Radiation Therapy Treatment Planning Research Articles

Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy.

Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning (DRL), autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints.

We integrated VTP with a real clinical TPS to establish a fully automated planning workflow guided by VTP. This integration allowed direct model training and evaluation using the clinical TPS. We designed the VTP network structure to approach the decision-making process in RT planning in a hierarchical manner that mirrors human planners. The VTP network was trained via the Q-learning framework. To assess the effectiveness of VTP, we conducted a prospective evaluation in the 2023 Planning Challenge organized by the American Association of Medical Dosimetrists (AAMD). We extended our evaluation to include 20 clinical H&N cancer patients, comparing the plans generated by VTP against their clinical plans.

In the prospective evaluation for the AAMD Planning Challenge, VTP achieved a plan score of 139.08 in the initial phase evaluating plan quality, and 15 min of planning time with the first place ranking in the adaptive phase competing for planning efficiency while meeting all plan quality requirements. For clinical cases, VTP-generated plans achieved an average VTP score of 125.33±11.12, which outperformed the corresponding clinical plans with an average score of117.76±13.56.

We successfully integrated VTP with the clinical TPS to achieve a fully automated treatment planning workflow. The compelling performance of VTP demonstrated its potential in automating H&N RT planning.

On polling directions for randomized direct-search approaches: application to beam angle optimization in intensity-modulated proton therapy

AbstractDeterministic direct-search methods have been successfully used to address real-world challenging optimization problems, including the beam angle optimization (BAO) problem in radiation therapy treatment planning. BAO is a highly non-convex optimization problem typically treated as the optimization of an expensive multi-modal black-box function which results in a computationally time consuming procedure. For the recently available modalities of radiation therapy with protons (instead of photons) further efficiency in terms of computational time is required despite the success of the different strategies developed to accelerate BAO approaches. Introducing randomization into otherwise deterministic direct-search approaches has been shown to lead to excellent computational performance, particularly when considering a reduced number (as low as two) of random poll directions at each iteration. In this study several randomized direct-search strategies are tested considering different sets of polling directions. Results obtained using a prostate and a head-and-neck cancer cases confirmed the high-quality results obtained by deterministic direct-search methods. Randomized strategies using a reduced number of polling directions showed difficulties for the higher dimensional search space (head-and-neck) and, despite the excellent mean results for the prostate cancer case, outliers were observed, a result that is often ignored in the literature. While, for general global optimization problems, mean results (or obtaining the global optimum once) might be enough for assessing the performance of the randomized method, in real-world problems one should not disregard the worst-case scenario and beware of the possibility of poor results since, many times, it is only possible to run the optimization problem once. This is even more important in healthcare applications where the mean patient does not exist and the best treatment possible must be assured for every patient.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

The potential of mixed carbon-helium beams for online treatment verification: a simulation and treatment planning study.

Recently, a new and promising approach for range verification was proposed. This method requires the use of two different ion species. Due to their equal magnetic rigidity, fully ionized carbon and helium ions can be simultaneously accelerated in accelerators like synchrotrons. At sufficiently high treatment energies, helium ions can exit the patient distally, reaching approximately three times the range
of carbon ions at an equal energy per nucleon. Therefore, the proposal involves adding a small helium fluence to the carbon ion beam and utilizing helium as an online range probe during radiation therapy. This work aims to develop a software framework for treatment planning and motion verification in range-guided radiation therapy using mixed carbon-helium beams. The developed framework is
based on the open-source treatment planning toolkit matRad. Dose distributions and helium radiographs were simulated using the open-source Monte Carlo package TOPAS. Beam delivery system parameters were obtained from the Heidelberg Ion Therapy Center, and imaging detectors along with reconstruction were facilitated by ProtonVDA. Methods for reconstructing the most likely patient positioning error scenarios and the motion phase of 4DCT are presented for prostate and lung cancer sites. The developed framework provides the capability to calculate and optimize treatment plans for mixed carbon-helium ion therapy. It can simulate the treatment process and generate helium radiographs for simulated patient geometry, including small beam views. Furthermore, motion reconstruction based on these radiographs seems possible with preliminary validation. The developed framework can be applied for further experimental work with the promising mixed carbon-helium ion implementation of range-guided radiotherapy. It offers opportunities for adaptation in particle therapy, improving dose accumulation, and enabling patient anatomy reconstruction during radiotherapy.&#xD.

