Purpose
For the implementation of personalised surveillance, it is important to create more awareness among HCPs with regard to the risk for locoregional recurrences (LRRs). The aim of this study is ...to evaluate the current awareness and estimations of individual risks for LRRs after completion of primary treatment for breast cancer among health care professionals (HCPs) in the Netherlands, without using any prediction tools.
Methods
A cross-sectional survey was performed among 60 HCPs working in breast cancer care in seven Dutch hospitals and 25 general practitioners (GPs). The survey consisted of eleven realistic surgically treated breast cancer cases. HCPs were asked to estimate the 5-year risk for LRRs for each case, which was compared to the estimations by the INFLUENCE-nomogram using one-sample Wilcoxon tests. Differences in estimations between HCPs with different specialities were determined using Kruskal–Wallis tests and Dunn tests.
Results
HCPs tended to structurally overestimate the 5-year risk for LRR on each case. Average overestimations ranged from 4.8 to 26.1%. Groups of HCPs with varying specialities differed significantly in risk estimations. GPs tended to overestimate the risk for LRRs on average the most (15.0%) and medical oncologists had the lowest average overestimation (2.7%).
Conclusions
It is important to create more awareness of the risk for LRRs, which is a pre-requisite for the implementation of personalised surveillance after breast cancer. Besides education for HCPs, the use of prediction models such as the INFLUENCE-nomogram can support in estimating an objective estimate of each individual patient’s risk.
The main objective of this study is to determine the current use of lung cancer diagnostic procedures in two large hospitals in the Netherlands, to explore deviations in guideline adherence between ...the hospitals, and to estimate the budget impact of the diagnostic work-up as well as the over- and underutilization.
A state transition model for the diagnostic pathway for lung cancer patients was developed using existing clinical practice guidelines (CPG) combined with a systematic literature. In addition to the CPGs depicting current practice, diagnostic utilization was gathered in two large hospitals representing an academic tertiary care hospital and a large regional teaching hospital for patients, who were selected from the Netherlands cancer registry.
The total population consisted of 376 patients with lung cancer. Not in all cases the guideline was followed, for instance in the usage of MR brain with stage III lung cancer patients (n = 70). The state-transition model predicts an average budget impact for the diagnostic pathway per patient estimated of € 2496 in the academic tertiary care hospital and € 2191 in the large regional teaching hospital.
The adherence to the CPG's differed between hospitals, which questions the adherence to CPG's in general. Adherence to CPG's could lead to less costs in the diagnostic pathway for lung cancer patients.
The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of ...registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer.
The PSCCR - Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well.
A collaboration agreement was signed in which the organisations retained data responsibility and accountability for 'their' registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients.
We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries.
To estimate the percentages of advanced-stage breast cancers (BCs) detected during the course of a steady-state screening programme when using different definitions of advanced BC.
Data of women aged ...49-74 years, diagnosed with BC in 2006-2015, were selected from the Netherlands Cancer Registry and linked to the screening registry. BCs were classified as screen-detected, interval or non-screened. Three definitions of advanced BC were used for comparison: TNM stage (III-IV), NM stage (N+ and/or M+) and T size (invasive tumour ≥15 mm). Analyses were performed assuming a 10% overdiagnosis rate. In sensitivity analyses, this assumption varied from 0 to 30%.
We included 46,734 screen-detected, 17,362 interval and 24,189 non-screened BCs. By TNM stage, 4.9% of screen-detected BCs were advanced, compared with 19.4% and 22.8% of interval and non-screened BCs, respectively (p < 0.001). Applying the other definitions led to higher percentages of advanced BC being detected. Depending on the definition interval, non-screened BCs had a 2-5-times risk of being advanced.
Irrespective of the definition, screen-detected BCs were less frequently in the advanced stage. These findings provide evidence of a stage shift to early detection and support the potential of mammographic screening to reduce treatment-related burdens and the mortality associated with BC.
Purpose
To extend the functionality of the existing INFLUENCE nomogram for locoregional recurrence (LRR) of breast cancer toward the prediction of secondary primary tumors (SP) and distant metastases ...(DM) using updated follow-up data and the best suitable statistical approaches.
Methods
Data on women diagnosed with non-metastatic invasive breast cancer were derived from the Netherlands Cancer Registry (
n
= 13,494). To provide flexible time-dependent individual risk predictions for LRR, SP, and DM, three statistical approaches were assessed; a Cox proportional hazard approach (COX), a parametric spline approach (PAR), and a random survival forest (RSF). These approaches were evaluated on their discrimination using the Area Under the Curve (AUC) statistic and on calibration using the Integrated Calibration Index (ICI). To correct for optimism, the performance measures were assessed by drawing 200 bootstrap samples.
