PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancerINTRODUCTION: The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. METHODS: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. RESULTS: Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). CONCLUSIONS: We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.
Cancer prevention with aspirin in hereditary colorectal cancer (Lynch syndrome), 10-year follow-up and registry-based 20-year data in the CAPP2 study: a double-blind, randomised, placebo-controlled trialBACKGROUND: Lynch syndrome is associated with an increased risk of colorectal cancer and with a broader spectrum of cancers, especially endometrial cancer. In 2011, our group reported long-term cancer outcomes (mean follow-up 55·7 months [SD 31·4]) for participants with Lynch syndrome enrolled into a randomised trial of daily aspirin versus placebo. This report completes the planned 10-year follow-up to allow a longer-term assessment of the effect of taking regular aspirin in this high-risk population. METHODS: In the double-blind, randomised CAPP2 trial, 861 patients from 43 international centres worldwide (707 [82%] from Europe, 112 [13%] from Australasia, 38 [4%] from Africa, and four [<1%] from The Americas) with Lynch syndrome were randomly assigned to receive 600 mg aspirin daily or placebo. Cancer outcomes were monitored for at least 10 years from recruitment with English, Finnish, and Welsh participants being monitored for up to 20 years. The primary endpoint was development of colorectal cancer. Analysis was by intention to treat and per protocol. The trial is registered with the ISRCTN registry, number ISRCTN59521990. FINDINGS: Between January, 1999, and March, 2005, 937 eligible patients with Lynch syndrome, mean age 45 years, commenced treatment, of whom 861 agreed to be randomly assigned to the aspirin group or placebo; 427 (50%) participants received aspirin and 434 (50%) placebo. Participants were followed for a mean of 10 years approximating 8500 person-years. 40 (9%) of 427 participants who received aspirin developed colorectal cancer compared with 58 (13%) of 434 who received placebo. Intention-to-treat Cox proportional hazards analysis revealed a significantly reduced hazard ratio (HR) of 0·65 (95% CI 0·43-0·97; p=0·035) for aspirin versus placebo. Negative binomial regression to account for multiple primary events gave an incidence rate ratio of 0·58 (0·39-0·87; p=0·0085). Per-protocol analyses restricted to 509 who achieved 2 years' intervention gave an HR of 0·56 (0·34-0·91; p=0·019) and an incidence rate ratio of 0·50 (0·31-0·82; p=0·0057). Non-colorectal Lynch syndrome cancers were reported in 36 participants who received aspirin and 36 participants who received placebo. Intention-to-treat and per-protocol analyses showed no effect. For all Lynch syndrome cancers combined, the intention-to-treat analysis did not reach significance but per-protocol analysis showed significantly reduced overall risk for the aspirin group (HR=0·63, 0·43-0·92; p=0·018). Adverse events during the intervention phase between aspirin and placebo groups were similar, and no significant difference in compliance between intervention groups was observed for participants with complete intervention phase data; details reported previously. INTERPRETATION: The case for prevention of colorectal cancer with aspirin in Lynch syndrome is supported by our results. FUNDING: Cancer Research UK, European Union, MRC, NIHR, Bayer Pharma AG, Barbour Foundation.
Impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England: a population-based studyEva Morris, Raph Goldacre, Enti Spata et al.|The Lancet. Gastroenterology & hepatology|2021 BACKGROUND: There are concerns that the COVID-19 pandemic has had a negative effect on cancer care but there is little direct evidence to quantify any effect. This study aims to investigate the impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England. METHODS: Data were extracted from four population-based datasets spanning NHS England (the National Cancer Cancer Waiting Time Monitoring, Monthly Diagnostic, Secondary Uses Service Admitted Patient Care and the National Radiotherapy datasets) for all referrals, colonoscopies, surgical procedures, and courses of rectal radiotherapy from Jan 1, 2019, to Oct 31, 2020, related to colorectal cancer in England. Differences in patterns of care were investigated between 2019 and 2020. Percentage reductions in monthly numbers and proportions were calculated. FINDINGS: As compared to the monthly average in 2019, in April, 2020, there was a 63% (95% CI 53-71) reduction (from 36 274 to 13 440) in the monthly number of 2-week referrals for suspected cancer and a 92% (95% CI 89-95) reduction in the number of colonoscopies (from 46 441 to 3484). Numbers had just recovered by October, 2020. This resulted in a 22% (95% CI 8-34) relative reduction in the number of cases referred for treatment (from a monthly average of 2781 in 2019 to 2158 referrals in April, 2020). By October, 2020, the monthly rate had returned to 2019 levels but did not exceed it, suggesting that, from April to October, 2020, over 3500 fewer people had been diagnosed and treated for colorectal cancer in England than would have been expected. There was also a 31% (95% CI 19-42) relative reduction in the numbers receiving surgery in April, 2020, and a lower proportion of laparoscopic and a greater proportion of stoma-forming procedures, relative to the monthly average in 2019. By October, 2020, laparoscopic surgery and stoma rates were similar to 2019 levels. For rectal cancer, there was a 44% (95% CI 17-76) relative increase in the use of neoadjuvant radiotherapy in April, 2020, relative to the monthly average in 2019, due to greater use of short-course regimens. Although in June, 2020, there was a drop in the use of short-course regimens, rates remained above 2019 levels until October, 2020. INTERPRETATION: The COVID-19 pandemic has led to a sustained reduction in the number of people referred, diagnosed, and treated for colorectal cancer. By October, 2020, achievement of care pathway targets had returned to 2019 levels, albeit with smaller volumes of patients and with modifications to usual practice. As pressure grows in the NHS due to the second wave of COVID-19, urgent action is needed to address the growing burden of undetected and untreated colorectal cancer in England. FUNDING: Cancer Research UK, the Medical Research Council, Public Health England, Health Data Research UK, NHS Digital, and the National Institute for Health Research Oxford Biomedical Research Centre.
An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validationBACKGROUND: PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS: Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS: In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS: The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.