A randomised controlled trial of Standard Of Care versus RadioAblaTion in Early Stage HepatoCellular Carcinoma (SOCRATES HCC).

BACKGROUND: Therapeutic options for early-stage hepatocellular carcinoma (HCC) in individual patients can be limited by tumor and location, liver dysfunction and comorbidities. Many patients with early-stage HCC do not receive curative-intent therapies. Stereotactic ablative body radiotherapy (SABR) has emerged as an effective, non-invasive HCC treatment option, however, randomized evidence for SABR in the first line setting is lacking. METHODS: Trans-Tasman Radiation Oncology Group (TROG) 21.07 SOCRATES-HCC is a phase II, prospective, randomised trial comparing SABR to other current standard of care therapies for patients with a solitary HCC ≤ 8 cm, ineligible for surgical resection or transplantation. The study is divided into 2 cohorts. Cohort 1 will compromise 118 patients with tumors ≤ 3 cm eligible for thermal ablation randomly assigned (1:1 ratio) to thermal ablation or SABR. Cohort 2 will comprise 100 patients with tumors > 3 cm up to 8 cm in size, or tumors ≤ 3 cm ineligible for thermal ablation, randomly assigned (1:1 ratio) to SABR or best other standard of care therapy including transarterial therapies. The primary objective is to determine whether SABR results in superior freedom from local progression (FFLP) at 2 years compared to thermal ablation in cohort 1 and compared to best standard of care therapy in cohort 2. Secondary endpoints include progression free survival, overall survival, adverse events, patient reported outcomes and health economic analyses. DISCUSSION: The SOCRATES-HCC study will provide the first randomized, multicentre evaluation of the efficacy, safety and cost effectiveness of SABR versus other standard of care therapies in the first line treatment of unresectable, early-stage HCC. It is a broad, multicentre collaboration between hepatology, interventional radiology and radiation oncology groups around Australia, coordinated by TROG Cancer Research. TRIAL REGISTRATION: anzctr.org.au, ACTRN12621001444875, registered 21 October 2021.

Genomic and Phenotypic Biomarkers for Precision Medicine Guidance in Advanced Prostate Cancer.

OPINION STATEMENT: Prostate cancer (PCa) is the second most diagnosed malignant neoplasm and is one of the leading causes of cancer-related death in men worldwide. Despite significant advances in screening and treatment of PCa, given the heterogeneity of this disease, optimal personalized therapeutic strategies remain limited. However, emerging predictive and prognostic biomarkers based on individual patient profiles in combination with computer-assisted diagnostics have the potential to guide precision medicine, where patients may benefit from therapeutic approaches optimally suited to their disease. Also, the integration of genotypic and phenotypic diagnostic methods is supporting better informed treatment decisions. Focusing on advanced PCa, this review discusses polygenic risk scores for screening of PCa and common genomic aberrations in androgen receptor (AR), PTEN-PI3K-AKT, and DNA damage response (DDR) pathways, considering clinical implications for diagnosis, prognosis, and treatment prediction. Furthermore, we evaluate liquid biopsy, protein biomarkers such as serum testosterone levels, SLFN11 expression, total alkaline phosphatase (tALP), neutrophil-to-lymphocyte ratio (NLR), tissue biopsy, and advanced imaging tools, summarizing current phenotypic biomarkers and envisaging more effective utilization of diagnostic and prognostic biomarkers in advanced PCa. We conclude that prognostic and treatment predictive biomarker discovery can improve the management of patients, especially in metastatic stages of advanced PCa. This will result in decreased mortality and enhanced quality of life and help design a personalized treatment regimen.

Adjuvant radiotherapy versus early salvage radiotherapy following radical prostatectomy (TROG 08.03/ANZUP RAVES): a randomised, controlled, phase 3, non-inferiority trial.

