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The role of adjuvant chemotherapy in stage II colon cancer continues to be debated. The presence of circulating tumor DNA (ctDNA) after surgery predicts very poor recurrence-free survival, whereas its absence predicts a low risk of recurrence. The benefit of adjuvant chemotherapy for ctDNA-positive patients is not well understood. We conducted a trial to assess whether a ctDNA-guided approach could reduce the use of adjuvant chemotherapy without compromising recurrence risk. Patients with stage II colon cancer were randomly assigned in a 2:1 ratio to have treatment decisions guided by either ctDNA results or standard clinicopathological features. For ctDNA-guided management, a ctDNA-positive result at 4 or 7 weeks after surgery prompted oxaliplatin-based or fluoropyrimidine chemotherapy. Patients who were ctDNA-negative were not treated. The primary efficacy end point was recurrence-free survival at 2 years. A key secondary end point was adjuvant chemotherapy use. Of the 455 patients who underwent randomization, 302 were assigned to ctDNA-guided management and 153 to standard management. The median follow-up was 37 months. A lower percentage of patients in the ctDNA-guided group than in the standard-management group received adjuvant chemotherapy (15% vs. 28%; relative risk, 1.82; 95% confidence interval [CI], 1.25 to 2.65). In the evaluation of 2-year recurrence-free survival, ctDNA-guided management was noninferior to standard management (93.5% and 92.4%, respectively; absolute difference, 1.1 percentage points; 95% CI, -4.1 to 6.2 [noninferiority margin, -8.5 percentage points]). Three-year recurrence-free survival was 86.4% among ctDNA-positive patients who received adjuvant chemotherapy and 92.5% among ctDNA-negative patients who did not. A ctDNA-guided approach to the treatment of stage II colon cancer reduced adjuvant chemotherapy use without compromising recurrence-free survival. (Supported by the Australian National Health and Medical Research Council and others; DYNAMIC Australian New Zealand Clinical Trials Registry number, ACTRN12615000381583.).

BackgroundBeing a parent alongside a cancer diagnosis presents unique challenges. It is unclear to what degree parenting considerations feature in routine care and how doctors approach treatment decision discussions.ObjectiveTo explore doctor perspectives regarding patients with cancer who have dependent children.MethodologyFocus groups and interviews conducted to ascertain doctor views. Responses were audio-recorded, transcribed and thematically analysed.ResultsTwenty-eight doctors participated: medical oncology (7), haematology (10), palliative care (8), and psycho-oncology (3). Participants observed cancer impacted upon parenting across several domains: psycho-social, practical, and family implications. Having dependent children was perceived to influence the patient experience and decision-making by patients and clinicians. Participants identified this cohort as emotionally demanding to care for with a range of psychological effects identified for doctors, particularly in highly challenging circumstances (single-parent and non-English speaking families, scenarios involving communication difficulties).ConclusionParticipants recognised the presence of dependent children to profoundly influence the experience of being both a parent and a patient with cancer. Identifying patients with parental responsibilities was noted as relevant for management at diagnosis through to death. Greater understanding of doctors’ experiences providing care for this cohort may inform the development of resources to assist doctors and their patients.

