Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer

BACKGROUND

The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning.

PURPOSE

This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment.

METHODS

We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n = 174), validation (10%, n = 24), and test (20%, n = 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network.

RESULTS

In the independent test set (n = 51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10-4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively.

CONCLUSIONS

The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning.

Stereotactic Radiation for Ultra-Central Non-Small Cell Lung Cancer: A Safety and Efficacy Trial (SUNSET)

PURPOSE

The use of stereotactic body radiation therapy for tumors in close proximity to the central mediastinal structures has been associated with a high risk of toxicity. This study (NCT03306680) aimed to determine the maximally tolerated dose of stereotactic body radiation therapy for ultracentral non-small cell lung carcinoma, using a time-to-event continual reassessment methodology.

METHODS AND MATERIALS

Patients with T1-3N0M0 (≤6 cm) non-small cell lung carcinoma were eligible. The maximally tolerated dose was defined as the dose of radiation therapy associated with a ≤30% rate of grade (G) 3 to 5 prespecified treatment-related toxicity occurring within 2 years of treatment. The starting dose level was 60 Gy in 8 daily fractions. The dose-maximum hotspot was limited to 120% and within the planning tumor volume; tumors with endobronchial invasion were excluded. This primary analysis occurred 2 years after completion of accrual.

RESULTS

Between March 2018 and April 2021, 30 patients were enrolled at 5 institutions. The median age was 73 years (range, 65-87) and 17 (57%) were female. Planning tumor volume was abutting proximal bronchial tree in 19 (63%), esophagus 5 (17%), pulmonary vein 1 (3.3%), and pulmonary artery 14 (47%). All patients received 60 Gy in 8 fractions. The median follow-up was 37 months (range, 8.9-51). Two patients (6.7%) experienced G3-5 adverse events related to treatment: 1 patient with G3 dyspnea and 1 G5 pneumonia. The latter had computed tomography findings consistent with a background of interstitial lung disease. Three-year overall survival was 72.5% (95% CI, 52.3%-85.3%), progression-free survival 66.1% (95% CI, 46.1%-80.2%), local control 89.6% (95% CI, 71.2%-96.5%), regional control 96.4% (95% CI, 77.2%-99.5%), and distant control 85.9% (95% CI, 66.7%-94.5%). Quality-of-life scores declined numerically over time, but the decreases were not clinically or statistically significant.

CONCLUSIONS

Sixty Gy in 8 fractions, planned and delivered with only a moderate hotspot, has a favorable adverse event rate within the prespecified acceptability criteria and results in excellent control for ultracentral tumors.

The role of medical physics experts in clinical trials: A guideline from the European Federation of Organisations for Medical Physics

The EFOMP working group on the Role of Medical Physics Experts (MPEs) in Clinical Trials was established in 2010, with experts from across Europe and different areas of medical physics. Their main aims were: (1) To develop a consensus guidance document for the work MPEs do in clinical trials across Europe. (2) Complement the work by American colleagues in AAPM TG 113 and guidance from National Member Organisations. (3) To cover external beam radiotherapy, brachytherapy, nuclear medicine, molecular radiotherapy, and imaging. This document outlines the main output from this working group. Giving guidance to MPEs, and indeed all Medical Physicists (MP) and MP trainees wishing to work in clinical trials. It also gives guidance to the wider multidisciplinary team, advising where MPEs must legally be involved, as well as highlighting areas where MPEs skills and expertise can really add value to clinical trials.

Impact of on-trial IGRT quality assurance in an international adaptive radiotherapy trial for participants with bladder cancer

BACKGROUND AND PURPOSE

Radiotherapy trial quality assurance (RT QA) is crucial for ensuring the safe and reliable delivery of radiotherapy trials, and minimizing inter-institutional variations. While previous studies focused on outlining and planning quality assurance (QA), this work explores the process of Image-Guided Radiotherapy (IGRT), and adaptive radiotherapy. This study presents findings from during-accrual QA in the RAIDER trial, evaluating concordance between online and offline plan selections for bladder cancer participants undergoing adaptive radiotherapy. RAIDER had two seamless stages; stage 1 assessed adherence to dose constraints of dose escalated radiotherapy (DART) and stage 2 assessed safety. The RT QA programme was updated from stage 1 to stage 2.

MATERIALS AND METHODS

Data from all participants in the adaptive arms (standard dose adaptive radiotherapy (SART) and DART) of the trial was requested (33 centres across the UK, Australia and New Zealand). Data collection spanned September 2015 to December 2022 and included the plans selected online, on Cone-Beam Computed Tomography (CBCT) data. Concordance with the plans selected offline by the independent RT QA central reviewer was evaluated.

RESULTS

Analysable data was received for 72 participants, giving a total of 884 CBCTs. The overall concordance rate was 83% (723/884). From stage 1 to stage 2 the concordance in the plans selected improved from 75% (369/495) to 91% (354/389).

CONCLUSION

During-accrual IGRT QA positively influenced plan selection concordance, highlighting the need for ongoing support when introducing a new technique. Overall, it contributes to advancing the understanding and implementation of QA measures in adaptive radiotherapy trials.

Choice of Adjuvant Radiotherapy Facility in Sinonasal Squamous Cell Carcinoma

OBJECTIVE

Undergoing surgery and adjuvant radiotherapy (aRT) at the same facility has been associated with higher overall survival (OS) in head and neck squamous cell carcinoma. Our study investigates whether undergoing surgery and aRT at the same academic facility is associated with higher OS compared with separate facilities in sinonasal squamous cell carcinoma (SNSCC).

METHODS

The 2006 to 2017 National Cancer Database was queried for patients with SNSCC undergoing surgery at an academic facility followed by aRT with or without adjuvant chemotherapy. Multivariable binary logistic and Cox proportional hazards regression models were implemented.

RESULTS

Of 419 patients satisfying inclusion criteria, 299 (71.4%) underwent surgery and aRT at the same academic facility. Residence in a less populated area (adjusted odds ratio [aOR] 1.75, 95% confidence interval [CI] 1.02-2.99, p = 0.042) and surgical facility case volume (aOR 2.51, 95% CI 1.21-5.21, p = 0.014) were associated with undergoing surgery and aRT at different facilities on multivariable logistic regression adjusting for patient demographics, clinicopathologic features, and adjuvant therapy (p < 0.05). Five-year OS was higher among patients undergoing surgery and aRT at the same academic facility (64% vs. 55%, p = 0.039). Undergoing surgery and aRT at different facilities remained associated with worse OS on multivariable Cox regression (aHR 1.90, 95% CI 1.09-3.32, p = 0.023).

CONCLUSION

Undergoing surgery and aRT at the same academic facility is associated with higher OS in SNSCC. Academic physicians should carefully consider the recommendation of aRT treatment facility based on the level of benefit that the patient may derive from coordinated multidisciplinary care.

LEVEL OF EVIDENCE

3 Laryngoscope, 2024.

Centralized Quality Assurance of Stereotactic Body Radiation Therapy for the Veterans Affairs Cooperative Studies Program Study Number 2005: A Phase 3 Randomized Trial of Lung Cancer Surgery or Stereotactic Radiotherapy for Operable Early-Stage Non-Small Cell Lung Cancer (VALOR)

PURPOSE

The phase 3 Veterans Affairs Lung Cancer Surgery Or Stereotactic Radiotherapy study implemented centralized quality assurance (QA) to mitigate risks of protocol deviations. This report summarizes the quality and compliance of the first 100 participants treated with stereotactic body radiation therapy (SBRT) in this study.

METHODS AND MATERIALS

A centralized QA program was developed to credential and monitor study sites to ensure standard-of-care lung SBRT treatments are delivered to participants. Requirements were adapted from protocols established by the National Cancer Institute's Image and Radiation Oncology Core, which provides oversight for clinical trials sponsored by the National Cancer Institute's National Clinical Trials Network.

RESULTS

The first 100 lung SBRT treatment plans were reviewed from April 2017 to October 2022. Tumor contours were appropriate in all submissions. Planning target volume (PTV) expansions were less than the minimum 5 mm requirement in 2% of cases. Critical organ-at-risk structures were contoured accurately for the proximal bronchial tree, trachea, esophagus, spinal cord, and brachial plexus in 75%, 92%, 100%, 100%, and 95% of cases, respectively. Prescriptions were appropriate in 98% of cases; 2 central tumors were treated using a peripheral tumor dose prescription while meeting organ-at-risk constraints. PTV V100% (the percentage of target volume that receives 100% or more of the prescription) values were above the protocol-defined minimum of 94% in all but 1 submission. The median dose maximum (Dmax) within the PTV was 125.4% (105.8%-149.0%; SD ± 8.7%), where values reference the percentage of the prescription dose. High-dose conformality (ratio of the volume of the prescription isodose to the volume of the PTV) and intermediate-dose compactness [R50% (ratio of the volume of the half prescription isodose to the volume of the PTV) and D2cm (the maximum dose beyond a 2 cm expansion of the PTV expressed as a percentage of the prescription dose)] were acceptable or deviation acceptable in 100% and 94% of cases, respectively.