A multiobjective beam angle optimization framework for intensity-modulated radiation therapy

Radiation therapy treatment planning is inherently a multiobjective problem, aiming to obtain the best tradeoffs between irradiating the tumor with the prescribed dose and sparing as much as possible the surrounding healthy organs. Many different multiobjective approaches have been proposed for the optimization of radiation intensities for fixed beam irradiation directions. However, multiobjective beam angle optimization is seldom considered. The purpose of this paper is to introduce a new multiobjective optimization framework that explicitly and simultaneously considers the optimization of intensities and also beam directions. Whilst multiobjective optimization of radiation intensities considering a fixed set of beam directions gives rise to a single Pareto front, beam angle optimization gives rise to the appearance of multiple Pareto fronts, each one associated with a given beam angle set. Our framework proposes a beam angle set choice based on the evaluation of non-dominated solutions belonging to different Pareto fronts, using a tree-based approach and a performance indicator to assess the quality of each Pareto front. The proposed approach, illustrated by head-and-neck cancer cases, allows for more flexibility in the calculation of solutions and a better understanding of the existing compromises between different objectives.

Method for Determining Radioresistance of Cancer Cell Lines Based on Cluster Analysis of Clonogenic Cell Survival Data

Background: The outcome of radiation therapy, the duration and quality of life of cancer patients significantly depend on the radiosensitivity (RS) of a cancerous tumor, and the duration of the patient’s relapse-free period is largely determined by the degree of its radioresistance (RR). Today the results of molecular mechanism investigation of cancer radioresistance and the classification of cancer cells according to their radiophenotypes mostly contribute to improving prognosis methods of treatment outcomes and increasing effectiveness of radiation therapy. In this work, we developed a classification method of cancer cells according to their radiosensitivity using machine learning based on the data analysis of clonogenic cell survival under ionizing radiation. Material and methods: The method consists of clustering parameters of experimental dose-effect relationships, which were approximated using the equation of a linear-quadratic (LQ) model, which is used to evaluate RS of cancer cells in radiobiology. The training of the statistical model included published experimental dataset of 96 cancer cell lines, for which parameters a, b and their ratio a/b of the LQ model were determined. Classification of cancer cells according to their radiosensitivity was carried out based on principal component analysis (PCA) in the parameter space (a, a/b), k-means clustering and hierarchical clustering methods. Results: Application of the developed statistical model to a large dataset of cancer cells made it possible to reliably separate radiosensitive and radioresistant (RR) cells into two clusters according to the parameters a and a/b. Application of the model to cancer cells with acquired RR, in which RS was suppressed as a result of exposure to irradiation or hypoxia, allowed tracing the shift of parent cells’ parameters from the RS cluster to the RR cell cluster. To study the genetic mechanisms of radiosensitivity, we performed bioinformatic analysis of the mutation distribution in genes encoding proteins in the cellular signalling pathways of cancer cells, i.e. proliferation, apoptosis, repair of damaged DNA molecules and antioxidant defence cellular system. Conclusion: The developed statistical model of radiophenotypic classification of cancer cells based on their radiosensitivity can be used in the development of radiation therapy treatment plans taking into account radiosensitivity of patient’s tumour. The model may be also helpful in a joint analysis of the phenotypic and genotypic characteristics of cancer cells, aiming at the elucidation of the molecular and genetic mechanisms of radiosensitivity and development of biomarkers of radioresistance.

Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography.

Segmentation of the prostate and surrounding organs at risk from computed tomography is required for radiation therapy treatment planning. We propose an automatic two-step deep learning-based segmentation pipeline that consists of an initial multi-organ segmentation network for organ localization followed by organ-specific fine segmentation. Initial segmentation of all target organs is performed using a hybrid convolutional-transformer model, axial cross-attention UNet. The output from this model allows for region of interest computation and is used to crop tightly around individual organs for organ-specific fine segmentation. Information from this network is also propagated to the fine segmentation stage through an image enhancement module, highlighting regions of interest in the original image that might be difficult to segment. Organ-specific fine segmentation is performed on these cropped and enhanced images to produce the final output segmentation. We apply the proposed approach to segment the prostate, bladder, rectum, seminal vesicles, and femoral heads from male pelvic computed tomography (CT). When tested on a held-out test set of 30 images, our two-step pipeline outperformed other deep learning-based multi-organ segmentation algorithms, achieving average dice similarity coefficient (DSC) of (prostate), (bladder), (rectum), (seminal vesicles), and (femoral heads). Our results demonstrate that a two-step segmentation pipeline with initial multi-organ segmentation and additional fine segmentation can delineate male pelvic CT organs well. The utility of this additional layer of fine segmentation is most noticeable in challenging cases, as our two-step pipeline produces noticeably more accurate and less erroneous results compared to other state-of-the-art methods on such images.

Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer.

We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.

GATE Monte Carlo approach to heterogeneity dose distribution in small fields used in radiation therapy

The Accurate dosage prediction in Radiation Therapy is challenging, prompting a need for precision beyond conventional clinical Treatment Planning Systems (TPS). Monte Carlo-based methods are sought for their superior accuracy. The aim of this study is to compare dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, focusing on smaller fields. This study was initiated with a homogeneous validation of the TrueBeam STX system, using measurements obtained from the Centre Hospitalier Interregional Edith Cavell (CHIREC) in Brussels. The validation compared dosimetric functions (Percentage Depth Dose (PDD), Dose profile (DP) and Collimator scatter fraction (CSF)) employing the GAMMA index with a 2% / 2 mm criterion tolerance. Following this, heterogeneous studies examined dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, with a specific focus on smaller fields. Simulations were conducted using both platforms on chest phantoms with heterogeneous slabs representing bone, lung, and heart, each housing a central tumor. The impact of electronic equilibrium on tumors for different small field sizes was evaluated. Results showed a remarkable 99% agreement between measurements and GATE calculations in the homogeneous validation of the TrueBeam STX system. However, in heterogeneous studies, ACUROS consistently overestimated lung doses by up to 8% compared to GATE simulation, especially evident with a flattening filter and smaller beam sizes at density interfaces. This highlights significant dose estimation discrepancies between ACUROS and GATE, emphasizing the need for precise calculations. The findings support exploring Monte Carlo-based methods for enhanced accuracy in Radiation Therapy treatment planning.

Evaluation of Radiation Therapy Treatment Plans in a Randomized Phase 2 Trial Comparing 2 Schedules of Twice-Daily Thoracic Radiation Therapy in Limited Stage Small Cell Lung Cancer

PurposeThere is limited clinical data for recommendations on how to deliver thoracic radiotherapy (TRT) concurrently with chemotherapy in limited stage (LS) small-cell lung cancer (SCLC). We reviewed radiotherapy treatment plans in a randomized phase II trial comparing high-dose with standard-dose twice-daily TRT to assess treatment planning techniques, dose-volume data for target volumes and organs at risk (OARs), evaluate compliance with the protocol, associations with radiation-induced toxicity and whether an imbalance in treatment planning parameters might be a reason for the large survival benefit of the higher dose (median overall survival 43.6 vs. 22.6 months). Methods and Materials170 patients were to receive four courses of platinum/etoposide and randomized to receive twice-daily TRT of 60 Gy/40 fractions (fx) or 45 Gy/30 fx. TRT treatment plans for those who received one or more fx of TRT (n=166) were analyzed. ResultsThe most common treatment planning technique was 3DCRT (67%). The 75th percentile of the reported dose-volume parameters for the OARs were within the protocol-recommended limits for both groups. Mean doses to the esophagus of 25.5 Gy (IQR: 20.2-31.3) [60 Gy/40 fx] and 24.3 Gy (IQR: 20.3-27.5) [45 Gy/30 fx] were associated with 21% and 18% ≥ grade 3 acute esophagitis, respectively. In the 60 Gy/40 fx group, a mean dose to the lungs of 16.5 Gy (IQR: 15.8-16.9), V20Gy of 29.5% (IQR: 28.8-30.4), and V5Gy of 65.6% (IQR: 61.5-68.7) led to ≥ grade 3 pneumonitis in 4% of the patients. There was no ≥ grade 3 pneumonitis in the 45 Gy group. Treatment planning techniques, the percentage change in volumes between original and re-delineated OARs, PTV volumes, relative doses, and laterality were well balanced between the randomly assigned groups. ConclusionConsidering that the incidences of severe radiation-induced toxicities were within the range of other recent trials, the reported doses to the OARs appear to be safe. Treatment planning parameters were well balanced between the randomly assigned groups, supporting that the survival benefit of the twice-daily 60 Gy/ 40 fx TRT schedule was due to the higher dose.

Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study

PurposeTo develop and externally validate an automatic Artificial Intelligence (AI) tool for delineating gross tumor volume (GTV) in esophageal squamous cell carcinoma (ESCC) patients, which can assist in the neo-adjuvant or radical radiation therapy treatment planning. Methods and MaterialsIn this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by two experts via consensus were used as ground truth. A three-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in three validation cohorts. The AI tool was compared against twelve board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance (ASD). Additionally, our previously established radiomics model for predicting pathological complete response (pCR) was utilized to compare AI-generated and ground truth contours, in order to assess the potential of the AI contouring tool in radiomics analysis. ResultsThe AI tool demonstrated good GTV contouring performance in multi-center validation cohorts, with median DSC values of 0.865, 0.876, and 0.866, and median ASD values of 0.939 mm, 0.789 mm, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of twelve board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = 0.003-0.048), reduced the intra- and inter-observer variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pCR prediction performance for these contours (P = 0.430) were observed. ConclusionsOur AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicated its potential on GTV contouring during radiation therapy treatment planning and radiomics studies.

Long-Term Outcomes After Concurrent Once- or Twice-Daily Chemoradiation in Limited-Stage Small Cell Lung Cancer: A Brief Report From the CONVERT Trial

CONVERT was a phase 3 international randomized clinical trial comparing once-daily (OD) and twice-daily (BD) radiation therapy (RT). This updated analysis describes the 6.5-year outcomes of these regimens delivered with conformal techniques. CONVERT (NCT00433563) randomized patients 1:1 between OD RT (66 Gy/33 fractions/6.5 weeks) and BD RT (45 Gy/30 fractions/3 weeks), both delivered with concurrent cisplatin/etoposide. Three-dimensional conformal RT was mandatory, intensity-modulated RT was permitted, and elective nodal irradiation was not allowed. Prophylactic cranial irradiation was delivered at the discretion of treating clinicians. RT treatment planning was subject to central quality assurance. Five hundred forty-seven patients were recruited at 73 centers. The median follow-up for the surviving cohort (n = 164) was 81.2 months. The median survival for the OD and BD arms were 25.4 months (95% CI, 21.1-30.9) and 30.0 months (95% CI, 25.3-36.5; hazard ratio, 1.13; 95% CI, 0.92-1.38; P = .247). Performance status and tumor volume were associated with survival on multivariate analysis. No treatment-related deaths occurred subsequent to the initial analysis performed in 2017. Regarding late toxicity, 7 patients in the OD arm developed grade 3 esophagitis, 4 of which went on to develop stricture or fistulation, compared with no patients in the BD arm. Grade 3 pulmonary fibrosis occurred in 2 and 3 patients in the OD and BD arms, respectively. As the CONVERT trial did not demonstrate the superiority of OD RT and this regimen had a slightly worse toxicity profile after 80 months of follow-up, 45 Gy BD should remain the standard of care in limited stage small cell lung cancer.

The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas

BackgroundAccurate gross tumor volume (GTV) delineation is a critical step in radiation therapy treatment planning. However, it is reader dependent and thus susceptible to intra- and inter-reader variability. GTV delineation of soft tissue sarcoma (STS) often relies on CT and MR images. PurposeThis study investigates the potential role of 18F-FDG PET in reducing intra- and inter-reader variability thereby improving reproducibility of GTV delineation in STS, without incurring additional costs or radiation exposure. Materials and MethodsThree readers performed independent GTV delineation of 61 patients with STS using first CT and MR followed by CT, MR, and 18F-FDG PET images. Each reader performed a total of six delineation trials, three trials per imaging modality group. Dice Similarity Coefficient (DSC) score and Hausdorff distance (HD) were used to assess both intra- and inter-reader variability using generated simultaneous truth and performance level estimation (STAPLE) GTVs as ground truth. Statistical analysis was performed using a Wilcoxon signed-ranked test. ResultsThere was a statistically significant decrease in both intra- and inter-reader variability in GTV delineation using CT, MR 18F-FDG PET images vs. CT and MR images. This was translated by an increase in the DSC score and a decrease in the HD for GTVs drawn from CT, MR and 18F-FDG PET images vs. GTVs drawn from CT and MR for all readers and across all three trials. ConclusionIncorporation of 18F-FDG PET into CT and MR images decreased intra- and inter-reader variability and subsequently increased reproducibility of GTV delineation in STS.

