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

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.

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

“…PCa does not have the same characteristics and severity in an Afro-American or Asian population [103]. Yet, most of the previously reviewed studies are based on Asian populations [20,26,28,37,42,50,59] and are probably not applicable to an African American population. Heintzelman [63] emphasizes this difference by showing that Afro-American patients in their study were clustered at the upper end of the pain index spectrum, even if these results were not significant.…”

“…Thus, Nicolae et al [58] demonstrated that compared to brachytherapists, ML helped to reduce radiotherapy planning time from a mean time of 18 min to shorter than 1 min with no significant difference regarding irradiated prostate volume. Kajikawa et al [59] built two CNN algorithms for optimization radiotherapy planning. With a CNN model trained with anatomical structure label dataset, the algorithm led to more contrasting results, with 70% accuracy for predicting the dosimetric eligibility of patients treated with intensity-modulated radiation therapy (IMRT).…”

Deep learning in medical imaging and radiation therapy

“…Dose estimation was also the aim of Kajikawa et al. who investigated the feasibility of DL in the automated determination of dosimetric eligibility of prostate cancer patients undergoing intensity‐modulated radiation therapy …”

Multicriterial CNN based beam generation for robotic radiosurgery of the prostate

“…Deep learning based methods have been studied for multiple tasks in medical imaging and radiation therapy including segmentation [2] and classification [3]. Other knowledge based methods are employed to optimize beam related parameters in intensity modulated radiation therapy (IMRT) [4,5] or to optimize beam orientations, positions, shapes, and weights directly (direct aperture optimization).…”