Sir Peter MacCallum Department of Oncology - Research Publications
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ItemImpact of COVID-19 on cancer service delivery: a follow-up international survey of oncology clinicians(ELSEVIER, 2020-10)BACKGROUND: The COVID-19 pandemic has had a vast impact on cancer service delivery around the world. Previously reported results from our international survey of oncology clinicians, conducted through March-April 2020, found that clinicians reported altering management in both the curative and palliative settings and not in proportion to the COVID-19 case burden in their region of practice. This follow-up survey, conducted from 27th September to 7th November 2020, aimed to explore how attitudes and practices evolved over the 2020 pandemic period. PARTICIPANTS AND METHODS: Participants were medical, radiation and surgical oncologist and trainees. Surveys were distributed electronically via ESMO and other collaborating professional societies. Participants were asked to compare their practice prior to the pandemic to both the period of March-April 2020, referred to as the 'early' period, and the current survey period, referred to as the 'later' period. RESULTS: One hundred and seventy-two oncology clinicians completed the survey. The majority of respondents were medical oncologists (n = 136, 79%) and many were from Europe (n = 82, 48%). In the 'early' period, 88% (n = 133) of clinicians reported altering their practice compared to 63% (n = 96) in the 'later' period. Compared to prior to the pandemic, clinicians reported fewer new patient presentations in the 'early' period and a trend towards more patients presenting with advanced disease in the 'later' period. CONCLUSIONS: Results indicate a swing back towards pre-COVID-19 practices despite an increase in the rate of cumulative COVID-19 cases across 2020. The impact of these changes on cancer associated morbidity and mortality remains to be measured over the months and years to come.
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ItemNo Preview AvailableReal world outcomes in KRAS G12C mutation positive non-small cell lung cancer(ELSEVIER IRELAND LTD, 2020-08)BACKGROUND: KRAS mutations are found in 20-30 % of non-small cell lung cancers (NSCLC) and were traditionally considered undruggable. KRASG12C mutation confers sensitivity to KRASG12C covalent inhibitors, however its prognostic impact remains unclear. This study assesses the frequency, clinical features, prevalence of brain metastases and outcomes in KRASG12C NSCLC in a real-world setting. METHODS: Patients enrolled in the prospective Thoracic Malignancies Cohort (TMC) between July 2012 to October 2019 with recurrent/metastatic non-squamous NSCLC, available KRAS results, and without EGFR/ALK/ROS1 gene aberrations, were selected. Data was extracted from TMC and patient records. Clinicopathologic features, treatment and overall survival (OS) was compared for KRAS wildtype (KRASWT) and KRAS mutated (KRASmut); and KRASG12C and other (KRASother) mutations. RESULTS: Of 1386 NSCLC patients, 1040 were excluded: non-metastatic/recurrent (526); unknown KRAS status (356); ALK/EGFR/ROS1 positive (154); duplicate (4). Of 346 patients analysed, 144 (42 %) were KRASmut, of whom 65 (45 %) were KRASG12C. All patients with KRASG12C were active or ex-smokers, compared to 92 % of KRASother and 83 % of KRASWT. The prevalence of brain metastases during follow-up was similar between KRASmut and KRASWT (33 % vs 40 %, p = 0.17), and KRASG12C and KRASother (40 % vs 41 %, p = 0.74). The proportion of patients receiving one or multiple lines of systemic therapy was comparable. OS was similar between KRASmut and KRASWT (p = 0.54), and KRASG12C and KRASother (p = 0.39). CONCLUSION: Patients with KRASmut and KRASWT, and KRASG12C and KRASother NSCLC have comparable clinical features, treatment and survival. While not prognostic, KRASG12C may be an important predictive biomarker as promising KRASG12C covalent inhibitors continue to be developed.
