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Rad Fan　19/11　2021年10月臨時増刊号　放射線治療情報BOOK 2021

出版社： メディカルアイ
発行日： 2021-09-30
分野： 医療技術  >  雑誌
ISSN： 13483498
雑誌名：
特集： 放射線治療情報BOOK 2021
電子書籍版： 2021-09-30
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目次

  • 放射線治療情報BOOK 2021

    序文
    ・ニューノーマル時代における放射線治療の心得

    巻頭言
    ・放射線治療情報BOOK2021の企画にあたって

    特集1　コロナ禍における放射線治療
    　・COVID-19に対する学会の取り組みについて
    　・COVID-19流行下でいかに放射線治療を行うか？
    　　　～乳癌・緩和的治療～
    　・施設間および在宅ワークにおける遠隔放射線治療計画の運用経験と課題
    　・コロナ禍における放射線治療部門の現場対応

    特集2　放射線治療のAI最前線
    　・放射線治療におけるAI利用の概要、課題、展望
    　・AI技術を用いた放射線治療効果予測について
    　・医療技術としてAIをどのように放射線治療の臨床現場に普及させるべきか

    特集3　III期肺癌に対する免疫放射線治療革命
    　　　　―VMATの効果的活用法―
    　・III期肺癌に対する免疫放射線治療：現状と展望
    　・III期肺癌に対するVMAT治療計画と運用(1)
    　・各施設のIII期肺癌に対するVMAT治療計画と運用(2)
    　・各施設のIII期肺癌に対するVMAT治療計画と運用(3)

    特集4　国内最新設備の導入と活用
    　・BNCTの現状と南東北BNCT研究センターでの運用について
    　・東日本重粒子センターでの重粒子線治療について
    　・Elekta Unityの機能と千葉大学における導入の現状

この書籍の参考文献

参考文献のリンクは、リンク先の都合等により正しく表示されない場合がありますので、あらかじめご了承下さい。

本参考文献は電子書籍掲載内容を元にしております。

【特集1 コロナ禍における放射線治療】

P.23 掲載の参考文献
1) 日本乳癌学会総務委員会. COVID-19に伴う乳癌診療トリアージについて. https://wwwjbcsgrjp/uploads/files/info/JBCS_triage20201030-3pdf.
 
2) 日本放射線腫瘍学会COVID-19対策アドホック委員会・コロナ対策実行グループ. COVID-19パンデミックにおける放射線治療 JASTRO提言 (第1.4版). https://jastro-covid19net/data/jastro_covid19_proposal_1_4pdf. 2021
 
3) Hughes KS et al : Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer : long-term follow-up of CALGB 9343. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 31 (19) : 2382-7, 2013
 
4) Kunkler IH et al : Breast-conserving surgery with or without irradiation in women aged 65 years or older with early breast cancer (PRIME II) : a randomised controlled trial. The Lancet Oncology 16 (3) : 266-73, 2015
 
5) Olivotto IA et al : Intervals longer than 20 weeks from breast-conserving surgery to radiation therapy are associated with inferior outcome for women with early-stage breast cancer who are not receiving chemotherapy. J Clin Oncol 27 (1) : 16-23, 2009
 
6) Bartelink H et al : Whole-breast irradiation with or without a boost for patients treated with breast-conserving surgery for early breast cancer : 20-year follow-up of a randomised phase 3 trial. The Lancet Oncology 16 (1) : 47-56, 2015
 
7) Smith BD et al : Radiation therapy for the whole breast : Executive summary of an American Society for Radiation Oncology (ASTRO) evidence-based guideline. Pract Radiat Oncol 8 (3) : 145-52, 2018
PubMed
8) 日本乳癌学会. 乳癌診療ガイドライン 1 治療編 2018年版.
 
9) Wang SL et al : Hypofractionated versus conventional fractionated postmastectomy radiotherapy for patients with high-risk breast cancer : a randomised, non-inferiority, open-label, phase 3 trial. The Lancet Oncology 20 (3) : 352-60, 2019
PubMed
10) Liu L et al : Comparing hypofractionated to conventional fractionated radiotherapy in postmastectomy breast cancer : a meta-analysis and systematic review. Radiat Oncol 15 (1) : 17, 2020
PubMed
11) group FT et al : First results of the randomised UK FAST Trial of radiotherapy hypofractionation for treatment of early breast cancer (CRUKE/04/015). Radiother Oncol 100 (1) : 93-100, 2011
 