BACKGROUND: Adjuvant radiotherapy has been shown to halve the risk of biochemical progression for patients with high-risk disease after radical prostatectomy. Early salvage radiotherapy could result in similar biochemical control with lower treatment toxicity. We aimed to compare biochemical progression between patients given adjuvant radiotherapy and those given salvage radiotherapy. METHODS: We did a phase 3, randomised, controlled, non-inferiority trial across 32 oncology centres in Australia and New Zealand. Eligible patients were aged at least 18 years and had undergone a radical prostatectomy for adenocarcinoma of the prostate with pathological staging showing high-risk features defined as positive surgical margins, extraprostatic extension, or seminal vesicle invasion; had an Eastern Cooperative Oncology Group performance status of 0-1, and had a postoperative prostate-specific antigen (PSA) concentration of 0·10 ng/mL or less. Patients were randomly assigned (1:1) using a minimisation technique via an internet-based, independently generated allocation to either adjuvant radiotherapy within 6 months of radical prostatectomy or early salvage radiotherapy triggered by a PSA of 0·20 ng/mL or more. Allocation sequence was concealed from investigators and patients, but treatment assignment for individual randomisations was not masked. Patients were stratified by radiotherapy centre, preoperative PSA, Gleason score, surgical margin status, and seminal vesicle invasion status. Radiotherapy in both groups was 64 Gy in 32 fractions to the prostate bed without androgen deprivation therapy with real-time review of plan quality on all cases before treatment. The primary endpoint was freedom from biochemical progression. Salvage radiotherapy would be deemed non-inferior to adjuvant radiotherapy if freedom from biochemical progression at 5 years was within 10% of that for adjuvant radiotherapy with a hazard ratio (HR) for salvage radiotherapy versus adjuvant radiotherapy of 1·48. The primary analysis was done on an intention-to-treat basis. This study is registered with ClinicalTrials.gov, NCT00860652. FINDINGS: Between March 27, 2009, and Dec 31, 2015, 333 patients were randomly assigned (166 to adjuvant radiotherapy; 167 to salvage radiotherapy). Median follow-up was 6·1 years (IQR 4·3-7·5). An independent data monitoring committee recommended premature closure of enrolment because of unexpectedly low event rates. 84 (50%) patients in the salvage radiotherapy group had radiotherapy triggered by a PSA of 0·20 ng/mL or more. 5-year freedom from biochemical progression was 86% (95% CI 81-92) in the adjuvant radiotherapy group versus 87% (82-93) in the salvage radiotherapy group (stratified HR 1·12, 95% CI 0·65-1·90; pnon-inferiority=0·15). The grade 2 or worse genitourinary toxicity rate was lower in the salvage radiotherapy group (90 [54%] of 167) than in the adjuvant radiotherapy group (116 [70%] of 166). The grade 2 or worse gastrointestinal toxicity rate was similar between the salvage radiotherapy group (16 [10%]) and the adjuvant radiotherapy group (24 [14%]). INTERPRETATION: Salvage radiotherapy did not meet trial specified criteria for non-inferiority. However, these data support the use of salvage radiotherapy as it results in similar biochemical control to adjuvant radiotherapy, spares around half of men from pelvic radiation, and is associated with significantly lower genitourinary toxicity. FUNDING: New Zealand Health Research Council, Australian National Health Medical Research Council, Cancer Council Victoria, Cancer Council NSW, Auckland Hospital Charitable Trust, Trans-Tasman Radiation Oncology Group Seed Funding, Cancer Research Trust New Zealand, Royal Australian and New Zealand College of Radiologists, Cancer Institute NSW, Prostate Cancer Foundation Australia, and Cancer Australia.

Voxel-wise correlation of positron emission tomography/computed tomography with multiparametric magnetic resonance imaging and histology of the prostate using a sophisticated registration framework.

