PMID- 30713000 OWN - NLM STAT- MEDLINE DCOM- 20200414 LR - 20200414 IS - 1873-4022 (Electronic) IS - 0958-3947 (Print) IS - 1873-4022 (Linking) VI - 44 IP - 4 DP - 2019 Winter TI - Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy. PG - e64-e70 LID - S0958-3947(19)30011-1 [pii] LID - 10.1016/j.meddos.2019.01.002 [doi] AB - Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a synthetic CT (SCT) from MRIs for patient setup and dose calculation. In this study, we aim to investigate the accuracy of dose calculation in prostate cancer radiotherapy using SCTs generated from MRIs using our learning-based method. We retrospectively investigated a total of 17 treatment plans from 10 patients, each having both planning CTs (pCT) and MRIs acquired before treatment. The SCT was registered to the pCT for generating SCT-based treatment plans. The original pCT-based plans served as ground truth. Clinically-relevant dose volume histogram (DVH) metrics were extracted from both ground truth and SCT-based plans for comparison and evaluation. Gamma analysis was performed for the comparison of absorbed dose distributions between SCT- and pCT-based plans of each patient. Gamma analysis of dose distribution on pCT and SCT within 1%/1 mm at 10% dose threshold showed greater than 99% pass rate. The average differences in DVH metrics for planning target volumes (PTVs) were less than 1%, and similar metrics for organs at risk (OAR) were not statistically different. The SCT images created from MR images using our proposed machine learning method are accurate for dose calculation in prostate cancer radiation treatment planning. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning. However, MR images are needed to further analyze geometric distortion effects. Digitally reconstructed radiograph (DRR) can be generated within our method, and their accuracy in patient setup needs further analysis. CI - Copyright (c) 2019 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved. FAU - Shafai-Erfani, Ghazal AU - Shafai-Erfani G AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Wang, Tonghe AU - Wang T AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Lei, Yang AU - Lei Y AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Tian, Sibo AU - Tian S AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Patel, Pretesh AU - Patel P AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Jani, Ashesh B AU - Jani AB AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Curran, Walter J AU - Curran WJ AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Liu, Tian AU - Liu T AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. FAU - Yang, Xiaofeng AU - Yang X AD - Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA. Electronic address: xyang43@emory.edu. LA - eng GR - R01 CA215718/CA/NCI NIH HHS/United States PT - Journal Article DEP - 20190201 PL - United States TA - Med Dosim JT - Medical dosimetry : official journal of the American Association of Medical Dosimetrists JID - 8908862 SB - IM MH - Humans MH - *Machine Learning MH - Magnetic Resonance Imaging MH - Male MH - Organs at Risk/radiation effects MH - Prostatic Neoplasms/diagnostic imaging/*radiotherapy MH - Radiotherapy Dosage MH - Radiotherapy Planning, Computer-Assisted/*methods MH - *Radiotherapy, Intensity-Modulated MH - Retrospective Studies MH - Tomography, X-Ray Computed PMC - PMC6669118 MID - NIHMS1519154 OTO - NOTNLM OT - MRI OT - SCT OT - Treatment planning EDAT- 2019/02/05 06:00 MHDA- 2020/04/15 06:00 PMCR- 2020/02/01 CRDT- 2019/02/05 06:00 PHST- 2018/10/13 00:00 [received] PHST- 2019/01/07 00:00 [revised] PHST- 2019/01/16 00:00 [accepted] PHST- 2019/02/05 06:00 [pubmed] PHST- 2020/04/15 06:00 [medline] PHST- 2019/02/05 06:00 [entrez] PHST- 2020/02/01 00:00 [pmc-release] AID - S0958-3947(19)30011-1 [pii] AID - 10.1016/j.meddos.2019.01.002 [doi] PST - ppublish SO - Med Dosim. 2019 Winter;44(4):e64-e70. doi: 10.1016/j.meddos.2019.01.002. Epub 2019 Feb 1.