Education 【 display / non-display 】

Education 【 display / non-display 】

• 2013

  -

  2016

  Sapporo Medical University   Graduate School of Medicine  

• 2003

  -

  2009

  Sapporo Medical University   医学部   医学科  

Professional Memberships 【 display / non-display 】

Professional Memberships 【 display / non-display 】

• 2019.09

  -

  Now

  日本癌治療学会

• 2017.10

  -

  Now

  日本核医学会

• 2012.10

  -

  Now

  日本放射線腫瘍学会

• 2011.04

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  Now

  日本医学放射線学会

Affiliation 【 display / non-display 】

Affiliation 【 display / non-display 】

• Sapporo Medical University   School of Medicine, Dept.of Radiology   助教  

Research Interests 【 display / non-display 】

Research Interests 【 display / non-display 】

• 腫瘍免疫

• 人工知能

• 放射線腫瘍学

Papers 【 display / non-display 】

Papers 【 display / non-display 】

• Prediction of Results of Radiotherapy With Ku70 Expression and an Artificial Neural Network

  TOMOKAZU HASEGAWA, MASANORI SOMEYA, MASAKAZU HORI, TAKAAKI TSUCHIYA, YUUKI FUKUSHIMA, YOSHIHISA MATSUMOTO, KOH-ICHI SAKATA

  In Vivo ( Anticancer Research USA Inc. )  34 ( 5 ) 2865 - 2872  2020.09

  　View Summary

  Background/Aim: Accurate prediction of radiotherapy results is indispensable for the individualized selection of treatment modalities of cancer. We examined the application of the artificial neural network (ANN) model in predicting radiotherapy results using clinical factors and immunohistochemical staining of Ku70 as inputs. Patients and Methods: We analyzed 79 prostate cancer patients with localized adenocarcinoma treated with radiotherapy between August 2001 and October 2010. We also analyzed 46 hypopharyngeal cancer patients with squamous cell carcinoma treated with radiotherapy between March 2002 and December 2009. The properly trained ANN analysis using a standard feedforward, back-propagation neural network was used to predict the radiotherapy treatment results. Results: The areas under the receiver-operating characteristic curve (AUC) were 0.939 for patients treated with intensity modulated radiotherapy (IMRT)+androgen deprivation therapy (ADT), 0.803 for IMRT alone, and 0.960 for 3D-conformal radiotherapy (CRT) alone in prostate cancer. Sensitivity and specificity were 85.7% and 90.4% for IMRT+ADT, 75.0% and 88.5% for IMRT alone, and 92.3% and 100% for 3D-CRT alone. The AUC was 0.901 for hypopharyngeal cancer. Sensitivity and specificity were 66.7% and 88.2%, respectively. Conclusion: We demonstrated a possibility to predict the radiotherapy treatment results in prostate and hypopharyngeal cancer using ANN in combination with Ku70 expression and clinical factors as inputs.

  DOI

• Expression of Ku70 predicts results of radiotherapy in prostate cancer.

  Tomokazu Hasegawa, Masanori Someya, Masakazu Hori, Yoshihisa Matsumoto, Kensei Nakata, Masanori Nojima, Mio Kitagawa, Takaaki Tsuchiya, Naoya Masumori, Tadashi Hasegawa, Koh-Ichi Sakata

  Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]   193 ( 1 ) 29 - 37  2017.01  [International journal]

  　View Summary

  BACKGROUND AND PURPOSE: Therapeutic strategy for prostate cancer is decided according to T stage, Gleason score, and prostate-specific antigen (PSA) level. These clinical factors are not accurate enough to predict individual risk of local failure of prostate cancer after radiotherapy. Parameters involved with radiosensitivity are required to improve the predictive capability for local relapse. PATIENTS AND METHODS: We analyzed 58 patients with localized adenocarcinoma of the prostate between August 2007 and October 2010 treated with 76 Gy of intensity-modulated radiotherapy (IMRT) as a discovery cohort and 42 patients between March 2001 and May 2007 treated with three-dimensional conformal radiotherapy (3D-CRT) as a validation cohort. Immunohistochemical examination for proteins involved in nonhomologous end-joining was performed using biopsy specimens. RESULTS: Ku70 expression was not correlated with various clinical parameters, such as the Gleason score and D'amico risk classification, indicating that Ku70 expression was an independent prognostic factor. The predictive value for PSA relapse was markedly improved after the combination of Gleason score and Ku70 expression, as compared with Gleason score alone. In patients treated with radiotherapy and androgen deprivation therapy (ADT), no relapses were observed in patients with Gleason score ≤7 or low Ku70 expression. In contrast, patients with Gleason score ≥8 and high Ku70 expression had high PSA relapse rates. In the validation cohort, similar results were obtained. CONCLUSION: Treatment with 76 Gy and ADT can be effective for patients with Gleason score ≤7 or low Ku70 expression, but is not enough for patients with Gleason score ≥8 and high Ku70 expression and, thus, require other treatment approaches.