OBJECTIVES: To develop a registration framework for correlating positron emission tomography/computed tomography (PET/CT) images with multiparametric magnetic resonance imaging (mpMRI) and histology of the prostate, thereby enabling voxel-wise analysis of imaging parameters. PATIENTS AND METHODS: In this prospective proof-of-concept study, nine patients scheduled for radical prostatectomy underwent mpMRI and PET/CT imaging before surgery. One had PET imaging using 18 F-fluoromethylcholine, five using 68 Ga-labelled prostate-specific membrane antigen (PSMA)-HBED-CC (PMSA-11), and three using a trial 68 Ga-labelled THP-PSMA tracer. PET/CT data were co-registered with mpMRI via the CT scan and an in vivo three-dimensional T2-weighted (T2w) MRI, and then co-registered with ground truth histology data using ex vivo MRI of the prostate specimen. Maximum and mean standardised uptake values (SUVmax and SUVmean ) were extracted from PET data using tumour annotations from histology, and Kolmogorov-Smirnov tests were used to compare between tumour- and benign-voxel values. Correlation analysis was performed between mpMRI and PET SUV tumour voxel values using Pearson's correlation coefficient and R2 statistics. RESULTS: PET/CT data from all nine patients were successfully registered with mpMRI and histology data. SUVmax and SUVmean ranged from 2.21 to 12.11 and 1.08 to 4.21, respectively. All patients showed the PET SUV values in benign and tumour voxels were from statistically different distributions. Correlation analysis showed no consistent trend between the T2w or apparent diffusion coefficient values and PET SUV. However, parameters from dynamic contrast-enhanced (DCE) MRI including the maximum enhancement, volume transfer constant (Ktrans ), and the initial area under the contrast agent concentration curve for the first 60 s after injection (iAUGC60), showed consistent positive correlations with PET SUV. Furthermore, R2* values from blood oxygen level-dependent (BOLD) MRI showed consistent negative correlations with PET SUV-voxel values. CONCLUSION: We have developed a novel framework for registering and correlating PET/CT data at a voxel-level with mpMRI and histology. Despite registration uncertainties, perfusion and oxygenation parameters from DCE MRI and BOLD imaging showed correlations with PET SUV. Further analysis will be performed on a larger patient cohort to quantify these proof-of-concept findings. Improved understanding of the correlation between mpMRI and PET will provide supportive information for focal therapy planning of the prostate.

Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

BACKGROUND: There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. MATERIAL AND METHODS: In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. RESULTS: Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 103 cells/mm2 and a relative deviation of 13.3 ± 0.8%. CONCLUSION: Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

On the use of a convolution-superposition algorithm for plan checking in lung stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) aims to deliver a highly conformal ablative dose to a small target. Dosimetric verification of SBRT for lung tumors presents a challenge due to heterogeneities, moving targets, and small fields. Recent software (M3D) designed for dosimetric verification of lung SBRT treatment plans using an advanced convolution-superposition algorithm was evaluated. Ten lung SBRT patients covering a range of tumor volumes were selected. 3D CRT plans were created using the XiO treatment planning system (TPS) with the superposition algorithm. Dose was recalculated in the Eclipse TPS using the AAA algorithm, M3D verification software using the collapsed-cone-convolution algorithm, and in-house Monte Carlo (MC). Target point doses were calculated with RadCalc software. Near-maximum, median, and near-minimum target doses, conformity indices, and lung doses were compared with MC as the reference calculation. M3D 3D gamma passing rates were compared with the XiO and Eclipse. Wilcoxon signed-rank test was used to compare each calculation method with XiO with a threshold of significance of p < 0.05. M3D and RadCalc point dose calculations were greater than MC by up to 7.7% and 13.1%, respectively, with M3D being statistically significant (s.s.). AAA and XiO calculated point doses were less than MC by 11.3% and 5.2%, respectively (AAA s.s.). Median and near-minimum and near-maximum target doses were less than MC when calculated with AAA and XiO (all s.s.). Near-maximum and median target doses were higher with M3D compared with MC (s.s.), but there was no difference in near-minimum M3D doses compared with MC. M3D-calculated ipsilateral lung V20 Gy and V5 Gy were greater than that calculated with MC (s.s.); AAA- and XiO-calculated V20 Gy was lower than that calculated with MC, but not statistically different to MC for V5 Gy. Nine of the 10 plans achieved M3D gamma passing rates greater than 95% and 80%for 5%/1 mm and 3%/1 mm criteria, respectively. M3D typically calculated a higher target and lung dose than MC for lung SBRT plans. The results show a range of calculated doses with different algorithms and suggest that M3D is in closer agree-ment with Monte Carlo, thus discrepancies between the TPS and M3D software will be observed for lung SBRT plans. M3D provides a useful supplement to verification of lung SBRT plans by direct measurement, which typically excludes patient specific heterogeneities.