RADCURE: An open-source head and neck cancer CT dataset for clinical radiation therapy insights.

This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ-at-risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom-built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor-Node-Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow-up is five years, with 60% of the cohort surviving until the last follow-up point. The dataset provides images and contours in DICOM CT and RT-STRUCT formats, respectively. We have standardized the nomenclature for individual contours-such as the gross primary tumor, gross nodal volumes, and 19 organs-at-risk-to enhance the RT-STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma-separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non-invasive biomarkers, or to forge prognostic models.

Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology

Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next-generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's 'segment anything' mode, and automatic segmentation using SAM's 'box prompt' mode that implements manual interaction via live prompts during segmentation. Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea. Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box-prompt method enables the users to augment auto-segmentation interactively and dynamically, leading to patient-specific auto-segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto-segmentation model in radiotherapy.

The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning.

Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.

Influence of Early Versus Delayed Hepatic Artery Perfusion Scan on 90Y Selective Internal Radiation Therapy Planning.

Purpose: The aim of this study was to evaluate the effect of an increase in the time interval between hepatic intra-arterial injection of 99mTc-macroaggregated albumin (MAA) and hepatic artery perfusion scintigraphy (HAPS) on the lung shunt fraction (LSF) and perfused volume (PV) calculations in the treatment planning of selective internal radiation therapy (SIRT). Methods: The authors enrolled 51 HAPS sessions from 40 patients diagnosed with primary or metastatic liver malignancy. All patients underwent scan at the first and fourth hour after hepatic arterial injection of 99mTc-MAA. Based on single-photon emission computed tomography images, LSF values were measured from each patient's first and fourth hour images. PV1 and PV4 were also calculated based on three-dimensional images using 5% and 10% cutoff threshold values and compared with each other. Results: The authors found that the median of LSF4 was statistically significantly higher than LSF1 (3.05 vs. 4.14, p ≤ 0.01). There was no statistically significant difference between PV1 and PV4 on the 10% (p = 0.72) thresholds. Conclusions: LSF values can be overestimated in case of delayed HAPS, potentially leading to treatment cancellation due to incorrectly high results in patients who could benefit from SIRT. Threshold-based PV values do not significantly change over time; nevertheless, keeping the short interval time would be safer.

Four-dimensional treatment planning strategies for dynamic tumor tracking.

Dynamic tumor tracking (DTT) is a motion management technique where the radiation beam follows a moving tumor in real time. Not modelling DTT beam motion in the treatment planning system leaves an organ at risk (OAR) vulnerable to exceeding its dose limit. This work investigates two planning strategies for DTT plans, the "Boolean OAR Method" and the "Aperture Sorting Method," to determine if they can successfully spare an OAR while maintaining sufficient target coverage. A step-and-shoot intensity modulated radiation therapy (sIMRT) treatment plan was re-optimized for 10 previously treated liver stereotactic ablative radiotherapy patients who each had one OAR very close to the target. Two planning strategies were investigated to determine which is more effective at sparing an OAR while maintaining target coverage: (1) the "Boolean OAR Method" created a union of an OAR's contours from two breathing phases (exhale and inhale) on the exhale phase (the planning CT) and protected this combined OAR during plan optimization, (2) the "Aperture Sorting Method" assigned apertures to the breathing phase where they contributed the least to an OAR's maximum dose. All 10 OARs exceeded their dose constraints on the original plan four-dimensional (4D) dose distributions and average target coverage was V100% =91.3%±2.9% (ranging from 85.1% to 94.8%). The "Boolean OAR Method" spared 7/10 OARs, and mean target coverage decreased to V100% =87.1%±3.8% (ranging from 80.7% to 93.7%). The "Aperture Sorting Method" spared 9/10 OARs and the mean target coverage remained high at V100% =91.7%±2.8% (ranging from 84.9% to 94.5%). 4D planning strategies are simple to implement and can improve OAR sparing during DTT treatments. The "Boolean OAR Method" improved sparing of OARs but target coverage was reduced. The "Aperture Sorting Method" further improved sparing of OARs and maintained target coverage.

