Artificial-intelligence-driven measurements of brain metastases' response to SRS compare favorably with current manual standards of assessment.

Background: Evaluation of treatment response for brain metastases (BMs) following stereotactic radiosurgery (SRS) becomes complex as the number of treated BMs increases. This study uses artificial intelligence (AI) to track BMs after SRS and validates its output compared with manual measurements. Methods: Patients with BMs who received at least one course of SRS and followed up with MRI scans were retrospectively identified. A tool for automated detection, segmentation, and tracking of intracranial metastases on longitudinal imaging, MEtastasis Tracking with Repeated Observations (METRO), was applied to the dataset. The longest three-dimensional (3D) diameter identified with METRO was compared with manual measurements of maximum axial BM diameter, and their correlation was analyzed. Change in size of the measured BM identified with METRO after SRS treatment was used to classify BMs as responding, or not responding, to treatment, and its accuracy was determined relative to manual measurements. Results: From 71 patients, 176 BMs were identified and measured with METRO and manual methods. Based on a one-to-one correlation analysis, the correlation coefficient was R2 = 0.76 (P = .0001). Using modified BM response classifications of BM change in size, the longest 3D diameter data identified with METRO had a sensitivity of 0.72 and a specificity of 0.95 in identifying lesions that responded to SRS, when using manual axial diameter measurements as the ground truth. Conclusions: Using AI to automatically measure and track BM volumes following SRS treatment, this study showed a strong correlation between AI-driven measurements and the current clinically used method: manual axial diameter measurements.

Clinical Experience With an Offline Adaptive Radiation Therapy Head and Neck Program: Dosimetric Benefits and Opportunities for Patient Selection.

PURPOSE: The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART. METHODS AND MATERIALS: Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis. RESULTS: In total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10-5), parotids (-68.2 ± 197.7 cGy, P = .001), and oral cavity (-238.7 ± 206.7 cGy, P < 10-5) with the adaptive plan. Normal tissue complication probabilities for xerostomia computed from mean parotid doses also decreased significantly with the adaptive plans (P = .008). We also observed systematic intratreatment volume reductions (ΔV) for GTVs and normal tissues. Candidate triggers were identified that predicted significant improvement with ART, including parotid ΔV = 7%, neck ΔV = 2%, and nodal GTV ΔV = 29%. CONCLUSIONS: Systematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.

A Potential Pitfall and Clinical Solutions in Surface-Guided Deep Inspiration Breath Hold Radiation Therapy for Left-Sided Breast Cancer.

Purpose: Deep inspiration breath hold (DIBH) is an effective technique to spare the heart in treating left-sided breast cancer. Surface-guided radiation therapy (SGRT) is increasingly applied in DIBH setup and motion monitoring. Patient-specific breathing behavior, either thoracically driven or abdominally driven (A-DIBH), should be unaltered, online identified, and monitored accordingly to ensure reproducible heart-sparing treatment. Methods and Materials: Sixty patients with left-sided breast cancer treated with SGRT were analyzed: 20 A-DIBH patients with vertical chest elevation (VCE ≤ 5 mm) were prospectively identified, and 40 control patients were retrospectively and randomly selected for comparison. At simulation, both free-breathing (FB) and DIBH computed tomography (CT) were acquired, guided by a motion surrogate placed around the xiphoid process. For SGRT treatment setups, the region of interest (ROI) was defined on the CT chest surface, and the surrogate-based setup was a backup. For all 60 patients, the VCE was measured as the average of the FB-to-DIBH elevations at the breast and xiphoid process, together with abdominal elevation. In the 40-patient control group, A-DIBH patients (VCE ≤ 5 mm) were identified. Of the 20 A-DIBH patients, 10 were treated with volumetric modulated arc therapy plans, and 10 patients were treated with tangent plans. Clinical DIBH plans were recalculated on FB CT to compare maximum dose (DMax), 5% of the maximum dose (D5%), mean dose (DMean), and V30Gy, V20Gy, and V5Gy of the heart and lungs and their significance. Results: In the 20 A-DIBH patients, VCE = 3 ± 2 mm, surrogate motion (9 ± 6 mm), and abdomen motion of 14 ± 5 mm are found. Heart dose reduction from FB to DIBH is significant (P < .01): ∆DMax = -8.4 ± 9.8 Gy, ∆D5% = -2.4 ± 4.4 Gy, and ∆DMean = -0.6 ± 0.9 Gy. Six out of 40 control patients (15%) are found to have VCE ≤ 5 mm. Conclusions: A-DIBH (VCE ≤ 5 mm) patient population is significant (15%), and they should be identified in the SGRT workflow and monitored accordingly. A new abdominal ROI or an abdominal surrogate should be used instead of the conventional chest-only ROI. Patient-specific DIBH should be preserved for higher reproducibility to ensure heart sparing.

