Cargando…
Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy
The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246080/ https://www.ncbi.nlm.nih.gov/pubmed/31994702 http://dx.doi.org/10.1093/jrr/rrz105 |
_version_ | 1783537877102100480 |
---|---|
author | Kai, Yudai Arimura, Hidetaka Ninomiya, Kenta Saito, Tetsuo Shimohigashi, Yoshinobu Kuraoka, Akiko Maruyama, Masato Toya, Ryo Oya, Natsuo |
author_facet | Kai, Yudai Arimura, Hidetaka Ninomiya, Kenta Saito, Tetsuo Shimohigashi, Yoshinobu Kuraoka, Akiko Maruyama, Masato Toya, Ryo Oya, Natsuo |
author_sort | Kai, Yudai |
collection | PubMed |
description | The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors. The pCT and daily CBCT images of 20 patients with prostate cancer were selected. The first 10 patients were employed for the development, and the second 10 patients for a validation test. The CTV position errors between the pCT and CBCT images were determined as reference CTV shifts (teacher data) after an automated bone-based registration. The anatomical features associated with rectum, bladder and prostate were calculated from the pCT and CBCT images. The features were fed as the input with the teacher data into five MLAs, i.e. three types of artificial neural networks, support vector regression (SVR) and random forests. Since the CTV shifts along the left–right direction were negligible, the MLAs were developed along the superior–inferior and anterior–posterior directions. The proposed framework was evaluated from the residual errors between the reference and predicted CTV shifts. In the validation test, the mean residual error with its standard deviation was 1.01 ± 1.09 mm in SVR using only one feature (one click), which was associated with positional difference of the upper rectal wall. The results suggested that MLAs with anatomical features could be useful in prediction of CTV shifts for prostate radiotherapy. |
format | Online Article Text |
id | pubmed-7246080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-72460802020-05-28 Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy Kai, Yudai Arimura, Hidetaka Ninomiya, Kenta Saito, Tetsuo Shimohigashi, Yoshinobu Kuraoka, Akiko Maruyama, Masato Toya, Ryo Oya, Natsuo J Radiat Res Regular Paper The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors. The pCT and daily CBCT images of 20 patients with prostate cancer were selected. The first 10 patients were employed for the development, and the second 10 patients for a validation test. The CTV position errors between the pCT and CBCT images were determined as reference CTV shifts (teacher data) after an automated bone-based registration. The anatomical features associated with rectum, bladder and prostate were calculated from the pCT and CBCT images. The features were fed as the input with the teacher data into five MLAs, i.e. three types of artificial neural networks, support vector regression (SVR) and random forests. Since the CTV shifts along the left–right direction were negligible, the MLAs were developed along the superior–inferior and anterior–posterior directions. The proposed framework was evaluated from the residual errors between the reference and predicted CTV shifts. In the validation test, the mean residual error with its standard deviation was 1.01 ± 1.09 mm in SVR using only one feature (one click), which was associated with positional difference of the upper rectal wall. The results suggested that MLAs with anatomical features could be useful in prediction of CTV shifts for prostate radiotherapy. Oxford University Press 2020-01-29 /pmc/articles/PMC7246080/ /pubmed/31994702 http://dx.doi.org/10.1093/jrr/rrz105 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of The Japanese Radiation Research Society and Japanese Society for Radiation Oncology. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Regular Paper Kai, Yudai Arimura, Hidetaka Ninomiya, Kenta Saito, Tetsuo Shimohigashi, Yoshinobu Kuraoka, Akiko Maruyama, Masato Toya, Ryo Oya, Natsuo Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title | Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title_full | Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title_fullStr | Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title_full_unstemmed | Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title_short | Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy |
title_sort | semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment ct images in prostate radiotherapy |
topic | Regular Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246080/ https://www.ncbi.nlm.nih.gov/pubmed/31994702 http://dx.doi.org/10.1093/jrr/rrz105 |
work_keys_str_mv | AT kaiyudai semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT arimurahidetaka semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT ninomiyakenta semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT saitotetsuo semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT shimohigashiyoshinobu semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT kuraokaakiko semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT maruyamamasato semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT toyaryo semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy AT oyanatsuo semiautomatedpredictionapproachoftargetshiftsusingmachinelearningwithanatomicalfeaturesbetweenplanningandpretreatmentctimagesinprostateradiotherapy |