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
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
536Informations de copyright
Copyright © 2023, Spandidos Publications.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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