A Phase III trial to investigate the timing of radiotherapy for prostate cancer with high-risk features: background and rationale of the Radiotherapy -- Adjuvant Versus Early Salvage (RAVES) trial.

OBJECTIVES: To test the hypothesis that observation with early salvage radiotherapy (SRT) is not inferior to 'standard' treatment with adjuvant RT (ART) with respect to biochemical failure in patients with pT3 disease and/or positive surgical margins (SMs) after radical prostatectomy (RP). To compare the following secondary endpoints between the two arms: patient-reported outcomes, adverse events, biochemical failure-free survival, overall survival, disease-specific survival, time to distant failure, time to local failure, cost utility analysis, quality adjusted life years and time to androgen deprivation. PATIENTS AND METHODS: The Radiotherapy - Adjuvant Versus Early Salvage (RAVES) trial is a phase III multicentre randomised controlled trial led by the Trans Tasman Radiation Oncology Group (TROG), in collaboration with the Urological Society of Australia and New Zealand (USANZ), and the Australian and New Zealand Urogenital and Prostate Cancer Trials Group (ANZUP). In all, 470 patients are planned to be randomised 1:1 to either ART commenced at ≤4 months of RP (standard of care) or close observation with early SRT triggered by a PSA level of >0.20 ng/mL (experimental arm). Eligible patients have had a RP for adenocarcinoma of the prostate with at least one of the following risk factors: positive SMs ± extraprostatic extension ± seminal vesicle involvement. The postoperative PSA level must be ≤0.10 ng/mL. Rigorous investigator credentialing and a quality assurance programme are designed to promote consistent RT delivery among patients. RESULTS: Trial is currently underway, with 258 patients randomised as of 31 October 2013. International collaborations have developed, including a planned meta-analysis to be undertaken with the UK Medical Research Council/National Cancer Institute of Canada Clinical Trials Group RADICALS (Radiotherapy and Androgen Deprivation In Combination with Local Surgery) trial and an innovative psycho-oncology sub-study to investigate a patient decision aid resource. CONCLUSION: On the current evidence available, it remains unclear if ART is equivalent or superior to observation with early SRT.

The rationale for a carbon ion radiation therapy facility in Australia.

Australia has taken a collaborative nationally networked approach to achieve particle therapy capability. This supports the under-construction proton therapy facility in Adelaide, other potential proton centres and an under-evaluation proposal for a hybrid carbon ion and proton centre in western Sydney. A wide-ranging overview is presented of the rationale for carbon ion radiation therapy, applying observations to the case for an Australian facility and to the clinical and research potential from such a national centre.

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy.

Background and purpose: Radiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR). Materials and methods: Multi-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR. Results: Prostate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated 'excellent' robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated 'good' robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85-0.93). Conclusion: When using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Towards optimal heterogeneous prostate radiotherapy dose prescriptions based on patient-specific or population-based biological features.

