Details of the Researcher

PHOTO

Yoichiro Yamamoto
Section
Graduate School of Medicine
Job title
Professor
Degree

  • M.D.,Ph.D. (Tohoku University)

Profile

Our mission is to elucidate novel knowledge through spatial and biomedical interpretation of multidimensional data, using analytical frameworks that traverse mathematical modelling, machine learning, and the life sciences. We aim to discover unidentified disease mechanisms, develop new therapies, and optimize individualized treatments.

Link:
・RIKEN, Center for Advanced Intelligence Project (Nihonbashi, Tokyo)
・Mathematical Intelligence for Medicine, Tohoku University Graduate School of Medicine (Joint Graduate School)

Research History 4

  • 2026 - Present
    Tohoku University Graduate School of Medicine　Department of Artificial Intelligence and Medical Science　Professor

  • 2025 - Present
    RIKEN　Advanced General Intelligence in Science Program (TRIP AGIS), Spatial multicellular foundation models for disease mechanisms　Theme Leader

  • 2023 - Present
    Tohoku University Graduate School of Medicine　Mathematical Intelligence for Medicine　Professor

  • 2018 - Present
    RIKEN Center for Advanced Intelligence Project　Biomedical Spatial Science Team　Team Director

Education 2

  • Tohoku University　Graduate School of Medicine

    2006 - 2009

  • Tohoku University　School of Medicine

    1998 - 2004

Awards 4

  1. 「科学技術への顕著な貢献 2020（ナイスステップな研究者）」

    2020　文部科学省 科学技術・学術政策研究所

  2. 武見奨励賞

    2020

  3. 理化学研究所と産業界の交流会 優秀出展パネル入賞

    2018

  4. 東北大学加齢医学研究所研究奨励賞

    2010

Papers 107

  1. Clinically informed intermediate reasoning enables generalizable prostate cancer prognostication through machine learning in limited settings

    Jun Akatsuka, Kotaro Tsutsumi, Mami Takadate, Yasushi Numata, Hiromu Morikawa, Atsushi Marugame, Hayato Takeda, Yuki Endo, Yuka Toyama, Takayuki Takahashi, Kaori Ono, Junya Iwazaki, Ryuji Ohashi, Akira Shimizu, Tomoharu Kiyuna, Maki Ogura, Masao Ueki, Takuma Kato, Toshiyuki China, Mikio Sugimoto, Hisamitsu Ide, Naoto Sassa, Naonori Ueda, Shigeo Horie, Toyonori Tsuzuki, Go Kimura, Yukihiro Kondo, Yoichiro Yamamoto

    npj Digital Medicine　9　19　2026/01

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1038/s41746-025-02193-x 　

    eISSN： 2398-6352

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    Abstract Machine learning has shown promise in medical image classification. However, its generalizability remains challenging. Here, we show that data-efficient pre-surgical prognostication of prostate cancer from biopsy specimens is enabled by versatile feature extraction from whole-mount histopathology and a clinically informed intermediate reasoning step. With data from multiple institutions, our pipeline resolved dual-domain shifts across specimen types and institutions and achieved consistent external validation, reinforced by comprehensive analyses of generalizability. This highlights the robustness of our prognostic approach when compared to the Gleason grading system. We establish an equitable, interpretable, and clinically applicable framework, supporting actionable decisions for prognosis and treatment planning, even in limited real-world clinical environments.

  2. Automated acquisition of explainable knowledge from unannotated histopathology images International-journal

    Yoichiro Yamamoto, Toyonori Tsuzuki, Jun Akatsuka, Masao Ueki, Hiromu Morikawa, Yasushi Numata, Taishi Takahara, Takuji Tsuyuki, Kotaro Tsutsumi, Ryuto Nakazawa, Akira Shimizu, Ichiro Maeda, Shinichi Tsuchiya, Hiroyuki Kanno, Yukihiro Kondo, Manabu Fukumoto, Gen Tamiya, Naonori Ueda, Go Kimura

    Nature communications　10　(1)　5642-5642　2019/12/18

    DOI： 10.1038/s41467-019-13647-8 　

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    Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.

  3. Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches International-journal

    Jun Akatsuka, Yoichiro Yamamoto, Tetsuro Sekine, Yasushi Numata, Hiromu Morikawa, Kotaro Tsutsumi, Masato Yanagi, Yuki Endo, Hayato Takeda, Tatsuro Hayashi, Masao Ueki, Gen Tamiya, Ichiro Maeda, Manabu Fukumoto, Akira Shimizu, Toyonori Tsuzuki, Go Kimura, Yukihiro Kondo

    Biomolecules　9　(11)　673　2019/10/30

    DOI： 10.3390/biom9110673 　

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    Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87-0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible.

  4. Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

    Yoichiro Yamamoto, Akira Saito, Ayako Tateishi, Hisashi Shimojo, Hiroyuki Kanno, Shinichi Tsuchiya, Ken-ichi Ito, Eric Cosatto, Hans Peter Graf, Rodrigo R. Moraleda, Roland Eils, Niels Grabe

    Scientific Reports　7　46732　2017/04

    DOI： 10.1038/srep46732 　

    ISSN： 2045-2322

  5. Tumour and immune cell dynamics explain the PSA bounce after prostate cancer brachytherapy

    Yoichiro Yamamoto, Chetan P. Offord, Go Kimura, Shigehiko Kuribayashi, Hayato Takeda, Shinichi Tsuchiya, Hisashi Shimojo, Hiroyuki Kanno, Ivana Bozic, Martin A. Nowak, Zeljko Bajzer, David Dingli

    British Journal of Cancer　115　(2)　195-202　2016/07

    DOI： 10.1038/bjc.2016.171 　

    ISSN： 0007-0920

    eISSN： 1532-1827

  6. Impact of Surgical Margin Control in Index Tumors on Prognosis After Radical Prostatectomy: A Focus on Zonal Origin

    Jun Akatsuka, Yoshihiko Ogata, Kotaro Obayashi, Mami Takadate, Shunsuke Ikuma, Hiroya Hasegawa, Hikaru Mikami, Hayato Takeda, Yuki Endo, Takayuki Takahashi, Kaori Ono, Yuka Toyama, Yoichiro Yamamoto, Go Kimura, Yukihiro Kondo

    Current Oncology　32　(8)　445-445　2025/08/07

    Publisher: MDPI AG

    DOI： 10.3390/curroncol32080445 　

    eISSN： 1718-7729

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    We investigated the clinical significance of positive surgical margins (PSMs) in index tumors following radical prostatectomy (RP), with particular attention to the tumor’s zonal origin. Among 1148 patients with localized prostate cancer who underwent RPs, 973 were included after excluding those who received perioperative therapy or had incomplete data. Index tumors were categorized by zonal origin: transition zone, peripheral zone, or central zone (CZ). Overall, PSMs were observed in 26.4% of index tumors. Although CZ index tumors were relatively uncommon (6.5%), they exhibited the highest PSM rate (42.9%) and showed the most aggressive pathological features. The 5-year biochemical recurrence (BCR)-free survival rate was significantly lower in patients with PSMs in index tumors than in those with negative surgical margins (45.6% vs. 86.8%, p < 0.0001). Notably, patients with PSMs in CZ index tumors had the worst outcomes, with a 5-year BCR-free survival rate of 22.0%. Multivariate analysis identified PSMs in index tumors as an independent predictor of BCR (HR: 3.4; 95% CI: 2.5–4.5), with a similar trend observed in early recurrence. These findings highlight the prognostic significance of PSMs in index tumors during RP, especially in CZ tumors, and emphasize the importance of securing local control in these cases.

