Sir Peter MacCallum Department of Oncology - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 25
-
ItemPredicting muscle loss during lung cancer treatment (PREDICT): protocol for a mixed methods prospective study(BMJ PUBLISHING GROUP, 2021-09)INTRODUCTION: Low muscle mass and low muscle attenuation (radiodensity), reflecting increased muscle adiposity, are prevalent muscle abnormalities in people with lung cancer receiving curative intent chemoradiation therapy (CRT) or radiation therapy (RT). Currently, there is a limited understanding of the magnitude, determinants and clinical significance of these muscle abnormalities in the lung cancer CRT/RT population. The primary objective of this study is to identify the predictors of muscle abnormalities (low muscle mass and muscle attenuation) and their depletion over time in people with lung cancer receiving CRT/RT. Secondary objectives are to assess the magnitude of change in these parameters and their association with health-related quality of life, treatment completion, toxicities and survival. METHODS AND ANALYSIS: Patients diagnosed with lung cancer and planned for treatment with CRT/RT are invited to participate in this prospective observational study, with a target of 120 participants. The impact and predictors of muscle abnormalities (assessed via CT at the third lumbar vertebra) prior to and 2 months post CRT/RT on the severity of treatment toxicities, treatment completion and survival will be assessed by examining the following variables: demographic and clinical factors, weight loss, malnutrition, muscle strength, physical performance, energy and protein intake, physical activity and sedentary time, risk of sarcopenia (Strength, Assistance in walking, Rise from a chair, Climb stairs, Falls history (SARC-F) score alone and with calf-circumference) and systemic inflammation. A sample of purposively selected participants with muscle abnormalities will be invited to take part in semistructured interviews to understand their ability to cope with treatment and explore preference for treatment strategies focused on nutrition and exercise. ETHICS AND DISSEMINATION: The PREDICT study received ethics approval from the Human Research Ethics Committee at Peter MacCallum Cancer Centre (HREC/53147/PMCC-2019) and Deakin University (2019-320). Findings will be disseminated through peer review publications and conference presentations.
-
ItemRadiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer(NATURE PORTFOLIO, 2021-09-03)Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
-
ItemPersonalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer(BMC, 2021-08-03)INTRODUCTION: Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer. METHODS: A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan. RESULTS: Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan. CONCLUSION: KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.
-
ItemMachine learning applications in radiation oncology(ELSEVIER, 2021-07)Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.
-
ItemDevelopment of a physical geometric phantom for deformable image registration credentialing of radiotherapy centers for a clinical trial(WILEY, 2021-07)PURPOSE: This study aimed to develop a physical geometric phantom for the deformable image registration (DIR) credentialing of radiotherapy centers for a clinical trial and tested the feasibility of the proposed phantom at multiple domestic and international institutions. METHODS AND MATERIALS: The phantom reproduced tumor shrinkage, rectum shape change, and body shrinkage using several physical phantoms with custom inserts. We tested the feasibility of the proposed phantom using 5 DIR patterns at 17 domestic and 2 international institutions (21 datasets). Eight institutions used the MIM software (MIM Software Inc, Cleveland, OH); seven used Velocity (Varian Medical Systems, Palo Alto, CA), and six used RayStation (RaySearch Laboratories, Stockholm, Sweden). The DIR accuracy was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). RESULTS: The mean and one standard deviation (SD) values (range) of DSC were 0.909 ± 0.088 (0.434-0.984) and 0.909 ± 0.048 (0.726-0.972) for tumor and rectum proxies, respectively. The mean and one SD values (range) of the HD value were 5.02 ± 3.32 (1.53-20.35) and 5.79 ± 3.47 (1.22-21.48) (mm) for the tumor and rectum proxies, respectively. In three patterns evaluating the DIR accuracy within the entire phantom, 61.9% of the data had more than a DSC of 0.8 in both tumor and rectum proxies. In two patterns evaluating the DIR accuracy by focusing on tumor and rectum proxies, all data had more than a DSC of 0.8 in both tumor and rectum proxies. CONCLUSIONS: The wide range of DIR performance highlights the importance of optimizing the DIR process. Thus, the proposed method has considerable potential as an evaluation tool for DIR credentialing and quality assurance.
-
ItemOut-of-field dose in stereotactic radiotherapy for paediatric patients(ELSEVIER, 2021-07)BACKGROUND AND PURPOSE: Stereotactic radiotherapy combines image guidance and high precision delivery with small fields to deliver high doses per fraction in short treatment courses. In preparation for extension of these treatment techniques to paediatric patients we characterised and compared doses out-of-field in a paediatric anthropomorphic phantom for small flattened and flattening filter free (FFF) photon beams. METHOD AND MATERIALS: Dose measurements were taken in several organs and structures outside the primary field in an anthropomorphic phantom of a 5 year old child (CIRS) using thermoluminescence dosimetry (LiF:Mg,Cu,P). Out-of-field doses from a medical linear accelerator were assessed for 6 MV flattened and FFF beams of field sizes between 2 × 2 and 10 × 10 cm2. RESULTS: FFF beams resulted in reduced out-of-field doses for all field sizes when compared to flattened beams. Doses for FFF and flattened beams converged for all field sizes at larger distances (>40 cm) from the central axis as leakage becomes the primary source of out-of-field dose. Rotating the collimator to place the MLC bank in the longitudinal axis of the patient was shown to reduce the peripheral doses measured by up to 50% in Varian linear accelerators. CONCLUSION: Minimising out-of-field doses by using FFF beams and aligning the couch and collimator to provide tertiary shielding demonstrated advantages of small field, FFF treatments in a paediatric setting.