Automatically tracking brain metastases after stereotactic radiosurgery.

Background and purpose: Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. Methods: The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. Results: A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was -8 ± 17%. The mean registration error was 1.5 ± 0.2 mm. Conclusions: Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.

Automated tracking of morphologic changes in weekly magnetic resonance imaging during head and neck radiotherapy.

BACKGROUND AND PURPOSE: Anatomic changes during head and neck radiotherapy can impact dose delivery, necessitate adaptive replanning, and indicate patient-specific response to treatment. We have developed an automated system to track these changes through longitudinal MRI scans to aid identification and clinical intervention. The purpose of this article is to describe this tracking system and present results from an initial cohort of patients. MATERIALS AND METHODS: The Automated Watchdog in Adaptive Radiotherapy Environment (AWARE) was developed to process longitudinal MRI data for radiotherapy patients. AWARE automatically identifies and collects weekly scans, propagates radiotherapy planning structures, computes structure changes over time, and reports important trends to the clinical team. AWARE also incorporates manual structure review and revision from clinical experts and dynamically updates tracking statistics when necessary. AWARE was applied to patients receiving weekly T2-weighted MRI scans during head and neck radiotherapy. Changes in nodal gross tumor volume (GTV) and parotid gland delineations were tracked over time to assess changes during treatment and identify early indicators of treatment response. RESULTS: N = 91 patients were tracked and analyzed in this study. Nodal GTVs and parotids both shrunk considerably throughout treatment (-9.7 ± 7.7% and -3.7 ± 3.3% per week, respectively). Ipsilateral parotids shrunk significantly faster than contralateral (-4.3 ± 3.1% vs. -2.9 ± 3.3% per week, p = 0.005) and increased in distance from GTVs over time (+2.7 ± 7.2% per week, p < 1 × 10-5 ). Automatic structure propagations agreed well with manual revisions (Dice = 0.88 ± 0.09 for parotids and 0.80 ± 0.15 for GTVs), but for GTVs the agreement degraded 4-5 weeks after the start of treatment. Changes in GTV volume observed by AWARE as early as one week into treatment were predictive of large changes later in the course (AUC = 0.79). CONCLUSION: AWARE automatically identified longitudinal changes in GTV and parotid volumes during radiotherapy. Results suggest that this system may be useful for identifying rapidly responding patients as early as one week into treatment.

Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.

BACKGROUND AND PURPOSE: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. MATERIALS AND METHODS: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2). Clinical dose-volume criteria were compared between the two scenarios (ECHO0 vs. ECHO1; ECHO1 vs. ECHO2; Wilcoxon signed-rank test; significance: p < 0.01). RESULTS: Small systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO1 = 6.2 (range: 0.4, 21) Gy vs. ECHO0 = 6.6 (range: 0.3, 22) Gy; p = 0.007), and the ECHO1 plans provided improved normal tissue sparing across a larger dose-volume range. Only in the ECHO2 plans, all patients fulfilled both MIMean and MPIMean criteria. The population median MIMean and MPIMean were considerably lower than those suggested by the trismus model (ECHO0: MIMean = 13 Gy vs. ≤42 Gy; MPIMean = 29 Gy vs. ≤68 Gy). CONCLUSIONS: Automated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning.

Automatic segmentation of brain metastases using T1 magnetic resonance and computed tomography images.

