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For three years, COVID-19 has circulated among our communities and around the world, fundamentally changing social interactions, health care systems, and service delivery. For people living with (and receiving treatment for) cancer, pandemic conditions presented significant additional hurdles in an already unstable and shifting environment, including disrupted personal contact with care providers, interrupted access to clinical trials, distanced therapeutic encounters, multiple immune vulnerabilities, and new forms of financial precarity. In a 2020 perspective in this journal, we examined how COVID-19 was reshaping cancer care in the early stages of the pandemic and how these changes might endure into the future. Three years later, and in light of a series of interviews with patients and their caregivers from the United States and Australia conducted during the pandemic, we return to consider the potential legacy effects of the pandemic on cancer care. While some challenges to care provision and survivorship were unforeseen, others accentuated and amplified existing problems experienced by patients, caregivers, and health care providers. Both are likely to have enduring effects in the "post-pandemic" world, raising the importance of focusing on lessons that can be learned for the future., (©2023 The Authors; Published by the American Association for Cancer Research.)

Introduction: Medical decision-making in oncology is a complicated process and to date there are few studies examining how patients with cancer make choices with respect to different features of their care. It is also unknown whether patient choices vary by geographical location and how location could account for observed rural and metropolitan cancer differences. This paper describes an ongoing study that aims to (1) examine patient and healthcare-related factors that influence choices of patients with cancer; (2) measure and quantify preferences of patients with cancer towards cancer care using a discrete choice experiment (DCE) and (3) explore preference heterogeneity between metropolitan and rural locations., Methods and Analysis: A DCE is being conducted to understand how patients with cancer choose between two clinical scenarios accounting for different patient and healthcare-related factors (and levels). Preliminary qualitative research was undertaken to guide the development of an appropriate DCE design including characteristics that are important and relevant to patients with cancer. A fractional factorial design using the D-efficiency criteria was used to estimate interactions among attributes. Multinomial logistic regression will be used for the primary DCE analysis and to control for sociodemographic and clinical characteristics., Ethics and Dissemination: The Barwon Health Human Research Ethics Committee approved the study. Findings from the study will be presented in national/international conferences and peer-reviewed journals. Our results will form the basis of a feasibility study to inform the development of a larger scale study into preferences of patients with cancer and their association with cancer outcomes., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objectives: Using the prediction of cancer outcome as a model, we have tested the hypothesis that through analysing routinely collected digital data contained in an electronic administrative record (EAR), using machine-learning techniques, we could enhance conventional methods in predicting clinical outcomes., Setting: A regional cancer centre in Australia., Participants: Disease-specific data from a purpose-built cancer registry (Evaluation of Cancer Outcomes (ECO)) from 869 patients were used to predict survival at 6, 12 and 24 months. The model was validated with data from a further 94 patients, and results compared to the assessment of five specialist oncologists. Machine-learning prediction using ECO data was compared with that using EAR and a model combining ECO and EAR data., Primary and Secondary Outcome Measures: Survival prediction accuracy in terms of the area under the receiver operating characteristic curve (AUC)., Results: The ECO model yielded AUCs of 0.87 (95% CI 0.848 to 0.890) at 6 months, 0.796 (95% CI 0.774 to 0.823) at 12 months and 0.764 (95% CI 0.737 to 0.789) at 24 months. Each was slightly better than the performance of the clinician panel. The model performed consistently across a range of cancers, including rare cancers. Combining ECO and EAR data yielded better prediction than the ECO-based model (AUCs ranging from 0.757 to 0.997 for 6 months, AUCs from 0.689 to 0.988 for 12 months and AUCs from 0.713 to 0.973 for 24 months). The best prediction was for genitourinary, head and neck, lung, skin, and upper gastrointestinal tumours., Conclusions: Machine learning applied to information from a disease-specific (cancer) database and the EAR can be used to predict clinical outcomes. Importantly, the approach described made use of digital data that is already routinely collected but underexploited by clinical health systems.