Results
Age, tumor grade, pT, pN, multifocality, type of surgery, hormonal receptor status, HER2-status, and adjuvant therapy were included as predictors. While all three approaches showed adequate calibration, the RSF approach offers the best optimism-corrected 5-year AUC for LRR (0.75, 95%CI: 0.74–0.76) and SP (0.67, 95%CI: 0.65–0.68). For the prediction of DM, all three approaches showed equivalent discrimination (5-year AUC: 0.77–0.78), while COX seems to have an advantage concerning calibration (ICI < 0.01). Finally, an online calculator of INFLUENCE 2.0 was created.
Conclusions
INFLUENCE 2.0 is a flexible model to predict time-dependent individual risks of LRR, SP and DM at a 5-year scale; it can support clinical decision-making regarding personalized follow-up strategies for curatively treated non-metastatic breast cancer patients.
Numerous prediction models have been developed to support treatment-related decisions for breast cancer patients. External validation, a prerequisite for implementation in clinical practice, has been ...performed for only a few models. This study aims to externally validate published clinical prediction models using population-based Dutch data.
Patient-, tumor- and treatment-related data were derived from the Netherlands Cancer Registry (NCR). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), scaled Brier score, and model calibration. Net benefit across applicable risk thresholds was evaluated with decision curve analysis.
After assessing 922 models, 87 (9%) were included for validation. Models were excluded due to an incomplete model description (n = 262 (28%)), lack of required data (n = 521 (57%)), previously validated or developed with NCR data (n = 45 (5%)), or the associated NCR sample size was insufficient (n = 7 (1%)). The included models predicted survival (33 (38%) overall, 27 (31%) breast cancer-specific, and 3 (3%) other cause-specific), locoregional recurrence (n = 7 (8%)), disease free survival (n = 7 (8%)), metastases (n = 5 (6%)), lymph node involvement (n = 3 (3%)), pathologic complete response (n = 1 (1%)), and surgical margins (n = 1 (1%)). Seven models (8%) showed poor (AUC<0.6), 39 (45%) moderate (AUC:0.6–0.7), 38 (46%) good (AUC:0.7–0.9), and 3 (3%) excellent (AUC≥0.9) discrimination. Using the scaled Brier score, worse performance than an uninformative model was found in 34 (39%) models.
Comprehensive registry data supports broad validation of published prediction models. Model performance varies considerably in new patient populations, affirming the importance of external validation studies before applying models in clinical practice. Well performing models could be clinically useful in a Dutch setting after careful impact evaluation.
•A total of 87 prediction models for breast cancer patients were externally validated using data of 271,040 Dutch patients.•Assessing model performance on discrimination and calibration, 34 models performed well, 26 moderately, and 27 poorly.•Validating models is crucial before clinical use. A high-performing model is ideal, but assessing actual benefit is preferable.
•The head and neck cancer incidence was nearly 25% less during the first COVID wave.•The lower incidence was mainly observed in oral cavity and laryngeal cancer.•During COVID, tumor stage and ...treatment distribution did not differ.•The expected increase in incidence during the remainder of 2020 was not observed.•Time-to-treatment was significantly shorter, regardless of first treatment modality.
Inevitably, the emergence of COVID-19 has impacted non-COVID care. Because timely diagnosis and treatment are essential, especially for patients with head and neck cancer (HNC) with fast-growing tumours in a functionally and aesthetically important area, we wished to quantify the impact of the COVID-19 pandemic on HNC care in the Netherlands.
This population-based study covered all, in total 8468, newly diagnosed primary HNC cases in the Netherlands in 2018, 2019 and 2020. We compared incidence, patient and tumour characteristics, primary treatment characteristics, and time-to-treatment in the first COVID-19 year 2020 with corresponding periods in 2018 and 2019 (i.e. pre-COVID).
The incidence of HNC was nearly 25% less during the first wave (n = 433) than in 2019 (n = 595) and 2018 (n = 598). In April and May 2020, the incidence of oral cavity and laryngeal carcinomas was significantly lower than in pre-COVID years. There were no shifts in tumour stage or alterations in initial treatment modalities.
Regardless of the first treatment modality and specific period, the median number of days between first visit to a HNC centre and start of treatment was significantly shorter during the COVID-19 year (26–28 days) than pre-COVID (31–32 days, p < 0.001).
The incidence of HNC during the Netherlands’ first COVID-19 wave was significantly lower than expected. The expected increase in incidence during the remainder of 2020 was not observed. Despite the overloaded healthcare system, the standard treatment for HNC patients could be delivered within a shorter time interval.
To determine prospectively overall and age-specific estimates of contralateral breast cancer (CBC) risk for young patients with breast cancer with or without BRCA1/2 mutations.
A cohort of 6,294 ...patients with invasive breast cancer diagnosed under 50 years of age and treated between 1970 and 2003 in 10 Dutch centers was tested for the most prevalent BRCA1/2 mutations. We report absolute risks and hazard ratios within the cohort from competing risk analyses.