An increasing number of patients with multiple brain metastases are being treated with stereotactic radiosurgery (SRS). Manually identifying and contouring all metastatic lesions is difficult and time-consuming, and a potential source of variability. Hence, we developed a 3D deep learning approach for segmenting brain metastases on MR and CT images. Five-hundred eleven patients treated with SRS were retrospectively identified for this study. Prior to radiotherapy, the patients were imaged with 3D T1 spoiled-gradient MR post-Gd (T1 + C) and contrast-enhanced CT (CECT), which were co-registered by a treatment planner. The gross tumor volume contours, authored by the attending radiation oncologist, were taken as the ground truth. There were 3 ± 4 metastases per patient, with volume up to 57 ml. We produced a multi-stage model that automatically performs brain extraction, followed by detection and segmentation of brain metastases using co-registered T1 + C and CECT. Augmented data from 80% of these patients were used to train modified 3D V-Net convolutional neural networks for this task. We combined a normalized boundary loss function with soft Dice loss to improve the model optimization, and employed gradient accumulation to stabilize the training. The average Dice similarity coefficient (DSC) for brain extraction was 0.975 ± 0.002 (95% CI). The detection sensitivity per metastasis was 90% (329/367), with moderate dependence on metastasis size. Averaged across 102 test patients, our approach had metastasis detection sensitivity 95 ± 3%, 2.4 ± 0.5 false positives, DSC of 0.76 ± 0.03, and 95th-percentile Hausdorff distance of 2.5 ± 0.3 mm (95% CIs). The volumes of automatic and manual segmentations were strongly correlated for metastases of volume up to 20 ml (r=0.97,p<0.001). This work expounds a fully 3D deep learning approach capable of automatically detecting and segmenting brain metastases using co-registered T1 + C and CECT.

Accuracy of surface-guided patient setup for conventional radiotherapy of brain and nasopharynx cancer.

PURPOSE: To evaluate the accuracy of surface-guided radiotherapy (SGRT) in cranial patient setup by direct comparison between optical surface imaging (OSI) and cone-beam computed tomography (CBCT), before applying SGRT-only setup for conventional radiotherapy of brain and nasopharynx cancer. METHODS AND MATERIALS: Using CBCT as reference, SGRT setup accuracy was examined based on 269 patients (415 treatments) treated with frameless cranial stereotactic radiosurgery (SRS) during 2018-2019. Patients were immobilized in customized head molds and open-face masks and monitored using OSI during treatment. The facial skin area in planning CT was used as OSI region of interest (ROI) for automatic surface alignment and the skull was used as the landmark for automatic CBCT/CT registration. A 6 degrees of freedom (6DOF) couch was used. Immediately after CBCT setup, an OSI verification image was captured, recording the SGRT setup differences. These differences were analyzed in 6DOFs and as a function of isocenter positions away from the anterior surface to assess OSI-ROI bias. The SGRT in-room setup time was estimated and compared with CBCT and orthogonal 2D kilovoltage (2DkV) setups. RESULTS: The SGRT setup difference (magnitude) is found to be 1.0 ± 2.5 mm and 0.1˚±1.4˚ on average among 415 treatments and within 5 mm/3˚ with greater than 95% confidence level (P < 0.001). Outliers were observed for very-posterior isocenters: 15 differences (3.6%) are >5.0mm and 9 (2.2%) are >3.0˚. The setup differences show minor correlations (|r| < 0.45) between translational and rotational DOFs and a minor increasing trend (<1.0 mm) in the anterior-to-posterior direction. The SGRT setup time is 0.8 ± 0.3 min, much shorter than CBCT (5 ± 2 min) and 2DkV (2 ± 1 min) setups. CONCLUSION: This study demonstrates that SGRT has sufficient accuracy for fast in-room patient setup and allows real-time motion monitoring for beam holding during treatment, potentially useful to guide radiotherapy of brain and nasopharynx cancer with standard fractionation.

Predictive dose accumulation for HN adaptive radiotherapy.

During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1-3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 [Formula: see text] 0.07/0.81 [Formula: see text] 0.04 (week 4), 0.79 [Formula: see text] 0.06/0.81 [Formula: see text] 0.05 (week 5) and 0.78 [Formula: see text] 0.06/0.82 [Formula: see text] (week 6). The DICE with the Demons model were 0.78 [Formula: see text] 0.08/0.82 [Formula: see text] 0.03 (week 4), 0.77 [Formula: see text] 0.07/0.82 [Formula: see text] 0.04 (week 5) and 0.75 [Formula: see text] 0.07/0.82 [Formula: see text] 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.

Differences between planned and delivered dose for head and neck cancer, and their consequences for normal tissue complication probability and treatment adaptation.