12) Murray Brunt A et al : Hypofractionated breast radiotherapy for 1 week versus 3 weeks (FAST-Forward) : 5-year efficacy and late normal tissue effects results from a multicentre, non-inferiority, randomised, phase 3 trial. Lancet 395 (10237) : 1613-26, 2020
PubMed
13) Coles CE et al : International Guidelines on Radiation Therapy for Breast Cancer During the COVID-19 Pandemic. Clin Oncol (R Coll Radiol) 32 (5) : 279-81, 2020
PubMed
14) Yerramilli D et al : Palliative Radiotherapy for Oncologic Emergencies in the setting of COVID-19 : Approaches to Balancing Risks and Benefits. Adv Radiat Oncol 5 (4) : 589-594 2020
PubMed
15) Thureau S et al : Adapting palliative radiation therapy for bone metastases during the Covid-19 pandemic : GEMO position paper. J Bone Oncol 13 ; 22 : 100291, 2020
 
16) Sapienza LG et al : Short-course palliative radiation therapy leads to excellent bleeding control : A single centre retrospective study. Clin Transl Radiat Oncol 14 : 40-6, 2019
PubMed
P.27 掲載の参考文献
1) JASTRO COVID-19対策アドホック委員会 : 第4回COVID-19全国実態調査結果報告.
 
2) 公益社団法人日本放射線腫瘍学会 : 遠隔放射線治療計画ガイドライン 2019.
 
3) NTTテクノクロス マジックコネクト HP (https://www.magicconnect.net/)
 
4) JASTROデータベース委員会 : 全国放射線治療施設の2017年定期構造調査報告 (第1報), 2021年8月13日
 
P.31 掲載の参考文献
1) World Health Organisation. Coronavirus Disease (Covid-19) situation report World Health Organisation ; 2020. Available from : https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports.
 
2) https://jastro-covid19.net/professional/ COVID-19パンデミックにおける放射線治療JASTRO提言 (第1.5版) 2021年3月2日 日本放射線腫瘍学会COVID-19対策アドホック委員会・コロナ対策実行クループ
 
3) JASTROホームページ : COVID-19特設ページ https://jastro-covid19.net/professional/
 
4) Future-proof Radiation therapist (RTT) practice in a pandemic-Lessons learnt from COVID-19 Maeve Kearney 2 Mary Coffey 2 Maddalena Rossib 1 YatTsangc 1 on behalf of the ESTRO ROSQC. RTTC. Technical Innovations & Patient Support in Radiation Oncology 17 : 18-24, 2021 (https://www.sciencedirect.com/science/article/pii/S2405632421000068)
 
5) COVID-19感染症への感染対策と治療患者への配慮. 日本診療放射線技師会 放射線治療分科会
 
6) 第4回COVID-19全国実態調査結果報告 JASTRO COVID-19対策アドホック委員会 https://www.jastro.or.jp/medicalpersonnel/news/20210331.pdf
 

【特集2 放射線治療のAI最前線】

P.38 掲載の参考文献
1) 日本放射線腫瘍学会編. 放射線治療における非剛体画像レジストレーション利用のためのガイドライン (DIRガイドライン) 2018.
 
2) Cardenas CE et al. Advances in Auto-Segmentation. Semin Radiat Oncol. 29 : 185-97, 2019
PubMed
3) Meyer P et al. Survey on deep learning for radiotherapy. Comput Biol Med. 98 : 126-46, 2018
PubMed
4) Ronneberger O et al. U-Net : Convolutional Networks for Biomedical Image Segmentation. MICCAI, 9351 : 234-241, 2015
 
5) Liu X et al. Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy. Front Oncol. 11 : 717039, 2021
PubMed
6) Chen X et al. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol. 160 : 175-84, 2021
 
7) Kajikawa T et al. A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients. J Radiat Res. 60 : 685-93, 2019
PubMed
8) Fan J et al. Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique. Med Phys. 46 : 370-81, 2019
PubMed
9) Dong P et al. Deep DoseNet : a deep neural network for accurate dosimetric transformation between different spatial resolutions and/or different dose calculation algorithms for precision radiation therapy. Phys Med Biol. 65 : 035010, 2020
PubMed
10) Kajikawa T et al. Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy. Phys Med. 80 : 186-92, 2020
PubMed
11) Raaymakers BW et al. First patients treated with a 1.5 T MRI-Linac : clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment. Phys Med Biol. 62 : L41-150, 2017
PubMed
12) Raaymakers BW et al. Integrated megavoltage portal imaging with a 1.5 T MRI linac. Phys Med Biol. 56 : N207-14, 2011
PubMed
13) Ito K et al. Feasibility of a Direct-Conversion Method from Magnetic Susceptibility to Relative Electron Density for Radiation Therapy Treatment Planning. International Journal of Medical Physics. Clinical Engineering and Radiation Oncology. 06 : 252-65, 2017
 