  7. Clinical and Economic Assessment of Robot‐Assisted Radical Prostatectomy With the Hinotori Surgical System: Insights From a Japanese Single‐Centre Study

    Kotaro Obayashi, Jun Akatsuka, Takuya Nishino, Mami Takadate, Hiroya Hasegawa, Hikaru Mikami, Hayato Takeda, Yuki Endo, Yuka Toyama, Takeshi Yamada, Yoichiro Yamamoto, Go Kimura, Yukihiro Kondo

    The International Journal of Medical Robotics and Computer Assisted Surgery　21　(3)　2025/06/21

    Publisher: Wiley

    DOI： 10.1002/rcs.70084 　

    ISSN： 1478-5951

    eISSN： 1478-596X

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    ABSTRACT Background We investigated the clinical and economic outcomes of robot‐assisted radical prostatectomy (RARP) using the hinotori surgical system (H‐RARP). Methods The analysis was conducted on 76 and 68 patients who underwent RARP using the da Vinci surgical system (D‐RARP) and H‐RARP, respectively. Safety, oncological, and functional outcomes were compared between H‐RARP and D‐RARP. Risk factors influencing medical costs in RARP were identified via linear regression analysis. Results Except for longer operative time in H‐RARP, clinical outcomes were comparable. The median medical costs per case were $12,426.8 for D‐RARP and $12,548.9 for H‐RARP. Operative time (coefficient: 0.0002, p < 0.001), pathological T stage (coefficient: 0.023, p = 0.030), and postoperative hospital stay (coefficient: 0.028, p < 0.001) were significant factors affecting medical costs; in contrast, the type of robot‐assisted surgical system was not a significant factor. Conclusions H‐RARP offers clinical outcomes comparable to those of D‐RARP while maintaining equivalent medical costs.

  8. Real-World Experience with Triplet Therapy for High-Volume Metastatic Castration-Sensitive Prostate Cancer: A Retrospective Cohort Study from a Japanese Academic Hospital

    Jun Akatsuka, Go Kimura, Mami Takadate, Hiroya Hasegawa, Hikaru Mikami, Kotaro Obayashi, Hayato Takeda, Yuki Endo, Shogo Imai, Yuka Toyama, Yoichiro Yamamoto, Yukihiro Kondo

    Journal of Nippon Medical School　92　(2)　204-215　2025/04/20

    Publisher: Medical Association of Nippon Medical School

    DOI： 10.1272/jnms.jnms.2025_92-212 　

    ISSN： 1345-4676

    eISSN： 1347-3409

  9. A Systematic Review of the Application of Artificial Intelligence in Colposcopy: Diagnostic Accuracy for Cervical Intraepithelial Neoplasia and Cervical Cancer

    Takayuki Takahashi, Yusuke Kobayashi, Rieko Sakurai, Keiko Matsuoka, Jun Akatsuka, Iori Kisu, Takashi Iwata, Jun Takayama, Motomichi Matsuzaki, Wataru Yamagami, Kouji Banno, Yoichiro Yamamoto, Hikaru Matsuoka, Gen Tamiya

    Clinical Medicine Insights: Oncology　19　2025/01

    Publisher: SAGE Publications

    DOI： 10.1177/11795549251374908 　

    ISSN： 1179-5549

    eISSN： 1179-5549

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    Background: Artificial intelligence (AI) is increasingly applied to colposcopy to enhance the detection of cervical intraepithelial neoplasia (CIN) and cervical cancer. We conducted a systematic review to summarize the diagnostic performance achieved by AI‑based colposcopic systems. Methods: Following the PRISMA 2020 guidelines, the PubMed database was searched using the search terms ‘artificial intelligence’ and ‘colposcop*’ for articles published between 2019 and 2024. From the initial 43 articles retrieved, 19 studies were selected based on specific inclusion criteria: original research articles, written in the English language, and relevant to CIN or cervical cancer diagnosis. For each, we extracted the sample size, AI architecture (e.g., convolutional neural networks, U-Net/DeepLab V3 + segmentation models, multimodal fusion networks), reference standard, and reported metrics (sensitivity, specificity, accuracy, and area under the curve). Results: Across multiple studies, AI systems demonstrated superior diagnostic accuracy, sensitivity, and specificity, particularly for early detection of high-risk lesions and classification of cervical abnormalities. Deep-learning models, such as convolutional neural networks, consistently outperformed conventional methods by reducing diagnostic variability and offering robust performance even in low-resource settings. The review also highlights the potential of AI for real-time diagnostics and its capacity to support clinical decision-making via automated systems. Conclusion: AI has the potential to revolutionize cervical cancer diagnosis and management by enhancing the accuracy and efficiency of colposcopic evaluations. However, challenges remain, including the development of standardized datasets, validation in diverse populations, and ethical considerations surrounding data privacy and access to technology. Continued research and development are crucial to harness AI’s global potential to improve patient outcomes.

  10. Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction

    Hayato Takeda, Jun Akatsuka, Tomonari Kiriyama, Yuka Toyama, Yasushi Numata, Hiromu Morikawa, Kotaro Tsutsumi, Mami Takadate, Hiroya Hasegawa, Hikaru Mikami, Kotaro Obayashi, Yuki Endo, Takayuki Takahashi, Manabu Fukumoto, Ryuji Ohashi, Akira Shimizu, Go Kimura, Yukihiro Kondo, Yoichiro Yamamoto

    Current Oncology　31　(11)　7180-7189　2024/11/15

    Publisher: MDPI AG

    DOI： 10.3390/curroncol31110530 　

    eISSN： 1718-7729

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    Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low–intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accuracy of deep learning analysis using multimodal medical data focused on clinically significant PCa in patients with PSA ≤ 20 ng/mL. Our cohort study included 178 consecutive patients who underwent ultrasound-guided prostate biopsy. Deep learning analyses were applied to predict clinically significant PCa. We generated receiver operating characteristic curves and calculated the corresponding area under the curve (AUC) to assess the prediction. The AUC of the integrated medical data using our multimodal deep learning approach was 0.878 (95% confidence interval [CI]: 0.772–0.984) in all patients without PSA restriction. Despite the reduced predictive ability of PSA when restricted to PSA ≤ 20 ng/mL (n = 122), the AUC was 0.862 (95% CI: 0.723–1.000), complemented by imaging data. In addition, we assessed clinical presentations and images belonging to representative false-negative and false-positive cases. Our multimodal deep learning approach assists physicians in determining treatment strategies by predicting clinically significant PCa in patients with PSA ≤ 20 ng/mL before biopsy, contributing to personalized medical workflows for PCa management.

  11. 医学領域におけるAIの新潮流 現状と今後の展開

    山本 陽一朗

    日本臨床細胞学会雑誌　63　(Suppl.2)　446-446　2024/10

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  12. 乳癌画像診断における診断補助人工知能(AI)ソフトウェア管理指針に関する検討

    片岡 正子, 井上 謙一, 藤岡 友之, 植田 琢也, 菊池 真理, 河口 浩介, 玉城 研太郎, 山本 陽一朗, 鈴木 貴, 戸井 雅和

    日本医学放射線学会秋季臨床大会抄録集　60回　S469-S469　2024/09

    Publisher: (公社)日本医学放射線学会

    ISSN： 0048-0428

    eISSN： 1347-7951

  13. 乳癌画像診断における診断補助人工知能(AI)ソフトウェア管理指針に関する検討

    片岡 正子, 井上 謙一, 藤岡 友之, 植田 琢也, 菊池 真理, 河口 浩介, 玉城 研太郎, 山本 陽一朗, 鈴木 貴, 戸井 雅和

    日本医学放射線学会秋季臨床大会抄録集　60回　S469-S469　2024/09

    Publisher: (公社)日本医学放射線学会

    ISSN： 0048-0428

    eISSN： 1347-7951

  14. Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology

    Aya Noguchi, Yasushi Numata, Takanori Sugawara, Hiroshu Miura, Kaori Konno, Yuzu Adachi, Ruri Yamaguchi, Masaharu Ishida, Takashi Kokumai, Daisuke Douchi, Takayuki Miura, Kyohei Ariake, Shun Nakayama, Shimpei Maeda, Hideo Ohtsuka, Masamichi Mizuma, Kei Nakagawa, Hiromu Morikawa, Jun Akatsuka, Ichiro Maeda, Michiaki Unno, Yoichiro Yamamoto, Toru Furukawa

    Scientific Reports　14　17059　2024/08

    DOI： 10.1038/s41598-024-67757-5 　

  15. Index tumor location affected early biochemical recurrence after radical prostatectomy in patients with negative surgical margin: a retrospective study

    Yoshihiko Ogata, Jun Akatsuka, Yuki Endo, Hikaru Mikami, Masato Yanagi, Hayato Takeda, Yuka Toyama, Yoichiro Yamamoto, Go Kimura, Yukihiro Kondo

