A generalizable dose prediction model for automatic radiotherapy planning based on physics-informed priors and large-kernel convolutions.

BackgroundPrevious knowledge‐based planning studies have demonstrated the feasibility of predicting three‐dimensional photon dose distributions and subsequently generating treatment plans. The steepness of dose fall‐off represents a critical metric for clinical plan evaluation; however, dose fall‐off similarity is frequently overlooked in dose prediction tasks. Our study introduces a novel automatic treatment planning methodology that specifically focuses on dose fall‐off reconstruction for nasopharyngeal carcinoma (NPC).PurposeOur study aims to establish an innovative methodology for automatic treatment plan generation that leverages dose fall‐off information derived from deep learning‐predicted dose distributions. Additionally, we propose and validate a comprehensive multicriteria rating strategy for dose prediction.MethodsWe incorporated 120 nasopharyngeal cancer cases in this study, distributing them into training (n = 90), validation (n = 10), and testing (n = 20) cohorts. Three distinct dose prediction models were trained: U‐Net, DoseNet, and Transformer. To determine the optimal dose prediction model, we developed a comprehensive multicriteria rating strategy that integrates mean absolute error, dose‐volume histogram analysis, and isodose dice similarity coefficients. Based on these predictions, we implemented two automatic planning approaches: (1) IsoPlans, which extracts isodose lines from the predicted dose distribution to generate radiotherapy contours as optimization objectives and (2) DVH‐IsoPlans, which enhances the first strategy by incorporating additional dose‐volume constraints to further optimize treatment planning parameters.ResultsThe multicriteria scores for the three dose prediction models (U‐Net, DoseNet, and Transformer) were 0.85, 0.84, and 0.82, respectively. The dose prediction model achieved a minimum mean absolute error of 2.71 Gy. In our clinical validation, 4 of the 20 generated IsoPlans failed to meet clinical requirements, whereas all 20 DVH‐IsoPlans successfully satisfied clinical requirements. The mean plan optimization time for the 20 test cases was significantly reduced from 870 to 560 s for IsoPlans and to 470 s for DVH‐IsoPlans, representing a substantial reduction of 37.5% and 50.5%, respectively.ConclusionsIn this study, a multicriteria rating strategy is proposed which combines pixel‐wise numerical evaluation, clinical parameter evaluation and physical dose fall‐off evaluation in order to rate the dose prediction models. Moreover, an automated planning workflow has been developed, enabling the rapid generation of treatment plans based on the isodose structures of the predicted dose. A self‐consistent dose prediction to automatic planning scheme based on isodose lines is proposed, which significantly reduces the time required for plan optimization.

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

Background and purposeMRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC. Materials and methodsAn in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes. ResultsThe planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%. ConclusionThe straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.

BackgroundRectal cancer patients are potential beneficiaries of adaptive radiotherapy (ART) which demands considerable resources. Currently, there is no definite guidance on what kind of patients and when will benefit from ART. This study aimed to develop and validate a methodology for estimating ART requirements in rectal cancer before treatment course.Methods and materialsThis study involved 66 rectal cancer patients from center 1 and 27 patients from center 2. The ART requirements were evaluated by comparing 8 dose volume histogram (DVH) metrics of targets and organs at risk (OARs) between planning and treatment fractions. Tolerance ranges of deviation of DVH metrics were derived from 10 patients and applied to assess fractional variability. Eighteen features, encompassing diagnostic, dosimetric, and time-related information, were utilized to formulate a stepwise logistic regression model for fraction-level ART requirement estimation. The super parameters were determined through 5-fold cross-validation with 250 training fractions and the methodology was validated with 109 internal testing fractions and 134 external testing fractions.ResultsThe area under the curve (AUC) of training dataset was 0.74 (95% CI: 0.61 to 0.85), while in the internal and external testing, the AUC achieved 0.76 (95% CI: 0.60–0.90) and 0.68 (95% CI: 0.56–0.81). Using a best (or clinical applicable) cut-off value of 33.4% (11%), the predictive model achieved a sensitivity of 46.2% (69.2%) and specificity of 97.9% (68.7%). During the modeling, 5 features were retained: Homogeneity index (OR = 6.06, 95% CI: 2.93–14.8), planning target volume (OR = 1.77, 95% CI: 1.17–2.69), fraction dose (OR = 45.37, 95% CI: 5.74–469), accumulated dose (OR = 2.29, 95% CI: 1.35–4.14), and whether neoadjuvant chemoradiotherapy (OR > 1000).ConclusionART requirements are associated with target volume, target dose homogeneity, fraction dose, dose accumulation and whether neoadjuvant radiotherapy. The predictive model exhibited the capability to predict fraction-level ART requirements.

The influence of Acuros XB on dose volume histogram metrics and tumour control probability modelling in locally advanced non-small cell lung cancer.