CONCLUSIONS

The first 100 participants randomized to SBRT in this study were appropriately treated without safety concerns. A response to the incorrect prescriptions led to preventative measures without further recurrences. The program was developed in a health care system without prior experience with a centralized radiation therapy QA program and may serve as a reference for other institutions.

Uncertainty estimation using a 3D probabilistic U-Net for segmentation with small radiotherapy clinical trial datasets

BACKGROUND AND OBJECTIVES

Bio-medical image segmentation models typically attempt to predict one segmentation that resembles a ground-truth structure as closely as possible. However, as medical images are not perfect representations of anatomy, obtaining this ground truth is not possible. A surrogate commonly used is to have multiple expert observers define the same structure for a dataset. When multiple observers define the same structure on the same image there can be significant differences depending on the structure, image quality/modality and the region being defined. It is often desirable to estimate this type of aleatoric uncertainty in a segmentation model to help understand the region in which the true structure is likely to be positioned. Furthermore, obtaining these datasets is resource intensive so training such models using limited data may be required. With a small dataset size, differing patient anatomy is likely not well represented causing epistemic uncertainty which should also be estimated so it can be determined for which cases the model is effective or not.

METHODS

We use a 3D probabilistic U-Net to train a model from which several segmentations can be sampled to estimate the range of uncertainty seen between multiple observers. To ensure that regions where observers disagree most are emphasised in model training, we expand the Generalised Evidence Lower Bound (ELBO) with a Constrained Optimisation (GECO) loss function with an additional contour loss term to give attention to this region. Ensemble and Monte-Carlo dropout (MCDO) uncertainty quantification methods are used during inference to estimate model confidence on an unseen case. We apply our methodology to two radiotherapy clinical trial datasets, a gastric cancer trial (TOPGEAR, TROG 08.08) and a post-prostatectomy prostate cancer trial (RAVES, TROG 08.03). Each dataset contains only 10 cases each for model development to segment the clinical target volume (CTV) which was defined by multiple observers on each case. An additional 50 cases are available as a hold-out dataset for each trial which had only one observer define the CTV structure on each case. Up to 50 samples were generated using the probabilistic model for each case in the hold-out dataset. To assess performance, each manually defined structure was matched to the closest matching sampled segmentation based on commonly used metrics.

RESULTS

The TOPGEAR CTV model achieved a Dice Similarity Coefficient (DSC) and Surface DSC (sDSC) of 0.7 and 0.43 respectively with the RAVES model achieving 0.75 and 0.71 respectively. Segmentation quality across cases in the hold-out datasets was variable however both the ensemble and MCDO uncertainty estimation approaches were able to accurately estimate model confidence with a p-value < 0.001 for both TOPGEAR and RAVES when comparing the DSC using the Pearson correlation coefficient.

CONCLUSIONS

We demonstrated that training auto-segmentation models which can estimate aleatoric and epistemic uncertainty using limited datasets is possible. Having the model estimate prediction confidence is important to understand for which unseen cases a model is likely to be useful.

Enhanced head and neck radiotherapy target definition through multidisciplinary delineation and peer review: A prospective single-center study

This study evaluates the benefit of weekly delineation and peer review by a multidisciplinary team (MDT) of radiation oncologists (ROs), radiologists (RXs), and nuclear medicine (NM) physicians in defining primary and lymph node tumor volumes (GTVp and GTVn) for head and neck cancer (HNC) radiotherapy. This study includes 30 consecutive HNC patients referred for definitive curative (chemo)-radiotherapy. Imaging data including head and neck MRI, [18F]-FDG-PET and CT scan were evaluated by the MDT. The RO identified the 'undeniable' tumor as GTVp_core and determined GTVp_max, representing the maximum tumoral volume. The MDT delineation (MDT-D) by RX and NM physicians outlined their respective primary GTVs (GTVp_RX and GTVp_NM). During the MDT meeting (MDT-M), these contours were discussed to reach a consensus on the final primary GTV (GTVp_final). In the comparative analysis of various GTVp delineations, we performed descriptive statistics and assessed two MDT-M factors: 1) the added value of MDT-M, which includes the section of GTVp_final outside GTVp_core but within GTVp_RX or GTVp_NM, and 2) the part of GTVp_final that deviates from GTVp_max, representing the area missed by the RO. For GTVn, discussions evaluated lymph node extent and malignancy, documenting findings and the frequency of disagreements. The average GTVp core and max volumes were 19.5 cc (range: 0.4-90.1) and 22.1 cc (range: 0.8-106.2), respectively. Compared to GTVp_core, MDT-D to GTVp_final added an average of 3.3 cc (range: 0-25.6) and spared an average of 1.3 cc (0-15.6). Compared to GTVp_max, MDT-D and -M added an average of 2.7 cc (range: 0-20.3) and removed 2.3 cc (0-21.3). The most frequent GTVn discussions included morphologically suspicious nodes not fixing on [18F]-FDG-PET and small [18F]-FDG-PET negative retropharyngeal lymph nodes. Multidisciplinary review of target contours in HNC is essential for accurate treatment planning, ensuring precise tumor and lymph node delineation, potentially improving local control and reducing toxicity.

Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation

PURPOSE

To validate a CT-based deep learning (DL) hippocampal segmentation model trained on a single-institutional dataset and explore its utility for multi-institutional contour quality assurance (QA).

METHODS

A DL model was trained to contour hippocampi from a dataset generated by an institutional observer (IO) contouring on brain MRIs from a single-institution cohort. The model was then evaluated on the RTOG 0933 dataset by comparing the treating physician (TP) contours to blinded IO and DL contours using Dice and Haussdorf distance (HD) agreement metrics as well as evaluating differences in dose to hippocampi when TP vs. IO vs. DL contours are used for planning. The specificity and sensitivity of the DL model to capture planning discrepancies was quantified using criteria of HD > 7 mm and Dmax hippocampi > 17 Gy.

RESULTS

The DL model showed greater agreement with IO contours compared to TP contours (DL:IO L/R Dice 74%/73%, HD 4.86/4.74; DL:TP L/R Dice 62%/65%, HD 7.23/6.94, all p < 0.001). Thirty percent of contours and 53 % of dose plans failed QA. The DL model achieved an AUC L/R 0.80/0.79 on the contour QA task via Haussdorff comparison and AUC of 0.91 via Dmax comparison. The false negative rate was 17.2%/20.5% (contours) and 5.8% (dose). False negative cases tended to demonstrate a higher DL:IO Dice agreement (L/R p = 0.42/0.03) and better qualitative visual agreement compared with true positive cases.

CONCLUSION

Our study demonstrates the feasibility of using a single-institutional DL model to perform contour QA on a multi-institutional trial for the task of hippocampal segmentation.

Choice of Adjuvant Radiotherapy Facility in Major Salivary Gland Cancer

OBJECTIVE

Undergoing surgery and adjuvant radiotherapy (aRT) at the same facility has been associated with higher overall survival (OS) in head and neck squamous cell carcinoma. Our study investigates whether undergoing surgery and aRT at the same academic facility is associated with higher OS in major salivary gland cancer (MSGC).

METHODS

The 2006-2018 National Cancer Database was queried for patients with MSGC undergoing surgery at an academic facility and then aRT. Multivariable binary logistic and Cox proportional hazards regression models were implemented.

RESULTS

Of 2801 patients satisfying inclusion criteria, 2130 (76.0%) underwent surgery and aRT at the same academic facility. Residence in a less populated area (adjusted odds ratio [aOR] 1.69, 95% confidence interval [CI] 1.16-2.45), treatment without adjuvant chemotherapy (aOR 1.97, 95% CI 1.41-2.76), and aRT duration (aOR 1.02, 95% CI 1.01-1.04) were associated with undergoing surgery and aRT at different facilities on multivariable logistic regression adjusting for patient demographics, clinicopathologic features, and adjuvant therapy (p < 0.01). Five-year OS was higher in patients undergoing surgery and aRT at the same academic facility (68.8% vs. 61.9%, p < 0.001). Undergoing surgery and aRT at different facilities remained associated with worse OS on multivariable Cox regression (aHR 1.41, 95% CI 1.10-1.81, p = 0.007).

