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.

Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low-dose 4D cone beam CT imaging.

BACKGROUND: The adoption of four-dimensional cone beam computed tomography (4DCBCT) for image-guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240 s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2 s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality. PURPOSE: This study investigates the impact of gantry velocity and angular separation between x-ray projections on image quality and its implication for fast low-dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x-ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state-of-the-art reconstruction methods. METHODS: This study considers fast low-dose 4DCBCT acquisitions (60-80 s, 200-projection scans). To assess the impact of adaptive gantry rotations, the angular position of x-ray projections from adaptive 4DCBCT acquisitions from a 30-patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2 s, 60 s, 120 s, 240 s) were simulated by sampling x-ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac-Torso (XCAT) digital phantom was used to simulate projections to remove patient-specific image quality variables. Image reconstruction was performed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), and Motion-Compensated-MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity-Index-Measure (SSIM), Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR), Tissue-Interface-Width-Diaphragm (TIW-D), and Tissue-Interface-Width-Tumor (TIW-T). RESULTS: Patient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB-reconstructions, average patient angular gaps produced SSIM-0.98, CNR-13.6, SNR-34.8, TIW-D-1.5 mm, and TIW-T-2.0 mm, static angular gap 40° produced SSIM-0.92, CNR-6.8, SNR-6.7, TIW-D-5.7 mm, and TIW-T-5.9 mm and ideal produced SSIM-1.00, CNR-13.6, SNR-34.8, TIW-D-1.5 mm, and TIW-T-2.0 mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts. CONCLUSION: Very fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion-compensated reconstruction is performed. Importantly, the angular separation between x-ray projections within each individual respiratory bin had minimal effect on the image quality of fast low-dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.

Central airway squamous metaplasia following radiation therapy mimicking local tumour recurrence.

Radiation therapy can result in injury to the lung parenchyma and central airways; the latter is less well documented in the literature. Here, we describe a 65-year-old Caucasian male, who developed focal endobronchial nodules and right main bronchial stenosis suggesting tumour recurrence, 32 months following curative intent concurrent chemoradiation therapy for Stage 3B squamous cell carcinoma of the lung. Computed tomography and positron emission tomography results are detailed. Flexible bronchoscopy with bronchial biopsies revealed squamous metaplasia rather than malignant tumour recurrence, with ongoing observation planned.

Central airway squamous metaplasia following radiation therapy mimicking local tumour recurrence.

Radiation therapy can result in injury to the lung parenchyma and central airways; the latter is less well documented in the literature. Here, we describe a 65-year-old Caucasian male, who developed focal endobronchial nodules and right main bronchial stenosis suggesting tumour recurrence, 32 months following curative intent concurrent chemoradiation therapy for Stage 3B squamous cell carcinoma of the lung. Computed tomography and positron emission tomography results are detailed. Flexible bronchoscopy with bronchial biopsies revealed squamous metaplasia rather than malignant tumour recurrence, with ongoing observation planned.

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation.

BACKGROUND AND PURPOSE: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on MRI-specific automatic QA. This work proposes a clinical target volume (CTV) delineation QA framework based on deep learning (DL) for MRI-guided prostate radiotherapy. MATERIALS AND METHODS: The proposed workflow utilized a 3D dropblock ResUnet++ (DB-ResUnet++) to generate multiple segmentation predictions via Monte Carlo dropout which were used to compute an average delineation and area of uncertainty. A logistic regression (LR) classifier was employed to classify the manual delineation as pass or discrepancy based on the spatial association between the manual delineation and the network's outputs. This approach was evaluated on a multicentre MRI-only prostate radiotherapy dataset and compared with our previously published QA framework based on AN-AG Unet. RESULTS: The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.92, a true positive rate (TPR) of 0.92 and a false positive rate of 0.09 with an average processing time per delineation of 1.3 min. Compared with our previous work using AN-AG Unet, this method generated fewer false positive detections at the same TPR with a much faster processing speed. CONCLUSION: To the best of our knowledge, this is the first study to propose an automatic delineation QA tool using DL with uncertainty estimation for MRI-guided prostate radiotherapy, which can potentially be used for reviewing prostate CTV delineation in multicentre clinical trials.

Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.

Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.

Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach.

In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume.

Patterns of curative treatment for non-small cell lung cancer in New South Wales, Australia.

INTRODUCTION: There is a lack of large population-based studies examining patterns of curative treatment for non-small cell lung cancer (NSCLC) in Australia. This study aimed to evaluate the utilization of curative treatment for NCSLC at a population level and identify factors associated with its use in New South Wales (NSW), Australia. METHODS: Patients diagnosed with localized or locoregional NSCLC between 2009 and 2014 were identified from the NSW Central Cancer Registry. Curative treatment was defined as surgery or radiotherapy with a 45 Gy minimum dose. Univariate and multivariable analyses were performed to investigate factors associated with the receipt of curative treatment. A Cox proportional-hazards regression model was used to analyze the factors associated with 2-year overall survival (OS). RESULTS: Of the 5722 patients diagnosed with NSCLC in the study period, 3355 (59%) patients received curative treatment and 2367 (41%) patients did not receive curative treatment. The receipt of curative treatment was significantly associated with younger patients, female gender, localized disease, and Charlson Comorbidity Index (CCI) = 0. The use of curative treatment increased significantly over time from 2009 (55%) to 2014 (63%) and varied significantly from 24% to 70% between local health districts (LHDs) of residence. Younger age, female gender, localized disease, CCI = 0, and overseas country of birth were significantly associated with 2-year OS. The 2-year OS significantly improved from 70% in 2009 to 77% in 2014 for patients who received curative treatment. CONCLUSION: The use of curative treatment for patients with potentially curable NSCLC was low at 59%. However, the use of curative treatment and survival have increased over time. Significant variation was noted in the use of curative treatment between LHDs.

Rates of MRI simulator utilisation in a tertiary cancer therapy centre.

Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.

Exploring cancer patients', caregivers', and clinicians' utilisation and experiences of telehealth services during COVID-19: A qualitative study.

OBJECTIVES: The COVID-19 pandemic has significantly impacted oncology. With pandemic restrictions limiting close contact between individuals, telehealth (the use of teleconferencing/videoconferencing to conduct real-time medical consultations) has been increasingly utilised. This qualitative study aimed to explore adult cancer patient, caregiver, and clinician (doctor, nurse, allied health) telehealth experiences during COVID-19 in urban and rural Australian settings and identify potential enablers and barriers to sustained telehealth implementation. METHODS: English-speaking participants completed semi-structured interviews regarding their telehealth experiences since March 2020. Interviews ceased when data saturation occurred. Iterative thematic analysis was conducted using NVivo 12 Pro. RESULTS: Thirty-four interviews (clinician=14, patient=13, caregiver=7) were conducted from April to August 2021. Analysis generated seven themes relating to telehealth use: 1) Acceptability as a form of consultation, 2) Impacts on healthcare provision, 3) Communication & relationships, 4) Efficient form of consultation, 5) Comfort of conducting telehealth in different environments, 6) Technological barriers and 7) Future preferences. CONCLUSIONS: The rapid uptake of telehealth during the pandemic has mostly been well-received, and telehealth can be appropriately used in oncology. PRACTICE IMPLICATIONS: Barriers including providing appropriate facilities, technology, and telehealth training; and selecting appropriate patients must be addressed to enable sustained telehealth use in future cancer care.

Stepping into the real world: a mixed-methods evaluation of the implementation of electronic patient reported outcomes in routine lung cancer care.