Dose-volume effects of breast cancer radiation therapy on the risk of second oesophageal cancer

The knowledge‐based treatment planning automation has evolved from academic research from the clinical application with the involvement of the major industry TPS vendors. Previous work proved the concept that IMRT or VMAT plans can be designed without or with minimal human intervention. Recently, the major industry vendors start releasing the automatic planning product such as Philips automatic treatment planning, Varian's Rapidplan, and RaySearch's autoplan. Many centers start to evaluate and use the automatic planning products from TPS vendors. It is more and more becoming evident that automatic planning will be routinely used in the clinic.The first presentation will focus on the knowledge modeling for clinical treatment planning. The presenter will introduce the algorithm and theory for developing the dose volume histogram (DVH) model, summarize the results on the validation of the DVH model, and discuss the application of DVH for the automatic treatment planning. The second presenter will focus on the clinical use of the automatic treatment planning. The presenter will give an overview of the several automatic treatment planning tools including the tools developed commercially and in‐house, present the early experience of using those tools, and discuss the application of the tools in the routine clinical use and in the quality control of the treatment planning.Learning Objectives: Describe the algorithm and theory behind the knowledge based treatment planning automation. Clinical experience of using automatic planning in clinical trial and patient treatment Early clinical experience of using commercial automatic planning approach The application of automatic planning approach to perform quality assurance of treatment plan. XD obtain the research funding from Varian Medical System, Philips Medical System and Funding support form Sister Institution Network Fund of MD Anderson, XD is also a founder of Global Oncology One

BackgroundAutomatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans.PurposeTo investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity‐modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization.MethodsFirst, dose distributions were predicted using a three‐dimensional (3D) Fusion Residual Unet (F‐ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient‐specific quality assurance (PSQA).ResultsThe predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V30Gy of left femoral head (p = 0.05), right femoral head (p = 0.004), and small intestine (p = 0.04). The V45Gy of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%/3 mm and 3%/2 mm for VMAT and IMRT plans, respectively.ConclusionsThe proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.

Pharyngeal Constrictor Dose–Volume Histogram Metrics and Patient-Reported Dysphagia in Head and Neck Radiotherapy

Introduction: Decision-making regarding radiotherapy techniques for patients with nasopharyngeal cancer requires a comparison of photon and proton plans generated using planning software, which requires time and expertise. We developed a fully automated decision tool to select patients for proton therapy that predicts proton therapy (XT) and photon therapy (PT) dose distributions using only patient CT image data, predicts xerostomia and dysphagia probability using predicted critical organ mean doses, and makes decisions based on the Netherlands' National Indication Protocol Proton therapy (NIPP) to select patients likely to benefit from proton therapy. Methods: This study used 48 nasopharyngeal patients treated at the Cancer Hospital of the Chinese Academy of Medical Sciences. We manually generated a photon plan and a proton plan for each patient. Based on this dose distribution, photon and proton dose prediction models were trained using deep learning (DL) models. We used the NIPP model to measure xerostomia levels 2 and 3, dysphagia levels 2 and 3, and decisions were made according to the thresholds given by this protocol. Results: The predicted doses for both photon and proton groups were comparable to those for manual plan (MP). The Mean Absolute Error (MAE) for each organ at risk in the photon and proton plans did not exceed 5% and showed a good performance of the dose prediction model. For proton, the normal tissue complication probability (NTCP) of xerostomia and dysphagia performed well, p > 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well, p > 0.05. For xerostomia p < 0.05 but the absolute deviation was 0.85% and 0.75%, which would not have a great impact on the prediction result. Among the 48 patients' decisions, 3 were wrong, and the correct rate was 93.8%. The area under curve (AUC) of operating characteristic curve (ROC) was 0.86, showing the good performance of the decision-making tool in this study. Conclusions: The decision tool based on DL and NTCP models can accurately select nasopharyngeal cancer patients who will benefit from proton therapy. The time spent generating comparison plans is reduced and the diagnostic efficiency of doctors is improved, and the tool can be shared with centers that do not have proton expertise. Trial registration: This study was a retrospective study, so it was exempt from registration.

This study aimed to evaluate the clinical validity of a dose-mimicking automated planning for volumetric-modulated arc therapy (VMAT) in patients with stage III non-small cell lung cancer (NSCLC), through direct comparison with a commercial dose volume histogram (DVH)-predicted system. We retrospectively analyzed volumetric-modulated arc therapy plans from 75 patients with stage III NSCLC treated at our institution (60 for training, 15 for testing). The dose-mimicking method was implemented using RatoGuide, and the DVH-predicted method was implemented using RapidPlan. The RatoGuide 3D dose-prediction model was trained on the 60 training cases. For each test case, a predicted dose distribution was generated and converted to a deliverable plan (RGDose) in Eclipse using vendor-provided objective functions. A RapidPlan model trained and generated deliverable plans (RPDose) for the same dataset. The clinical plan dose distribution (CliDose) was the reference. We compared dose distributions and DVH parameters among RGDose, RPDose and CliDose. Mean absolute errors (MAEs) relative to CliDose were 0.83 ± 0.66% (targets) and 2.06 ± 3.14% (organs at risk [OARs]) for RGDose, and 0.88 ± 0.66% (targets) and 2.49 ± 3.63% (OARs) for RPDose. There were no significant differences in OAR DVH parameters between RGDose and CliDose. In contrast, compared to CliDose, RPDose showed a significant reduction in the Esophagus D1cc and a significant increase in the Lungs V5Gy. The dose-mimicking method more faithfully reproduced the original clinical plans than the conventional DVH-predicted system, suggesting that dose-mimicking method can capture complex inter-OAR trade-offs and consistently reflect planner intent.

