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Background: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt‐60‐based systems and linear accelerators with C‐arm, O‐ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality‐specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient‐specific basis. However, manually generating treatment plans for each modality across every patient is time‐consuming and clinically impractical. Purpose: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning‐based convolutional neural networks. The baseline network performs a single‐task (ST), predicting dose for a single modality. Our proposed multi‐task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient‐specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient‐specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. Methods: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two‐tailed Wilcoxon signed‐rank test. Results: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [–0.0865, –0.0712]). Conclusion: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre‐treatment decision‐making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site‐agnostic dose prediction model, enhancing its generalizability and applicability. [ABSTRACT FROM AUTHOR]

Background: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone‐beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image‐guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation‐based checks and independent verification of volumetric dose distribution, as measurement‐based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time‐sensitive nature of online ART procedures. Purpose: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. Methods: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre‐clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. Results: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre‐treatment checks by 3–4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. Conclusion: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor‐intensive work. [ABSTRACT FROM AUTHOR]

Background: Throughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes‐for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time‐consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population‐based models. Purpose: This study extends our prior research by introducing a minimalist approach to patient‐specific adaptive dose prediction. In contrast to our prior method, which involved fine‐tuning a pre‐trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient‐specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient‐specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population‐based DL model. Methods: We selected 33 patients to train an adaptive population‐based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient‐specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed‐rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them. Results: The average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed‐rank test yielded two‐tailed p‐value = 2.9802×10−8$2.9802\ \times \ {10}^{ - 8}$, indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval = [−2.1610, −1.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two‐tailed p‐values < 0.05. Conclusion: Our study demonstrates the potential of patient‐specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment. [ABSTRACT FROM AUTHOR]

Background: MRI‐only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR‐to‐sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. Purpose: The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. Methods: To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in‐phase (IP) MR images. Dixon images provide better bone contrast than T2‐weighted images as inputs to a modified multi‐channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). Results: We compared model performance with and without bony structure constraints using single‐ and multi‐channel inputs. Among all the models, multi‐channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. Conclusion: Modified multi‐channel CycleGAN with bony structure constraints, taking Dixon‐constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy. [ABSTRACT FROM AUTHOR]

Background: The framework of adaptive radiation therapy (ART) was crafted to address the underlying sources of intra‐patient variation that were observed throughout numerous patients' radiation sessions. ART seeks to minimize the consequential dosimetric uncertainty resulting from this daily variation, commonly through treatment planning re‐optimization. Re‐optimization typically consists of manual evaluation and modification of previously utilized optimization criteria. Ideally, frequent treatment plan adaptation through re‐optimization on each day's computed tomography (CT) scan may improve dosimetric accuracy and minimize dose delivered to organs at risk (OARs) as the planning target volume (PTV) changes throughout the course of treatment. Purpose: Re‐optimization in its current form is time‐consuming and inefficient. In response to this ART bottleneck, we propose a deep learning based adaptive dose prediction model that utilizes a head and neck (H&N) patient's initial planning data to fine‐tune a previously trained population model towards a patient‐specific model. Our fine‐tuned, patient‐specific (FT‐PS) model, which is trained using the intentional deep overfit learning (IDOL) method, may enable clinicians and treatment planners to rapidly evaluate relevant dosimetric changes daily and re‐optimize accordingly. Methods: An adaptive population (AP) model was trained using adaptive data from 33 patients. Separately, 10 patients were selected for training FT‐PS models. The previously trained AP model was utilized as the base model weights prior to re‐initializing model training for each FT‐PS model. Ten FT‐PS models were separately trained by fine‐tuning the previous model weights based on each respective patient's initial treatment plan. From these 10 patients, 26 ART treatment plans were withheld from training as the test dataset for retrospective evaluation of dose prediction performance between the AP and FT‐PS models. Each AP and FT‐PS dose prediction was compared against the ground truth dose distribution as originally generated during the patient's course of treatment. Mean absolute percent error (MAPE) evaluated the dose differences between a model's prediction and the ground truth. Results: MAPE was calculated within the 10% isodose volume region of interest for each of the AP and FT‐PS models dose predictions and averaged across all test adaptive sessions, yielding 5.759% and 3.747% respectively. MAPE differences were compared between AP and FT‐PS models across each test session in a test of statistical significance. The differences were statistically significant in a paired t‐test with two‐tailed p‐value equal to 3.851×10−9$3.851 \times {10}^{ - 9}$ and 95% confidence interval (CI) equal to [−2.483, −1.542]. Furthermore, MAPE was calculated using each individually segmented structure as an ROI. Nineteen of 24 structures demonstrated statistically significant differences between the AP and FT‐PS models. Conclusion: We utilized the IDOL method to fine‐tune a population‐based dose prediction model into an adaptive, patient‐specific model. The averaged MAPE across the test dataset was 5.759% for the population‐based model versus 3.747% for the fine‐tuned, patient‐specific model, and the difference in MAPE between models was found to be statistically significant. Our work demonstrates the feasibility of patient‐specific models in adaptive radiotherapy, and offers unique clinical benefit by utilizing initial planning data that contains the physician's treatment intent. [ABSTRACT FROM AUTHOR]