CONCLUSION

Undergoing surgery and aRT at the same academic facility is associated with higher OS in MSGC. Although undergoing surgery and aRT at the same academic facility is impractical for all patients, academic physicians should consider same-facility treatment for complex patients who would most benefit from clear multidisciplinary communication.

LEVEL OF EVIDENCE

4 Laryngoscope, 134:3620-3632, 2024.

Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases

PURPOSE

This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments.

METHODS AND MATERIALS

The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95).

RESULTS

We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection.

CONCLUSIONS

In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

Voxel-Level Dosimetry for Combined Iodine 131 Radiopharmaceutical Therapy and External Beam Radiation Therapy Treatment Paradigms for Head and Neck Cancer

PURPOSE

Targeted radiopharmaceutical therapy (RPT) in combination with external beam radiation therapy (EBRT) shows promise as a method to increase tumor control and mitigate potential high-grade toxicities associated with re-treatment for patients with recurrent head and neck cancer. This work establishes a patient-specific dosimetry framework that combines Monte Carlo-based dosimetry from the 2 radiation modalities at the voxel level using deformable image registration (DIR) and radiobiological constructs for patients enrolled in a phase 1 clinical trial combining EBRT and RPT.

METHODS AND MATERIALS

Serial single-photon emission computed tomography (SPECT)/computed tomography (CT) patient scans were performed at approximately 24, 48, 72, and 168 hours postinjection of 577.2 MBq/m2 (15.6 mCi/m2) CLR 131, an iodine 131-containing RPT agent. Using RayStation, clinical EBRT treatment plans were created with a treatment planning CT (TPCT). SPECT/CT images were deformably registered to the TPCT using the Elastix DIR module in 3D Slicer software and assessed by measuring mean activity concentrations and absorbed doses. Monte Carlo EBRT dosimetry was computed using EGSnrc. RPT dosimetry was conducted using RAPID, a GEANT4-based RPT dosimetry platform. Radiobiological metrics (biologically effective dose and equivalent dose in 2-Gy fractions) were used to combine the 2 radiation modalities.

RESULTS

The DIR method provided good agreement for the activity concentrations and calculated absorbed dose in the tumor volumes for the SPECT/CT and TPCT images, with a maximum mean absorbed dose difference of -11.2%. Based on the RPT absorbed dose calculations, 2 to 4 EBRT fractions were removed from patient EBRT treatments. For the combined treatment, the absorbed dose to target volumes ranged from 57.14 to 75.02 Gy. When partial volume corrections were included, the mean equivalent dose in 2-Gy fractions to the planning target volume from EBRT + RPT differed -3.11% to 1.40% compared with EBRT alone.

CONCLUSIONS

This work demonstrates the clinical feasibility of performing combined EBRT + RPT dosimetry on TPCT scans. Dosimetry guides treatment decisions for EBRT, and this work provides a bridge for the same paradigm to be implemented within the rapidly emerging clinical RPT space.

Randomized Phase 3 Trial of the Hypoxia Modifier Nimorazole Added to Radiation Therapy With Benefit Assessed in Hypoxic Head and Neck Cancers Determined Using a Gene Signature (NIMRAD)

PURPOSE

Tumor hypoxia is an adverse prognostic factor in head and neck squamous cell carcinoma (HNSCC). We assessed whether patients with hypoxic HNSCC benefited from the addition of nimorazole to definitive intensity modulated radiation therapy (IMRT).

METHODS AND MATERIALS

NIMRAD was a phase 3, multicenter, placebo-controlled, double-anonymized trial of patients with HNSCC unsuitable for concurrent platinum chemotherapy or cetuximab with definitive IMRT (NCT01950689). Patients were randomized 1:1 to receive IMRT (65 Gy in 30 fractions over 6 weeks) plus nimorazole (1.2 g/m2 daily, before IMRT) or placebo. The primary endpoint was freedom from locoregional progression (FFLRP) in patients with hypoxic tumors, defined as greater than or equal to the median tumor hypoxia score of the first 50 patients analyzed (≥0.079), using a validated 26-gene signature. The planned sample size was 340 patients, allowing for signature generation in 85% and an assumed hazard ratio (HR) of 0.50 for nimorazole effectiveness in the hypoxic group and requiring 66 locoregional failures to have 80% power in a 2-tail log-rank test at the 5% significance level.

RESULTS

Three hundred thirty-eight patients were randomized by 19 centers in the United Kingdom from May 2014 to May 2019, with a median follow-up of 3.1 years (95% CI, 2.9-3.4). Hypoxia scores were available for 286 (85%). The median patient age was 73 years (range, 44-88; IQR, 70-76). There were 36 (25.9%) locoregional failures in the hypoxic group, in which nimorazole + IMRT did not improve FFLRP (adjusted HR, 0.72; 95% CI, 0.36-1.44; P = .35) or overall survival (adjusted HR, 0.96; 95% CI, 0.53-1.72; P = .88) compared with placebo + IMRT. Similarly, nimorazole + IMRT did not improve FFLRP or overall survival in the whole population. In total (N = 338), 73% of patients allocated nimorazole adhered to the drug for ≥50% of IMRT fractions. Nimorazole + IMRT caused more acute nausea compared with placebo + IMRT (Common Terminology Criteria for Adverse Events version 4.0 G1+2: 56.6% vs 42.4%, G3: 10.1% vs 5.3%, respectively; P < .05).

CONCLUSIONS

Addition of the hypoxia modifier nimorazole to IMRT for locally advanced HNSCC in older and less fit patients did not improve locoregional control or survival.

Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer

Purpose

To evaluate the feasibility to use a standard Ethos planning template to treat left-sided breast cancer with regional lymph nodes.

Material/Methods

The tuning cohort of 5 patients was used to create a planning template. The validation cohort included 15 patients treated for a locally advanced left breast cancer randomly enrolled. The Ethos planning template was tuned using standard 3 partial arc VMAT and two collimator rotation configurations: 45/285/345° and 30/60/330°. Re-planning was performed automatically using the template without editing. The study was conducted with a schedule of 42.3 Gy in 18 fractions to the breast/chestwall, internal mammary chain (IMC) and regional lymph nodes ("Nodes"). The PTV was defined as a 3D extension of the CTV with a margin of 7 mm, excluding the 5mm below the skin. The manual treatment plans were performed using Eclipse treatment planning system with AAA and PO algorithms (v15.6) and a manual arc VMAT configuration and imported in Ethos TPS (v1.1) for a dose calculation with Ethos Acuros algorithm. The automated plans were compared with the manual plans using PTV and CTV coverage, homogeneity and conformity indices (HI and CN) and doses to organs at risk (OAR) via DVH metrics. For each plan, the patient quality assurance (QA) were performed using Mobius3D and gamma index. Finally, two breast radiation oncologists performed a blinded assessment of the clinical acceptability of each of the three plans (manual and automated) for each patient.

Results

The manual and automated plans provided suitable treatment planning as regards dose constraints. The dosimetric comparison showed the CTV_breast D99% were significantly improved with both automated plans (p< 0,002) while PTV coverage was comparable. The doses to the organs at risk were equivalent for the three plans. Concerning treatment delivery, the Ethos-45° and Ethos-30° plans led to an increase in MUs compared to the manual plans, without affecting the beam on time. The average gamma index pass rates remained consistently above 98% regardless of the type of plan utilized. In the blinded evaluation, clinicians 1 and 2 assessed 13 out of 15 plans for Ethos 45° and 11 out of 15 plans for Ethos 30° as clinically acceptable.

Conclusion

Using a standard planning template for locally advanced breast cancer, the Ethos TPS provided automated plans that were clinically acceptable and comparable in quality to manually generated plans. Automated plans also dramatically reduce workflow and operator variability.

Facility Volume and Changing Facilities for Postoperative Radiotherapy in Salivary Gland Cancer

OBJECTIVES

Changing location of postoperative radiotherapy (PORT) after treatment at a high-volume facility (HVF) is associated with worse survival in various head and neck cancers. Our study investigates this relationship in salivary gland cancer (SGC).

METHODS

The 2004-2016 National Cancer Database was queried for all cases of adult SGC treated with surgery and PORT with or without adjuvant chemotherapy. Patients with multiple cancer diagnoses, metastatic disease, or unknown PORT facility were excluded. Reporting facilities with >95th percentile annual case volume were classified as HVFs, the remainder were classified low-volume facilities (LVFs).