Independent validation is essential to justify use of models of breast cancer risk prediction and inform decisions about prevention options and screening. Few independent validations had been done using cohorts for common breast cancer risk prediction models, and those that have been done had small sample sizes and short follow-up periods, and used earlier versions of the prediction tools. We aimed to validate the relative performance of four commonly used models of breast cancer risk and assess the effect of limited data input on each one's performance. In this validation study, we used the Breast Cancer Prospective Family Study Cohort (ProF-SC), which includes 18 856 women from Australia, Canada, and the USA who did not have breast cancer at recruitment, between March 17, 1992, and June 29, 2011. We selected women from the cohort who were 20–70 years old and had no previous history of bilateral prophylactic mastectomy or ovarian cancer, at least 2 months of follow-up data, and information available about family history of breast cancer. We used this selected cohort to calculate 10-year risk scores and compare four models of breast cancer risk prediction: the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm model (BOADICEA), BRCAPRO, the Breast Cancer Risk Assessment Tool (BCRAT), and the International Breast Cancer Intervention Study model (IBIS). We compared model calibration based on the ratio of the expected number of breast cancer cases to the observed number of breast cancer cases in the cohort, and on the basis of their discriminatory ability to separate those who will and will not have breast cancer diagnosed within 10 years as measured with the concordance statistic (C-statistic). We did subgroup analyses to compare the performance of the models at 10 years in BRCA1 or BRCA2 mutation carriers (ie, BRCA-positive women), tested non-carriers and untested participants (ie, BRCA-negative women), and participants younger than 50 years at recruitment. We also assessed the effect that limited data input (eg, restriction of the amount of family history and non-genetic information included) had on the models' performance. After median follow-up of 11·1 years (IQR 6·0–14·4), 619 (4%) of 15 732 women selected from the ProF-SC cohort study were prospectively diagnosed with breast cancer after recruitment, of whom 519 (84%) had histologically confirmed disease. BOADICEA and IBIS were well calibrated in the overall validation cohort, whereas BRCAPRO and BCRAT underpredicted risk (ratio of expected cases to observed cases 1·05 [95% CI 0·97–1·14] for BOADICEA, 1·03 [0·96–1·12] for IBIS, 0·59 [0·55–0·64] for BRCAPRO, and 0·79 [0·73–0·85] for BRCAT). The estimated C-statistics for the complete validation cohort were 0·70 (95% CI 0·68–0·72) for BOADICEA, 0·71 (0·69–0·73) for IBIS, 0·68 (0·65–0·70) for BRCAPRO, and 0·60 (0·58–0·62) for BCRAT. In subgroup analyses by BRCA mutation status, the ratio of expected to observed cases for BRCA-negative women was 1·02 (95% CI 0·93–1·12) for BOADICEA, 1·00 (0·92–1·10) for IBIS, 0·53 (0·49–0·58) for BRCAPRO, and 0·97 (0·89–1·06) for BCRAT. For BRCA-positive participants, BOADICEA and IBIS were well calibrated, but BRCAPRO underpredicted risk (ratio of expected to observed cases 1·17 [95% CI 0·99–1·38] for BOADICEA, 1·14 [0·96–1·35] for IBIS, and 0·80 [0·68–0·95] for BRCAPRO). We noted similar patterns of calibration for women younger than 50 years at recruitment. Finally, BOADICEA and IBIS predictive scores were not appreciably affected by limiting input data to family history for first-degree and second-degree relatives. Our results suggest that models that include multigenerational family history, such as BOADICEA and IBIS, have better ability to predict breast cancer risk, even for women at average or below-average risk of breast cancer. Although BOADICEA and IBIS performed similarly, further improvements in the accuracy of predictions could be possible with hybrid models that incorporate the polygenic risk component of BOADICEA and the non-family-history risk factors included in IBIS. US National Institutes of Health, National Cancer Institute, Breast Cancer Research Foundation, Australian National Health and Medical Research Council, Victorian Health Promotion Foundation, Victorian Breast Cancer Research Consortium, Cancer Australia, National Breast Cancer Foundation, Queensland Cancer Fund, Cancer Councils of New South Wales, Victoria, Tasmania, and South Australia, and Cancer Foundation of Western Australia.

Glioblastoma has a poor prognosis with median survival of 12–14 months. Long-term survivors (LTS), alive at least 2 years from diagnosis, comprise 13% of this population. This study aims to provide a clinical profile of LTS at two institutions in Melbourne, Australia. Histological diagnosis of glioblastoma from 1st January 2006 to 31st December 2012 were identified from pathology/oncology databases. Demographic, treatment and survival characteristics were recorded (follow-up to 31st December 2015). Relevant inter-group statistics were used to identify differences between LTS and those surviving less than 2 years. Survival estimated by Kaplan–Meier. 776 patients were identified with 154 surviving > 2 years. Compared with patients surviving < 2 years, LTS were more likely to be younger (median age 56 vs. 65 years, p < .001), have ECOG 0–2 (97 vs. 65%, p < .001), gross tumour resection (91 vs. 61%, p < .001), and receive chemoradiotherapy (94 vs. 40%, p < .001). Most common presenting symptoms amongst LTS were headache (42%), seizure (28%) and speech disturbance (16%). Of LTS, 111 patients (72%) progressed at a median of 20.1 months from diagnosis, with 46% undergoing a second craniotomy. The most common non-surgical second line treatments were temozolomide (41%), followed by radiotherapy (12%). One-third of LTS received three or more lines of treatment, and 10% underwent three or more craniotomies. LTS of glioblastoma (20%) are more likely to be younger, have unilateral tumours, good performance status and undergo multimodality treatment. These data may assist in predicting LTS at diagnosis and understanding their clinical journey to facilitate planning of treatment and supportive care.

Background To perform virtual re-executions of a breast cancer clinical trial with a time-to-event outcome to demonstrate what would have happened if the trial had used various Bayesian adaptive designs instead. Methods We aimed to retrospectively “re-execute” a randomised controlled trial that compared two chemotherapy regimens for women with metastatic breast cancer (ANZ 9311) using Bayesian adaptive designs. We used computer simulations to estimate the power and sample sizes of a large number of different candidate designs and shortlisted designs with the either highest power or the lowest average sample size. Using the real-world data, we explored what would have happened had ANZ 9311 been conducted using these shortlisted designs. Results We shortlisted ten adaptive designs that had higher power, lower average sample size, and a lower false positive rate, compared to the original trial design. Adaptive designs that prioritised small sample size reduced the average sample size by up to 37% when there was no clinical effect and by up to 17% at the target clinical effect. Adaptive designs that prioritised high power increased power by up to 5.9 percentage points without a corresponding increase in type I error. The performance of the adaptive designs when applied to the real-world ANZ 9311 data was consistent with the simulations. Conclusion The shortlisted Bayesian adaptive designs improved power or lowered the average sample size substantially. When designing new oncology trials, researchers should consider whether a Bayesian adaptive design may be beneficial.