Evaluation of Equations for Unbound Serum Concentration Prediction of Carbamazepine and Carbamazepine-10,11-epoxide in Polytherapy Pediatric Patients with Epilepsy

Objective To compare the automatic and manual plans of intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma and investigate whether the automatic plan possesses advantages over the manual plan. Methods Clinical data of 97 nasopharyngeal carcinoma patients were retrospectively analyzed. All patients received IMRT with Eclipse treatment planning system (Version 11.0) using manual optimization. The same plans were optimized successively in Pinnacle3(version 9.10) treatment planning system using automatic plan software package module (provided by Prof. Zhang XD from MD Anderson Cancer Center). The D95(dose in 95% of planning target volume, PTV), homogeneity index, conformal index and normal organ dose were statistically compared between two plans. Results The PTV coverage and homogeneity did not significantly differ between two plans. Compared with the manual plan, the automatic plan could more effectively protect the normal organs. Two plans significantly differed in a majority of organ at risk (OAR). The mean dose was decreased by 270-1870 cGy. Conclusions For nasopharyngeal carcinoma patients, IMRT via the automatic plan can meet the clinical requirement for target prescription dose and reduce the dose of normal organs. Key words: Automatic plan; Manual plan; Nasopharyngeal neoplasms/radiotherapy

Purpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning.Methods and Materials: A total of 40 WBRT plans from a single institution were included in this study under IRB approval. Twenty WBRT plans, 10 with single energy (SE, 6MV) and 10 with mixed energy (ME, 6/15MV), were randomly selected as training dataset to develop the methodology for automatic planning. The rest 10 SE cases and 10 ME cases served as validation. The auto-planning process consists of three steps. First, an energy prediction model was developed to automate energy selection. This model establishes an anatomy-energy relationship based on principle component analysis (PCA) of the gray level histograms from training cases' digitally reconstructed radiographs (DRRs). Second, a random forest (RF) model generates an initial fluence map using the selected energies. Third, the balance of overall dose contribution throughout the breast tissue is realized by automatically selecting anchor points and applying centrality correction. The proposed method was tested on the validation dataset. Non-parametric equivalence test was performed for plan quality metrics using one-sided Wilcoxon Signed-Rank test.Results: For validation, the auto-planning system suggested same energy choices as clinical-plans in 19 out of 20 cases. The mean (standard deviation, SD) of percent target volume covered by 100% prescription dose was 82.5% (4.2%) for auto-plans, and 79.3% (4.8%) for clinical-plans (p > 0.999). Mean (SD) volume receiving 105% Rx were 95.2 cc (90.7 cc) for auto-plans and 83.9 cc (87.2 cc) for clinical-plans (p = 0.108). Optimization time for auto-plan was <20 s while clinical manual planning takes between 30 min and 4 h.Conclusions: We developed an automatic treatment planning system that generates WBRT plans with optimal energy selection, clinically comparable plan quality, and significant reduction in planning time, allowing for near-real-time planning.

The prediction of an additional space for the dose sparing of organs at risk (OAR) in radiotherapy is still difficult. In this pursuit, the present study was envisaged to find out the factors affecting the bladder and rectum dosimetry of cervical cancer. Additionally, the relationship between the dose-volume histogram (DVH) parameters and the geometry and plan dose-volume optimization parameters of the bladder/rectum was established to develop the dose prediction models and guide the planning design for lower OARs dose coverage directly. Thirty volume modulated radiation therapy (VMAT) plans from cervical cancer patients were randomly chosen to build the dose prediction models. The target dose coverage was evaluated. Dose prediction models were established by univariate and multiple linear regression among the dosimetric parameters of the bladder/rectum, the geometry parameters (planning target volume (PTV), volume of bladder/rectum, overlap volume of bladder/rectum (OV), and overlapped volume as a percentage of bladder/rectum volume (OP)), and corresponding plan dose-volume optimization parameters of the nonoverlapping structures (the structure of bladder/rectum outside the PTV (NOS)). Finally, the accuracy of the prediction models was evaluated by tracking d = (predicted dose-actual dose)/actual in additional ten VMAT plans. V30, V35, and V40 of the bladder and rectum were found to be multiple linearly correlated with the relevant OP and corresponding dose-volume optimization parameters of NOS (regression R2 > 0.99, P < 0.001). The variations of these models were less than 0.5% for bladder and rectum. Percentage of bladder and rectum within the PTV and the dose-volume optimization parameters of NOS could be used to predict the dose quantitatively. The parameters of NOS as a limited condition could be used in the plan optimization instead of limiting the dose and volume of the entire OAR traditionally, which made the plan optimization more unified and convenient and strengthened the plan quality and consistency.