BACKGROUND AND PURPOSE: Anatomical changes induce differences between planned and delivered dose. Adaptive radiotherapy (ART) may reduce these differences but the optimal implementation is insufficiently clear. The aims of this study were to quantify the difference between planned and delivered dose in HNC patients, assess the consequential difference in normal tissue complication probability (ΔNTCP) and to explore the value of ΔNTCP as an objective selection strategy for ART. MATERIALS AND METHODS: For 52 patients, daily doses were accumulated to estimate the delivered dose. The difference from planned dose was analyzed for CTVs and 9 organs-at-risk (OAR). ΔNTCP was calculated for xerostomia, dysphagia, parotid gland dysfunction and tube feeding dependency at 6 months. ART was deemed necessary if ΔNTCP was >5%. The positive predicted value (PPV) was calculated for identification of ART-patients by clinical judgement, and ΔNTCP at fraction 10 and 15. RESULTS: ΔNTCP >5% was seen five times for dysphagia and twice for the other toxicities. Only 5/9 patients with any ΔNTCP >5% clinically received ART, although ART had been done for 13/52 patients (PPV: 0.38). PPV was 0.86 and 0.75 for accumulated dose at fraction 10 and 15, respectively, using a ΔNTCP cut-off for the allocation of ART of 5%. Using other ΔNTCP cut-offs did not substantially improve PPV. With this cut-off the negative predictive value was 0.93 for ΔNTCP method of fraction 10 and fraction 15, and 0.90 for clinical judgement. CONCLUSION: To identify patients accurately for ART, NTCP calculations based on the dose differences between planned and delivered dose at fraction 10 are superior to clinical judgement.

A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma.

First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUVmax). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.

Magnetic Resonance-based Response Assessment and Dose Adaptation in Human Papilloma Virus Positive Tumors of the Oropharynx treated with Radiotherapy (MR-ADAPTOR): An R-IDEAL stage 2a-2b/Bayesian phase II trial.

BACKGROUND: Current standard radiotherapy for oropharynx cancer (OPC) is associated with high rates of severe toxicities, shown to adversely impact patients' quality of life. Given excellent outcomes of human papilloma virus (HPV)-associated OPC and long-term survival of these typically young patients, treatment de-intensification aimed at improving survivorship while maintaining excellent disease control is now a central concern. The recent implementation of magnetic resonance image - guided radiotherapy (MRgRT) systems allows for individual tumor response assessment during treatment and offers possibility of personalized dose-reduction. In this 2-stage Bayesian phase II study, we propose to examine weekly radiotherapy dose-adaptation based on magnetic resonance imaging (MRI) evaluated tumor response. Individual patient's plan will be designed to optimize dose reduction to organs at risk and minimize locoregional failure probability based on serial MRI during RT. Our primary aim is to assess the non-inferiority of MRgRT dose adaptation for patients with low risk HPV-associated OPC compared to historical control, as measured by Bayesian posterior probability of locoregional control (LRC). METHODS: Patients with T1-2 N0-2b (as per AJCC 7th Edition) HPV-positive OPC, with lymph node <3 cm and <10 pack-year smoking history planned for curative radiotherapy alone to a dose of 70 Gy in 33 fractions will be eligible. All patients will undergo pre-treatment MRI and at least weekly intra-treatment MRI. Patients undergoing MRgRT will have weekly adaptation of high dose planning target volume based on gross tumor volume response. The stage 1 of this study will enroll 15 patients to MRgRT dose adaptation. If LRC at 6 months with MRgRT dose adaptation is found sufficiently safe as per the Bayesian model, stage 2 of the protocol will expand enrollment to an additional 60 patients, randomized to either MRgRT or standard IMRT. DISCUSSION: Multiple methods for safe treatment de-escalation in patients with HPV-positive OPC are currently being studied. By leveraging the ability of advanced MRI techniques to visualize tumor and soft tissues through the course of treatment, this protocol proposes a workflow for safe personalized radiation dose-reduction in good responders with radiosensitive tumors, while ensuring tumoricidal dose to more radioresistant tumors. MRgRT dose adaptation could translate in reduced long term radiation toxicities and improved survivorship while maintaining excellent LRC outcomes in favorable OPC. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03224000; Registration date: 07/21/2017.