BACKGROUND: To realize the broader benefits of electronic patient-reported outcome measures (ePROMs) in routine care, we used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to inform the translation of a clinically effective ePROM system (hereafter referred to as the PRM system) into practice. The study aimed to evaluate the processes and success of implementing the PRM system in the routine care of patients diagnosed with lung cancer. METHOD: A controlled before-and-after mixed-methods study was undertaken. Data sources included a self-report questionnaire and interviews with healthcare providers, electronic health record data for PRMs patients and historical controls, and field notes. Descriptive statistics, logistic regression modelling, negative binomial models, generalized estimating equations and repeated measures ANOVA were used to analyze quantitative data. Qualitative data was thematically analyzed. RESULTS: A total of 48/79 eligible people diagnosed with lung cancer completed 90 assessments during the 5-month implementation period (RE-AIM reach). Every assessment breached the pre-defined threshold and care coordinators reviewed and actioned 95.6% of breaches, resulting in 146 referrals to allied health services, most frequently for social work (25.3%), dietetics (18.5%), physiotherapy (18.5%) and occupational therapy (17.1%). PRMs patients had significantly fewer visits to the cancer assessment unit for problematic symptoms (M = 0.23 vs. M = 0.43; p = 0.035), and were significantly more likely to be offered referrals (71% vs. 29%, p < 0.0001) than historical controls (RE-AIM effect). The levels of 'organizational readiness for implementing change' (ORIC) did not show much differences between baseline and follow-up, though this was already high at baseline; but significantly more staff reported improved confidence when asking patients to complete assessments (64.7% at baseline vs. 88.2% at follow-up, p = 0.0046), and when describing the assessment tool to patients (64.7% at baseline vs. 76.47% at follow-up, p = 0.0018) (RE-AIM adoption). A total of 78 staff received PRM system training, and 95.6% of the PRM system alerts were actioned (RE-AIM implementation); and all lung cancer care coordinators were engaged with the PRM system beyond the end of the study period (RE-AIM maintenance). CONCLUSION: This study demonstrates the potential of the PRM system in enhancing the routine care of lung cancer patients, through leveraging the capabilities of automated web-based care options. Research has shown the clear benefits of using electronically collected patient-reported outcome measures (ePROMs) for cancer patients and health services. However, we need to better understand how to implement ePROMs as part of routine care. This study evaluated the processes and outcomes of implementing an ePROMs system in the routine care of patients diagnosed with lung cancer. Key findings included: (a) a majority of eligible patients completed the scheduled assessments; (b) patient concerns were identified in every assessment, and care coordinators reviewed and actioned almost all of these, including making significantly more referrals to allied health services; (c) patients completing assessments regularly were less likely to present to the cancer assessment unit with problematic symptoms, suggesting that ePROMs identified patient concerns early and this led to a timely response to concerns; (d) staff training and engagement was high, and staff reporting increased confidence when asking patients to complete assessments and when describing the assessment tool to patients at the end of the implementation period. This study shows that implementing ePROMs in routine care is feasible and can lead to improvements in patient care.

Quality indicators for radiation oncology.

Quality Indicators, based on clinical practice guidelines, have been used in medicine and within oncology to measure quality of care for over twenty years. However, radiation oncology quality indicators are sparse. This article describes the background to the development of current national and international, general and tumour site-specific radiation oncology quality indicators in use. We explore challenges and opportunities to expand their routine prospective collection and feedback to help drive improvements in the quality of care received by people undergoing radiation therapy.

Reducing 4DCBCT imaging dose and time: exploring the limits of adaptive acquisition and motion compensated reconstruction.