    BMC Urology　24　(1)　2024/05/18

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1186/s12894-024-01499-4 　

    eISSN： 1471-2490

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    Abstract Background Index tumors are the most aggressive tumors of the prostate. However, their clinical significance remains unclear. This study aimed to assess the incidence of index tumor location according to the zonal origin and whether these locations affect the prognosis after radical prostatectomy in patients with negative surgical margins. Methods This single-centered, retrospective study evaluated 1,109 consecutive patients who underwent radical prostatectomies. An index tumor was defined as the largest tumor in the prostate gland. We detected these locations based on McNeal's zonal origin using whole-mount sections. Biochemical recurrence (BCR) free survival curves were generated using the Kaplan–Meier method. Univariate and multivariate analyses using the Cox proportional hazards model were performed to determine the predictive factors for early BCR (within 1-year). Results A total of 621 patients with negative surgical margins who did not receive adjuvant therapy were included in this study. The index tumor were located in the transitional zone in 191 patients (30.8%), the peripheral zone in 399 patients (64.3%), and the central zone in 31 patients (5.0%). In total, 22 of 621 patients (3.5%) experienced early BCR and 70 patients (11.2%) experienced overall BCR at a median follow-up of 61.7 months. According to the index tumor location, the early BCR-free rates were 99.5%, 95.7 %, and 83.3% in the transitional, peripheral, and central zones, respectively. On multivariate analysis, the index tumor in the central zone was an independent predictor of early BCR with negative surgical margins following radical prostatectomy, followed by prostatectomy pathological grade, index tumor in the peripheral zone, and high prostate-specific antigen level. Conclusions We assessed the significance of index tumor location in patients with negative surgical margins following radical prostatectomy. Index tumors located in the central zone, although infrequent, were the strongest predictive factors for early BCR. Our results may allow urologists and patients to reconsider the therapeutic strategies for prostate cancer.

  16. ディープラーニングによる腹腔洗浄細胞診標本を用いた膵癌患者の予後予測

    野口 彩, 沼田 康志, 菅原 隆譲, 三浦 弘守, 石田 晶玄, 堂地 大輔, 前田 一郎, 海野 倫明, 山本 陽一朗, 古川 徹

    日本病理学会会誌　113　(1)　282-282　2024/02

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  17. 機械学習による膵癌病理組織を用いた術後膵癌の再発予測

    山口 るり, 森川 啓, 赤塚 純, 沼田 康志, 水間 正道, 海野 倫明, 三宅 顕光, 田宮 元, 山本 陽一朗, 古川 徹

    日本病理学会会誌　113　(1)　400-400　2024/02

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  18. The Current Status of Comprehensive Genomic Profiling in the Management of Metastatic Castration-Resistant Prostate Cancer: A Study from a Cooperative Hospital for Cancer Genomic Medicine in Japan

    Jun Akatsuka, Go Kimura, Mami Taniuchi, Sayuri Hiraoka, Tomoko Sahara, Takuma Iwai, Hiroya Hasegawa, Hikaru Mikami, Kotaro Obayashi, Hayato Takeda, Yuki Endo, Yuka Toyama, Yoichiro Yamamoto, Takeshi Yamada, Yukihiro Kondo

    Journal of Nippon Medical School　91　(5)　2024

  19. Machine Learning of Histopathological Images Predicts Recurrences of Resected Pancreatic Ductal Adenocarcinoma With Adjuvant Treatment

    Ruri Yamaguchi, Hiromu Morikawa, Jun Akatsuka, Yasushi Numata, Aya Noguchi, Takashi Kokumai, Masaharu Ishida, Masamichi Mizuma, Kei Nakagawa, Michiaki Unno, Akimitsu Miyake, Gen Tamiya, Yoichiro Yamamoto, Toru Furukawa

    Pancreas　2023/12/21

    Publisher: Ovid Technologies (Wolters Kluwer Health)

    DOI： 10.1097/mpa.0000000000002289 　

    ISSN： 0885-3177

    eISSN： 1536-4828

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    Objectives Pancreatic ductal adenocarcinoma is an intractable disease with frequent recurrence after resection and adjuvant therapy. The present study aimed to clarify whether artificial intelligence–assisted analysis of histopathological images can predict recurrence in patients with pancreatic ductal adenocarcinoma who underwent resection and adjuvant chemotherapy with tegafur/5-chloro-2,4-dihydroxypyridine/potassium oxonate. Materials and Methods Eighty-nine patients were enrolled in the study. Machine-learning algorithms were applied to 10-billion-scale pixel data of whole-slide histopathological images to generate key features using multiple deep autoencoders. Areas under the curve were calculated from receiver operating characteristic curves using a support vector machine with key features alone and by combining with clinical data (age and carbohydrate antigen 19-9 and carcinoembryonic antigen levels) for predicting recurrence. Supervised learning with pathological annotations was conducted to determine the significant features for predicting recurrence. Results Areas under the curves obtained were 0.73 (95% confidence interval, 0.59–0.87) by the histopathological data analysis and 0.84 (95% confidence interval, 0.73–0.94) by the combinatorial analysis of histopathological data and clinical data. Supervised learning model demonstrated that poor tumor differentiation was significantly associated with recurrence. Conclusions Results indicate that machine learning with the integration of artificial intelligence–driven evaluation of histopathological images and conventional clinical data provides relevant prognostic information for patients with pancreatic ductal adenocarcinoma.

  20. 前立腺癌治療Front Line : AIの活用による前立腺癌の再発予測

    山本 陽一朗, 木村 剛

    Prostate cancer front line　12　(1)　2-7　2023/09

    Publisher: 東京 : アステラス製薬

    ISSN： 2186-7003

  21. 医療画像とAI 形態情報の可能性に挑む

    山本 陽一朗

    泌尿器外科　36　(臨増)　687-687　2023/08

    Publisher: 医学図書出版(株)

    ISSN： 0914-6180

  22. 乳癌学会におけるAI(artificial intelligence)の活用に向けた取り組み 画像と病理を中心に

    片岡 正子, 井上 謙一, 植田 琢也, 藤岡 友之, 菊池 真理, 山本 陽一朗, 玉城 研太朗, 鈴木 貴, 戸井 雅和

    Rad Fan　21　(7)　17-21　2023/06

    Publisher: (株)メディカルアイ

    ISSN： 1348-3498

  23. 病理におけるAI(artificial intelligence)の動向と展望

    鈴木 貴, 山本 陽一朗, 植田 琢也, 玉城 研太朗, 藤岡 友之, 井上 謙一, 菊池 真理, 片岡 正子

    日本乳癌学会総会プログラム抄録集　31回　63-63　2023/06

    Publisher: (一社)日本乳癌学会

  24. 乳癌画像診断におけるAI(artificial intelligence)の動向と展望

    片岡 正子, 井上 謙一, 植田 琢也, 藤岡 友之, 菊池 真理, 山本 陽一朗, 玉城 研太朗, 鈴木 貴

    日本乳癌学会総会プログラム抄録集　31回　63-63　2023/06

    Publisher: (一社)日本乳癌学会

  25. 医療とAI 医療AIの課題と展望

    山本 陽一朗

    日本病理学会会誌　112　(1)　181-181　2023/03

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  26. 細胞画像とAI-形態情報の可能性に挑む-

    山本陽一朗

    日本解剖学会総会・全国学術集会抄録集(CD-ROM)　128th　2023

  27. 形態情報の可能性に挑む-医療AIの課題と展望-

    山本陽一朗

    日本臨床分子形態学会総会・学術集会講演プログラム・要旨集　55th　2023

  28. 高悪性度前立腺癌を判別する次世代超音波AIシステム

    赤塚 純, 木村 剛, 三神 晃, 柳 雅人, 遠藤 勇気, 武田 隼人, 戸山 友香, 高橋 亮, 山本 陽一朗, 近藤 幸尋

    日本癌治療学会学術集会抄録集　60回　O24-5　2022/10

    Publisher: (一社)日本癌治療学会

  29. 知識を紡ぐ医療AI

    山本 陽一朗

    日本外科学会定期学術集会抄録集　122回　FS-1　2022/04

    Publisher: (一社)日本外科学会

  30. 医療におけるAI 知っておきたいコトと今後の展望

    山本 陽一朗

    日本皮膚科学会雑誌　132　(2)　333-333　2022/02

    Publisher: (公社)日本皮膚科学会

    ISSN： 0021-499X

    eISSN： 1346-8146

  31. Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

    Takayuki Takahashi, Hikaru Matsuoka, Rieko Sakurai, Jun Akatsuka, Yusuke Kobayashi, Masaru Nakamura, Takashi Iwata, Kouji Banno, Motomichi Matsuzaki, Jun Takayama, Daisuke Aoki, Yoichiro Yamamoto, Gen Tamiya

