Evidence map›Paper›PMID 37869640›Full record

ArticleExperimental and therapeutic medicine2023

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.

Ryo Kamizaki, Masahiro Kuroda, Wlla E Al-Hammad, Nouha Tekiki, Hinata Ishizaka, Kazuhiro Kuroda, Kohei Sugimoto, Masataka Oita, Yoshinori Tanabe, Majd Barham and 6 more

Open access · diamondAbstract read
In one paragraph

Article in Experimental and therapeutic medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 3 institutions in 3 countries.

Ryo KamizakiDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Masahiro KurodaDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Wlla E Al-HammadDepartment of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.
Nouha TekikiDepartment of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan.
Hinata IshizakaDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Kazuhiro KurodaDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Kohei SugimotoDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Masataka OitaGraduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan.
Yoshinori TanabeDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Majd BarhamDepartment of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine.
Irfan SugiantoDepartment of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia.
Yuki NakamitsuDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Masaki HiranoDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Yuki MutoDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan.
Hiroki IharaDepartment of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.
Soichi SugiyamaDepartment of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan.
Okayama University · JPAn-Najah National University · PSHasanuddin University · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BCDIBHDNNheart doseMLRT

Identifiers

PMID37869640
PMCPMC10587874
OpenAlexW4387264439

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.