This study investigates the dose and time limits of adaptive 4DCBCT acquisitions (adaptive-acquisition) compared with current conventional 4DCBCT acquisition (conventional-acquisition). We investigate adaptive-acquisitions as low as 60 projections (∼25 s scan, 6 projections per respiratory phase) in conjunction with emerging image reconstruction methods. 4DCBCT images from 20 patients recruited into the adaptive CT acquisition for personalized thoracic imaging clinical study (NCT04070586) were resampled to simulate faster and lower imaging dose acquisitions. All acquisitions were reconstructed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), motion compensated FDK (MCFDK), motion compensated MKB (MCMKB) and simultaneous motion estimation and image reconstruction (SMEIR) algorithms. All reconstructions were compared against conventional-acquisition 4DFDK-reconstruction using Structural SIMilarity Index (SSIM), signal-to-noise ratio (SNR), contrast-to-noise-ratio (CNR), tissue interface sharpness diaphragm (TIS-D), tissue interface sharpness tumor (TIS-T) and center of mass trajectory (COMT) for difference in diaphragm and tumor motion. All reconstruction methods using 110-projection adaptive-acquisition (11 projections per respiratory phase) had a SSIM of greater than 0.92 relative to conventional-acquisition 4DFDK-reconstruction. Relative to conventional-acquisition 4DFDK-reconstruction, 110-projection adaptive-acquisition MCFDK-reconstructions images had 60% higher SNR, 10% higher CNR, 30% higher TIS-T and 45% higher TIS-D on average. The 110-projection adaptive-acquisition SMEIR-reconstruction images had 123% higher SNR, 90% higher CNR, 96% higher TIS-T and 60% higher TIS-D on average. The difference in diaphragm and tumor motion compared to conventional-acquisition 4DFDK-reconstruction was within submillimeter accuracy for all acquisition reconstruction methods. Adaptive-acquisitions resulted in faster scans with lower imaging dose and equivalent or improved image quality compared to conventional-acquisition. Adaptive-acquisition with motion compensated-reconstruction enabled scans with as low as 110 projections to deliver acceptable image quality. This translates into 92% lower imaging dose and 80% less scan time than conventional-acquisition.

Trends in the use of short-course radiation therapy for rectal cancer in New South Wales, Australia.

INTRODUCTION: Trends in the use of short-course radiation therapy (RT) for rectal cancer in Australia are unknown. The purpose of this study was to compare short-course RT and long-course chemoradiation (CRT) utilisation in the neoadjuvant treatment of rectal cancer in New South Wales (NSW). METHODS: Patients who received neoadjuvant RT (2009-2014) for rectal cancer were identified from the NSW Central Cancer Registry. Univariate and multivariable analyses were performed to investigate factors associated with receipt of short-course RT. RESULTS: A total of 1196 (81%) patients received long-course CRT, and 274 (19%) patients received short-course RT. Receipt of short-course RT was associated with older age: 54% in patients ≥80 years, and 11% in patients <50 years (P < 0.0001). Patients with T2 disease (30%) were more likely to receive short-course RT, compared with T3 (19%) or T4 (8%) disease (P = 0.002). Patients with N0 (23%) disease were more likely to be treated with short-course RT, compared with N+ (16%) (P = 0.03). The proportion of short-course RT delivered to patients with Charlson Comorbidity Index (CCI) ≥ 2 (28%) was higher than patients with CCI = 0 (17%) (P = 0.002). There was wide variation in the proportion of short-course RT used across residence local health districts (5-29%) (P < 0.0001). CONCLUSION: In rectal cancer patients treated with neoadjuvant RT in NSW, 19% received short-course RT. The use of short-course RT was associated with older age, comorbidities and less advanced disease. Wide variation across NSW was identified and future research investigating factors for the variation will be useful.

Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial.

Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.

The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose.

BACKGROUND AND PURPOSE: We present the first implementation of Adaptive 4D cone beam CT (4DCBCT) that adapts the image hardware (gantry rotation speed and kV projections) in response to the patient's real-time respiratory signal. Adaptive 4DCBCT was applied on lung cancer patients to reduce the scan time and imaging dose in the ADaptive CT Acquisition for Personalised Thoracic imaging (ADAPT) trial. MATERIALS AND METHODS: The ADAPT technology measures the patient's real-time respiratory signal and uses mathematical optimisation and external circuitry attached to the linear accelerator to modulate the gantry rotation speed and kV projection rate to reduce scan times and imaging dose. For each patient, ADAPT scans were acquired on two treatment fractions and reconstructed with a motion compensated reconstruction algorithm and compared to the current state-of-the-art four-minute 4DCBCT acquisition (conventional 4DCBCT). We report on the scan time, imaging dose and image quality for the first four adaptive 4DCBCT patients. RESULTS: The ADAPT imaging dose was reduced by 85% and scan times were 73 ± 12 s representing a 70% reduction compared to the 240 s conventional 4DCBCT scan. The contrast-to-noise ratio was improved from 9.2 ± 3.9 with conventional 4DCBCT to 11.7 ± 4.1 with ADAPT. DISCUSSION: The ADAPT trial represents the first time that gantry rotation speed and projection acquisition have been adapted and optimised in real-time in response to changes in the patient's breathing. ADAPT demonstrates substantially reduced scan times and imaging dose for clinical 4DCBCT imaging that could enable more efficient and optimised lung cancer radiotherapy.