    Journal of Gynecologic Oncology　33　(5)　2022

    Publisher: Asian Society of Gynecologic Oncology; Korean Society of Gynecologic Oncology and Colposcopy

    DOI： 10.3802/jgo.2022.33.e57 　

    ISSN： 2005-0380

    eISSN： 2005-0399

  32. A data-driven ultrasound approach discriminates pathological high grade prostate cancer International-journal

    Jun Akatsuka, Yasushi Numata, Hiromu Morikawa, Tetsuro Sekine, Shigenori Kayama, Hikaru Mikami, Masato Yanagi, Yuki Endo, Hayato Takeda, Yuka Toyama, Ruri Yamaguchi, Go Kimura, Yukihiro Kondo, Yoichiro Yamamoto

    Scientific Reports　12　(1)　860-860　2022/01

    DOI： 10.1038/s41598-022-04951-3 　

    More details Close

    Accurate prostate cancer screening is imperative for reducing the risk of cancer death. Ultrasound imaging, although easy, tends to have low resolution and high inter-observer variability. Here, we show that our integrated machine learning approach enabled the detection of pathological high-grade cancer by the ultrasound procedure. Our study included 772 consecutive patients and 2899 prostate ultrasound images obtained at the Nippon Medical School Hospital. We applied machine learning analyses using ultrasound imaging data and clinical data to detect high-grade prostate cancer. The area under the curve (AUC) using clinical data was 0.691. On the other hand, the AUC when using clinical data and ultrasound imaging data was 0.835 (p = 0.007). Our data-driven ultrasound approach offers an efficient tool to triage patients with high-grade prostate cancers and expands the possibility of ultrasound imaging for the prostate cancer detection pathway.

  33. Introduction of Japan Association for Medical Artificial Intelligence

    井元 清哉, 鎌谷 洋一郎, 瀬々 潤, 田宮 元, 浜本 隆二, 山口 類, 山本 陽一朗

    腫瘍内科 = Clinical oncology / 腫瘍内科編集委員会 編　28　(3)　296-299　2021/09

    Publisher: 東京 : 科学評論社

    ISSN： 1881-6568

  34. 医療AIの活用に向けて知っておきたいこと

    山本 陽一朗

    日本癌学会総会記事　80回　[ML9]-[ML9]　2021/09

    Publisher: (一社)日本癌学会

    ISSN： 0546-0476

  35. 形態病理と分子病理の新たな展望 HER2陽性乳癌

    前田 一郎, 沼田 康志, 森川 啓, 小穴 良保, 津川 浩一郎, 山本 陽一朗

    日本乳癌学会総会プログラム抄録集　29回　14-14　2021/07

    Publisher: (一社)日本乳癌学会

  36. AI診断の新たな展望 知識を紡ぐ医療AI技術

    山本 陽一朗

    日本乳癌学会総会プログラム抄録集　29回　21-21　2021/07

    Publisher: (一社)日本乳癌学会

  37. Outcomes of starting low-dose pazopanib in patients with metastatic renal cell carcinoma who do not meet eligibility criteria for clinical trials

    Jun Akatsuka, Go Kimura, Kotaro Obayashi, Kotaro Tsutsumi, Masato Yanagi, Yuki Endo, Hayato Takeda, Tatsuro Hayashi, Yuka Toyama, Yasutomo Suzuki, Tsutomu Hamasaki, Yoichiro Yamamoto, Yukihiro Kondo

    Urological Science　32　(3)　104-110　2021/07/01

    Publisher: Wolters Kluwer Medknow Publications

    DOI： 10.4103/UROS.UROS_145_20 　

    ISSN： 1879-5234 1879-5226

  38. The emerging role of artificial intelligence in the reporting of prostate pathology International-journal

    Lars Egevad, Brett Delahunt, Hemamali Samaratunga, Toyonori Tsuzuki, Yoichiro Yamamoto, John Yaxley, Pekka Ruusuvuori, Kimmo Kartasalo, Martin Eklund

    Pathology　53　(5)　565-567　2021/06

    Publisher: Elsevier BV

    DOI： 10.1016/j.pathol.2021.04.002 　

    ISSN： 0031-3025

  39. 乳腺病理最近のトピックス ゲノム異常と形態学

    前田 一郎, 山本 陽一朗

    日本病理学会会誌　110　(1)　178-178　2021/03

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  40. HER2陽性乳癌

    前田一郎, 前田一郎, 前田一郎, 沼田康志, 森川啓, 小穴良保, 津川浩一郎, 山本陽一朗

    日本乳癌学会学術総会(CD-ROM)　29th　2021

  41. Intraductal carcinoma of the prostate(IDC-P)は根治的前立腺全摘除術後のPSA再発に対する独立した予後因子である 傾向スコアマッチング解析

    赤塚 純, 木村 剛, 都築 豊徳, 三神 晃, 柳 雅人, 遠藤 勇気, 武田 隼人, 林 達郎, 山本 陽一朗, 近藤 幸尋

    日本泌尿器科学会総会　108回　1295-1295　2020/12

    Publisher: (一社)日本泌尿器科学会総会事務局

  42. 関節リウマチにT細胞大顆粒リンパ球性白血病とアミロイドーシスを合併した1剖検例

    松浦 勇匡, 立石 文子, 山口 晃典, 橋本 幸始, 上條 祐司, 山本 陽一朗, 菅野 祐幸

    診断病理　37　(4)　334-340　2020/10

    Publisher: (一社)日本病理学会

    ISSN： 1345-6431

  43. AI診断の時代 AIと予後予測、そして知識獲得

    山本 陽一朗

    日本癌治療学会学術集会抄録集　58回　SSY8-1　2020/10

    Publisher: (一社)日本癌治療学会

  44. デジタルサイトロジー、AI 医療におけるAIの新たな可能性

    山本 陽一朗

    日本臨床細胞学会雑誌　59　(Suppl.1)　124-124　2020/05

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  45. AIと細胞 細胞画像に秘められた可能性の探求

    山本 陽一朗

    日本臨床細胞学会雑誌　58　(Suppl.2)　477-477　2019/10

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  46. 前立腺全摘後のPSA再発におけるIntraductal carcinoma of the prostateの影響

    赤塚 純, 木村 剛, 都築 豊徳, 三神 晃, 柳 雅人, 遠藤 勇気, 武田 隼人, 林 達郎, 堤 光太郎, 山本 陽一朗, 近藤 幸尋

    日本癌治療学会学術集会抄録集　57回　P94-7　2019/10

    Publisher: (一社)日本癌治療学会

  47. Cancer research using artificial intelligence technologies on the frontiers of medical science How is AI recognizing cells? The uncharted values of cellular images

    山本 陽一朗

    日本癌学会総会記事　78回　IS8-4　2019/09

    Publisher: (一社)日本癌学会

    ISSN： 0546-0476

  48. 病理学における人工知能

    山本 陽一朗

    診断病理　36　(2)　73-78　2019/04

    Publisher: (一社)日本病理学会

    ISSN： 1345-6431

  49. 医療とAI 現状と展望

    山本 陽一朗

    中部日本整形外科災害外科学会雑誌　62　(春季学会)　3-3　2019/03

    Publisher: (一社)中部日本整形外科災害外科学会

    ISSN： 0008-9443

    eISSN： 1349-0885

  50. 人工知能と医療~AIからみた細胞像~

    山本陽一朗

    医学と工学からみた交通安全対策抄録集　2019　2019

  51. How is AI recognizing cells? The uncharted values of cellular images

    山本陽一朗

    日本癌学会学術総会抄録集(Web)　78th　2019

  52. AI技術の医療応用 新規バイオマーカー探索へ向けて

    山本 陽一朗

    日本分子腫瘍マーカー研究会誌　34　11-12　2019/01

    Publisher: 日本分子腫瘍マーカー研究会

    eISSN： 2433-8575

  53. AIの病理学分野への応用 現状と展望

    山本 陽一朗

    日本病理学会会誌　107　(2)　55-55　2018/10

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  54. 異分野融合-AIは医師を超えられるか～AIの現状と未来～ AIの医療分野への応用 AIがみる細胞像

