Sir Peter MacCallum Department of Oncology - Research Publications
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ItemImaging immunity in patients with cancer using positron emission tomography(NATURE PORTFOLIO, 2022-04-07)Immune checkpoint inhibitors and related molecules can achieve tumour regression, and even prolonged survival, for a subset of cancer patients with an otherwise dire prognosis. However, it remains unclear why some patients respond to immunotherapy and others do not. PET imaging has the potential to characterise the spatial and temporal heterogeneity of both immunotherapy target molecules and the tumor immune microenvironment, suggesting a tantalising vision of personally-adapted immunomodulatory treatment regimens. Personalised combinations of immunotherapy with local therapies and other systemic therapies, would be informed by immune imaging and subsequently modified in accordance with therapeutically induced immune environmental changes. An ideal PET imaging biomarker would facilitate the choice of initial therapy and would permit sequential imaging in time-frames that could provide actionable information to guide subsequent therapy. Such imaging should provide either prognostic or predictive measures of responsiveness relevant to key immunotherapy types but, most importantly, guide key decisions on initiation, continuation, change or cessation of treatment to reduce the cost and morbidity of treatment while enhancing survival outcomes. We survey the current literature, focusing on clinically relevant immune checkpoint immunotherapies, for which novel PET tracers are being developed, and discuss what steps are needed to make this vision a reality.
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ItemBlood-Derived Extracellular Vesicle-Associated miR-3182 Detects Non-Small Cell Lung Cancer Patients(MDPI, 2022-01)With five-year survival rates as low as 3%, lung cancer is the most common cause of cancer-related mortality worldwide. The severity of the disease at presentation is accredited to the lack of early detection capacities, resulting in the reliance on low-throughput diagnostic measures, such as tissue biopsy and imaging. Interest in the development and use of liquid biopsies has risen, due to non-invasive sample collection, and the depth of information it can provide on a disease. Small extracellular vesicles (sEVs) as viable liquid biopsies are of particular interest due to their potential as cancer biomarkers. To validate the use of sEVs as cancer biomarkers, we characterised cancer sEVs using miRNA sequencing analysis. We found that miRNA-3182 was highly enriched in sEVs derived from the blood of patients with invasive breast carcinoma and NSCLC. The enrichment of sEV miR-3182 was confirmed in oncogenic, transformed lung cells in comparison to isogenic, untransformed lung cells. Most importantly, miR-3182 can successfully distinguish early-stage NSCLC patients from those with benign lung conditions. Therefore, miR-3182 provides potential to be used for the detection of NSCLC in blood samples, which could result in earlier therapy and thus improved outcomes and survival for patients.
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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.
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ItemRobust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?(PUBLIC LIBRARY SCIENCE, 2020-02-25)In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLMall). The fitted model performance was externally validated (n = 40). The performance of GLMall (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLMall compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLMall led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients.
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ItemNodal metabolic tumour volume on baseline 18F-FDG PET/CT and overall survival in stage II and III NSCLC patients undergoing curative-intent chemoradiotherapy/radiotherapy(WILEY, 2021-10)INTRODUCTION: This study aims to investigate whether nodal metabolic tumour volume (nMTV) and nodal total lesion glycolysis (nTLG) on Fluorine-18 fluoro-deoxy-glucose positron emission tomography-computed tomography (18 F-FDG PET/CT) in inoperable node-positive stage II and III non-small cell lung cancer (NSCLC) are independent predictors of overall survival (OS) in patients undergoing curative-intent chemoradiotherapy/radiotherapy (CRT/RT). METHODS: Data from two prospective trials between 2004 and 2016 were analysed retrospectively. Primary, nodal and total metabolic tumour volume and total lesion glycolysis (pMTV, nMTV, tMTV, pTLG, nTLG and tTLG, respectively) were derived from baseline 18 F-FDG PET/CT. Cox regressions were used to model OS by 18 F-FDG PET/CT parameters adjusting for overall stage. RESULTS: 89 patients with stage II (8%) and stage III (92%) were included. The median age at diagnosis was 67 years; 62% were male. The median follow-up was 6.9 years; the median OS was 2.2 years (95% CI 1.7-3.1). The median pMTV, nMTV and tMTV were 14 mL (range 0-360), 8 mL (range 0-250) and 34 mL (range 3-384), respectively. In 3 patients, the primary lesion could not be delineated from the central hilar mass. There was no association between nMTV (adjusted HR 1.04, 95% CI 0.95-1.15, P-value 0.43), pMTV (adjusted HR 1.0, 95% CI 0.96-1.04, P-value 0.92), tMTV (adjusted HR 1.0, 95% CI 0.97-1.04, P-value 0.88), nTLG, pTLG or tTLG and OS. Consistent results were noted when patients with central hilar lesions were excluded from analysis. CONCLUSION: In node-positive stage II and III NSCLC patients who underwent 18 F-FDG PET/CT-guided target delineation curative-intent concurrent CRT/RT, metabolic parameters did not appear to provide independent prognostication.