After a median follow-up of 12.5 years, 578 CBCs were observed in our study population. CBC risk for BRCA1 and BRCA2 mutation carriers was two to three times higher than for noncarriers (hazard ratios, 3.31 95% CI, 2.41 to 4.55; P < .001 and 2.17 95% CI,1.22 to 3.85; P = .01, respectively). Ten-year cumulative CBC risks were 21.1% (95% CI, 15.4 to 27.4) for BRCA1, 10.8% (95% CI, 4.7 to 19.6) for BRCA2 mutation carriers and 5.1% (95% CI, 4.5 to 5.7) for noncarriers. Age at diagnosis of the first breast cancer was a significant predictor of CBC risk in BRCA1/2 mutation carriers only; those diagnosed before age 41 years had a 10-year cumulative CBC risk of 23.9% (BRCA1: 25.5%; BRCA2: 17.2%) compared with 12.6% (BRCA1: 15.6%; BRCA2: 7.2%) for those 41 to 49 years of age (P = .02); our review of published studies showed ranges of 24% to 31% before age 40 years (BRCA1: 24% to 32%; BRCA2:17% to 29%) and 8% to 21% after 40 years (BRCA1: 11% to 52%; BRCA2: 7% to 18%), respectively.
Age at first breast cancer is a strong risk factor for cumulative CBC risk in BRCA1/2 mutation carriers. Considering the available evidence, age-specific risk estimates should be included in counseling.
The number of breast cancer survivors increases, but information about long-term adverse health effects in breast cancer survivors is sparse. We aimed to get an overview of the health effects for ...which survivors visit their general practitioner up to 14 years after diagnosis.
We retrieved data on 11,671 women diagnosed with breast cancer in 2000–2016 and 23,242 age and sex matched controls from the PSCCR-Breast Cancer, a database containing data about cancer diagnosis, treatment and primary healthcare. We built Cox regression models for 685 health effects, with time until the health effect as the outcome and survivor/control and cancer treatment as predictors. Models were built separately for four age groups (aged 18/44, 45/59, 60/74 and 75/89) and two follow-up periods (1/4 and 5/14 years after diagnosis).
229 health effects occurred statistically significantly more often in survivors than in controls (p < 0.05). Health effects varied by age, time since diagnosis and treatment, but coughing, respiratory and urinary infections, fatigue, sleep problems, osteoporosis and lymphedema were statistically significantly increased in breast cancer survivors. Osteoporosis and chest symptoms were associated with hormone therapy; respiratory and skin infections with chemotherapy and lymphedema and skin infections with axillary dissection.
Breast cancer survivors may experience numerous adverse health effects up to 14 years after diagnosis. Insight in individual risks may assist healthcare professionals in managing patient expectations and improve monitoring, detection and treatment of adverse health effects.
•Breast cancer survivors experience adverse effects up to 14 years after diagnosis.•Most common were respiratory/urinary infections, fatigue and sleep problems.•Adverse effects vary by age, time since diagnosis and treatment.•Our results may aid monitoring, detection and treatment of adverse effects.
Survival estimates from diagnosis are of limited importance for (ex-)breast cancer patients who survived several years, as it includes information on already deceased patients. This study analysed ...the 10-year conditional risk of recurrent breast cancer in specific prognostic subgroups. Second, we investigated 10-year conditional overall survival (OS) and relative survival (RS), adjusted for confounding.
All women diagnosed in 2005 with operated T1-2N0-1 breast cancer were selected from the Netherlands Cancer Registry. Patients were classified into T1N0, T1N1, T2N0 and T2N1 stage. Ten-year conditional recurrence rates were calculated from diagnosis, and for patients without an event (local LR, regional recurrence RR, distant metastasis DM or death) every year following diagnosis. Ten-year conditional OS was calculated using multivariable Cox regression. RS was estimated by dividing patient survival rates by those of the general Dutch population.
We included 7969 patients: 52.3% had T1N0, 15.3% T1N1, 19.9% T2N0 and 12.5% T2N1 stage. For T1N0, 10-year LR rates changed from 4.6% at diagnosis to 0.5% in year 10. RR rates changed from 2.3% to 0.2%, and DM rates changed from 7.8% to 0.6%. For T2N1 stage, the LR, RR and DM rates changed from 6.2% to 0.8%, 5.2%–0.4% and 19.6%–1.5%, respectively. For the luminal A subtype, LR, RR and DM rates changed from 3.9% to 0.4%, 1.7%–0.5% and 7.3%–1.1%, while for triple negative, these rates changed from 5.6% to 0.7%, 4.9%–0.2% and 16.7%–0%, respectively. Differences between subgroups attenuated over time, and all recurrence rates became ≤1.5% in year 10. Ten-year OS and RS, adjusted for confounding, showed declining risk differences between subgroups over time.
Differences in recurrence rates, OS and RS between prognostic subgroups declined as years passed by. These results highlight the importance of taking into account disease-free years to more accurately predict (ex-)breast cancer patients' prognosis over time.
•Survival estimates from diagnosis are less important for breast cancer survivors.•Conditional survival includes the numbers of years survived following diagnosis.•Differences in recurrence risks between prognostic subgroups declined over time.•Differences in overall and relative survival between subgroups declined as well.•Conditional survival and recurrences provide patients better insight in prognosis.
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