    山本 陽一朗

    日本癌治療学会学術集会抄録集　56回　SSY9-2　2018/10

    Publisher: (一社)日本癌治療学会

  55. 革新的技術による新規バイオマーカー探索 AI技術の医療応用 新規バイオマーカー探索へ向けて

    山本 陽一朗

    日本分子腫瘍マーカー研究会プログラム・講演抄録　38回　26-27　2018/09

    Publisher: 日本分子腫瘍マーカー研究会

  56. An infant case of diffuse cerebrospinal lesions and cardiomyopathy caused by a BOLA3 mutation

    Makoto Nishioka, Yuji Inaba, Mitsuo Motobayashi, Yosuke Hara, Ryusuke Numata, Yoshiro Amano, Kunihiko Shingu, Yoichiro Yamamoto, Kei Murayama, Akira Ohtake, Yozo Nakazawa

    Brain and Development　40　(6)　484-488　2018/06/01

    Publisher: Elsevier B.V.

    DOI： 10.1016/j.braindev.2018.02.004 　

    ISSN： 1872-7131 0387-7604

  57. ICT、AIを病理診断に活かす AI技術の病理学への応用 現状と展望

    山本 陽一朗

    日本病理学会会誌　107　(1)　186-186　2018/04

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  58. Histopathological findings of pregnancy-induced hypertension: histopathology of early-onset type reflects two-stage disorder theory

    Ayako Tateishi, Satoshi Ohira, Yoichiro Yamamoto, Hiroyuki Kanno

    Virchows Archiv　472　(4)　635-642　2018/04/01

    Publisher: Springer Verlag

    DOI： 10.1007/s00428-018-2315-3 　

    ISSN： 1432-2307 0945-6317

  59. 人工知能と癌研究

    山本 陽一朗

    日本癌学会総会記事　76回　SP2-5　2017/09

    Publisher: (一社)日本癌学会

    ISSN： 0546-0476

  60. より深くAIある医療へ 人工知能がみた癌細胞 AIの医療応用の実例と可能性

    山本 陽一朗

    日本皮膚科学会雑誌　127　(5)　836-836　2017/05

    Publisher: (公社)日本皮膚科学会

    ISSN： 0021-499X

    eISSN： 1346-8146

  61. 人工知能からみた細胞の姿

    山本 陽一朗

    日本臨床細胞学会雑誌　56　(Suppl.1)　132-132　2017/04

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  62. 乳癌の治療戦略に迫る 細胞診と針生検の位置づけ 乳癌ネオアジュバンド療法に対する人工知能を用いたコンパニオン診断

    山本 陽一朗

    日本臨床細胞学会雑誌　56　(Suppl.1)　163-163　2017/04

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  63. Information Technologyと病理診断 ビッグデータからdeep learningまで 病理学における人工知能研究の現状と可能性

    山本 陽一朗

    日本病理学会会誌　106　(1)　205-205　2017/03

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  64. Digital Pathologyを用いた肝細胞がんの形態学解析による再発予測の検討

    齋藤 彰, 藤田 浩司, 山本 陽一朗, 硲 彰一, 坂本 和彦, 永野 浩昭, 黒田 雅彦

    日本病理学会会誌　106　(1)　277-277　2017/03

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  65. 人工知能からみた乳癌細胞

    山本陽一朗

    日本乳癌画像研究会プログラム・抄録集　26th　2017

  66. 乳癌ネオアジュバンド療法に対する人工知能を用いたコンパニオン診断

    山本陽一朗

    日本臨床細胞学会雑誌(Web)　56　2017

    ISSN： 1882-7233

  67. Artificial intelligence and mathematical analysis in the pathology field

    Yoichiro Yamamoto

    Recent Advances and Research Updates　1-7　2017

  68. Quantitative analysis of nuclear shape in oral squamous cell carcinoma is useful for predicting the chemotherapeutic response

    Maki Ogura, Yoichiro Yamamoto, Hitoshi Miyashita, Hiroyuki Kumamoto, Manabu Fukumoto

    Medical Molecular Morphology　49　(2)　76-82　2016/06

    DOI： 10.1007/s00795-015-0121-4 　

    ISSN： 1860-1480

    eISSN： 1860-1499

  69. 妊娠高血圧症候群における病型と病理組織学的所見の比較検討

    立石 文子, 大平 哲史, 山本 陽一朗, 塩沢 丹里, 菅野 祐幸

    日本病理学会会誌　105　(1)　334-334　2016/04

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  70. 人工知能を用いた形態学的細胞タイピング 乳管内増殖性病変の良悪性判別

    山本 陽一朗, 立石 文子, 下条 久志, 菅野 祐幸

    日本病理学会会誌　105　(1)　370-370　2016/04

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  71. 腫瘍内新生血管の定量化と遺伝子ビッグデータ解析 Glioblastomaの年齢依存的特徴

    長谷川 航平, 山本 陽一朗, 一萬田 正二郎, 立石 文子, 大谷 真紀, 小林 実喜子, 下条 久志, 菅野 祐幸

    日本病理学会会誌　105　(1)　593-593　2016/04

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  72. Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

    Masatoshi Yamada, Akira Saito, Yoichiro Yamamoto, Eric Cosatto, Atsushi Kurata, Toshitaka Nagao, Ayako Tateishi, Masahiko Kuroda

    Journal of Pathology Informatics　7　(1)　1　2016/01/29

    Publisher: Medknow Publications

    DOI： 10.4103/2153-3539.175380 　

    ISSN： 2153-3539 2229-5089

  73. A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix

    Akira Saito, Yasushi Numata, Takuya Hamada, Tomoyoshi Horisawa, Eric Cosatto, Hans-Peter Graf, Masahiko Kuroda, Yoichiro Yamamoto

    Journal of Pathology Informatics　7　(1)　2016/01/29

    Publisher: Medknow Publications

    DOI： 10.4103/2153-3539.189699 　

    ISSN： 2153-3539 2229-5089

  74. 乳腺細胞診のDCIS 診断のための新たなアプローチ 形態情報マイクロアレイ 乳管内増殖性病変における筋上皮細胞の定量的評価と臨床応用

    山本 陽一朗

    日本臨床細胞学会雑誌　54　(Suppl.2)　477-477　2015/10

    Publisher: (公社)日本臨床細胞学会

    ISSN： 0387-1193

    eISSN： 1882-7233

  75. 形態情報マイクロアレイ:乳管内増殖性病変における筋上皮細胞の定量的評価と臨床応用

    山本陽一朗

    日本臨床細胞学会雑誌(Web)　54　2015

    ISSN： 1882-7233

  76. Supratentorial extraventricular anaplastic ependymoma in an adult with repeated intratumoral hemorrhage

    Naotaka Iwamoto, Yasuo Murai, Yoichiro Yamamoto, Koji Adachi, Akira Teramoto

    Brain Tumor Pathology　31　(2)　138-143　2014/04

    DOI： 10.1007/s10014-013-0146-0 　

    ISSN： 1433-7398

    eISSN： 1861-387X

  77. Inferior vena cava tumor thrombus after partial nephrectomy for renal cell carcinoma International-journal

    Jun Akatsuka, Yasutomo Suzuki, Tsutomu Hamasaki, Takao Shindo, Masato Yanagi, Go Kimura, Yoichiro Yamamoto, Yukihiro Kondo

    BMC research notes　7　198-198　2014/03/29

    DOI： 10.1186/1756-0500-7-198 　

    More details Close

    BACKGROUND: Partial nephrectomy is now the gold standard treatment for small renal tumors. Local recurrence is a major problem after partial nephrectomy, and local recurrence in the remnant kidney after partial nephrectomy is common. CASE PRESENTATION: A 77-year-old man underwent right partial nephrectomy for a T1 right renal cell carcinoma. Microscopic examination revealed a clear cell renal carcinoma, grade 2, stage pT3a. Although the surgical margin was negative, the carcinoma invaded the perirenal fat, and vascular involvement was strongly positive. Thirty months after partial nephrectomy, an enhanced computed tomographic scan showed local recurrence of the renal cell carcinoma extending into the inferior vena cava without renal mass. Hence, we performed right radical nephrectomy and intracaval thrombectomy. Microscopic examination revealed a clear cell carcinoma grade 2, stage pT3a + b. The patient is still alive with no evidence of recurrence 10 months post-procedure. CONCLUSION: To our knowledge, local recurrence of renal cell carcinoma extending into the inferior vena cava after partial nephrectomy has not been reported in the literature. Our case report emphasizes the importance of strict surveillance of patients after partial nephrectomy, especially for those with renal cell carcinoma positive for microvessel involvement.