RESULTS

A total of 7885 patients met inclusion criteria, of which 418 (5.3%) were treated at an HVF. Patients treated at an HVF had higher rates clinical nodal positivity (18.2% vs. 14.0%, p < 0.001) and clinical T3/T4 (27.3% vs. 20.7%, p = 0.001) disease. Patients at HVFs changed facility for PORT at lower rates (18.9% vs. 24.5%, p = 0.009). Patients treated at an HVF had higher 5-year overall survival (5-OS) than those treated at an LVF (79.0% vs. 72.0%, p = 0.042). Patients treated at an HVF that changed PORT facility had worse 5-OS (60.8% vs. 83.2%, p < 0.001). Radiation facility change was an independent predictor of worse survival in patients treated at an HVF (HR: 8.99 [3.15-25.67], p < 0.001) but not for patients treated at a LVF (HR: 1.11 [0.98-1.25], p = 0.109).

CONCLUSIONS

Patients treated at an HVF changing facility for PORT for SGC experience worse survival. Our data suggest patients treated surgically at an HVF should be counseled to continue their PORT at the same institution.

LEVEL OF EVIDENCE

3 Laryngoscope, 2024.

Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.

Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities

Although radiotherapy continues to evolve as a mainstay of the oncological armamentarium, research and innovation in radiotherapy in low-income and middle-income countries (LMICs) faces challenges. This third Series paper examines the current state of LMIC radiotherapy research and provides new data from a 2022 survey undertaken by the International Atomic Energy Agency and new data on funding. In the context of LMIC-related challenges and impediments, we explore several developments and advances-such as deep phenotyping, real-time targeting, and artificial intelligence-to flag specific opportunities with applicability and relevance for resource-constrained settings. Given the pressing nature of cancer in LMICs, we also highlight some best practices and address the broader need to develop the research workforce of the future. This Series paper thereby serves as a resource for radiation professionals.

Quality Assurance in Radiotherapy (RT)-Specific Trials: Indian Scenario

AIMS

There is evidence that proper radiotherapy trial quality assurance (RTTQA) translates into improved outcomes for patients. However, the practice of RTTQA is heterogeneous and implemented in a diverse manner across trials. In this paper, we review the RTTQA report for randomised trials (RCT) conducted in India and present our experience with RTTQA for various clinical trials and highlight the key achievements and challenges.

MATERIALS AND METHODS

Search was performed using the keywords and the variations thereof for "radiotherapy" and author affiliations from India, its states and major metropolitan cities. Pubmed search filters were used to restrict results to RCT published in the past 5 years (2019-2024). Reporting of RTTQA procedures from publications and protocols was documented along with the protocol-specified dosimetric goals. We also evaluated a few clinical trials performed in the Department of Radiation Oncology at Tata Medical Center. The different RTTQA procedures and results for four representative clinical trials have been described.

RESULTS

A formal RTTQA process was reported by only one out of 24 randomised controlled trials and formal dosimetric goals were pre-specified by 9 of 13 trials where IMRT was used as treatment. RTTQA requirements were tailored for each clinical trial at Tata Medical Center. For the HYPORT trial, the RTTQA process focused on ensuring the matchline doses were homogenous. HYPORT B trial commissioned the use of a simultaneous integrated boost technique which emphasised conformal avoidance of dose spillage to contralateral breast and lung. HYPORT Adjuvant and PROPARA trials are multicentre clinical trials. While HYPORT Adjuvant focussed on ensuring that the dose delivery met the predefined constraints, segmentation of the target volume was important for the PROPARA trial.

CONCLUSION

We demonstrate different RTTQA procedures required for representative clinical trials and highlight key challenges encountered.

Essential requirements for reporting radiation therapy in breast cancer clinical trials: An international multi-disciplinary consensus endorsed by the European Society for Radiotherapy and Oncology (ESTRO)

The European Society for Radiotherapy and Oncology (ESTRO) has advocated the establishment of guidelines to optimise precision radiotherapy (RT) in conjunction with contemporary therapeutics for cancer care. Quality assurance in RT (QART) plays a pivotal role in influencing treatment outcomes. Clinical trials incorporating QART protocols have demonstrated improved survival rates with minimal associated toxicity. Nonetheless, in routine clinical practice, there can be variability in the indications for RT, dosage, fractionation, and treatment planning, leading to uncertainty. In pivotal trials reporting outcomes of systemic therapy for breast cancer, there is limited information available regarding RT, and the potential interaction between modern systemic therapy and RT remains largely uncharted. This article is grounded in a consensus recommendation endorsed by ESTRO, formulated by international breast cancer experts. The consensus was reached through a modified Delphi process and was presented at an international meeting convened in Florence, Italy, in June 2023. These recommendations are regarded as both optimal and essential standards, with the latter aiming to define the minimum requirements. A template for a case report form (CRF) has been devised, which can be utilised by all clinical breast cancer trials involving RT. Optimal requirements include adherence to predefined RT planning protocols and centralised QART. Essential requirements aim to reduce variations and deviations from the guidelines in RT, even when RT is not the primary focus of the trial. These recommendations underscore the significance of implementing these practices in both clinical trials and daily clinical routines to generate high-quality data.

Radiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials.

Implementation and Efficacy of a Large-Scale Radiation Oncology Case-Based Peer-Review Quality Program across a Multinational Cancer Network

PURPOSE

With expansion of academic cancer center networks across geographically-dispersed sites, ensuring high-quality delivery of care across all network affiliates is essential. We report on the characteristics and efficacy of a radiation oncology peer-review quality assurance (QA) system implemented across a large-scale multinational cancer network.

METHODS AND MATERIALS

Since 2014, weekly case-based peer-review QA meetings have been standard for network radiation oncologists with radiation oncology faculty at a major academic center. This radiotherapy (RT) QA program involves pre-treatment peer-review of cases by disease site, with disease-site subspecialized main campus faculty members. This virtual QA platform involves direct review of the proposed RT plan as well as supporting data, including relevant pathology and imaging studies for each patient. Network RT plans were scored as being concordant or nonconcordant based on national guidelines, institutional recommendations, and/or expert judgment when considering individual patient-specific factors for a given case. Data from January 1, 2014, through December 31, 2019, were aggregated for analysis.

RESULTS

Between 2014 and 2019, across 8 network centers, a total of 16,601 RT plans underwent peer-review. The network-based peer-review case volume increased over the study period, from 958 cases in 2014 to 4,487 in 2019. A combined global nonconcordance rate of 4.5% was noted, with the highest nonconcordance rates among head-and-neck cases (11.0%). For centers that joined the network during the study period, we observed a significant decrease in the nonconcordance rate over time (3.1% average annual decrease in nonconcordance, P = 0.01); among centers that joined the network prior to the study period, nonconcordance rates remained stable over time.

CONCLUSIONS

Through a standardized QA platform, network-based multinational peer-review of RT plans can be achieved. Improved concordance rates among newly added network affiliates over time are noted, suggesting a positive impact of network membership on the quality of delivered cancer care.

Indicators to evaluate quality of care in head and neck cancer in Spain

PURPOSE

This study aimed to develop a set of criteria and indicators to evaluate the quality of care of patients with head and neck cancer (HNC).

METHODS

A systematic literature review was conducted to identify valuable criteria/indicators for the assessment of the quality of care in HNC. With the aid of a technical group, a scientific committee of oncologists specialised in HNC used selected criteria to propose indicators that were evaluated with a two-round Delphi method. Indicators on which consensus was achieved were then prioritised by the scientific committee to develop a final set of indicators.

RESULTS

We proposed a list of 50 indicators used in the literature or developed by us to be evaluated with a Delphi method. There was consensus on the appropriateness of 47 indicators in the first round; the remaining 3 achieved consensus in the second round. The 50 indicators were scored to prioritise them, leading to a final selection of 29 indicators related to structure (3), process (22), or outcome (4) and covering diagnosis, treatment, follow-up, and health outcomes in patients with HNC. Easy-to-use index cards were developed for each indicator, with their criterion, definition, formula for use in real-world clinical practice, rationale, and acceptable level of attainment.

CONCLUSIONS

We have developed a set of 29 evidence-based and expert-supported indicators for evaluating the quality of care in HNC, covering diagnosis, treatment, follow-up, and health outcomes.

Systematic Implementation of Effective Quality Assurance Processes for the Assessment of Radiation Target Volumes in Head and Neck Cancer

PURPOSE

Significant heterogeneity exists in clinical quality assurance (QA) practices within radiation oncology departments, with most chart rounds lacking prospective peer-reviewed contour evaluation. This has the potential to significantly affect patient outcomes, particularly for head and neck cancers (HNC) given the large variance in target volume delineation. With this understanding, we incorporated a prospective systematic peer contour-review process into our workflow for all patients with HNC. This study aims to assess the effectiveness of implementing prospective peer review into practice for our National Cancer Institute Designated Cancer Center and to report factors associated with contour modifications.