BACKGROUND: Escalation of prescribed dose in prostate cancer (PCa) radiotherapy enables improvement in tumor control at the expense of increased toxicity. Opportunities for reduction of treatment toxicity may emerge if more efficient dose escalation can be achieved by redistributing the prescribed dose distribution according to the known heterogeneous, spatially-varying characteristics of the disease. PURPOSE: To examine the potential benefits, limitations and characteristics of heterogeneous boost dose redistribution in PCa radiotherapy based on patient-specific and population-based spatial maps of tumor biological features. METHOD: High-resolution prostate histology images, from a cohort of 63 patients, annotated with tumor location and grade, provided patient-specific "maps" and a population-based "atlas" of cell density and tumor probability. Dose prescriptions were derived for each patient based on a heterogeneous redistribution of the boost dose to the intraprostatic lesions, with the prescription maximizing patient tumor control probability (TCP). The impact on TCP was assessed under scenarios where the distribution of population-based biological data was ignored, partially included, or fully included in prescription generation. Heterogeneous dose prescriptions were generated for three combinations of maps and atlas, and for conventional fractionation (CF), extreme hypo-fractionation (EH), moderate hypo-fractionation (MH), and whole Pelvic RT + SBRT Boost (WPRT + SBRT). The predicted efficacy of the heterogeneous prescriptions was compared with equivalent homogeneous dose prescriptions. RESULTS: TCPs for heterogeneous dose prescriptions were generally higher than those for homogeneous dose prescriptions. TCP escalation by heterogeneous dose prescription was the largest for CF. When only using population-based atlas data, the generated heterogeneous dose prescriptions of 55 to 58 patients (out of 63) had a higher TCP than for the corresponding homogeneous dose prescriptions. The TCPs of the heterogeneous dose prescriptions generated with the population-based atlas and tumor probability maps did not differ significantly from those using patient-specific biological information. The generated heterogeneous dose prescriptions achieved significantly higher TCP than homogeneous dose prescriptions in the posterior section of the prostate. CONCLUSION: Heterogeneous dose prescriptions generated via biologically-optimized dose redistribution can produce higher TCP than the homogeneous dose prescriptions for the majority of the patients in the studied cohort. For scenarios where patient-specific biological information was unavailable or partially available, the generated heterogeneous dose prescriptions can still achieve TCP improvement relative to homogeneous dose prescriptions.

Improving motion management in radiation therapy: findings from a workshop and survey in Australia and New Zealand.

Motion management has become an integral part of radiation therapy. Multiple approaches to motion management have been reported in the literature. To allow the sharing of experiences on current practice and emerging technology, the University of Sydney and the New South Wales/Australian Capital Territory branch of the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) held a two-day motion management workshop. To inform the workshop program, participants were invited to complete a survey prior to the workshop on current use of motion management techniques and their opinion on the effectiveness of each approach. A post-workshop survey was also conducted, designed to capture changes in opinion as a result of workshop participation. The online workshop was the most well attended ever hosted by the ACPSEM, with over 300 participants and a response to the pre-workshop survey was received from at least 60% of the radiation therapy centres in Australia and New Zealand. Motion management is extensively used in the region with use of deep inspiration breath-hold (DIBH) reported by 98% of centres for left-sided breast treatments and 91% for at least some right-sided breast treatments. Surface guided radiation therapy (SGRT) was the most popular session at the workshop and survey results showed that the use of SGRT is likely to increase. The workshop provided an excellent opportunity for the exchange of knowledge and experience, with most survey respondents indicating that their participation would lead to improvements in the quality of delivery of treatments at their centres.

Local hero: A phase II study of local therapy only (stereotactic radiosurgery and / or surgery) for treatment of up to five brain metastases from HER2+ breast cancer. (TROG study 16.02).