Background: The Psychosocial Assessment Tool 2.0 (PAT2.0) is a recently developed screening measure for assessing psychosocial risk in families caring for a child with cancer. This study aimed to assess the external validity of the PAT2.0 in an Australian pediatric oncology sample. Further aims included examining mothers' and fathers' PAT2.0 scores, change in psychosocial risk over time, and the relationship between treatment intensity and psychosocial risk., Procedure: Parents of 143 children newly diagnosed with cancer completed the PAT2.0 at diagnosis (T1) and 6-8 months later (T2). A treatment intensity measure (ITR-2) was completed by two clinical oncologists., Results: The PAT2.0 stratified families into a 3-tiered risk framework and was consistent with existing data from the authors of the scale. The majority of families were stratified into the Universal (lowest risk) category; more than one-third of families had some elevated psychosocial risk. PAT2.0 scores of mothers and fathers were correlated and psychosocial risk remained relatively stable between T1 and T2. Treatment intensity scores were not related to PAT2.0 scores at T2., Conclusions: Findings support the external validity of the PAT2.0 as a psychosocial screener. Mothers' and fathers' ratings of risk are similar; however, multi-informant use of the PAT2.0 may be clinically useful. Psychosocial risk, as measured by the PAT2.0, is a relatively stable construct over the first months of treatment and is independent of treatment intensity., (Copyright 2009 Wiley-Liss, Inc.)

Purpose: To assess the feasibility of performing central molecular analyses of fresh medulloblastomas obtained from multiple institutions and using these data to identify prognostic markers for contemporaneously treated patients., Materials and Methods: Ninety-seven samples of medulloblastoma were collected. Tumor content in samples was judged by frozen section review. Tumor ERBB2 protein and MYCC, MYCN, and TRKC mRNA levels were measured blind to clinical details using Western blotting and real-time polymerase chain reaction, respectively. Histopathologic and clinical review of each case was also performed. All data were subjected to independent statistical analysis., Results: Sample acquisition and analysis times ranged from 3 to 6 days. Eighty-six samples contained sufficient tumor for analysis, including 38 classic, 30 nodular desmoplastic, and 18 large-cell anaplastic (LCA) medulloblastomas. Protein and mRNA were extracted from 81 and 49 tumors, respectively. ERBB2 was detected in 40% (n=32 of 81) of tumors, most frequently in LCA disease (P=.005), and was independently associated with a poor prognosis (P=.031). A combination of clinical characteristics and ERBB2 expression provided a highly accurate means of discriminating disease risk. One hundred percent (n=26) of children with clinical average-risk, ERBB2-negative disease were alive at 5 years, with a median follow-up of 5.6 years, compared with only 54% for children with average-risk, ERBB2-positive tumors (n=13; P=.0001). TRKC, MYCC, and MYCN expression and histopathologic subtype were not associated with prognosis in this study., Conclusion: Central and rapid molecular analysis of frozen medulloblastomas collected from multiple institutions is feasible. ERBB2 expression and clinical risk factors together constitute a highly accurate disease risk stratification tool.

The improving prognosis for children with cancer refocuses attention to long-term outcomes with an emphasis on quality of life. Few studies have examined relationships and differences in reported results between the parent, child and clinician. We examined parent-proxy and clinician-reported functional status and health-related quality of life for children and adolescents with acute lymphoblastic leukemia (ALL). Children and adolescents, 5-18 years, in the maintenance phase of treatment for ALL attending the Haematology/Oncology outpatient clinic at the Royal Children's Hospital, Melbourne, were eligible. Measures included: 1) parent-reported functional health and well-being (Child Health Questionnaire [CHQ]); 2) parent-reported condition specific quality of life (Pediatric Cancer Quality of Life inventory [PCQL]); 3) clinician ratings of physical and psychosocial health; and 4) clinical indicators. Insufficient numbers of older patients prohibited collection of adolescent self-reports. We had a 94% response and 31 participants. Mean time since diagnosis: 1.5 (SD 0.4) years. Parents reported significantly lower functioning and well-being than population norms for all CHQ scales, whereas cancer-specific quality of life was comparable to PCQL norms. Clinician reports of the child's global physical and psychosocial health were moderately associated with each other (r(s) = 0.56, p < 0.001), and with the parent-reported physical (r(s) = 0.47, p < 0.01) and psychosocial (r(s) = 0.56, p < 0.001) CHQ summary scores. Clinician reports of the child's psychosocial health were not associated with any clinical indicators reported regularly. The results demonstrate that the social, physical and emotional health and well-being of children with ALL is significantly poorer than the health of their community-based peers. Routinely collected indicators of clinical progress conceal the psychosocial burden of ALL. Data on health, well-being and quality of life can easily be incorporated into clinical care., (Copyright 2002 Wiley-Liss, Inc.)