  78. 腎部分切除後に下大静脈内進展を来した再発性腎細胞癌の1例

    赤塚 純, 鈴木 康友, 濱崎 務, 新藤 貴雄, 柳 雅人, 木村 剛, 山本 陽一郎, 近藤 幸尋

    日本癌治療学会誌　48　(3)　2882-2882　2013/09

    Publisher: (一社)日本癌治療学会

    ISSN： 0021-4671

  79. 当院における術中断端迅速診断の検討

    栗田智子, 飯田信也, 山下浩二, 柳原恵子, 岩本美樹, 山本陽一朗, 土屋眞一, 内田英二, 武井寛幸

    日本臨床外科学会雑誌　74　2013

    ISSN： 1345-2843

  80. 腎原発のPrimitive neuroectodermal tumour(PNET)の検討

    石井英昭, 山本陽一朗, 大橋隆治, 土屋眞一

    日本病理学会会誌　102　(1)　2013

    ISSN： 0300-9181

  81. 術前内分泌療法を施行した3症例の検討

    栗田智子, 武井寛幸, 飯田信也, 山下浩二, 柳原恵子, 岩本美樹, 山本陽一朗, 土屋眞一, 内田英二

    日本癌治療学会学術集会(CD-ROM)　51st　(3)　2206-2206　2013

    Publisher: (一社)日本癌治療学会

    ISSN： 0021-4671

  82. Age-Related Decrease of the Retinal Vasculature Area Identified with a Novel Computer-Aided Analysis System

    Yuuka Yamamoto, Yoichiro Yamamoto, Atsushi Marugame, Maki Ogura, Akira Saito, Kouichi Ohta, Manabu Fukumoto, Toshinori Murata

    Tohoku Journal of Experimental Medicine　228　(3)　229-237　2012/11

    DOI： 10.1620/tjem.228.229 　

    ISSN： 0040-8727

    eISSN： 1349-3329

  83. 癌細胞核の特徴量の計測による化学療法効果の予測

    小掠 真貴, 山本 陽一朗, 宮下 仁, 齋藤 彰, 熊本 裕行, 福本 学

    日本病理学会会誌　101　(1)　326-326　2012/03

    Publisher: (一社)日本病理学会

    ISSN： 0300-9181

  84. A case of late recurrence of urothelial carcinoma in an ileal conduit after radical cystectomy and ileal conduit diversion for bladder cancer

    Masato Yanagi, Taiji Nishimura, Go Kimura, Yukihiro Kondo, Keiichi Yamazaki, Ken Kojo, Takahiro Igarashi, Kei Hosoda, Yoichiro Yamamoto

    Acta Urologica Japonica　58　(2)　109-112　2012/02

    ISSN： 0018-1994

  85. 乳癌幹細胞およびNicheのin situ探索:薄層広視野樹脂包埋法と乳癌幹細胞

    山本陽一朗

    日本医科大学医学会雑誌　8　(4)　2012

    ISSN： 1349-8975

  86. 胃癌におけるp53の役割

    石井英昭, 山本陽一朗, 原田大, 大橋隆治, 土屋眞一

    日本病理学会会誌　101　(1)　2012

    ISSN： 0300-9181

  87. Roles of fine-needle aspiration and core needle biopsy in the diagnosis of breast cancer

    Tomoko Kurita, Shin-ichi Tsuchiya, Yasuhiko Watarai, Yoichiro Yamamoto, Oi Harada, Keiko Yanagihara, Shinya Iida, Koji Yamashita, Shun-suke Haga, Eiji Uchida

    Breast Cancer　19　(1)　23-29　2012/01

    DOI： 10.1007/s12282-010-0251-4 　

    ISSN： 1340-6868

  88. 前立腺癌に対する密封小線源療法 PSA低下率の悪い症例の臨床病理学的検討

    武田 隼人, 木村 剛, 林 達郎, 藤原 博通, 近藤 幸尋, 栗林 茂彦, 宮下 次廣, 汲田 伸一郎, 山本 陽一朗

    日本泌尿器科学会雑誌　102　(2)　379-379　2011/03

    Publisher: (一社)日本泌尿器科学会

    DOI： 10.5980/jpnjurol.102.379_4 　

    ISSN： 0021-5287

  89. 兄弟骨髄移植後に肝細胞癌を発症した一例:ドナー由来かレシピエント由来か

    福本基, 李立, 橋谷田真樹, 山本陽一朗, 山本陽一朗, 福本学

    日本病理学会会誌　100　(1)　2011

    ISSN： 0300-9181

  90. A case of breast angiosarcoma

    TSUCHIYA Kyoko, YAMAMOTO Yoichiro, MATSUBARA Miyuki, YANAGIHARA Keiko, IIDA Shinya, HAGA Shunsuke, NAITO Zenya, TSUGAWA Kouichiro

    The Journal of the Japanese Society of Clinical Cytology　50　(5)　295-300　2011

    Publisher: The Japanese Society of Clinical Cytology

    DOI： 10.5795/jjscc.50.295 　

    ISSN： 0387-1193

    eISSN： 1882-7233

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    Background : Primary breast angiosarcoma is extremely rare. We report a case of painful, rapidly growing angiosarcoma.<br>Case : A 40-year-old woman finding a lump and strain in her right breast 1.5 years earlier noted rapid, painful growth in the last three months. Fine-needle aspiration (FNA) detected spindle-shaped tumor cells in a massive amount of blood components, with a tubular, net-like structure. Core needle biopsy (CNB) showed immunocytochemical staining positive for CD31, CD34, and Factor VIII. The final diagnosis was high-grade angiosarcoma. Postoperatively, we found a dark-red 72×55 mm tumor with intratumorous hemorrhage.<br>Conclusion : Angiosarcoma tumors usually grow rapidly and painlessly, but intratumorous hemorrhage may cause pain as in our case. Angiosarcoma is rare but highly malignant, making it important to provide sufferers with useful knowledge, starting with a correct diagnosis.

  91. 前立腺全摘除術後のPSA再発に対する救済放射線療法 : 予後因子の検討

    木村 剛, 栗林 茂彦, 斎藤 友香, 木全 亮二, 松沢 一郎, 鈴木 康友, 浜崎 務, 近藤 幸尋, 宮下 次廣, 汲田 伸一郎, 山本 陽一朗

    日本泌尿器科学会雑誌　102　(2)　387　2011

    Publisher: 一般社団法人 日本泌尿器科学会

    DOI： 10.5980/jpnjurol.102.387_2 　

    ISSN： 0021-5287

    eISSN： 1884-7110

  92. A case of hepatocellular carcinoma developed after allogeneic bone marrow transplantation

    Li Li, Masaki Hashiyada, Makoto Kume, Motoi Fukumoto, Yoichiro Yamamoto, Masato Funayama, Yuzo Yamamoto, Manabu Fukumoto

    Pathology International　60　(12)　795-797　2010/12

    DOI： 10.1111/j.1440-1827.2010.02601.x 　

    ISSN： 1320-5463

  93. The uneven irradiation of a target cell and its dynamic movement can mathematically explain incubation period for the induction of cancer by internally deposited radionuclides

    Yoichiro Yamamoto, Nobuteru Usuda, Yoichi Oghiso, Yoshikazu Kuwahara, Manabu Fukumoto

    Health Physics　99　(3)　388-393　2010/09

    DOI： 10.1097/HP.0b013e3181cd4153 　

    ISSN： 0017-9078

    eISSN： 1538-5159

  94. Histological type of Thorotrast-induced liver tumors associated with the translocation of deposited radionuclides

    Yoichiro Yamamoto, Junichi Chikawa, Yoshinobu Uegaki, Nobuteru Usuda, Yoshikazu Kuwahara, Manabu Fukumoto