METHODS AND MATERIALS

Starting in November 2020, our department adopted a systematic QA process with real-time metrics, in which contours for all patients with HNC treated with radiation therapy were prospectively peer reviewed and graded. Contours were graded with green (unnecessary), yellow (minor), or red (major) colors based on the degree of peer-recommended modifications. Contours from November 2020 through September 2021 were included for analysis.

RESULTS

Three hundred sixty contours were included. Contour grades were made up of 89.7% green, 8.9% yellow, and 1.4% red grades. Physicians with >12 months of clinical experience were less likely to have contour changes requested than those with <12 months (8.3% vs 40.9%; P < .001). Contour grades were significantly associated with physician case load, with physicians presenting more than the median number of 50 cases having significantly less modifications requested than those presenting <50 (6.7% vs 13.3%; P = .013). Physicians working with a resident or fellow were less likely to have contour changes requested than those without a trainee (5.2% vs 12.6%; P = .039). Frequency of major modification requests significantly decreased over time after adoption of prospective peer contour review, with no red grades occurring >6 months after adoption.

CONCLUSIONS

This study highlights the importance of prospective peer contour-review implementation into systematic clinical QA processes for HNC. Physician experience proved to be the highest predictor of approved contours. A growth curve was demonstrated, with major modifications declining after prospective contour review implementation. Even within a high-volume academic practice with subspecialist attendings, >10% of patients had contour changes made as a direct result of prospective peer review.

A proposed framework for the implementation of head and neck cancer treatment at a new cancer center from a radiation oncology perspective

BACKGROUND

Establishing a new head and neck cancer (HNC) treatment center requires multidisciplinary team management and expertise. To our knowledge, there are no clear recommendations or guidelines in the literature for the commencement of HNC radiation therapy (RT) at a new cancer center. We propose a novel framework outlining the necessary components required to set-up a new radiation therapy HNC treatment.

METHODS

We reviewed the infrastructure and methodology in the commencement of HNC radiation therapy in our cancer care center and invited several external, experienced metropolitan head and neck radiation oncologists to develop a novel consensus guideline that may be used by new RT centers to treat HNC. Recommendations were presented to our internal and external staff specialists using a survey questionnaire with ratings utilized to determine consensus using pre-defined thresholds as per the American Society of Clinical Oncology Guidelines Methodology Manual.

CONCLUSION

This consensus recommendation aims to improve RT utilization whilst advocating for optimal patient outcomes by presenting a framework for new radiation therapy centers ready to step up and manage the treatment of head and neck cancer patients. We propose these evidence-based consensus guidelines endorsed by external HNC radiation oncologists.

Combinatorial Approaches for Chemotherapies and Targeted Therapies With Radiation: United Efforts to Innovate in Patient Care

Combinatorial therapies consisting of radiation therapy (RT) with systemic therapies, particularly chemotherapy and targeted therapies, have moved the needle to augment disease control across nearly all disease sites for locally advanced disease. Evaluating these important combinations to incorporate more potent therapies with RT will aid our understanding of toxicity and efficacy for patients. This article discusses multiple disease sites and includes a compilation of contributions from expert Red Journal editors from each disease site. Leveraging improved systemic control with novel agents, we must continue efforts to study novel treatment combinations with RT.

CT number calibration audit in photon radiation therapy

BACKGROUND

Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs.

PURPOSE

The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe ).

METHODS

A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute-specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute-specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute-specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m , respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT-ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT-ρ calibration curves because the available tissue-equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM , respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT-ρ or CT-ρe calibration curve.

RESULTS

The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were -1.1 ± 1.2 g/cm3 and -0.2 ± 1.1, -0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and -0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN-SR2) was -1.8 ± 0.4, implying that the tissue equivalency of the BRN-SR2 plug was slightly inferior to that of other plugs.

CONCLUSIONS

Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes.

Stereotactic ablative body radiotherapy for primary kidney cancer (TROG 15.03 FASTRACK II): a non-randomised phase 2 trial

BACKGROUND

Stereotactic ablative body radiotherapy (SABR) is a novel non-invasive alternative for patients with primary renal cell cancer who do not undergo surgical resection. The FASTRACK II clinical trial investigated the efficacy of SABR for primary renal cell cancer in a phase 2 trial.

METHODS

This international, non-randomised, phase 2 study was conducted in seven centres in Australia and one centre in the Netherlands. Eligible patients aged 18 years or older had biopsy-confirmed diagnosis of primary renal cell cancer, with only a single lesion; were medically inoperable, were at high risk of complications from surgery, or declined surgery; and had an Eastern Cooperative Oncology Group performance status of 0-2. A multidisciplinary decision that active treatment was warranted was required. Key exclusion criteria were a pre-treatment estimated glomerular filtration rate of less than 30 mL/min per 1·73 m2, previous systemic therapies for renal cell cancer, previous high-dose radiotherapy to an overlapping region, tumours larger than 10 cm, and direct contact of the renal cell cancer with the bowel. Patients received either a single fraction SABR of 26 Gy for tumours 4 cm or less in maximum diameter, or 42 Gy in three fractions for tumours more than 4 cm to 10 cm in maximum diameter. The primary endpoint was local control, defined as no progression of the primary renal cell cancer, as evaluated by the investigator per Response Evaluation Criteria in Solid Tumours (version 1.1). Assuming a 1-year local control of 90%, the null hypothesis of 80% or less was considered not to be worthy of proceeding to a future randomised controlled trial. All patients who commenced trial treatment were included in the primary outcome analysis. This trial is registered with ClinicalTrials.gov, NCT02613819, and has completed accrual.

FINDINGS

Between July 28, 2016, and Feb 27, 2020, 70 patients were enrolled and initiated treatment. Median age was 77 years (IQR 70-82). Before enrolment, 49 (70%) of 70 patients had documented serial growth on initial surveillance imaging. 49 (70%) of 70 patients were male and 21 (30%) were female. Median tumour size was 4·6 cm (IQR 3·7-5·5). All patients enrolled had T1-T2a and N0-N1 disease. 23 patients received single-fraction SABR of 26 Gy and 47 received 42 Gy in three fractions. Median follow-up was 43 months (IQR 38-60). Local control at 12 months from treatment commencement was 100% (p<0·0001). Seven (10%) patients had grade 3 treatment-related adverse events, with no grade 4 adverse events observed. Grade 3 treatment-related adverse events were nausea and vomiting (three [4%] patients), abdominal, flank, or tumour pain (four [6%]), colonic obstruction (two [3%]), and diarrhoea (one [1%]). No treatment-related or cancer-related deaths occurred.

INTERPRETATION

To our knowledge, this is the first multicentre prospective clinical trial of non-surgical definitive therapy in patients with primary renal cell cancer. In a cohort with predominantly T1b or larger disease, SABR was an effective treatment strategy with no observed local failures or cancer-related deaths. We observed an acceptable side-effect profile and renal function after SABR. These outcomes support the design of a future randomised trial of SABR versus surgery for primary renal cell cancer.

FUNDING

Cancer Australia Priority-driven Collaborative Cancer Research Scheme.

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography

Purpose

Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning.

Methods and Materials

Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations.

Results

The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively.

Conclusions

Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.

Radiotherapy trial quality assurance processes: a systematic review

Quality assurance remains a neglected component of many trials, particularly for technical interventions, such as surgery and radiotherapy, for which quality of treatment is an important component in defining outcomes. We aimed to evaluate evidence for the processes used in radiotherapy quality assurance of clinical trials. A systematic review was undertaken focusing on use of a pre-trial outlining benchmark case and subsequent on-trial individual case reviews of outlining for recruited patients. These pre-trial and on-trial checks are used to ensure consistency and standardisation of treatment for each patient recruited to the trial by confirming protocol compliance. Non-adherence to the trial protocol has been shown to have a negative effect on trial outcomes. 29 studies published between January, 2000, and December, 2022, were identified that reported on either outlining benchmark case results or outlining individual case review results, or both. The trials identified varied in their use of radiotherapy quality assurance practices and reporting of outcomes was inconsistent. Deviations from trial protocols were frequent, particularly regarding outlining. Studies correlating benchmark case results with on-trial individual case reviews provided mixed results, meaning firm conclusions could not be drawn regarding the influence of the pre-trial benchmark case on subsequent on-trial performance. The optimal radiotherapy quality assurance processes were unclear, and there was little evidence available. Improved reporting of outcomes from radiotherapy quality assurance programmes is needed to develop an evidence base for the optimal approach to radiotherapy quality assurance in trials.