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor spread is necessary. This paper proposes a solution to auto-segment these volumes in oropharyngeal cancer patients using a two-channel 3D U-Net architecture. The first channel feeds the network with the patient's CT image providing anatomical context, whereas the second channel provides the network with tumor location and morphological information. Radiation therapy simulation computer tomography scans and their corresponding manually delineated CTV and gross tumor volume (GTV) delineations from 285 oropharyngeal patients previously treated at MD Anderson Cancer Center were used in this study. CTV and GTV delineations underwent rigorous group peer-review prior to the start of treatment delivery. The convolutional network's parameters were fine-tuned using a training set of 210 patients using 3-fold cross-validation. During hyper-parameter selection, we use a score based on the overlap (dice similarity coefficient (DSC)) and missed volumes (false negative dice (FND)) to minimize any possible under-treatment. Three auto-delineated models were created to estimate tight, moderate, and wide CTV margin delineations. Predictions on our test set (75 patients) resulted in auto-delineations with high overlap and close surface distance agreement (DSC > 0.75 on 96% of cases for tight and moderate auto-delineation models and 97% of cases having mean surface distance ⩽ 5.0 mm) to the ground-truth. We found that applying a 5 mm uniform margin expansion to the auto-delineated CTVs would cover at least 90% of the physician CTV volumes for a large majority of patients; however, determination of appropriate margin expansions for auto-delineated CTVs merits further investigation.

Prospective in silico study of the feasibility and dosimetric advantages of MRI-guided dose adaptation for human papillomavirus positive oropharyngeal cancer patients compared with standard IMRT.

PURPOSE: We aim to determine the feasibility and dosimetric benefits of a novel MRI-guided IMRT dose-adaption strategy for human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPC). MATERIALS/METHODS: Patients with locally advanced HPV+ OPC underwent pre-treatment and in-treatment MRIs every two weeks using RT immobilization setup. For each patient, two IMRT plans were created (i.e. standard and adaptive). The prescription dose for the standard plans was 2.12 Gy/fx for 33 fractions to the initial PTV. For adaptive plans, a new PTVadaptive was generated based on serial MRIs in case of detectable tumor shrinkage. Prescription dose to PTVadaptive was 2.12 Gy/fx to allow for maximum dose to the residual disease. Any previously involved volumes received minimally a floor dose of 50.16 Gy. Uninvolved elective nodal volumes were prescribed 50.16 Gy in 1.52 Gy/fx. Dosimetric parameters of organs at risk (OARs) were recorded for standard vs. adaptive plans. Normal tissue complication probability (NTCP) for toxicity endpoints was calculated using literature-derived multivariate logistic regression models. RESULTS: Five patients were included in this pilot study, 3 men and 2 women. Median age was 58 years (range 45-69). Three tumors originated at the tonsillar fossa and two at the base of tongue. The average dose to 95% of initial PTV volume was 70.7 Gy (SD,0.3) for standard plans vs. 58.5 Gy (SD,2.0) for adaptive plans. The majority of OARs showed decrease in dosimetric parameters using adaptive plans vs. standard plans, particularly swallowing related structures. The average reduction in the probability of developing dysphagia ≥ grade2, feeding tube persistence at 6-month post-treatment and hypothyroidism at 1-year post-treatment was 11%, 4%, and 5%, respectively. The probability of xerostomia at 6-month was only reduced by 1% for adaptive plans vs. standard IMRT. CONCLUSION: These in silico results showed that the proposed MRI-guided adaptive approach is technically feasible and advantageous in reducing dose to OARs, especially swallowing musculature.

Omitting cardiophrenic lymph nodes in the treatment of patients with Hodgkin lymphoma via modified involved-site radiation therapy.

Cardiophrenic lymph nodes (CPLNs) are occasionally involved in Hodgkin lymphoma (HL). We characterized the incidence of CPLN involvement among 169 HL patients and evaluated outcomes after treatment with omission of the CPLN region from the involved-site radiation therapy (ISRT) field. Three types of RT fields were used: standard (S)-ISRT, reduced-dose (RD)-ISRT (lower dose to CPLNs, standard to other sites), or modified (M)-ISRT (omission of CPLNs). CPLNs were involved at diagnosis in 29 patients (17%). Of the 20 patients who received RT after complete response to chemotherapy, 4(20%) received S-ISRT, 8(40%) RD-ISRT, and 8(40%) M-ISRT. The four-year progression-free survival was 94.7%. One relapse occurred at a non-CPLN site after RD-ISRT. The mean heart dose and volume of the heart that received 25 Gy was higher for S-ISRT patients compared to M-ISRT (p = .043 and p = .025, respectively). Re-planning the M-ISRT cases as S-ISRT resulted in significant increase in cardiac doses.

Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function.