-
ItemStudy protocol of the LARK (TROG 17.03) clinical trial: a phase II trial investigating the dosimetric impact of Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring(BMC, 2021-05-03)BACKGROUND: Stereotactic Ablative Body Radiotherapy (SABR) is a non-invasive treatment which allows delivery of an ablative radiation dose with high accuracy and precision. SABR is an established treatment for both primary and secondary liver malignancies, and technological advances have improved its efficacy and safety. Respiratory motion management to reduce tumour motion and image guidance to achieve targeting accuracy are crucial elements of liver SABR. This phase II multi-institutional TROG 17.03 study, Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring (LARK), aims to investigate and assess the dosimetric impact of the KIM real-time image guidance technology. KIM utilises standard linear accelerator equipment and therefore has the potential to be a widely available real-time image guidance technology for liver SABR. METHODS: Forty-six patients with either hepatocellular carcinoma or oligometastatic disease to the liver suitable for and treated with SABR using Kilovoltage Intrafraction Monitoring (KIM) guidance will be included in the study. The dosimetric impact will be assessed by quantifying accumulated patient dose distribution with or without the KIM intervention. The patient treatment outcomes of local control, toxicity and quality of life will be measured. DISCUSSION: Liver SABR is a highly effective treatment, but precise dose delivery is challenging due to organ motion. Currently, there is a lack of widely available options for performing real-time tumour localisation to assist with accurate delivery of liver SABR. This study will provide an assessment of the impact of KIM as a potential solution for real-time image guidance in liver SABR. TRIAL REGISTRATION: This trial was registered on December 7th 2016 on ClinicalTrials.gov under the trial-ID NCT02984566 .
-
ItemMLC tracking for lung SABR is feasible, efficient and delivers high-precision target dose and lower normal tissue dose(ELSEVIER IRELAND LTD, 2021-02)BACKGROUND AND PURPOSE: The purpose of this work is to present the clinical experience from the first-in-human trial of real-time tumor targeting via MLC tracking for stereotactic ablative body radiotherapy (SABR) of lung lesions. METHODS AND MATERIALS: Seventeen patients with stage 1 non-small cell lung cancer (NSCLC) or lung metastases were included in a study of electromagnetic transponder-guided MLC tracking for SABR (NCT02514512). Patients had electromagnetic transponders inserted near the tumor. An MLC tracking SABR plan was generated with planning target volume (PTV) expanded 5 mm from the end-exhale gross tumor volume (GTV). A clinically approved comparator plan was generated with PTV expanded 5 mm from a 4DCT-derived internal target volume (ITV). Treatment was delivered using a standard linear accelerator to continuously adapt the MLC based on transponder motion. Treated volumes and reconstructed delivered dose were compared between MLC tracking and comparator ITV-based treatment. RESULTS: All seventeen patients were successfully treated with MLC tracking (70 successful fractions). MLC tracking treatment delivery time averaged 8 minutes. The time from the start of CBCT to the end of treatment averaged 22 minutes. The MLC tracking PTV for 16/17 patients was smaller than the ITV-based PTV (range -1.6% to 44% reduction, or -0.6 to 18 cc). Reductions in mean lung dose (27 cGy) and V20Gy (50 cc) were statistically significant (p < 0.02). Reconstruction of treatment doses confirmed a statistically significant improvement in delivered GTV D98% (p < 0.05) from planned dose compared with the ITV-based plans. CONCLUSION: The first treatments with lung MLC tracking have been successfully performed in seventeen SABR patients. MLC tracking for lung SABR is feasible, efficient and delivers high-precision target dose and lower normal tissue dose.
-
ItemA Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images(FRONTIERS MEDIA SA, 2021-05-07)BACKGROUND: Muscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans. METHODS: Attenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours. RESULTS: We trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%. CONCLUSIONS: This work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.
-
ItemOn the reduction of aperture complexity in kidney SABR(WILEY, 2021-04)BACKGROUND: Stereotactic ablative body radiotherapy (SABR) of primary kidney cancers is confounded by motion. There is a risk of interplay effect if the dose is delivered using volumetric modulated arc therapy (VMAT) and flattening filter-free (FFF) dose rates due to target and linac motion. This study aims to provide an efficient way to generate plans with minimal aperture complexity. METHODS: In this retrospective study, 62 patients who received kidney SABR were reviewed. For each patient, two plans were created using internal target volume based motion management, on the average intensity projection of a four-dimensional CT. In the first plan, optimization was performed using a knowledge-based planning model based on delivered clinical plans in our institution. In the second plan, the optimization was repeated, with a maximum monitor unit (MU) objective applied in the optimization. Dose-volume, conformity, and complexity metric (with the field edge metric and the modulation complexity score) were compared between the two plans. Results are shown in terms of median (first quartile - third quartile). RESULTS: Similar dosimetry was obtained with and without the utilization of an objective on the MU. However, complexity was reduced by using the objective on the MUs (modulation complexity score = 0.55 (0.50-0.61) / 0.33 (0.29-0.36), P-value < 10-10 , with/without the MU objective). Reduction of complexity was driven by a larger aperture area (area aperture variability = 0.68 (0.64-0.73) / 0.42 (0.37-0.45), P-value < 10-10 , with/without the MU objective). Using the objective on the MUs resulted in a more spherical dose distribution (sphericity 50% isodose = 0.73 (0.69-0.75) / 0.64 (0.60-0.68), P-value < 10-8 , with/without the MU objective) reducing dose to organs at risk given respiratory motion. CONCLUSIONS: Aperture complexity is reduced in kidney SABR by using an objective on the MU delivery with VMAT and FFF dose rate.