    Cancer Science　101　(2)　336-340　2010/02

    DOI： 10.1111/j.1349-7006.2009.01401.x 　

    ISSN： 1347-9032

  95. 眼底写真における血管面積の検討

    山本裕香, 山本陽一朗, 村田敏規, 太田浩一, 福本学

    日本眼科学会雑誌　114　2010

    ISSN： 0029-0203

  96. 浸潤性小葉癌(histiocytoid type)の細胞像

    葉山綾子, 土屋眞一, 佐藤春明, 釜口晴美, 川本雅司, 山本陽一朗, 渡会泰彦, 松原美幸

    日本臨床細胞学会雑誌　49　2010

    ISSN： 0387-1193

  97. 破骨細胞様巨細胞を伴う乳癌の3例

    浅川一恵, 土屋眞一, 大塚俊司, 山本陽一朗, 川本雅司, 松原美幸, 渡會泰彦

    日本臨床細胞学会雑誌　49　2010

    ISSN： 0387-1193

  98. 内部被ばくにおける発癌潜伏期間:不均等被ばくと被ばく細胞移動に基づいた数理モデルの構築

    山本陽一朗, 川本雅司, 土屋眞一, 福本学

    日本病理学会会誌　99　(1)　2010

    ISSN： 0300-9181

  99. Long Incubation Period for the Induction of Cancer by Thorotrast is Attributed to the Uneven Irradiation of Liver Cells at the Microscopic Level

    Yoichiro Yamamoto, Nobuteru Usuda, Toshihiro Takatsuji, Yoshikazu Kuwahara, Manabu Fukumoto

    Radiation Research　171　(4)　494-503　2009/04

    DOI： 10.1667/RR1492.1 　

    ISSN： 0033-7587

  100. Clinically relevant radioresistant cells efficiently repair DNA double-strand breaks induced by X-rays

    Yoshikazu Kuwahara, Li Li, Taisuke Baba, Hironobu Nakagawa, Tsutomu Shimura, Yoichiro Yamamoto, Yasuhito Ohkubo, Manabu Fukumoto

    Cancer Science　100　(4)　747-752　2009/04

    DOI： 10.1111/j.1349-7006.2009.01082.x 　

    ISSN： 1347-9032

  101. 発がんと治療の数理解析

    山本陽一朗, 福本学

    日本臨床分子形態学会総会・学術集会講演プログラム・要旨集　41st　2009

  102. ミトコンドリアDNAのD-loop領域に放射線被ばくに特異的な変異は存在するか?

    栗原愛, 山本陽一朗, 福本学

    日本病理学会会誌　97　(1)　2008

    ISSN： 0300-9181

  103. 高齢者における肺類上皮血管内皮腫の2例

    玉川空樹, 山本陽一朗, 簡野直子, 森谷卓也, 福本学

    日本病理学会会誌　97　(1)　2008

    ISSN： 0300-9181

  104. 宇宙飛行がラット肝のCytochrome P450遺伝子とストレス関連遺伝子の発現におよぼす影響

    馬場泰輔, 山本陽一朗, 福本学

    日本病理学会会誌　96　(1)　2007

    ISSN： 0300-9181

  105. 内部放射線障害のモデルとしてのトロトラスト症肝内胆管癌と血管肉腫における非癌部の組織学的背景の差異

    山本陽一朗, 森武三郎, 立野紘雄, 福本学

    日本病理学会会誌　96　(1)　2007

    ISSN： 0300-9181

  106. 拡張型膵管胆道合流異常の胆嚢癌・胆管癌同時重複発癌の1根治切除例

    渡部泰弘, 片寄友, 力山敏樹, 小野川徹, 山本久仁治, 吉田寛, 林洋毅, 山本陽一朗, 福本学, 海野倫明

    日本すい管胆道合流異常研究会プロシーディングス　29th　2006

    ISSN： 0912-425X

  107. 放射線耐性を理解する上での問題点

    桑原 義和, 馬場 泰輔, 王 ル, 村田 和浩, 栗原 愛, 海老島 茂子, 山本 陽一朗, 福本 学

    放射線生物研究　12　(4)　380-386　2006

    Publisher:

    ISSN： 0441-747X

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Books and Other Publications 12

  1. The Complete Guide to Artificial Intelligence (Newton Special Edition): AI for Science Is Accelerating Scientific Discovery and Transforming Modern Society

    Shota Imai, Yibin Xu, Koji Hashimoto, Yoshitaka Moriwaki, Yoichiro Yamamoto

    Newton　2026/07

    ISBN: 9784315530384

  2. The Scientific Revolution of AI

    Shota Imai, Koji Hashimoto, Yoichiro Yamamoto

    Newton　2025/01

  3. Expectations for development of medical care and biomedical science research brought about by the evolution of neural networks

    Naonori Ueda, Yoichiro Yamamoto

    2024/11

    ISBN: 9784758125864

  4. AI・シミュレーションによる科学的発見は可能か？複雑な生命現象・疾患の未知なるメカニズムを解く

    山本陽一朗/企画

    実験医学 Vol.42 No.1, 羊土社　2024/01

    ISBN: 9784758125758

  5. What artificial intelligence brings to the medical field

    Yoichiro Yamamoto

    Journal of Clinical and Experimental Medicine, 263, 8, pp636-640　2017/11

  6. 【デジタルパソロジー/サイトロジーとAIの進歩】前立腺がんにおける医療AIの現状

    赤塚 純, 山本 陽一朗

    医学のあゆみ 284(3) 201-205 2023年, 医歯薬出版株式会社　2023/01

  7. 医療・医学の進歩と発展に貢献する医療AI技術の開発 : 病理学と数理解析学の融合研究

    山本 陽一朗, 矢口 雅英, 大場 豪

    東京 : 文部科学省科学技術学術政策研究所科学技術動向研究センター　2021

  8. Exploring a new world in pathological images

    Yoichiro Yamamoto

    Journal of Clinical and Experimental Medicine, 274, 9, pp759-764.　2020/08

  9. Pathology and artificial intelligence: toward trustable AI

    Yoichiro Yamamoto

    Experimental Medicine, vol.37, No,16, pp2690-2695　2019/10

  10. Applications of Artificial Intelligence Techniques in Pathology Field

    Yoichiro Yamamoto

    The Medical Frontline, 74, 3, pp99 -105　2019/03

  11. Medical applications of artificial intelligence~How to Apply

    Yoichiro Yamamoto

    Pathology and Clinical Medicine, 36, 9, pp915 -919　2018/09

  12. Artificial intelligence in the pathology field

    Yoichiro Yamamoto, Manabu Fukumoto

    Pathology and Clinical Medicine, 35, 1, pp18 -25　2017/01

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Research Projects 10

  1. Spatial transcriptome analysis of intraductal and invasive cancer cells in prostate cancer

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (C)

    Institution: Aichi Medical University

    2024/04/01 - 2027/03/31

  2. Exploring histologic risk factors for relapse in ulcerative colitis using artificial intelligence

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Scientific Research (C)

    Institution: Kitasato University

    2022/04/01 - 2026/03/31

  3. Challenge toward the Control of Intractable Cancer through Understanding of Molecular, Cellular, and Interorgan Networks

    Yoichiro Yamamoto

    System: Moonshot

    2020/12 - 2026

  4. AIによる前立腺癌病理情報に基づく遺伝子変異診断法の開発

    加藤 真史, 山本 陽一朗, 都築 豊徳

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 基盤研究(C)