Radiotherapy quality assurance in the TROG 12.01 randomised trial and its impact on loco-regional failure

Introduction

There is no consensus as to what specifically constitutes head and neck cancer radiotherapy quality assurance (HNC RT QA). The aims of this study are to (1) describe the RT QA processes used in the TROG 12.01 study, (2) review the RT QA processes undertaken for all patients with loco-regional failure (LRF), and (3) provide prospective data to propose a consensus statement regarding the minimal components and optimal timing of HNC RT QA.

Materials and methods

All patients undergoing RT QA in the original TROG 12.01 study were included in this substudy. All participating sites completed IMRT credentialling and a clinical benchmark case. Real-time (pre-treatment) RT QA was performed for the first patient of each treating radiation oncologist, and for one in five of subsequent patients. Protocol violations were deemed major if they related to contour and/or dose of gross tumour volume (GTV), high dose planning target volume (PTVhd), or critical organs of risk (spinal cord, mandible, and brachial plexus).

Results

Thirty HNROs from 15 institutions accrued 182 patients. There were 28 clinical benchmark cases, 27 pre-treatment RT QA cases, and 38 post-treatment cases. Comprehensive RT QA was performed in 65/182 (36%) treated patients. Major protocol violations were found in 5/28 benchmark cases, 5/27 pre-treatment cases, and 6/38 post-treatment cases. An independent review of all nine LRF cases showed major protocol violations in four of nine cases.

Conclusion

Only pre-treatment RT QA can improve patient outcomes. The minimal components of RT QA in HNC are GTVs, PTVhd, and critical organs at risk. What constitutes major dosimetric violations needs to be harmonised.

Analysis of Retrospective Versus Prospective Peer Review in a Multisite Academic Radiation Department

Purpose

Our multisite academic radiation department reviewed our experience with transitioning from weekly primarily retrospective to daily primarily prospective peer review to improve plan quality and decrease the rate of plan revisions after treatment start.

Methods and Materials

This study was an institutional review board-approved prospective comparison of radiation treatment plan review outcomes of plans reviewed weekly (majority within 1 week after treatment start) versus plans reviewed daily (majority before treatment start, except brachytherapy, frame-based radiosurgery, and some emergent plans). Deviations were based on peer comments and considered major if plan revisions were recommended before the next fraction and minor if modifications were suggested but not required. Categorical variables were compared using χ2 distribution tests of independence; means were compared using independent t tests.

Results

In all, 798 patients with 1124 plans were reviewed: 611 plans weekly and 513 plans daily. Overall, 76 deviations (6.8%) were noted. Rates of any deviation were increased in the daily era (8.6% vs 5.2%; P = .026), with higher rates of major deviations in the daily era (4.1% vs 1.6%; P = .012). Median working days between initial simulation and treatment was the same across eras (8 days). Deviations led to a plan revision at a higher rate in the daily era (84.1% vs 31.3%; P < .001).

Conclusions

Daily prospective peer review is feasible in a multisite academic setting. Daily peer review with emphasis on prospective plan evaluation increased constructive plan feedback, plan revisions, and plan revisions being implemented before treatment start.

Optimising of axillary therapy in breast cancer: lessons from the past to plan for a better future

In this narrative review, we aim to explore the ability of radiation therapy to eradicate breast cancer regional node metastasis. It is a journey through data of older trials without systemic therapy showing the magnitude of axillary therapy (surgery versus radiation) on cancer control. Considering that both systemic and loco-regional therapies were shown to reduce any recurrence with a complex interaction, our review includes surgical, radiation, and radiobiology consideration for breast cancer, and provide our view of future practise. The aim is to provide information optimise radiation therapy in the era of primary systemic therapy.

Beam-wise dose composition learning for head and neck cancer dose prediction in radiotherapy

Automatic and accurate dose distribution prediction plays an important role in radiotherapy plan. Although previous methods can provide promising performance, most methods did not consider beam-shaped radiation of treatment delivery in clinical practice. This leads to inaccurate prediction, especially on beam paths. To solve this problem, we propose a beam-wise dose composition learning (BDCL) method for dose prediction in the context of head and neck (H&N) radiotherapy plan. Specifically, a global dose network is first utilized to predict coarse dose values in the whole-image space. Then, we propose to generate individual beam masks to decompose the coarse dose distribution into multiple field doses, called beam voters, which are further refined by a subsequent beam dose network and reassembled to form the final dose distribution. In particular, we design an overlap consistency module to keep the similarity of high-level features in overlapping regions between different beam voters. To make the predicted dose distribution more consistent with the real radiotherapy plan, we also propose a dose-volume histogram (DVH) calibration process to facilitate feature learning in some clinically concerned regions. We further apply an edge enhancement procedure to enhance the learning of the extracted feature from the dose falloff regions. Experimental results on a public H&N cancer dataset from the AAPM OpenKBP challenge show that our method achieves superior performance over other state-of-the-art approaches by significant margins. Source code is released at https://github.com/TL9792/BDCLDosePrediction.

Treatment planning for patients with low rectal cancer in a multicenter prospective organ preservation study

BACKGROUND

Non-surgical management of rectal cancer relies on (chemo)radiotherapy as the definitive treatment modality. This study reports and evaluates the clinical high dose radiotherapy treatment plans delivered to patients with low resectable rectal cancer in a Danish multicenter trial.

METHODS

The Danish prospective multicenter phase II Watchful Waiting 2 trial (NCT02438839) investigated definitive chemoradiation for non-surgical management of low rectal cancer. Three Danish centers participated in the trial and committed to protocol-specified treatment planning and delivery requirements. The protocol specified a dose of 50.4 Gy in 28 fractions to the elective volume (CTV-/PTV-E) and a concomitant boost of 62 Gy in 28 fractions to the primary target volume (CTV-/PTV-T).

RESULTS

The trial included 108 patients, of which 106 treatment plans were available for retrospective analysis. Dose coverage planning goals for the main target structures were fulfilled for 94% of the treatment plans. However, large intercenter differences in doses to organs-at-risk (OARs) were seen, especially for the intestines. Five patients had a V60Gy>10 cm3 for the intestines and two patients for the bladder.

CONCLUSION

Prescribed planning goals for target coverage were fulfilled for 94% of the treatment plans, however analysis of OAR doses and volumes indicated intercenter variations. Dose escalation to 62 Gy (as a concomitant boost to the primary tumor) introduced no substantial high dose volumes (>60 Gy) to the bladder and intestines. The treatment planning goals may be used for future prospective evaluation of highdose radiotherapy for organ preservation for low rectal cancer.

Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study

PURPOSE

To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV.

METHODS AND MATERIALS

In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification.

RESULTS

Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5-19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs.

CONCLUSION

DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.

Enhancing dosimetric practices through knowledge-based predictive models: a case study on VMAT prostate irradiation

Introduction

Acquisition of dosimetric knowledge by radiation therapy planners is a protracted and complex process. This study delves into the impact of empirical predictive models based on the knowledge-based planning (KBP) methodology, aimed at detecting suboptimal results and homogenizing and improving existing practices for prostate cancer. Moreover, the dosimetric effect of implementing these models into routine clinical practice was also assessed.

Materials and methods

Based on the KBP method, we analyzed 25 prostate treatment plans performed using VMAT by expert operators, aiming to correlate dose indicators with patient geometry. The D a v g C a v ( G y ) , V 45 G y C a v ( c c ) , and V 15 G y C a v ( c c ) of the peritoneal cavity and the V 60 G y ( % ) and V 70 G y ( % ) of the rectum and bladder were linked to geometric characteristics such as the distance from the planning target volume (PTV) to the organs at risk (OAR), the volume of the OAR, or the overlap between the PTV and the OAR. In the second phase, the KBP was used in routine clinical practice in a prospective cohort of 25 patients and compared with the 41 patient plans calculated before implementing the tool.

Results

Using linear regression, we identified strong geometric predictive factors for the peritoneal cavity, rectum, and bladder (R 2   > 0.8), with an average prescribed dose of 97.8%, covering 95% of the target volume. The use of the model led to a significant dose reduction ( Δ ) for all evaluated OARs. This trend was most notable for Δ V 15 G y C a v = - 171.5  cc  ( p = 0.003 ) . Significant reductions were also obtained in average doses to the rectum and bladder, Δ D a v g R e c t =   - 2.3   G y   ( p = 0.040 ) , and Δ D a v g V e s s =   - 3.3   G y   ( p = 0.039 ) respectively. Based on this model, we reduced the number of plans with OAR constraints above the clinical recommendations from 19% to 8%.

