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Development of Deep Learning Chest X-Ray Model for Cardiac Dose Prediction in Left-Sided Breast Cancer Radiotherapy - 2022

Development Of Deep Learning Chest X-Ray Model For Cardiac Dose Prediction In Left-Sided Breast Cancer Radiotherapy

Research Paper on Development Of Deep Learning Chest X-Ray Model For Cardiac Dose Prediction In Left-Sided Breast Cancer Radiotherapy

Research Area:  Machine Learning


Deep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially high risk of cardiac irradiation and need for DIBH radiotherapy. We used 103 pairs of anteroposterior and lateral chest X-ray data of left-sided breast cancer patients (training cohort: n = 59, validation cohort: n = 19, test cohort: n = 25). All patients underwent breast-conserving surgery followed by DIBH radiotherapy: the treatment plan consisted of three-dimensional, two opposing tangential radiation fields. The prescription dose of the planning target volume was 42.56 Gy in 16 fractions. A convolutional neural network-based regression model was developed to predict the mean heart dose (∆MHD) reduction between free-breathing (MHDFB) and DIBH. The model performance is evaluated as a binary classifier by setting the cutoff value of ∆MHD > 1 Gy. The patient characteristics were as follows: the median (IQR) age was 52 (47–61) years, MHDFB was 1.75 (1.14–2.47) Gy, and ∆MHD was 1.00 (0.52–1.64) Gy. The classification performance of the developed model showed a sensitivity of 85.7%, specificity of 90.9%, a positive predictive value of 92.3%, a negative predictive value of 83.3%, and a diagnostic accuracy of 88.0%. The AUC value of the ROC curve was 0.864. The proposed model could predict ∆MHD in breast radiotherapy, suggesting the potential of a classifier in which patients are more desirable for DIBH.

Deep Learning
Chest X-Ray Model
Cardiac Dose Prediction
Left-Sided Breast Cancer
Deep inspiration breath-hold

Author(s) Name:  Yutaro Koide, Takahiro Aoyama, Hidetoshi Shimizu, Tomoki Kitagawa, Risei Miyauchi, Hiroyuki Tachibana & Takeshi Kodaira

Journal name:  Scientific Reports

Conferrence name:  

Publisher name:  Springer Nature

DOI:  10.1038/s41598-022-16583-8

Volume Information:  volume 12, Article number: 13706 (2022)