This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Knowledge-based radiation treatment planning: A data-driven method survey.

Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to deliver an accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). To improve the effectiveness of the treatment planning, deep learning methods are widely adopted to predict dose distribution maps for clinical treatment planning. In this paper, we present a novel multi-constraint dose prediction model based on generative adversarial network, named Mc-GAN, to automatically predict the dose distribution map from the computer tomography (CT) images and the masks of PTV and OARs. Specifically, the generator is an embedded UNet-like structure with dilated convolution to capture both the global and local information. During the feature extraction, a dual attention module (DAM) is embedded to force the generator to take more heed of internal semantic relevance. To improve the prediction accuracy, two additional losses, i.e., the locality-constrained loss (LCL) and the self-supervised perceptual loss (SPL), are introduced besides the conventional global pixel-level loss and adversarial loss. Concretely, the LCL tries to focus on the predictions of locally important areas while the SPL aims to prevent the predicted dose maps from the possible distortion at the feature level. Evaluated on two in-house datasets, our proposed Mc-GAN has been demonstrated to outperform other state-of-the-art methods in almost all PTV and OARs criteria. 

Multi-constraint generative adversarial network for dose prediction in radiotherapy

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 is the mainstay treatment for most patients with cancer. During radiotherapy planning, it is essential to generate a clinically acceptable dose distribution map. In practice, dosimetrists need to tweak the planning in an iterative trial-and-error manner according to their experience, making traditional workflow time-consuming and subjective. Although some deep learning-based dose prediction models have been proposed, they always require additional handcrafted features, such as planning target volume (PTV) and organs at risk (OAR) segmentation maps, as network inputs. To overcome these limitations, we propose a multi-task attention adversarial network (MtAA-NET) to automatically complete dose planning using only CT images. In particular, our framework consists of a main dose distribution prediction task to generate a dose map and an auxiliary segmentation task to provide additional anatomical information of the PTV and OAR for the main task, implemented by a shared encoder and two exclusive decoders. To effectively integrate dosimetric and anatomical features, we introduce an attention mechanism embedded cross-task feature fusion (CtFF) module to further fuse the features from different tasks in a deep supervision manner. Note that the generated attention maps reveal the attention of our model in the PTV area of the intermediate features, providing an explainable evaluation of the quality of the predicted dose maps. Experimental results on an in-house cervical cancer dataset and a public head and neck cancer dataset demonstrate the superiority of the proposed method compared with state-of-the-art methods. 

Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction

Dose prediction via distance-guided deep learning: initial development for nasopharyngeal carcinoma radiotherapy.

Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

Abstract Background CEA and systemic inflammation were reported to correlate with proliferation, invasion, and metastasis of colorectal cancer. This study investigated the prognostic significance of the preoperative CEA and systemic inflammation response index (C-SIRI) in patients with resectable colorectal cancer. Methods Two hundred seventeen CRC patients were recruited from Chongqing Medical University, the first affiliated hospital, between January 2015 and December 2017. Baseline characteristics, preoperative CEA level, and peripheral monocyte, neutrophil, and lymphocyte counts were retrospectively reviewed. The optimal cutoff value for SIRI was defined as 1.1, and for CEA, the best cutoff values were 4.1 ng/l and 13.0 ng/l. Patients with low levels of CEA (&lt; 4.1 ng/l) and SIRI (&lt; 1.1) were assigned a value of 0, those with high levels of CEA (≥ 13.0 ng/l) and SIRI (≥ 1.1) were assigned a value of 3, and those with CEA (4.1–13.0 ng/l) and SIRI (≥ 1.1), CEA (≥ 13.0 ng/l), and SIRI (&lt; 1.1) were assigned a value of 2. Those with CEA (&lt; 4.1 ng/l) and SIRI (≥ 1.1) and CEA (4.1–13.0 ng/l) and SIRI (&lt; 1.1) were assigned a value of 1. The prognostic value was assessed based on univariate and multivariate survival analysis. Results Preoperative C-SIRI was statistically correlated with gender, site, stage, CEA, OPNI, NLR, PLR, and MLR. However, no difference was observed between C-SIRI and age, BMI, family history of cancer, adjuvant therapy, and AGR groups. Among these indicators, the correlation between PLR and NLR is the strongest. In addition, high preoperative C-SIRI was significantly correlated with poorer overall survival (OS) (HR: 2.782, 95% CI: 1.630–4.746, P &lt; 0.001) based on univariate survival analysis. Moreover, it remained an independent predictor for OS (HR: 2.563, 95% CI: 1.419–4.628, p = 0.002) in multivariate Cox regression analysis. Conclusion Our study showed that preoperative C-SIRI could serve as a significant prognostic biomarker in patients with resectable colorectal cancer. 

High preoperative CEA and systemic inflammation response index (C-SIRI) predict unfavorable survival of resectable colorectal cancer

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Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy

High preoperative CEA and systemic inflammation response index (C-SIRI) predict unfavorable survival of resectable colorectal cancer