  DOI PubMed

• Prediction of late adverse events in pelvic cancer patients receiving definitive radiotherapy using radiation-induced gamma-H2AX foci assay.

  Masanori Someya, Tomokazu Hasegawa, Asako J Nakamura, Takaaki Tsuchiya, Mio Kitagawa, Toshio Gocho, Sho Mafune, Yutaro Ikeuchi, Hiroshi Tauchi, Koh-Ichi Sakata

  Journal of radiation research   64 ( 6 ) 948 - 953  2023.11  [International journal]

  　View Summary

  Radiation can induce DNA double-stranded breaks, which are typically detected by the fluorescence of phosphorylated histone H2AX. In this study, we examined the usefulness of the dynamics of radiation-induced gamma-H2AX foci of peripheral blood lymphocytes (PBLs), as a marker of DNA repair ability, in predicting late adverse events from radiotherapy. A total of 46 patients with cervical, vaginal and anal canal cancers treated with radical radiotherapy between 2014 and 2019 were included in this analysis. Concurrent chemotherapy was administered in 36 cases (78.3%). Peripheral blood was obtained before treatment, and then irradiated ex vivo with 1 Gy X-ray. The ratio of radiation-induced gamma-H2AX foci in PBLs measured at 30 min and at 4 h was defined as the foci decay ratio (FDR). With a median follow-up of 54 months, 9 patients (19.6%) were observed to have late genitourinary or gastrointestinal (GU/GI) toxicity. The FDR ranged from 0.51 to 0.74 (median 0.59), with a significantly higher incidence of Grade 1 or higher late adverse events in the FDR ≥ 0.59 group. In multivariate analysis, FDR ≥ 0.59 and hypertension also emerged as significant factors associated with the development of late toxicities. Overall, our results suggest that measurement of radiation-induced gamma-H2AX foci in PBLs may predict the risk of late GU/GI toxicities from chemoradiotherapy, which can enable tailoring the radiation dose to minimize adverse effects.

  DOI PubMed

• Analysis of treatment response with proteins related to tumor immunity in postoperative irradiated cervical cancer patients

  Shoh Mafune, Masanori Someya, Tomokazu Hasegawa, Takaaki Tsuchiya, Mio Kitagawa, Toshio Gocho, Ryu Okuda, Masahiro Iwasaki, Motoki Matsuura, Terufumi Kubo, Yoshihiko Hirohashi, Toshihiko Torigoe, Tsuyoshi Saito, Koh-ichi Sakata

     2023.11

  DOI

• 化学放射線+免疫療法を行った3期NSCLCにおける、末梢血リンパ細胞のTCRレパトア解析

  染谷 正則, 長谷川 智一, 北川 未央, 土屋 高旭, 後町 俊夫, 眞船 翔, 金関 貴幸, 蒔田 芹奈, 鳥越 俊彦, 坂田 耕一

  日本癌治療学会学術集会抄録集 ( (一社)日本癌治療学会 )  61回   O46 - 3  2023.10

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Misc 【 display / non-display 】

Misc 【 display / non-display 】

• 化学放射線療法+免疫療法を行った3期切除不能非小細胞肺癌における,末梢血リンパ細胞のTCRレパトア解析

  染谷正則, 長谷川智一, 北川未央, 土屋高旭, 眞船翔, 後町俊夫, 池内佑太郎, 金関貴幸, 鳥越俊彦, 坂田耕一

  日本免疫治療学会学術集会プログラム・抄録集   20th  2023

  J-GLOBAL

• 肺動脈シャントを伴う気管支動脈蔓状血管腫に対して血管塞栓術を施行した1例

  長谷川 智一, 廣川 直樹, 北川 未央, 斉藤 正人, 宇佐見 陽子, 鷲尾 嘉一, 荒谷 和紀, 佐藤 大志, 晴山 雅人

  IVR: Interventional Radiology ( (一社)日本インターベンショナルラジオロジー学会 )  27 ( 4 ) 456 - 456  2012.11