14) Chen S et al. Technical Note : U-net Generated Synthetic CT images for Magnetic Resonance Imaging-Only Prostate Intensity-Modulated Radiation Therapy Treatment Planning. Med Phys. 45 (12) : 5659-5665, 2018
PubMed
15) Simon L et al. Artificial intelligence for quality assurance in radiotherapy. Cancer Radiother. 24 ; S1278-3218 (21) 00110-4, 2021
 
16) Tomori S et al. A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance. Medical Physics. 45 : 4055-65, 2018
 
17) Valdes G et al. IMRT QA using machine learning : A multi-institutional validation. J Appl Clin Med Phys. 18 : 279-84, 2017
PubMed
18) Ono T et al. Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning. Med Phys. 46 : 3823-32, 2019
PubMed
19) Hirashima H et al. Improvement of prediction and classification performance for gamma passing rate by using plan complexity and dosiomics features. Radiother Oncol. 153 : 250-7, 2020
PubMed
20) Sakai M et al. Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy. Med Phys. 48 : 991-1002, 2021
PubMed
21) Kimura Y et al. Error detection model developed using a multi-task convolutional neural network in patient-specific quality assurance for volumetric-modulated arc therapy. Med Phys. 73 : 57-64, 2021
 
22) Kimura Y et al. Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy. Phys Med. 73 : 57-64, 2020
PubMed
23) Aerts HJ et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 5 : 4006, 2014
PubMed
24) Hosny A et al. Deep learning for lung cancer prognostication : A retrospective multi-cohort radiomics study. 15 : e1002711, 2018
PubMed
25) Ana M. Barragan-Montero et al. Deep learning dose prediction for IMRT of esophageal cancer : The effect of data quality and quantity on model performance. Phys Med. 83 : 52-63, 2021
PubMed
P.42 掲載の参考文献
1) Trevor Hastie et al : 2nd edn. New York : Springer, 2008.
 
2) Philippe Lambin et al : Radiomics extracting more information from medical images using advanced feature analysis. Eur J Cancer 48 (4) : 441-6, 2012
PubMed
3) Virendra Kumar et al : Radiomics : the process and the challenges. Magn Reson Imaging 30 (9) : 1234-1248, 2012
PubMed
4) Ke Nie et al : Rectal Cancer : Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI. Clin Cancer Res 22 (21) : 5256-5264, 2016
PubMed
5) Zijian Zhang et al : A predictive model for distinguishing radiation necrosis from tumour progressio after gamma knife radiosurgery based on radiomic features from MR images. Eur Radio 28 (6) : 2255-2263, 2017
 
6) Daisuke Kawahara et al : Prediction of radiation pneumonitis after definitive radiotherapy for locally advanced non-small Cel lung cancer using multi-region radiomics analysis. Sci Rep 11 (1) : 16232, 2021
 
7) Andre Diamant et al : Deep learning in head & neck cancer outcome prediction. Sco Rep 9 (1) : 2764, 2019
 
8) Yiwen Xu et al : Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clin Cancer Res 25 (11) : 3266-3275, 2019
PubMed
P.45 掲載の参考文献
1) Bertholet J et al : Patterns of practice for adaptive and real-time radiation therapy (POP-ART RT) part II : Offline and online plan adaption for interfractional changes. Radiother Oncol 153 : 88-96, 2020
PubMed
2) Wang C et al : Artificial Intelligence in Radiotherapy Treatment Planning : Present and Future. Technol Cancer Res Treat 18 : doi : 10.1177/1533033819873922, 2019
 
3) Vandewinckele L et al : Overview of artificial intelligence-based applications in radiotherapy : Recommendations for Implementation and quality assurance. Radiother Oncol 153 : 55-66, 2020
PubMed
4) Huq MS et al : A method for evaluating quality assurance needs in radiation therapy. Int J Radiat Oncol Biol Phys 71 (1 Suppl) : 170-173, 2008
 