Translation of oncology multidisciplinary team meeting (MDM) recommendations into clinical practice.

BACKGROUND: Multidisciplinary team meeting (MDM) processes differ according to clinical setting and tumour site. This can impact on decision making. This study aimed to evaluate the translation of MDM recommendations into clinical practice across solid tumour MDMs at an academic centre. METHODS: A retrospective audit of oncology records was performed for nine oncology MDMs held at Liverpool Hospital, NSW, Australia from 1/2/17-31/7/17. Information was collected on patient factors (age, gender, country of birth, language, postcode, performance status, comorbidities), tumour factors (diagnosis, stage) and MDM factors (number of MDMs, MDM recommendation). Management was audited up to a year post MDM to record management and identify reasons if discordant with MDM recommendations. Univariate and multivariable regression analyses were performed to assess for factors associated with concordant management. RESULTS: Eight hundred thirty-five patients were discussed, median age was 65 years and 51.4% were males. 70.8% of patients were presented at first diagnosis, 77% discussed once and treatment recommended in 73.2%. Of 771 patients assessable for concordance, management was fully concordant in 79.4%, partially concordant in 12.8% and discordant in 7.8%. Concordance varied from 84.5% for lung MDM to 97.6% for breast MDMs. On multivariable analysis, breast and upper GI MDMs and discussion at multiple MDMs were significantly associated with concordant management. The most common reason for discordant management was patient/guardian decision (28.3%). CONCLUSION: There was variability in translation of MDM recommendations into clinical practice by tumour site. Routine measurement of implementation of MDM recommendations should be considered as a quality indicator of MDM practice.

Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction.

Conventional 4DCBCT captures 1320 projections across 4 min. Adaptive 4DCBCT has been developed to reduce imaging dose and scan time. This study investigated reconstruction algorithms that best complement adaptive 4DCBCT acquisition for reducing imaging dose and scan time whilst maintaining or improving image quality compared to conventional 4DCBCT acquisition using real patient data from the first 10 adaptive 4DCBCT patients. Adaptive 4DCBCT was implemented in the ADaptive CT Acquisition for Personalized Thoracic imaging clinical trial. Adaptive 4DCBCT modulates gantry rotation speed and kV acquisition rate in response to the patient's real-time respiratory signal, ensuring even angular spacing between projections at each respiratory phase. We examined the first 10 lung cancer radiotherapy patients that received adaptive 4DCBCT. Fast, 200-projection scans over 60-80 s, and slower, 600-projection scans over ∼240 s, were obtained after routine patient treatment and compared against conventional 4DCBCT acquisition. Adaptive 4DCBCT acquisitions were reconstructed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), Motion Compensated FDK (MCFDK) and Motion Compensated MKB (MCMKB) algorithms. Reconstructions were assessed via, Structural SIMilarity (SSIM), Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), Tissue Interface Sharpness of Diaphragm (TIS-D) and Tumor (TIS-T). The 200- and 600-projection adaptive 4DCBCT acquisition corresponded to 85% and 55% reduction in imaging dose, shorter and similar scan times of approximately 90 s and 236 s respectively, compared to conventional 4DCBCT acquisition. 200- and 600-projection adaptive 4DCBCT reconstructions achieved more than 0.900 SSIM relative to conventional 4DCBCT acquisition. Compared to conventional 4DCBCT acquisition, 200-projection adaptive 4DCBCT reconstructions achieved higher SNR, CNR, TIS-T, TIS-D with motion compensated algorithms, MCFDK (208%, 159%, 174%, 247%) and MCMKB (214%, 173%, 266%, 245%) respectively. The 200-projection adaptive 4DCBCT MCFDK- and MCMKB-reconstruction results show image quality improvements are possible even with 85% fewer projections acquired. We established acquisition-reconstruction protocols that provide substantial reductions in imaging time and dose whilst improving image quality.