Background Prior to 2020, the use of telehealth in cancer care was limited, but COVID‐19 necessitated its rapid and widespread adoption into routine care delivery. This study aimed to evaluate perceptions of telehealth through a dyadic exploration of matched cancer patient‐ and clinician‐reported acceptability data and to explore factors that may predict greater suitability for telehealth. Methods A prospective, cross‐sectional, exploratory survey study assessed (matched) patient‐ and clinician‐reported perceptions of telehealth consultations occurring at a metropolitan, tertiary‐based cancer centre in Victoria, Australia. Results One‐hundred and fifty‐five matched patient‐ and clinician‐reported data were included. High rates of acceptability with telehealth were reported by patients (93%) and clinicians (91%), who mostly shared concordant views (86%). Factors significantly associated with increased acceptability for telehealth, included, for clinicians, greater familiarity with the patient (OR 8.20, 95% CI: 1.50–45.06, p = 0.02), and younger patient age (OR 1.06, 95% CI: 0.99–1.13, p = 0.05), and for patients was earlier stage disease (≤stage III) (OR 5.29, 95% CI: 1.08–25.82, p = 0.04). Lower acceptability for telehealth according to clinicians was associated with poorer patient performance status (OR 0.04, 95% CI 1.00–1.08, p = 0.04) and for patients with the need for an interpreter (0R 0.06, 95% CI: 0.008–0.51, p = 0.009). Conclusion While overall telehealth is acceptable in cancer care, our findings raise important implications for future service development, notably that it may be less optimal for patients with higher complexity of need—including those with more advanced disease, poorer performance status, those less well known to treating clinicians and those identified to have additional language barriers. This matched survey of cancer patient and clinician views of telehealth as they reflected on the same recent consultation showed both groups found it high acceptable. However lower ratings of acceptability were associated with less familiarity between patient and clinician, when cancer was more advanced or if an interpreter was required.

Background The association between body mass index (BMI) and risk of breast cancer depends on time of life, but it is unknown whether this association depends on a woman’s familial risk. Methods We conducted a prospective study of a cohort enriched for familial risk consisting of 16,035 women from 6701 families in the Breast Cancer Family Registry and the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer followed for up to 20 years (mean 10.5 years). There were 896 incident breast cancers (mean age at diagnosis 55.7 years). We used Cox regression to model BMI risk associations as a function of menopausal status, age, and underlying familial risk based on pedigree data using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), all measured at baseline. Results The strength and direction of the BMI risk association depended on baseline menopausal status ( P  < 0.001); after adjusting for menopausal status, the association did not depend on age at baseline ( P  = 0.6). In terms of absolute risk, the negative association with BMI for premenopausal women has a much smaller influence than the positive association with BMI for postmenopausal women. Women at higher familial risk have a much larger difference in absolute risk depending on their BMI than women at lower familial risk. Conclusions The greater a woman’s familial risk, the greater the influence of BMI on her absolute postmenopausal breast cancer risk. Given that age-adjusted BMI is correlated across adulthood, maintaining a healthy weight throughout adult life is particularly important for women with a family history of breast cancer.

Background Culturally and/or linguistically diverse (CALD) patients have unique health needs and may face multiple barriers when accessing healthcare. This study explored the association between of CALD status on treatment received and outcomes for patients with pancreatic cancer. Methods Data were extracted from the multi-site PURPLE Pancreatic cancer Translational Registry between January 2016 and April 2023. Registry data was supplemented by country of birth and preferred language data from linkage with hospital administrative datasets. CALD status was defined by being born overseas in a non-main English-speaking country and/or having a preferred language other than English. Descriptive statistics were used to analyse demographic data. Survival analysis was conducted using Kaplan-Meier estimates to generate survival curves, with comparisons assessed via the log-rank test. Moreover, univariable and multivariable Cox proportional hazards regression were employed. Results Of 1796 patients with pancreatic cancer enrolled at seven participating institutions, 1451 (80.8%) had their CALD status determined; with 661 (46%) identified as CALD. The CALD population were older (median age 72 vs. 68 years; P  < 0.001), with a worse performance status (Eastern Cooperative Oncology Group score > 1: 20 vs. 13%, P  = 0.004) and a greater number of comorbidities (Charlson Comorbidity Index > 3: 55 vs. 43%, P  < 0.001). The use of neoadjuvant therapy in resectable/borderline resectable disease was similar. However, fewer CALD patients proceeded to curative-intent surgery following neoadjuvant therapy (30% vs. 51%, P  = 0.041). In the metastatic setting, a higher proportion of CALD patients were offered best supportive care (50% vs. 41%; P  = 0.021). Overall, there were no significant differences identified in the progression-free, recurrence-free or overall survival for CALD patients with pancreatic cancer across all stages of disease. Conclusions CALD status was associated with multiple adverse prognostic factors (age, PS, comorbidities), which likely impacted differences in treatment received and challenges analysis of the independent impact of CALD status on outcomes. Notably, however, there were no striking differences by CALD status in treatment delivered or survival outcomes.

