Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.

/pdf/deep-learning-method-for-prediction-of-patient-specific-dose-2931c6jxrn.pdf

Deep learning method for prediction of patient-specific dose distribution in breast cancer

Radiotherapy treatment planning dose prediction can be used to ensure plan quality and guide automatic plan. One of the dose prediction methods is incorporating historical treatment planning data into algorithms to estimate the dose–volume histogram (DVH) of organ for new patients. Although DVH is used extensively in treatment plan quality and radiotherapy prognosis evaluation, three-dimensional dose distribution can describe the radiation effects more explicitly. The purpose of this retrospective study was to predict the dose distribution of breast cancer radiotherapy by means of deformable registration into atlas images with historical treatment planning data that were considered to achieve expert level. The atlas cohort comprised 20 patients with left-sided breast cancer, previously treated by volumetric-modulated arc radiotherapy. The registration-based prediction technique was applied to 20 patients outside the atlas cohort. This study evaluated and compared three different approaches: registration to the most similar image from a dataset of individual atlas images (SIM), registration to all images from a database of individual atlas images with the average method (WEI_A), and the weighted method (WEI_F). The dose prediction performance of each strategy was quantified using nine metrics, including the region of interest dose error, 80% and 100% prescription area dice similarity coefficients (DSCs), and γ metrics. A Friedman test and a nonparametric exact Wilcoxon signed rank test were performed to compare the differences among groups. The clinical doses of all cases served as the gold standard. The WEI_F method could achieve superior dose prediction results to those of WEI_A. WEI_F outperformed SIM in the organ-at-risk mean absolute difference (MAD). When using the WEI_F method, the MAD values for the ipsilateral lung, heart, and whole lung were 197.9 ± 42.9, 166 ± 55.1, 122.3 ± 25.5, and 55.3 ± 42.2 cGy, respectively. Moreover, SIM exhibited superior prediction in the DSC and γ metrics. When using the SIM method, the means of the 80% and 100% prescription area DSCs, 33γ metric, and 55γ metric were 0.85 ± 0.05, 0.84 ± 0.05, 0.64 ± 0.13, and 0.84 ± 0.10, respectively. The plan target volume and spinal cord MAD when using SIM and WEI were 235.6 ± 158.4 cGy versus 227.4 ± 144.0 cGy ($$p > 0.05$$) and 61.4 ± 44.9 cGy versus 55.3 ± 42.2 cGy ($$p > 0.05$$), respectively. A predicted dose distribution that approximated the clinical plan could be generated using the methods presented in this study.

/pdf/radiotherapy-dose-distribution-prediction-for-breast-cancer-3hjl3ghbud.pdf

Radiotherapy dose distribution prediction for breast cancer using deformable image registration.

Purpose Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models. Materials and Methods A total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”. Results The model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%). Conclusions A consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.

Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection

The healthcare system is transforming as a result of new technology invention and implementation, which promotes therapeutic interventions and treatment outcomes for improved patient care. Numerous diverse parameters like gene variation, sociodemographics, lifestyle/environmental elements, should be measured to expedite precision/personalized medicine (PM). As a result, one of the most important difficulties in PM is bridging the translational gap in the clinical context by transforming big, multimodal data into decision support tools. Deep learning (DL) offers a novel technique to overcome these challenges, enabling the building or acquisition of accuracy, multimodal predictive models that could help the PM vision become a reality shortly. DL along with modern artificial intelligence (AI), in particular, provides tools in the form of algorithms that could pave the way for an individualized malignancy treatment in a more precise and customized manner by allowing early diagnosis and precise treatment options. Machine learning (ML) techniques applied to genomic datasets have a lot of potentials to deliver PM. DL has the power to accurately analyze tumor data and recommend the best possible treatment for cancer patients with a personalized approach. The capacity to anticipate how a patient will respond to a medication using DL techniques will aid in the reevaluation of treatment decisions, as well as the reduction of errors and disease-related financial burden. This chapter discusses the overall characteristics of DL and PM, as well as the importance of DL in precision oncology drug discovery and development, genomics, and biomarker datasets. It also outlines the potential of different technologies of DL in cancer diagnosis, detection, and prediction. It also highlights the applications of DL in translational oncology and its clinical benefits. Various challenges and limitations associated with DL techniques, such as like unique genomic patterns, small sample size, multidisciplinary expertise, and clinical trials, are also addressed. 