Research Projects 【 display / non-display 】

Research Projects 【 display / non-display 】

• Prediction Model for Tumor Immune Activation and Radiotherapy Effectiveness Using Machine Learning

  Grant-in-Aid for Scientific Research (C)

  Project Year :

  2022.04

  -

  2025.03

   

  長谷川 智一

• Development of a method for predicting tumor immune activation by radiotherapy

  Grant-in-Aid for Scientific Research (C)

  Project Year :

  2019.04

  -

  2022.03

   

  HASEGAWA TOMOKAZU

  　View Summary

  The objective is to create a prediction model of tumor immune activation by radiotherapy using machine learning methods with high accuracy and easy clinical application. As a preliminary step, a radiotherapy effect prediction model was created by immunohistochemistry using biopsy specimens. Both prostate and hypopharyngeal cancers showed improved prediction accuracy in the analysis using ANN compared to the conventional method. In addition, using samples of cervical cancer, we used QuPath software to classify and quantify the immunohistochemistry staining decisions, and verified that there was no significant difference compared to the results of manual counting. In the future, we will perform the same analysis on samples of mesopharyngeal carcinoma, aiming for objective evaluation and automation of the determination method of immunohistochemical staining.

• Development of a practical radiosensitivity prediction method for clinical applications

  Grant-in-Aid for Scientific Research (C)

  Project Year :

  2018.04

  -

  2021.03

   

  Sakata Koh-ichi

  　View Summary

  Using biopsy and surgical specimens of breast and cervical cancers, we analyzed the correlation between various protein expressions and radiotherapy outcomes. Radiotherapy of cervical cancer increased the expression of PD-L1 in tumor cells, and there was a strong correlation between the expression of PD-L1 in tumor cells after radiotherapy and overall survival. This suggests that irradiation-induced immunological changes may affect the radiotherapy effect. In patients with early-stage breast cancer treated with postoperative radiotherapy, XRCC4 expression rates in tumor cells were significantly correlated with intra-breast recurrence. By type of recurrence, there was a correlation with XRCC4 expression in True recurrence. True recurrence may be due to the low radiosensitivity of tumor cells.

• Prediction of results of radiotherapy using expression of proteins involved with repair of DNA

  Grant-in-Aid for Young Scientists (B)

  Project Year :

  2017.04

  -

  2019.03

   

  HASEGAWA TOMOKAZU

  　View Summary

  We examined the application of an artificial neural network (ANN) model to predict the outcome of radiation therapy using immunohistochemical staining and clinical factors of Ku70 for prostate and hypopharyngeal cancer. Age, Gleason score, biopsy positive rate, pre-treatment PSA value, risk classification, prostate volume were used as clinical factors in analysis of prostate cancer. Similarly, in hypopharyngeal cancer, age, gender, performance status, clinical T staging and subsite were used as clinical factors. The treatment result prediction by ANN was a result that the sensitivity, the specificity, and the area under curve (AUC) of the ROC curve based on the prediction result were all superior in prediction ability as compared with the conventional method.

• Prediction of local tumor control and acute radiation toxicity in pelvic cancer patients using lymphocyte biomarker.

  Grant-in-Aid for Scientific Research (C)

  Project Year :

  2015.04

  -

  2018.03

   

  Someya Masanori

  　View Summary

  To predict individual treatment effects and acute toxicity of patients who underwent pelvic radiation therapy, blood lymphocytes were collected from 141 patients who underwent definitive radiation therapy for non metastatic prostate cancer at our hospital, we measured the DNA-dependent protein kinase (DNA-PK) activity, which is considered to be involved in DNA damage repair, and the expression analysis of microRNA-410, 221 and 99a. As a result, we showed that the DNA-PK activity of lymphocytes was a significant predictor of biochemical relapse in prostate cancer patients and that a combination of miRNA-410 and 221 predicted acute gastrointestinal toxicities with higher accuracy than conventional dose-volume histogram analysis.

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