Conclusions

The KBP methodology established a robust and personalized predictive model for dose estimation to organs at risk in prostate cancer. Implementing the model resulted in improved sparing of these organs. Notably, it yields a solid foundation for harmonizing dosimetric practices, alerting us to suboptimal results, and improving our knowledge.

Analysis of Radiation Therapy Quality Assurance in NRG Oncology RTOG 0848

PURPOSE

NRG/Radiation Therapy Oncology Group 0848 is a 2-step randomized trial to evaluate the benefit of the addition of concurrent fluoropyrimidine and radiation therapy (RT) after adjuvant chemotherapy (second step) for patients with resected pancreatic head adenocarcinoma. Real-time quality assurance (QA) was performed on each patient who underwent RT. This analysis aims to evaluate adherence to protocol-specified contouring and treatment planning and to report the types and frequencies of deviations requiring revisions.

METHODS AND MATERIALS

In addition to a web-based contouring atlas, the protocol outlined step-by-step instructions for generating the clinical treatment volume through the creation of specific regions of interest. The planning target volume was a uniform 0.5 cm clinical treatment volume expansion. One of 2 radiation oncology study chairs independently reviewed each plan. Plans with unacceptable deviations were returned for revision and resubmitted until approved. Treatment started after final approval of the RT plan.

RESULTS

From 2014 to 2018, 354 patients were enrolled in the second randomization. Of these, 160 patients received RT and were included in the QA analysis. Resubmissions were more common for patients planned with 3-dimensional conformal RT (43%) than with intensity modulated RT (31%). In total, at least 1 resubmission of the treatment plan was required for 33% of patients. Among patients requiring resubmission, most only needed 1 resubmission (87%). The most common reasons for resubmission were unacceptable deviations with respect to the preoperative gross target volume (60.7%) and the pancreaticojejunostomy (47.5%).

CONCLUSION

One-third of patients required resubmission to meet protocol compliance criteria, demonstrating the continued need for expending resources on real-time, pretreatment QA in trials evaluating the use of RT, particularly for pancreas cancer. Rigorous QA is critically important for clinical trials involving RT to ensure that the true effect of RT is assessed. Moreover, RT QA serves as an educational process through providing feedback from specialists to practicing radiation oncologists on best practices.

Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.

The Feasibility of Quality Assurance in the TOPGEAR International Phase 3 Clinical Trial of Neoadjuvant Chemoradiation Therapy for Gastric Cancer (an Intergroup Trial of the AGITG/TROG/NHMRC CTC/EORTC/CCTG)

PURPOSE

The TOPGEAR phase 3 trial hypothesized that adding preoperative chemoradiation therapy (CRT) to perioperative chemotherapy will improve survival in patients with gastric cancer. Owing to the complexity of gastric irradiation, a comprehensive radiation therapy quality assurance (RTQA) program was implemented. Our objective is to describe the RTQA methods and outcomes.

METHODS AND MATERIALS

RTQA was undertaken in real time before treatment for the first 5 patients randomized to CRT from each center. Once acceptable quality was achieved, RTQA was completed for one-third of subsequent cases. RTQA consisted of evaluating (1) clinical target volume and organ-at-risk contouring and (2) radiation therapy planning parameters. Protocol violations between high- (20+ patients enrolled) and low-volume centers were compared using the Fisher exact test.

RESULTS

TOPGEAR enrolled 574 patients, of whom 286 were randomized to receive preoperative CRT and 203 (71%) were included for RTQA. Of these, 67 (33%) and 136 (67%) patients were from high- and low-volume centers, respectively. The initial RTQA pass rate was 72%. In total, 28% of cases required resubmission. In total, 200 of 203 cases (99%) passed RTQA before treatment. Cases from low-volume centers required resubmission more often (44/136 [33%] vs 13/67 [18%]; P = .078). There was no change in the proportion of cases requiring resubmission over time. Most cases requiring resubmission had multiple protocol violations. At least 1 aspect of the clinical target volume had to be adjusted in all cases. Inadequate coverage of the duodenum was most common (53% major violation, 25% minor violation). For the remaining cases, the resubmission process was triggered secondary to poor contour/plan quality.

CONCLUSIONS

In a large multicenter trial, RTQA is feasible and effective in achieving high-quality treatment plans. Ongoing education should be performed to ensure consistent quality during the entire study period.

Systematic Review and Meta-analysis of the Association Between Radiation Therapy Treatment Volume and Patient Outcomes

PURPOSE

Evidence of a volume-outcome association in cancer surgery has shaped the centralization of cancer services; however, it is unknown whether a similar association exists for radiation therapy. The objective of this study was to determine the association between radiation therapy treatment volume and patient outcomes.

METHODS AND MATERIALS

This systematic review and meta-analysis included studies that compared outcomes of patients who underwent definitive radiation therapy at high-volume radiation therapy facilities (HVRFs) versus low-volume facilities (LVRFs). The systematic review used Ovid MEDLINE and Embase. For the meta-analysis, a random effects model was used. Absolute effects and hazard ratios (HRs) were used to compare patient outcomes.

RESULTS

The search identified 20 studies assessing the association between radiation therapy volume and patient outcomes. Seven of the studies looked at head and neck cancers (HNCs). The remaining studies covered cervical (4), prostate (4), bladder (3), lung (2), anal (2), esophageal (1), brain (2), liver (1), and pancreatic cancer (1). The meta-analysis demonstrated that HVRFs were associated with a lower chance of death compared with LVRFs (pooled HR, 0.90; 95% CI, 0.87- 0.94). HNCs had the strongest evidence of a volume-outcome association for both nasopharyngeal cancer (pooled HR, 0.74; 95% CI, 0.62-0.89) and nonnasopharyngeal HNC subsites (pooled HR, 0.80; 95% CI, 0.75-0.84), followed by prostate cancer (pooled HR, 0.92; 95% CI, 0.86-0.98). The remaining cancer types showed weak evidence of an association. The results also demonstrate that some centers defined as HVRFs are undertaking very few procedures per annum (<5 radiation therapy cases per year).

CONCLUSIONS

An association between radiation therapy treatment volume and patient outcomes exists for most cancer types. Centralization of radiation therapy services should be considered for cancer types with the strongest volume-outcome association, but the effect on equitable access to services needs to be explicitly considered.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

BACKGROUND AND PURPOSE

Inter-hospital inequalities in head and neck cancer (HNC) survival may exist due to variation in radiotherapy treatment-related factors. This study investigated inter-hospital variation in data collection, primary radiotherapy treatment, and survival in HNC patients from an Australian setting.

MATERIALS AND METHODS

Data collected in oncology information systems (OIS) from seven Australian hospitals was extracted for 3,182 adults treated with curative radiotherapy, with or without surgery or chemotherapy, for primary, non-metastatic squamous cell carcinoma of the head and neck (2000-2017). Death data was sourced from the National Death Index using record linkage. Multivariable Cox regression was used to assess the association between survival and hospital.

RESULTS

Inter-hospital variation in data collection, primary radiotherapy dose, and five-year HNC-related death was detected. Completion of eleven fields ranged from 66%-98%. Primary radiotherapy treated Tis-T1N0 glottic and any stage oral cavity and oropharynx cancers received significantly different time-corrected biologically equivalent dose in two gray fractions (EQD2T) by hospital, with observed deviation from Australian radiotherapy guidelines. Increased EQD2T dose was associated with a reduced risk of five-year HNC-related death in all patients and those treated with primary radiotherapy. Hospital, tumour site, and T and N classification were also identified as independent prognostic factors for five-year HNC-related death in all patients treated with radiotherapy.

CONCLUSION

Unexplained variation exists in HNC-related death in patients treated at Australian hospitals. Available routinely collected data in OIS are insufficient to explain variation in survival. Innovative data collection, extraction, and classification practices are needed to inform clinical practice.

Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble

PURPOSE

The manual segmentation of organ structures in radiation oncology treatment planning is a time-consuming and highly skilled task, particularly when treating rare tumors like sacral chordomas. This study evaluates the performance of automated deep learning (DL) models in accurately segmenting the gross tumor volume (GTV) and surrounding muscle structures of sacral chordomas.

METHODS AND MATERIALS

An expert radiation oncologist contoured 5 muscle structures (gluteus maximus, gluteus medius, gluteus minimus, paraspinal, piriformis) and sacral chordoma GTV on computed tomography images from 48 patients. We trained 6 DL auto-segmentation models based on 3-dimensional U-Net and residual 3-dimensional U-Net architectures. We then implemented an average and an optimally weighted average ensemble to improve prediction performance. We evaluated algorithms with the average and standard deviation of the volumetric Dice similarity coefficient, surface Dice similarity coefficient with 2- and 3-mm thresholds, and average symmetric surface distance. One independent expert radiation oncologist assessed the clinical viability of the DL contours and determined the necessary amount of editing before they could be used in clinical practice.

