A method of using deep learning to predict three‐dimensional dose distributions for intensity‐modulated radiotherapy of rectal cancer

Abstract

Purpose To develop and test a three‐dimensional (3D) deep learning model for predicting 3D voxel‐wise dose distributions for intensity‐modulated radiotherapy (IMRT). Methods A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training–validating set and the remaining as the testing set. A 3D deep learning model named 3D U‐Res‐Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V50, V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired‐samples t test. The model was also compared with 3D U‐Net and the same architecture model without beam configurations input (named as 3D U‐Res‐Net_O). Results The 3D U‐Res‐Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from −1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U‐Res‐Net_O and being slightly superior to 3D U‐Net. Conclusions This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel‐wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

Development and evaluation of radiotherapy deep learning dose prediction models for breast cancer

“…Furthermore, several studies used a three dimensional (3D) U-net architecture for predicting dose distributions, sometimes combined with other well-known CNN architectures such as DenseNet [7] , or extended with beam configuration information [9] , [19] . Both studies that included beam configuration, report an improvement in prediction compared to only using anatomical information.…”

Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies

“…There have been some studies on ATP techniques using DL neural networks ( 20 – 23 , 28 – 30 ). Unlike conventional inverse optimization radiotherapy treatment planning with trial and error, ATP can be summarized into two steps: obtaining the predicted dose distribution and generating an automated executable plan ( 6 ).…”

“…Second, given the high safety requirements of medical applications, generating executable clinical automated treatment plan based on 3D dose distribution in closed commercial software architecture remains a challenge. To the best of our knowledge, previous studies have generally focused on algorithms to improve the accuracy of 3D dose distribution prediction ( 21 – 23 ) or have used the open-source toolkit matRad to generate radiation treatment planning for educational purposes and research ( 20 , 24 ).…”

A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

“…For these sites, the spatial relationships of the tumor with organs at risk vary among different patients and the beam setups also vary much more than for prostate. Zhou et al (27) also improved a 3D U-Res-Net model performance to predict 3D dose distribution for postoperative rectal cancer patients treated with IMRT considering beam configurations input.…”

“…In recent years, a number of deep learning (DL)-based ATP techniques have been proposed using various DL neural networks (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). Several review articles on AI in radiation oncology (34)(35)(36), and radiotherapy treatment planning (37)(38)(39), have been published, which demonstrated the interests on AI and the significance of ATP, summarization of the achievements and challenges, as well as insightful discussion on future studies.…”