5) Scorsetti M et al : Applying failure mode effects and criticality analysis in radiotherapy : lessons learned and perspectives of enhancement. Radiother Oncol 94 (3) : 367-374, 2010
PubMed
6) Huq MS et al : The report of Task Group 100 of the AAPM : Application of risk analysis methods to radiation therapy quality management. Med Phys 43 (7) : 4209, 2016
PubMed

【特集3 III期肺癌に対する免疫放射線治療革命 - VMATの効果的活用法 - 】

P.51 掲載の参考文献
1) Bradley JD et al : Standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage IIIA or IIIB non-small-cell lung cancer (RTOG 0617) : a randomised, two-by-two factorial phase 3 study. Lancet Oncol 16 (2) : 187-199, 2015
 
2) Ngwa W et al : Using immunotherapy to boost the abscopal effect. Nature Reviews Cancer 18 (5) : 313-322, 2018
PubMed
3) Shaverdian N et al : Previous radiotherapy and the clinical activity and toxicity of pembrolizumab in the treatment of non-small-cell lung cancer : a secondary analysis of the KEYNOTE-001 phase 1 trial. Lancet Oncol 18 (7) : 895-903, 2017
PubMed
4) Antonia SJ et al : Durvalumab after Chemoradiotherapy in Stage III Non-Small-Cell Lung Cancer. N Engl J Med 377 (20) : 1919-1929, 2017
PubMed
5) Antonia SJ et al : Overall Survival with Durvalumab after Chemoradiotherapy in Stage III NSCLC. N Engl J Med 379 (24) : 2342-2350, 2018
PubMed
6) Spigel DR et al : Five-year survival outcomes with durvalumab after chemoradiotherapy in unresectable stage III NSCLC : An update from the PACIFIC trial. Journal of Clinical Oncology 39 : 8511-8511, 2021
 
7) Durm GA et al : A phase 2 trial of consolidation pembrolizumab following concurrent chemoradiation for patients with unresectable stage III non-small cell lung cancer : Hoosier Cancer Research Network LUN 14-179. Cancer 126 : 4353-4361, 2020
PubMed
8) Peters S et al : Safety evaluation of nivolumab added concurrently to radiotherapy in a standard first line chemo-radiotherapy regimen in stage III non-small cell lung cancer-The ETOP NICOLAS trial. Lung Cancer 133 : 83-87, 2019
 
9) Jabbour SK et al : Pembrolizumab Plus Concurrent Chemoradiation Therapy in Patients With Unresectable, Locally Advanced, Stage III Non-Small Cell Lung Cancer : The Phase 2 KEYNOTE-799 Nonrandomized Trial. JAMA Oncol, 2021
PubMed
P.59 掲載の参考文献
1) Faivre-Finn C et al : Four-year survival with durvalumab after chemoradiotherapy in stage III NSCLC-an update from the PACIFIC trial. J Thorac Oncol 16 (5) : 860-867, 2021
 
2) Hu X et al : Is IMRT Superior or Inferior to 3DCRT in Radiotherapy for NSCLC? A Meta-Analysis. PLoS One 11 (4) : e0151988, 2016
 
3) 日本放射線腫瘍学会編 : 放射線治療計画ガイドライン 2020年版. 41-45, 金原出版株式会社, 東京, 2020
 
4) Chui CS et al : The effects of intra-fraction organ motion on the delivery of intensity-modulated field with a multileaf collimator. Med Phys 30 (7) : 1736-1746, 2003
PubMed
P.63 掲載の参考文献
1) Morimoto M et al : Elective Nodal Irradiation for Non-small Cell Lung Cancer Complicated With Chronic Obstructive Pulmonary Disease Affects Immunotherapy After Definitive Chemoradiotherapy. Anticancer Res. 40 (12) : 6957-6970, 2020
PubMed
2) Tsujino, K et al : Predictive value of dose-volume histogram parameters for predicting radiation pneumonitis after concurrent chemoradiation for lung cancer. Int. J. Radiat. Oncol. Biol. Phys. 55 (1) : 110-115, 2003
 