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ItemImpact of COVID-19 on cancer service delivery; results from an international survey of oncology clinicians(WILEY, 2020-11)OBJECTIVES: To report clinician-perceived changes to cancer service delivery in response to COVID-19. DESIGN: Multidisciplinary Australasian cancer clinician survey in collaboration with the European Society of Medical Oncology. SETTING: Between May and June 2020 clinicians from 70 countries were surveyed; majority from Europe (n=196; 39%) with 1846 COVID-19 cases per million people, Australia (AUS)/New Zealand (NZ) (n=188; 38%) with 267/236 per million and Asia (n=75; 15%) with 121 per million at time of survey distribution. PARTICIPANTS: Medical oncologists (n=372; 74%), radiation oncologists (n=91; 18%) and surgical oncologists (n=38; 8%). RESULTS: Eighty-nine per cent of clinicians reported altering clinical practices; more commonly among those with versus without patients diagnosed with COVID-19 (n=142; 93% vs n=225; 86%, p=0.03) but regardless of community transmission levels (p=0.26). More European clinicians (n=111; 66.1%) had treated patients diagnosed with COVID-19 compared with Asia (n=20; 27.8%) and AUS/NZ (n=8; 4.8%), p<0.001. Many clinicians (n=307; 71.4%) reported concerns that reduced access to standard treatments during the pandemic would negatively impact patient survival. The reported proportion of consultations using telehealth increased by 7.7-fold, with 25.1% (n=108) of clinicians concerned that patient survival would be worse due to this increase. Clinicians reviewed a median of 10 fewer outpatients/week (including non-face to face) compared with prior to the pandemic, translating to 5010 fewer specialist oncology visits per week among the surveyed group. Mental health was negatively impacted for 52.6% (n=190) of clinicians. CONCLUSION: Clinicians reported widespread changes to oncology services, in regions of both high and low COVID-19 case numbers. Clinician concerns of potential negative impacts on patient outcomes warrant objective assessment, with system and policy implications for healthcare delivery at large.
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ItemLung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer(NATURE PUBLISHING GROUP, 2017-08-22)INTRODUCTION: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. METHODS: Prospective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell's c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284). RESULTS: The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. CONCLUSIONS: The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.
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ItemDynamic Thromboembolic Risk Modelling to Target Appropriate Preventative Strategies for Patients with Non-Small Cell Lung Cancer(MDPI, 2019-01)Prevention of cancer-associated thromboembolism (TE) remains a significant clinical challenge and priority world-wide safety initiative. In this prospective non-small cell lung cancer (NSCLC) cohort, longitudinal TE risk profiling (clinical and biomarker) was undertaken to develop risk stratification models for targeted TE prevention. These were compared with published models from Khorana, CATS, PROTECHT, CONKO, and CATS/MICA. The NSCLC cohort of 129 patients, median follow-up 22.0 months (range 5.6-31.3), demonstrated a hypercoagulable profile in >75% patients and TE incidence of 19%. High TE risk patients were those receiving chemotherapy with baseline fibrinogen ≥ 4 g/L and d-dimer ≥ 0.5 mg/L; or baseline d-dimer ≥ 1.5 mg/L; or month 1 d-dimer ≥ 1.5 mg/L. The model predicted TE with 100% sensitivity and 34% specificity (c-index 0.67), with TE incidence 27% vs. 0% for high vs. low-risk. A comparison using the Khorana, PROTECHT, and CONKO methods were not discriminatory; TE incidence 17⁻25% vs. 14⁻19% for high vs. low-risk (c-index 0.51⁻0.59). Continuous d-dimer (CATS/MICA model) was also not predictive of TE. Independent of tumour stage, high TE risk was associated with cancer progression (HR 1.9, p = 0.01) and mortality (HR 2.2, p = 0.02). The model was tested for scalability in a prospective gastrointestinal cancer cohort with equipotency demonstrated; 80% sensitivity and 39% specificity. This proposed TE risk prediction model is simple, practical, potent and can be used in the clinic for real-time, decision-making for targeted thromboprophylaxis. Validation in a multicentre randomised interventional study is underway (ACTRN12618000811202).
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ItemEvaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients(OXFORD UNIV PRESS, 2020-07)OBJECTIVE: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. METHODS: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. RESULTS: The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. DISCUSSION AND CONCLUSION: The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.