Background The use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been associated with reduced breast cancer risk, but it is not known if this association extends to women at familial or genetic risk. We examined the association between regular NSAID use and breast cancer risk using a large cohort of women selected for breast cancer family history, including 1054 BRCA1 or BRCA2 mutation carriers. Methods We analyzed a prospective cohort ( N  = 5606) and a larger combined, retrospective and prospective, cohort ( N  = 8233) of women who were aged 18 to 79 years, enrolled before June 30, 2011, with follow-up questionnaire data on medication history. The prospective cohort was further restricted to women without breast cancer when medication history was asked by questionnaire. Women were recruited from seven study centers in the United States, Canada, and Australia. Associations were estimated using multivariable Cox proportional hazards regression models adjusted for demographics, lifestyle factors, family history, and other medication use. Women were classified as regular or non-regular users of aspirin, COX-2 inhibitors, ibuprofen and other NSAIDs, and acetaminophen (control) based on self-report at follow-up of ever using the medication for at least twice a week for ≥1 month prior to breast cancer diagnosis. The main outcome was incident invasive breast cancer, based on self- or relative-report (81% confirmed pathologically). Results From fully adjusted analyses, regular aspirin use was associated with a 39% and 37% reduced risk of breast cancer in the prospective (HR = 0.61; 95% CI = 0.33–1.14) and combined cohorts (HR = 0.63; 95% CI = 0.57–0.71), respectively. Regular use of COX-2 inhibitors was associated with a 61% and 71% reduced risk of breast cancer (prospective HR = 0.39; 95% CI = 0.15–0.97; combined HR = 0.29; 95% CI = 0.23–0.38). Other NSAIDs and acetaminophen were not associated with breast cancer risk in either cohort. Associations were not modified by familial risk, and consistent patterns were found by BRCA1 and BRCA2 carrier status, estrogen receptor status, and attained age. Conclusion Regular use of aspirin and COX-2 inhibitors might reduce breast cancer risk for women at familial or genetic risk.

Background Alcohol consumption and cigarette smoking are associated with an increased risk of breast cancer (BC), but it is unclear whether these associations vary by a woman’s familial BC risk. Methods Using the Prospective Family Study Cohort, we evaluated associations between alcohol consumption, cigarette smoking, and BC risk. We used multivariable Cox proportional hazard models to estimate hazard ratios (HR) and 95% confidence intervals (CI). We examined whether associations were modified by familial risk profile (FRP), defined as the 1-year incidence of BC predicted by Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), a pedigree-based algorithm. Results We observed 1009 incident BC cases in 17,435 women during a median follow-up of 10.4 years. We found no overall association of smoking or alcohol consumption with BC risk (current smokers compared with never smokers HR 1.02, 95% CI 0.85–1.23; consuming ≥ 7 drinks/week compared with non-regular drinkers HR 1.10, 95% CI 0.92–1.32), but we did observe differences in associations based on FRP and by estrogen receptor (ER) status. Women with lower FRP had an increased risk of ER-positive BC associated with consuming ≥ 7 drinks/week (compared to non-regular drinkers), whereas there was no association for women with higher FRP. For example, women at the 10th percentile of FRP (5-year BOADICEA = 0.15%) had an estimated HR of 1.46 (95% CI 1.07–1.99), whereas there was no association for women at the 90th percentile (5-year BOADICEA = 4.2%) (HR 1.07, 95% CI 0.80–1.44). While the associations with smoking were not modified by FRP, we observed a positive multiplicative interaction by FRP ( p interaction  = 0.01) for smoking status in women who also consumed alcohol, but not in women who were non-regular drinkers. Conclusions Moderate alcohol intake was associated with increased BC risk, particularly for women with ER-positive BC, but only for those at lower predicted familial BC risk (5-year BOADICEA < 1.25). For women with a high FRP (5-year BOADICEA ≥ 6.5%) who also consumed alcohol, being a current smoker was associated with increased BC risk.