Introduction, A decade ago, stereotactic radiosurgery (SRS) without whole brain radiotherapy (WBRT) was emerging as preferred treatment for oligometastatic brain metastases. Studies of cavity SRS after neurosurgery were underway. Data specific to metastatic HER2 breast cancer (MHBC), describing intracranial, systemic and survival outcomes without WBRT, were lacking. A Phase II study was designed to address this gap. Method, Adults with MHBC, performance status 0-2, ≤ five BrM, receiving/planned to receive HER2-targeted therapy were eligible. Exclusions included leptomeningeal disease and prior WBRT. Neurosurgery allowed ≤6 weeks before registration and required for BrM >4 cm. Primary endpoint was 12-month requirement for WBRT. Secondary endpoints; freedom from (FF-) local failure (LF), distant brain failure (DBF), extracranial disease failure (ECDF), overall survival (OS), cause of death, mini-mental state examination (MMSE), adverse events (AE). Results, Twenty-five patients accrued Decembers 2016-2020. The study closed early after slow accrual. Thirty-seven BrM and four cavities received SRS. Four cavities and five BrM were observed. At 12 months: one patient required WBRT (FF-WBRT 95 %, 95 % CI 72-99), FFLF 91 % (95 % CI 69-98), FFDBF 57 % (95 % CI 34-74), FFECDF 64 % (95 % CI 45-84), OS 96 % (95 % CI 74-99). Two grade 3 AE occurred. MMSE was abnormal for 3/24 patients at baseline and 1/17 at 12 months. Conclusion, At 12 months, SRS and/or neurosurgery provided good control with low toxicity. WBRT was not required in 95 % of cases. This small study supports the practice change from WBRT to local therapies for MHBC BrM.

Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy.

Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.

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.

Comparison of TLD calibration methods for 192Ir dosimetry.

For the purpose of dose measurement using a high-dose rate (192)Ir source, four methods of thermoluminescent dosimeter (TLD) calibration were investigated. Three of the four calibration methods used the (192)Ir source. Dwell times were calculated to deliver 1 Gy to the TLDs irradiated either in air or water. Dwell time calculations were confirmed by direct measurement using an ionization chamber. The fourth method of calibration used 6 MV photons from a medical linear accelerator, and an energy correction factor was applied to account for the difference in sensitivity of the TLDs in (192)Ir and 6 MV. The results of the four TLD calibration methods are presented in terms of the results of a brachytherapy audit where seven Australian centers irradiated three sets of TLDs in a water phantom. The results were in agreement within estimated uncertainties when the TLDs were calibrated with the (192)Ir source. Calibrating TLDs in a phantom similar to that used for the audit proved to be the most practical method and provided the greatest confidence in measured dose. When calibrated using 6 MV photons, the TLD results were consistently higher than the (192)Ir-calibrated TLDs, suggesting this method does not fully correct for the response of the TLDs when irradiated in the audit phantom.

A survey of compliance with stereotactic ablative body radiotherapy quality recommendations.

INTRODUCTION: Many publications have proposed quality standards for stereotactic ablative body radiotherapy (SABR). However, data on the level of compliance with these guidelines is lacking in the literature. This study aimed to understand how these guidelines are applied in the clinic and to identify barriers to implementing such recommendations. METHODS: Interviews were conducted with multidisciplinary staff at radiation oncology centres across New South Wales formulated around the RANZCR Guidelines for Safe Practice of Stereotactic Body (Ablative) Radiation Therapy. The interview responses were grouped into 20 topics, assessed against the guidelines and thematically analysed. RESULTS: Good compliance with the guidelines was found, with more than 80% of centres achieving satisfactory results in more than half the topics. The areas with the lowest compliance were auditing, risk assessment and reporting recommendations. Barriers to the quality of SABR treatments included limited training opportunities, low patient numbers and a lack of clear requirements on comprehensive auditing and reporting. CONCLUSION: Overall, the centres surveyed reported good compliance with most of the RANZCR SABR guidelines. The tasks with the lowest compliance were those that monitor quality outcomes. Potential strategies for improvement include inclusion in clinical trials and the use of databases which link treatment parameters, dosimetry and outcomes. Further work will focus on the barriers identified in this survey and propose practical solutions to improve compliance in these areas.

U-Net Architecture for Prostate Segmentation: The Impact of Loss Function on System Performance.

