Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery.

BC DIBH DNN ML RT heart dose

Journal

Experimental and therapeutic medicine
ISSN: 1792-1015
Titre abrégé: Exp Ther Med
Pays: Greece
ID NLM: 101531947

Informations de publication

Date de publication:
Nov 2023
Historique:
received: 08 06 2023
accepted: 01 09 2023
medline: 23 10 2023
pubmed: 23 10 2023
entrez: 23 10 2023
Statut: epublish

Résumé

Increased heart dose during postoperative radiotherapy (RT) for left-sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath-hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left-sided BC. However, treatment planning and DIBH for RT are laborious, time-consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre-select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right-left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve-receiver operating characteristic of 0.88, for a cut-off value of 300 cGy. The present study suggested that ML can be used to pre-select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC.

Identifiants

pubmed: 37869640
doi: 10.3892/etm.2023.12235
pii: ETM-26-5-12235
pmc: PMC10587874
doi:

Types de publication

Journal Article

Langues

eng

Pagination

536

Informations de copyright

Copyright © 2023, Spandidos Publications.

Déclaration de conflit d'intérêts

The authors declare that they have no competing interests.

Références

Med Phys. 2020 Jun;47(5):e178-e184
pubmed: 32418338
Lancet. 2011 Nov 12;378(9804):1707-16
pubmed: 22019144
Radiol Oncol. 2021 Jan 29;55(2):212-220
pubmed: 33600676
J Appl Clin Med Phys. 2016 Nov 08;17(6):60-68
pubmed: 27929481
J Appl Clin Med Phys. 2022 Jun;23(6):e13609
pubmed: 35460150
Breastfeed Med. 2014 Mar;9(2):73-8
pubmed: 24180472
Int J Radiat Oncol Biol Phys. 1996 Jan 1;34(1):235-42
pubmed: 12118557
J Appl Clin Med Phys. 2011 Jan 31;12(2):3451
pubmed: 21587195
Breast Cancer (Dove Med Press). 2022 Jul 20;14:175-186
pubmed: 35899145
Radiat Oncol. 2021 Aug 17;16(1):154
pubmed: 34404441
Rep Pract Oncol Radiother. 2020 Jul-Aug;25(4):656-666
pubmed: 32617080
Jpn J Clin Oncol. 2018 May 01;48(5):476-479
pubmed: 29635375
BMC Cancer. 2013 May 07;13:230
pubmed: 23651532
Acta Med Okayama. 2021 Jun;75(3):307-314
pubmed: 34176934
Sci Rep. 2022 Aug 12;12(1):13706
pubmed: 35961992
Mol Clin Oncol. 2021 Sep;15(3):193
pubmed: 34349992
Int J Radiat Oncol Biol Phys. 2015 Dec 1;93(5):1127-35
pubmed: 26581149
BMC Cancer. 2020 Oct 12;20(1):989
pubmed: 33046044
Clin Transl Radiat Oncol. 2019 Aug 13;19:39-45
pubmed: 31485490
Lancet. 2005 Dec 17;366(9503):2087-106
pubmed: 16360786
J Radiat Res. 2020 May 22;61(3):447-456
pubmed: 32100831
Front Oncol. 2022 Apr 21;12:845037
pubmed: 35530354
Case Rep Oncol. 2017 Jan 16;10(1):37-51
pubmed: 28203163
J Radiat Res. 2021 Aug 31;:
pubmed: 34467396

Auteurs

Ryo Kamizaki (R)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Department of Radiology, Matsuyama Red Cross Hospital, Matsuyama, Ehime 790-8524, Japan.

Masahiro Kuroda (M)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.

Wlla E Al-Hammad (WE)

Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.
Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan.

Nouha Tekiki (N)

Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.

Hinata Ishizaka (H)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.

Kazuhiro Kuroda (K)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Department of Health and Welfare Science, Graduate School of Health and Welfare Science, Okayama Prefectural University, Okayama 719-1197, Japan.

Kohei Sugimoto (K)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan.

Masataka Oita (M)

Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan.

Yoshinori Tanabe (Y)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.

Majd Barham (M)

Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine.

Irfan Sugianto (I)

Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia.

Yuki Nakamitsu (Y)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.

Masaki Hirano (M)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Department of Radiology, Osaka Red Cross Hospital, Osaka 543-8555, Japan.

Yuki Muto (Y)

Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Department of Radiology, Oomoto Hospital, Okayama 700-0924, Japan.

Hiroki Ihara (H)

Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.

Soichi Sugiyama (S)

Department of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.

Classifications MeSH