RESULTS

Quantitatively, the ensembles performed the best across all structures. The optimal ensemble (volumetric Dice similarity coefficient, average symmetric surface distance) was (85.5 ± 6.4, 2.6 ± 0.8; GTV), (94.4 ± 1.5, 1.0 ± 0.4; gluteus maximus), (92.6 ± 0.9, 0.9 ± 0.1; gluteus medius), (85.0 ± 2.7, 1.1 ± 0.3; gluteus minimus), (92.1 ± 1.5, 0.8 ± 0.2; paraspinal), and (78.3 ± 5.7, 1.5 ± 0.6; piriformis). The qualitative evaluation suggested that the best model could reduce the total muscle and tumor delineation time to a 19-minute average.

CONCLUSIONS

Our methodology produces expert-level muscle and sacral chordoma tumor segmentation using DL and ensemble modeling. It can substantially augment the streamlining and accuracy of treatment planning and represents a critical step toward automated delineation of the clinical target volume in sarcoma and other disease sites.

Consistency in contouring of organs at risk by artificial intelligence vs oncologists in head and neck cancer patients

BACKGROUND

In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial, patients are selected for proton treatment based on simulated reductions of Normal Tissue Complication Probability (NTCP) for proton compared to photon treatment at the referring departments. After inclusion in the trial, immobilization, scanning, contouring and planning are repeated at the national proton centre. The new contours could result in reduced expected NTCP gain of the proton plan, resulting in a loss of validity in the selection process. The present study evaluates if contour consistency can be improved by having access to AI (Artificial Intelligence) based contours.

MATERIALS AND METHODS

The 63 patients in the DAHANCA 35 pilot trial had a CT from the local DAHANCA centre and one from the proton centre. A nationally validated convolutional neural network, based on nnU-Net, was used to contour OARs on both scans for each patient. Using deformable image registration, local AI and oncologist contours were transferred to the proton centre scans for comparison. Consistency was calculated with the Dice Similarity Coefficient (DSC) and Mean Surface Distance (MSD), comparing contours from AI to AI and oncologist to oncologist, respectively. Two NTCP models were applied to calculate NTCP for xerostomia and dysphagia.

RESULTS

The AI contours showed significantly better consistency than the contours by oncologists. The median and interquartile range of DSC was 0.85 [0.78 - 0.90] and 0.68 [0.51 - 0.80] for AI and oncologist contours, respectively. The median and interquartile range of MSD was 0.9 mm [0.7 - 1.1] mm and 1.9 mm [1.5 - 2.6] mm for AI and oncologist contours, respectively. There was no significant difference in Δ NTCP.

CONCLUSIONS

The study showed that OAR contours made by the AI algorithm were more consistent than those made by oncologists. No significant impact on the Δ NTCP calculations could be discerned.

Outcomes among oropharyngeal and oral cavity cancer patients treated with postoperative volumetric modulated arctherapy

Background

Presently, there are few published reports on postoperative radiation therapy for oropharyngeal and oral cavity cancers treated with IMRT/VMAT technique. This study aimed to assess the oncological outcomes of this population treated with postoperative VMAT in our institution, with a focus on loco-regional patterns of failure.

Material and methods

Between 2011 and 2019, 167 patients were included (40% of oropharyngeal cancers, and 60% of oral cavity cancers). The median age was 60 years. There was 64.2% of stage IV cancers. All patients had both T and N surgery. 34% had a R1 margin, 42% had perineural invasion. 72% had a positive neck dissection and 42% extranodal extension (ENE). All patients were treated with VMAT with simultaneous integrated boost with three dose levels: 66Gy in case of R1 margin and/or ENE, 59.4-60Gy on the tumor bed, and 54Gy on the prophylactic areas. Concomittant cisplatin was administrated concomitantly when feasible in case of R1 and/or ENE.

Results

The 1- and 2-year loco-regional control rates were 88.6% and 85.6% respectively. Higher tumor stage (T3/T4), the presence of PNI, and time from surgery >45 days were significant predictive factors of worse loco-regional control in multivariate analysis (p=0.02, p=0.04, and p=0.02). There were 17 local recurrences: 11 (64%) were considered as infield, 4 (24%) as marginal, and 2 (12%) as outfield. There were 9 regional recurrences only, 8 (89%) were considered as infield, and 1 (11%) as outfield. The 1- and 2-year disease-free survival (DFS) rates were 78.9% and 71.8% respectively. The 1- and 2-year overall survival (OS) rates were 88.6% and 80% respectively. Higher tumor stage (T3/T4) and the presence of ENE were the two prognostic factors significantly associated with worse DFS and OS in multivariate analysis.

Conclusion

Our outcomes for postoperative VMAT for oral cavity and oropharyngeal cancers are encouraging, with high rates of loco-regional control. However, the management of ENE still seems challenging.

The Importance of Quality Assurance in Radiation Oncology Clinical Trials

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.

Thermal neutron detection and track recognition method in reference and out-of-field radiotherapy FLASH electron fields using Timepix3 detectors

Objective.This work presents a method for enhanced detection, imaging, and measurement of the thermal neutron flux.Approach. Measurements were performed in a water tank, while the detector is positioned out-of-field of a 20 MeV ultra-high pulse dose rate electron beam. A semiconductor pixel detector Timepix3 with a silicon sensor partially covered by a6LiF neutron converter was used to measure the flux, spatial, and time characteristics of the neutron field. To provide absolute measurements of thermal neutron flux, the detection efficiency calibration of the detectors was performed in a reference thermal neutron field. Neutron signals are recognized and discriminated against other particles such as gamma rays and x-rays. This is achieved by the resolving power of the pixel detector using machine learning algorithms and high-resolution pattern recognition analysis of the high-energy tracks created by thermal neutron interactions in the converter.Main results. The resulting thermal neutrons equivalent dose was obtained using conversion factor (2.13(10) pSv·cm2) from thermal neutron fluence to thermal neutron equivalent dose obtained by Monte Carlo simulations. The calibrated detectors were used to characterize scattered radiation created by electron beams. The results at 12.0 cm depth in the beam axis inside of the water for a delivered dose per pulse of 1.85 Gy (pulse length of 2.4μs) at the reference depth, showed a contribution of flux of 4.07(8) × 103particles·cm-2·s-1and equivalent dose of 1.73(3) nSv per pulse, which is lower by ∼9 orders of magnitude than the delivered dose.Significance. The presented methodology for in-water measurements and identification of characteristic thermal neutrons tracks serves for the selective quantification of equivalent dose made by thermal neutrons in out-of-field particle therapy.

Fully automated volumetric modulated arc therapy planning for locally advanced rectal cancer: feasibility and efficiency

BACKGROUND

Volumetric modulated arc therapy (VMAT) for locally advanced rectal cancer (LARC) has emerged as a promising technique, but the planning process can be time-consuming and dependent on planner expertise. We aimed to develop a fully automated VMAT planning program for LARC and evaluate its feasibility and efficiency.

METHODS

A total of 26 LARC patients who received VMAT treatment and the computed tomography (CT) scans were included in this study. Clinical target volumes and organs at risk were contoured by radiation oncologists. The automatic planning program, developed within the Raystation treatment planning system, used scripting capabilities and a Python environment to automate the entire planning process. The automated VMAT plan (auto-VMAT) was created by our automated planning program with the 26 CT scans used in the manual VMAT plan (manual-VMAT) and their regions of interests. Dosimetric parameters and time efficiency were compared between the auto-VMAT and the manual-VMAT created by experienced planners. All results were analyzed using the Wilcoxon signed-rank sum test.

RESULTS

The auto-VMAT achieved comparable coverage of the target volume while demonstrating improved dose conformity and uniformity compared with the manual-VMAT. V30 and V40 in the small bowel were significantly lower in the auto-VMAT compared with those in the manual-VMAT (p < 0.001 and < 0.001, respectively); the mean dose of the bladder was also significantly reduced in the auto-VMAT (p < 0.001). Furthermore, auto-VMAT plans were consistently generated with less variability in quality. In terms of efficiency, the auto-VMAT markedly reduced the time required for planning and expedited plan approval, with 93% of cases approved within one day.

CONCLUSION

We developed a fully automatic feasible VMAT plan creation program for LARC. The auto-VMAT maintained target coverage while providing organs at risk dose reduction. The developed program dramatically reduced the time to approval.

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck

PURPOSE

In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics.

METHODS

A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices.

RESULTS

The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net.

CONCLUSION

All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.