LENAS: Learning-Based Neural Architecture Search and Ensemble for 3-D Radiotherapy Dose Prediction.

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners. Moreover, the complexity of model ensembles is a major concern, as it requires maintaining multiple models during inference, leading to increased computational cost and storage overhead. In this study, we propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3-D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance and significant diversity. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network. Furthermore, to preserve high-level semantic information, we design a hybrid loss to optimize the student network, enabling it to recover the knowledge embedded within the teacher networks. The proposed method has been evaluated on two public datasets: 1) OpenKBP and 2) AIMIS. Extensive experimental results demonstrate the effectiveness of our method and its superior performance to the state-of-the-art methods. Code: github.com/hust-linyi/LENAS.

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomography (sCT) is generated from MRI to provide electron density information for dose calculation. Bone/air regions produce little MRI signal which could lead to electron density misclassification in sCT. Establishing the dosimetric impact of this error could inform quality assurance (QA) procedures using MRI-only RT planning or compensatory methods for accurate dosimetric calculation. The aim of this study was to investigate if Hounsfield unit (HU) voxel misassignments from sCT images result in dosimetric errors in clinical treatment plans. Fourteen H&N cancer patients undergoing same-day CT and 3T MRI simulation were retrospectively identified. MRI was deformed to the CT using multimodal deformable image registration. sCTs were generated from T1w DIXON MRIs using a commercially available deep learning-based generator (MRIplanner, Spectronic Medical AB, Helsingborg, Sweden). Tissue voxel assignment was quantified by creating a CT-derived HU threshold contour. CT/sCT HU differences for anatomical/target contours and tissue classification regions including air (<250 HU), adipose tissue (-250 HU to -51 HU), soft tissue (-50 HU to 199 HU), spongy (200 HU to 499 HU) and cortical bone(>500 HU) were quantified. t-test was used to determine if sCT/CT HU differences were significant. The frequency of structures that had a HU difference>80 HU (the CT window-width setting for intra-cranial structures) was computed to establish structure classification accuracy. Clinical intensity modulated radiation therapy (IMRT) treatment plans created on CT were retrospectively recalculated on sCT images and compared using the gamma metric. The mean ratio of sCT HUs relative to CT for air, adipose tissue, soft tissue, spongy and cortical bone were 1.7±0.3, 1.1±0.1, 1.0±0.1, 0.9±0.1 and 0.8±0.1 (value of 1 indicates perfect agreement). T-tests (significance set at t=0.05) identified differences in HU values for air, spongy and cortical bone in sCT images compared to CT. The structures with sCT/CT HU differences>80 HU of note were the left and right (L/R) cochlea and mandible (>79% of the tested cohort), the oral cavity (for 57% of the tested cohort), the epiglottis (for 43% of the tested cohort) and the L/R TM joints (occurring>29% of the cohort). In the case of the cochlea and TM joints, these structures contain dense bone/air interfaces. In the case of the oral cavity and mandible, these structures suffer the additional challenge of being positionally altered in CT versus MRI simulation (due to a non-MR safe immobilizing bite block requiring absence of bite block in MR). Finally, the epiglottis HU assignment suffers from its small size and unstable positionality. Plans recalculated on sCT yielded global/local gamma pass rates of 95.5%±2% (3mm, 3%) and 92.7%±2.1% (2mm, 2%). The largest mean differences in D95, Dmean , D50 dose volume histogram (DVH) metrics for organ-at-risk (OAR) and planning tumor volumes (PTVs) were 2.3%±3.0% and 0.7%±1.9% respectively. In this cohort, HU differences of CT and sCT were observed but did not translate into a reduction in gamma pass rates or differences in average PTV/OAR dose metrics greater than 3%. For sites such as the H&N where there are many tissue interfaces we did not observe large scale dose deviations but further studies using larger retrospective cohorts are merited to establish the variation in sCT dosimetric accuracy which could help to inform QA limits on clinical sCT usage.