Deep Learning and Precision Medicine

BACKGROUND Neural-network methods have been widely used for the prediction of dose distributions in radiotherapy. However, the prediction accuracy of existing methods may be degraded by the problem of dose imbalance. In this work, a new loss function is proposed to alleviate the dose imbalance and achieve more accurate prediction results. The U-Net architecture was employed to build a prediction model. Our study involved a total of 110 patients with left-breast cancer, who were previously treated by volumetric-modulated arc radiotherapy. The patient dataset was divided into training and test subsets of 100 and 10 cases, respectively. We proposed a novel 'sharp loss' function, and a parameter γ was used to adjust the loss properties. The mean square error (MSE) loss and the sharp loss with different γ values were tested and compared using the Wilcoxon signed-rank test. RESULTS The sharp loss achieved superior dose prediction results compared to those of the MSE loss. The best performance with the MSE loss and the sharp loss was obtained when the parameter γ was set to 100. Specifically, the mean absolute difference values for the planning target volume were 318.87 ± 30.23 for the MSE loss versus 144.15 ± 16.27 for the sharp loss with γ = 100 (p   0.05), and 194.30 ± 14.51 versus 168.58 ± 25.97 (p < 0.05), respectively. CONCLUSIONS The sharp loss function could significantly improve the accuracy of radiotherapy dose prediction.

/pdf/sharp-loss-a-new-loss-function-for-radiotherapy-dose-4srcmljplz.pdf

Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks.

Due to the complexity of advanced radiotherapy techniques, treatment planning process is usually time consuming and plan quality can vary considerably among planners and institutions. It is also impractical to generate all possible treatment plans based on available radiotherapy techniques and select the best option for a specific patient. Automatic dose prediction will be very helpful in these situations, while there were a few studies of three-dimensional (3D) dose prediction for patients who received radiotherapy. The purpose of this work was to develop a novel atlas-based method to predict 3D dose prediction and to evaluate its performance. Previously treated nineteen left-sided post-mastectomy breast cancer patients and sixteen prostate cancer patients were included in this study. One patient was arbitrarily chosen as the reference for each type of cancer and all the remaining patients' computed tomography (CT) images and contours were aligned to it using deformable image registration (DIR). Deformable vector field (DVF) for each patient i (DVF i-ref) was used to deform the original 3D dose matrix of that patient. CT scan of a test patient was also registered with the same reference patient using DIR and both direct DVF (DVFtest-ref) and inverse DVF ([Formula: see text]) were derived. Similarity of atlas patients to the test patient was determined based on the similarity of DVFtest-ref to atlas DVFs (DVF i-ref) and appropriate weighting factors were calculated. Patients' doses in the atlas were deformed again using [Formula: see text] to transform them from the reference patient's coordinates to the test patient's coordinates and the final 3D dose distribution for the test patient was predicted by summing the weighted individual 3D dose distributions. Performance of our method was evaluated and the results revealed that the proposed method was able to predict the 3D dose distributions accurately. The mean dose difference between clinical and predicted 3D dose distributions were 0.9  ±  1.1 Gy and 1.9  ±  1.2 Gy for breast and prostate plans. The proposed dose prediction method can be used to improve planning quality and facilitate plan comparisons.

/pdf/an-atlas-based-method-to-predict-three-dimensional-dose-3iu6ucvao5.pdf

An atlas-based method to predict three-dimensional dose distributions for cancer patients who receive radiotherapy

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An atlas-based method to predict three-dimensional dose distributions for cancer patients who receive radiotherapy