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Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network

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Abstract

The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through automated planning method; however, few studies have used the deep learning method for optimization of planning. The purpose of this study was to propose an automated method based on a convolutional neural network (CNN) for predicting the dosimetric eligibility of patients with prostate cancer undergoing IMRT. Sixty patients with prostate cancer who underwent IMRT were included in the study. Treatment strategy involved division of the patients into two groups, namely, meeting all dose constraints and not meeting all dose constraints, by experienced medical physicists. We used AlexNet (i.e., one of common CNN architectures) for CNN-based methods to predict the two groups. An AlexNet CNN pre-trained on ImageNet was fine-tuned. Two dataset formats were used as input data: planning computed tomography (CT) images and structure labels. Five-fold cross-validation was used, and performance metrics included sensitivity, specificity, and prediction accuracy. Class activation mapping was used to visualize the internal representation learned by the CNN. Prediction accuracies of the model with the planning CT image dataset and that with the structure label dataset were 56.7 ± 9.7% and 70.0 ± 11.3%, respectively. Moreover, the model with structure labels focused on areas associated with dose constraints. These results revealed the potential applicability of deep learning to the treatment planning of patients with prostate cancer undergoing IMRT.

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Authors and Affiliations

  1. Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan

    Tomohiro Kajikawa, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori & Keiichi Jingu

  2. Department of Radiology, National Hospital Organization Sendai Medical Center, Sendai, Japan

    Seiji Tomori

  3. Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan

    Ken Takeda

Authors
  1. Tomohiro Kajikawa
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  2. Noriyuki Kadoya
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  3. Kengo Ito
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  4. Yoshiki Takayama
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  5. Takahito Chiba
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  6. Seiji Tomori
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  7. Ken Takeda
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  8. Keiichi Jingu
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Correspondence to Noriyuki Kadoya.

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The authors have no relevant conflicts of interest to disclose.

Ethical approval

All procedures involving human participants were in accordance with the ethical standards of the Institutional Review Board (IRB) and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. IRB approval was obtained and the requirement for patients’ informed consent was waived.

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Kajikawa, T., Kadoya, N., Ito, K. et al. Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network. Radiol Phys Technol 11, 320–327 (2018). https://doi.org/10.1007/s12194-018-0472-3

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  • DOI: https://doi.org/10.1007/s12194-018-0472-3

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