3) Chun SG et al : Impact of Intensity-Modulated Radiation Therapy Technique for Locally Advanced Non-Small-Cell Lung Cancer : A Secondary Analysis of the NRG Oncology RTOG 0617 Randomized Clinical Trial. J. Clin. Oncol. 35 (1) : 56-62, 2017
PubMed
4) Patel PR et al : Effect of increasing experience on dosimetric and clinical outcomes in the management of malignant pleural mesothelioma with intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys. 83 (1) : 362-368, 2012
PubMed
5) Tsukita Y et al : Intensity-modulated radiation therapy with concurrent chemotherapy followed by durvalumab for stage III non-small cell lung cancer : A multi-center retrospective study. Radiother Oncol. 160 : 266-272, 2021
PubMed
6) Ueda Y et al : The promising treatment schedule of concurrent chemoradiotherapy for stage III non-small cell lung cancer : Alternative for conventional fractionation using mathematical analysis. Anticancer Res. 40 (7) : 4095-4104, 2020
PubMed
7) Imamura F et al : Novel chemoradiotherapy with concomitant boost thoracic radiation and concurrent cisplatin and vinorelbine for stage IIIA and IIIB non-small-cell lung cancer. Clin Lung Cancer. 15 : 281-286, 2014
PubMed
8) Wada K et al : Radiation dose escalation in accelerated hyperfractionated radiotherapy for stage III non-small-cell lung cancer. Anticancer Res. 38 (10) : 5951-5958, 2018
PubMed
9) Mayo CS et al : Hybrid IMRT for Treatment of cancers of the lung and esophagus. Int J Radiat Oncol Biol Phys. 71 (5) : 1408-1418, 2008
PubMed
10) Chan OSH et al : The superiority of hybrid-volumetric arc therapy (VMAT) technique over double arcs VMAT and 3D-conformal technique in the treatment of locally advanced non-small cell lung cancer-A planning study. Radiother Oncol. 101 (2) : 298-302, 2011
PubMed
11) Ueda Y et al : Knowledge-based planning for oesophageal cancers using a model trained with plans from a different treatment planning system. Acta Oncol. 59 (3) : 274-283, 2020
 
12) Miyazaki M et al : Oesophageal Cancer : Conformal Radiotherapy vs. Hybrid-VMAT Technique With Two Different Treatment Planning Systems. In Vivo. 34 (1) : 331-337, 2020
PubMed

【特集4 国内最新設備の導入と活用】

P.69 掲載の参考文献
1) Coderre JA et al : The radiation biology of boron neutron capture therapy. Radiat Res 151 : 1-18, 1999
PubMed
2) Kato I et al : Effectiveness of boron neutron capture therapy for recurrent head and neck malignancies. Appl Radiat Isot 67 : S37-42, 2009
PubMed
3) Hirose K et al : Boron neutron capture therapy using cyclotron-based epithermal neutron source and borofalan ((10) B) for recurrent or locally advanced head and neck cancer (JHN002) : An open-label phase II trial. Radiother Oncol, 2020
PubMed
4) Kawabata S et al : Accelerator-based BNCT for patients with recurrent glioblastoma : a multicenter phase II study. Neurooncol Adv 3 : vdab067, 2021
PubMed
5) Kato T et al : Design and construction of an accelerator-based boron neutron capture therapy (AB-BNCT) facility with multiple treatment rooms at the Southern Tohoku BNCT Research Center. Appl Radiat Isot 156, 108961, 2020
PubMed
P.74 掲載の参考文献
1) Kawamura H et al : Quality of life in prostate cancer patients receiving particle radiotherapy : A review of the literature. Int J Urol. 27 (1) : 24-9, 2020
PubMed
2) Cui X et al : Effects of carbon ion beam on putative colon cancer stem cells and its comparison with X-rays. Cancer Res 71 (10) : 3676-87, 2011
PubMed
P.78 掲載の参考文献
1) Prescilla Uijtewaal et al : Dosimetric evaluation of MRI-guided multi-leaf collimator tracking and trailing for lung stereotactic body radiation therapy. 48 (4) : 1520-1532, 2021
PubMed
2) Dennis Winkel et al : Adaptive radiotherapy : The Elekta Unity MR-linac concept. 2 : 54-59, 2019
 
3) Anita M Werensteijn-Honingh et al : Feasibility of stereotactic radiotherapy using a 1.5 T MR-linac : Multi-fraction treatment of pelvic lymph node oligometastases. 134 : 50-54, 2019
 
4) Anders S Bertelsen et al : First clinical experiences with a high field 1.5 T MR linac. 58 (10) : 1352-1357, 2019
 
5) Elekta HP (https://www.elekta.co.jp/)
 
6) Peter S N van Rossum et al : Diffusion-weighted magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemoradiotherapy in esophageal cancer. 115 (2) : 163-70, 2015
PubMed
7) Ernst S Kooreman et al : ADC measurements on the Unity MR-linac-A recommendation on behalf of the Elekta Unity MR-linac consortium. 153 : 106-113, 2020
 

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