    Institution: 名古屋大学

    2022/04/01 - 2025/03/31

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    先行研究「前立腺癌遺伝子変異を予測する新規病理学的スクリーニング法の開発」では病理学的予後不良因子であるIntraductal carcinoma of the prostate (IDC-P)と前立腺癌遺伝子変異との相関関係を検討する研究の解析を行っている。ここではmHSPC（ホルモン未治療転移性前立腺癌）の前立腺生検標本をNGS（次世代シークエンサー）として遺伝子パネル検査により108遺伝子についての変異解析を行いこれらはすでに検査が終了しそのデータを取得してる。現在は病理学的検討結果と遺伝子変異結果の内容について解析段階にある。この解析が終了しだい、計画修正を行い前立腺生検HE標本の予備評価さらに固定不良例では標本再作成を行いバーチャルスライド化の準備を行うが、これら未染標本は先行研究ですでに作製完了済の状態にありバーチャルスライド化の準備はほぼ整った状況にある。ここから遺伝子変異解析結果および生検HE標本を理化学研究所AIP (革新知能総合研究センター)に送りスーパーコンピューターRAIDENを用いたAIによる教師無し学習により、癌の病理学的特徴と遺伝子異常の関連づけを本年度開始する予定である。最終的には転移性前立腺癌のHE標本から得られる病理情報を元に、AIにより前立腺癌組織中の遺伝子変異が予測可能な病理学的因子を導き出すことを目的としている。本研究により病理情報による補助診断で遺伝子変異が予測可能となり治療選択に繋げるのみならず、無効な遺伝子検査を回避することが実現可能となる。

  5. 前立腺導管癌の分子生物学的特徴及び治療予測への人工知能の応用

    都築 豊徳, 山本 陽一朗, 小林 恭, 羽賀 博典, 佐々 直人, 高原 大志, 溝脇 尚志

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 基盤研究(C)

    Institution: 愛知医科大学

    2021/04/01 - 2024/03/31

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    前立腺癌の予後予測の上で、導管内癌 (IDC-P)が存在することが予後不良因子であることが様々なデーター解析から裏付けられている。2022年に発刊される予定のWHOの泌尿器腫瘍の教科書において、研究者は執筆の中心として参画し、IDC-Pの定義並びにその臨床病理学的意義の記載を行った。 前立腺癌の放射線治療において、前立腺生検内にIDC-Pが存在することが予後不良であることを示した研究結果をまとめた。また、それらのデーターを元にして論文を作製、提出した。2022年4月にその結果がCancer Scienceにacceptされた。（尚、論文自体は4月末のacceptであることから、DOI numberはまだない。)これらの症例は全てwhole slide imageが作製されており、このデーターと予後とを組み合わせて、人工知能による予後解析を行う準備並びに態勢を整えた。既にてwhole slide image上では病変部並びにそれに対応するGleason score (生物学的悪性度の形態学的指標）のannotationが遂行されている。今後、そのデーターを元にして解析を進める。 前立腺癌組織におけるIDC-PとPTEN等の分子生物学的マーカー検索を行う対象症例並びに検討すべき標本は抽出・選択された。これらに対する病理学的な診断・解析は既に終了してしる。現在は免疫組織を遂行するための標本の作製過程中である。今後は免疫染色を遂行し、そのデーターの解析と病理組織学的検討でえられた結果をまとめていく予定である。

  6. 組織中および尿中ビッグデータに基づいたBCG注入後膀胱癌再発予測システムの開発

    木村 剛, 山本 陽一朗, 齋藤 彰

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 基盤研究(C)

    Institution: 日本医科大学

    2016/04/01 - 2023/03/31

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    新型コロナ感染症蔓延による非常事態宣言などで，研究は滞ることがあった．この間，BCG膀胱内注入後膀胱癌再発に関連すると考えられる免疫染色を中心に症例数を増やし作業を進めた．研究分担者とはwebミーティングを通し，研究内容について綿密に話し合いを行った．また，尿路腫瘍の予後因子等について解析を進め，結果を以下のごとく学会発表した． 1. Endo Y, Kimura G et al. Prognostic impact of serum cytokeratin 19 fragments in patient with metastatic urothelial cancer treated with immune checkpoint inihibitors. ASCO-GU, Feb 2022. 2. Ikuma S, Kimura G et al. Diagnostic performance and identification of factors for misdiagnosis in Vesical Imaging-Reporting and Data System (VI-RADS). ASCO-GU, Feb 2022. 3. Yanagi M, Kimura G et al. How can we reduce the rate of incomplete resection in patients with non-muscle-invasive bladder cancer (NMIBC) undergoing first transurethral resection of bladder tumor (TURBT)? -Impact of two-loop wide resection of surrounding tumor tissue-. EMUC, Nov 2021.

  7. Unveiling the Black Box of Medical Artificial Intelligence

    Yamamoto Yoichiro

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Challenging Research (Pioneering)

    Institution: Institute of Physical and Chemical Research

    2018/06 - 2023/03

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    We challenged the black box problem in medical data classification by deep learning, which is a characteristic of modern AI, aiming at safe and accurate application of artificial intelligence (AI) to medical care. Through the analysis of AI-based classification results, we were able to promote the understanding of deep learning analysis in medicine. Furthermore, we have succeeded in improving the accuracy and explainability of the classification system by combining it with clinical data and other multimodal methods. We plan to contribute to the development of this field by applying this technology to a wider range of diseases in the future.

  8. Development of companion diagnostics for cancer using stacked Artificial Intelligence Competitive

    Yoichiro Yamamoto

    Offer Organization: AMED

    2016 - 2019

  9. Quantitative morphometric analysis of breast cancer invasion using a machine learning approach

    Yamamoto Yoichiro

    Offer Organization: Japan Society for the Promotion of Science

    System: Grants-in-Aid for Scientific Research

    Category: Grant-in-Aid for Young Scientists (B)

    2015/04/01 - 2018/03/31

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    We found that histological types of breast tumors could be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.

  10. 超広視野ナノレベル構造観測による乳癌幹細胞ｉｎ ｓｉｔｕ同定法の開発

    山本 陽一朗

    Offer Organization: 日本学術振興会

    System: 科学研究費助成事業

    Category: 若手研究(B)

    2012/04/01 - 2014/03/31

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    初年度、ピックアップした乳癌のLuminal A, Luminal B, Basal-like(triple negative), HER2+(ER-,PgR-)の4タイプにおいて、薄層広視野樹脂包埋法によりBCL-like cellの存在を確認した。しかしながら、これらの病理標本では、がん幹細胞マーカーであるCD44+の免疫組織化学的な同定が困難であった。そのため今年度は、複数の免疫組織学的な検討および、病理組織標本のバーチャルスライド化を行い、それぞれの癌細胞の核特徴量および分布形式をNEC齋藤彰博士の協力のもと画像解析を用いて定量化した。具体的には画像解析用に、各症例の乳癌における異なる部位（癌中心部、先端部）のバーチャルスライドを作成し、乳癌各部位における癌細胞の核内構造変化を100種類以上の核特徴量を用いて画像解析した。 結果として、癌の中心部と先端部では癌細胞核内構造特徴量の平均値および分散値が異なる可能性が示唆された。これは、部位によるBCL-like cellの存在比率の違いの可能性を示唆するため、現在、免疫組織化学的および遺伝子解析的に更なる検討を行っている。

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Media Coverage 11

  1. AI detected unknown signs of cancer and outperformed doctors in recurrence prediction accuracy

    Newton press　Newton　pp18-19

    2025/01

    Type: Newspaper, magazine

  2. Deep learning predicts the 1-year prognosis of pancreatic cancer patients using positive peritoneal washing cytology

    NIKKEI etc.

    2024/08

    Type: Internet

  3. Artificial intelligence identifies previously unknown features associated with cancer recurrence

    20 Newspapers (SANKEI, ASAHI, YOMIURI, MAINICHI etc.)

    2019/12

    Type: Newspaper, magazine

  4. Cancer cells are judged with AI

    The Nikkei (National newspaper)

    2016/09/19

    Type: Newspaper, magazine

  5. “NISTEP Selection” by National Institute of Science and Technology Policy (MEXT)

    Yahoo news, etc.

    2020/12

    Type: Internet

  6. Research News in Japan: Artificial intelligence identifies previously unknown features associated with cancer recurrence

    NHK NEWS WEB etc. (70 articles)

    2019/12

    Type: Internet

  7. Research interview, Comments

    President Family

    2019

    Type: Newspaper, magazine

  8. Potential of cutting-edge "medical AI" ~From diagnosis to Treatment~

    Yahoo news, Toyo economic online, etc.

    2018/08/07

    Type: Internet

  9. Cancer image, AI analysis

    Nikkei Business Daily

    2018/05/08

    Type: Newspaper, magazine

  10. AI as an analysis tool cuts out the future of medical treatment

    Beautiful skin café (magazine)

    2018/01/01

    Type: Newspaper, magazine

  11. Research interview

    2016/01

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