Jan 14.
7. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, TheyAre Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub2015 Nov 18.
8. Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, Kohane IS,Saria S. The Clinician and Dataset Shift in Artificial Intelligence. N Engl J Med.
2021 Jul 15;385(3):283-286. doi: 10.1056/NEJMc2104626.
9. Wang R, Liu H, Toyonaga T, Shi L, Wu J, Onofrey JA, Tsai YJ, Naganawa M, Ma T,
Liu Y, Chen MK, Mecca AP, O'Dell RS, van Dyck CH, Carson RE, Liu C. Generationof synthetic PET images of synaptic density and amyloid from 18 F-FDG images
using deep learning. Med Phys. 2021 Sep;48(9):5115-5129. doi: 10.1002/mp.15073.
Epub 2021 Jul 27.
10. EANM Radiobiology Working Group:; Pouget JP, Konijnenberg M, Eberlein U,Glatting G, Gabina PM, Herrmann K, Holm S, Strigari L, van Leeuwen FWB,Lassmann M. An EANM position paper on advancing radiobiology for shaping thefuture of nuclear medicine. Eur J Nucl Med Mol Imaging. 2023 Jan;50(2):242-246.
doi: 10.1007/s00259-022-05934-2.