PURPOSE: Automating and standardizing the contouring of clinical target volumes (CTVs) can reduce interphysician variability, which is one of the largest sources of uncertainty in head and neck radiation therapy. In addition to using uniform margin expansions to auto-delineate high-risk CTVs, very little work has been performed to provide patient- and disease-specific high-risk CTVs. The aim of the present study was to develop a deep neural network for the auto-delineation of high-risk CTVs. METHODS AND MATERIALS: Fifty-two oropharyngeal cancer patients were selected for the present study. All patients were treated at The University of Texas MD Anderson Cancer Center from January 2006 to August 2010 and had previously contoured gross tumor volumes and CTVs. We developed a deep learning algorithm using deep auto-encoders to identify physician contouring patterns at our institution. These models use distance map information from surrounding anatomic structures and the gross tumor volume as input parameters and conduct voxel-based classification to identify voxels that are part of the high-risk CTV. In addition, we developed a novel probability threshold selection function, based on the Dice similarity coefficient (DSC), to improve the generalization of the predicted volumes. The DSC-based function is implemented during an inner cross-validation loop, and probability thresholds are selected a priori during model parameter optimization. We performed a volumetric comparison between the predicted and manually contoured volumes to assess our model. RESULTS: The predicted volumes had a median DSC value of 0.81 (range 0.62-0.90), median mean surface distance of 2.8 mm (range 1.6-5.5), and median 95th Hausdorff distance of 7.5 mm (range 4.7-17.9) when comparing our predicted high-risk CTVs with the physician manual contours. CONCLUSIONS: These predicted high-risk CTVs provided close agreement to the ground-truth compared with current interobserver variability. The predicted contours could be implemented clinically, with only minor or no changes.

Illustrated instructions for mechanical quality assurance of a medical linear accelerator.

PURPOSE: The purpose of this study was to develop and test a set of illustrated instructions for effective training for mechanical quality assurance (QA) of medical linear accelerators (linac). METHODS: Illustrated instructions were created for mechanical QA and underwent several steps of review, testing, and refinement. Eleven testers with no recent QA experience were then recruited from our radiotherapy department (one student, two computational scientists, and eight dosimetrists). This group was selected because they have experience of radiation therapy but no preconceived ideas about how to do QA. The following parameters were progressively decalibrated on a Varian C-series linac: Group A = gantry angle, ceiling laser position, X1 jaw position, couch longitudinal position, physical graticule position (five testers); Group B = Group A + wall laser position, couch lateral and vertical position, collimator angle (three testers); Group C = Group B + couch angle, wall laser angle, and optical distance indicator (three testers). Testers were taught how to use the linac and then used the instructions to try to identify these errors. An experienced physicist observed each session, giving support on machine operation as necessary. RESULTS: Testers were able to follow the instructions. They determined gantry, collimator, and couch angle errors within 0.4°, 0.3°, and 0.9° of the actual changed values, respectively. Laser positions were determined within 1 mm and jaw positions within 2 mm. Couch position errors were determined within 2 mm and 3 mm for lateral/longitudinal and vertical errors, respectively. Accessory-positioning errors were determined within 1 mm. Optical distance indicator errors were determined within 2 mm when comparing with distance sticks and 6 mm when using blocks, indicating that distance sticks should be the preferred approach for inexperienced staff. CONCLUSIONS: Inexperienced users were able to follow these instructions and catch errors within the criteria suggested by AAPM TG-142 for linacs used for intensity-modulated radiation therapy. These instructions are, therefore, suitable for QA training.

Evaluation of diffusion weighted imaging for tumor delineation in head-and-neck radiotherapy by comparison with automatically segmented 18F-fluorodeoxyglucose positron emission tomography.