Segmentation of the prostate gland from magnetic resonance images is rapidly becoming a standard of care in prostate cancer radiotherapy treatment planning. Automating this process has the potential to improve accuracy and efficiency. However, the performance and accuracy of deep learning models varies depending on the design and optimal tuning of the hyper-parameters. In this study, we examine the effect of loss functions on the performance of deep-learning-based prostate segmentation models. A U-Net model for prostate segmentation using T2-weighted images from a local dataset was trained and performance compared when using nine different loss functions, including: Binary Cross-Entropy (BCE), Intersection over Union (IoU), Dice, BCE and Dice (BCE + Dice), weighted BCE and Dice (W (BCE + Dice)), Focal, Tversky, Focal Tversky, and Surface loss functions. Model outputs were compared using several metrics on a five-fold cross-validation set. Ranking of model performance was found to be dependent on the metric used to measure performance, but in general, W (BCE + Dice) and Focal Tversky performed well for all metrics (whole gland Dice similarity coefficient (DSC): 0.71 and 0.74; 95HD: 6.66 and 7.42; Ravid 0.05 and 0.18, respectively) and Surface loss generally ranked lowest (DSC: 0.40; 95HD: 13.64; Ravid -0.09). When comparing the performance of the models for the mid-gland, apex, and base parts of the prostate gland, the models' performance was lower for the apex and base compared to the mid-gland. In conclusion, we have demonstrated that the performance of a deep learning model for prostate segmentation can be affected by choice of loss function. For prostate segmentation, it would appear that compound loss functions generally outperform singles loss functions such as Surface loss.

Focal Boost in Prostate Cancer Radiotherapy: A Review of Planning Studies and Clinical Trials.

BACKGROUND: Focal boost radiotherapy was developed to deliver elevated doses to functional sub-volumes within a target. Such a technique was hypothesized to improve treatment outcomes without increasing toxicity in prostate cancer treatment. PURPOSE: To summarize and evaluate the efficacy and variability of focal boost radiotherapy by reviewing focal boost planning studies and clinical trials that have been published in the last ten years. METHODS: Published reports of focal boost radiotherapy, that specifically incorporate dose escalation to intra-prostatic lesions (IPLs), were reviewed and summarized. Correlations between acute/late ≥G2 genitourinary (GU) or gastrointestinal (GI) toxicity and clinical factors were determined by a meta-analysis. RESULTS: By reviewing and summarizing 34 planning studies and 35 trials, a significant dose escalation to the GTV and thus higher tumor control of focal boost radiotherapy were reported consistently by all reviewed studies. Reviewed trials reported a not significant difference in toxicity between focal boost and conventional radiotherapy. Acute ≥G2 GU and late ≥G2 GI toxicities were reported the most and least prevalent, respectively, and a negative correlation was found between the rate of toxicity and proportion of low-risk or intermediate-risk patients in the cohort. CONCLUSION: Focal boost prostate cancer radiotherapy has the potential to be a new standard of care.

Viability of focal dose escalation to prostate cancer intraprostatic lesions using HDR prostate brachytherapy.

PURPOSE: This study aimed to determine the viability of focal dose escalation to prostate cancer intraprostatic lesions (IPLs) from multiparametric magnetic resonance (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA-PET) images using high-dose-rate (HDR) prostate brachytherapy (pBT). METHODS AND MATERIALS: Retrospective data from 20 patients treated with HDR pBT was utilized. The interobserver contouring variability of 5 observers was quantified using the dice similarity coefficient (DSC) and mean distance to agreement (MDA). Uncertainty in propagating IPL contours to trans-rectal ultrasound (TRUS) was quantified using a tissue equivalent prostate phantom. Feasibility of incorporating IPLs into HDR pBT planning was tested on retrospective patient data. RESULTS: The average observer DSC was 0.65 (PSMA-PET) and 0.52 (mpMRI). The uncertainty in propagating IPL contours was 0.6 mm (PSMA-PET), and 0.4 mm (mpMRI). Uncertainties could be accounted for by expanding IPL contours by 2 mm to create IPL PTVs. The mean D98% achieved using HDR pBT was 166% and 135% for the IPL and IPL PTV contours, respectively. CONCLUSIONS: Focal dose escalation to IPLs identified on either PSMA-PET or mpMRI is viable using TRUS-based HDR pBT. Utilizing HDR pBT allows dose escalation of up to 166% of the prescribed dose to the prostate.