BACKGROUND AND PURPOSE: Diffusion weighted (DW) MRI may facilitate target volume delineation for head-and-neck (HN) radiation treatment planning. In this study we assessed the use of a dedicated, geometrically accurate, DW-MRI sequence for target volume delineation. The delineations were compared with semi-automatic segmentations on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images and evaluated for interobserver variation. METHODS AND MATERIALS: Fifteen HN cancer patients underwent both DW-MRI and FDG-PET for RT treatment planning. Target delineation on DW-MRI was performed by three observers, while for PET a semi-automatic segmentation was performed using a Gaussian mixture model. For interobserver variation and intermodality variation, volumes, overlap metrics and Hausdorff distances were calculated from the delineations. RESULTS: The median volumes delineated by the three observers on DW-MRI were 10.8, 10.5 and 9.0 cm3 respectively, and was larger than the median PET volume (8.0 cm3). The median conformity index of DW-MRI for interobserver variation was 0.73 (range 0.38-0.80). Compared to PET, the delineations on DW-MRI by the three observers showed a median dice similarity coefficient of 0.71, 0.69 and 0.72 respectively. The mean Hausdorff distance was small with median (range) distances between PET and DW-MRI of 2.3 (1.5-6.8), 2.5 (1.6-6.9) and 2.0 (1.35-7.6) mm respectively. Over all patients, the median 95th percentile distances were 6.0 (3.0-13.4), 6.6 (4.0-24.0) and 5.3 (3.4-26.0) mm. CONCLUSION: Using a dedicated DW-MRI sequence, target volumes could be defined with good interobserver agreement and a good overlap with PET. Target volume delineation using DW-MRI is promising in head-and-neck radiotherapy, combined with other modalities, it can lead to more precise target volume delineation.

Deep-Inspiration Breath-Hold Intensity Modulated Radiation Therapy to the Mediastinum for Lymphoma Patients: Setup Uncertainties and Margins.

PURPOSE: Patient setup for treating large target volumes can be challenging. In the present study, we measured the local uncertainties in the treatment of mediastinal lymphoma and investigated the need for region-specific planning target volume (PTV) margins. METHODS AND MATERIALS: The data from 30 patients who had undergone radiation therapy for mediastinal lymphoma were retrospectively analyzed. A computed tomography (CT)-on-rails (CTOR) system in the treatment room was used for daily image guidance. The total PTV was split into 6 regions: neck, supraclavicular fossa, axilla, mediastinum, upper heart, and lower heart. The total PTV and the 6 local regions were separately aligned to the planning CT scan using automatic rigid registration. The residual local errors using 3 setup strategies were investigated: no image guidance, CTOR setup to total PTV, and simulated cone beam CT setup to the mediastinum. Errors were recorded in the anteroposterior, superoinferior, and right-left directions separately. Using the residual error calculations, the margins required to cover 95% of the clinical target volume for 90% of the patients was estimated. RESULTS: For each patient, 12 to 21 days of daily CTOR data were available for analysis. The residual errors for the total PTV and mediastinum setups were both smaller than those with no image guidance. The lower heart region had more uncertainty with all 3 setup strategies. Margin analysis revealed that the magnitude of the margin is dependent on the imaging strategy, direction, and local region inside the PTV. Margins >7 mm are necessary to account for uncertainty in the neck, lower heart, and axilla regions even under daily CT guidance. CONCLUSIONS: Setup uncertainties in the mediastinum are not uniform and are dependent on target location and imaging strategy. However, with the appropriate margin, we can target regions that might not be visualized with the available on-board imager system.

Patterns-of-failure guided biological target volume definition for head and neck cancer patients: FDG-PET and dosimetric analysis of dose escalation candidate subregions.

BACKGROUND: To identify the radio-resistant subvolumes in pretreatment FDG-PET by mapping the spatial location of the origin of tumor recurrence after IMRT for head-and-neck squamous cell cancer to the pretreatment FDG-PET/CT. METHODS: Patients with local/regional recurrence after IMRT with available FDG-PET/CT and post-failure CT were included. For each patient, both pre-therapy PET/CT and recurrence CT were co-registered with the planning CT (pCT). A 4-mm radius was added to the centroid of mapped recurrence growth target volumes (rGTV's) to create recurrence nidus-volumes (NVs). The overlap between boost-tumor-volumes (BTV) representing different SUV thresholds/margins combinations and NVs was measured. RESULTS: Forty-seven patients were eligible. Forty-two (89.4%) had type A central high dose failure. Twenty-six (48%) of type A rGTVs were at the primary site and 28 (52%) were at the nodal site. The mean dose of type A rGTVs was 71Gy. BTV consisting of 50% of the maximum SUV plus 10mm margin was the best subvolume for dose boosting due to high coverage of primary site NVs (92.3%), low average relative volume to CTV1 (41%), and least average percent voxels outside CTV1 (19%). CONCLUSIONS: The majority of loco-regional recurrences originate in the regions of central-high-dose. When correlated with pretreatment FDG-PET, the majority of recurrences originated in an area that would be covered by additional 10mm margin on the volume of 50% of the maximum FDG uptake.