Stratified breast cancer follow-up using a continuous state partially observable Markov decision process

Frequency and duration of follow-up for breast cancer patients is still under discussion. Currently, in the Netherlands follow-up consists of annual mammography for the first five years after treatment and does not depend on the personal risk of developing a locoregional recurrence or a second primary tumor. The aim of this study is to gain insight in how to allocate resources for optimal and personalized follow-up. We formulate a discrete-time Partially Observable Markov Decision Process (POMDP) over a finite horizon with both discrete and continuous states, in which the size of the tumor is modeled as a continuous state. Transition probabilities are obtained from data of the Netherlands Cancer Registry. We show that the optimal value function of the POMDP is piecewise linear and convex and provide an alternative representation for it. Under some reasonable conditions on the dynamics of the POMDP, the optimal value function can be obtained from the parameters of the underlying probability distributions only. Finally, we present results for a stratification of the patients based on their age to show how this model can be applied in practice

Survival after Locoregional Recurrence or Second Primary Breast Cancer: Impact of the Disease-Free Interval

The association between the disease-free interval (DFI) and survival after a locoregional recurrence (LRR) or second primary (SP) breast cancer remains uncertain. The objective of this study is to clarify this association to obtain more information on expected prognosis. Women first diagnosed with early breast cancer between 2003–2006 were selected from the Netherlands Cancer Registry. LRRs and SP tumours within five years of first diagnosis were examined. The five-year period was subsequently divided into three equal intervals. Prognostic significance of the DFI on survival after a LRR or SP tumour was determined using Kaplan-Meier estimates and multivariable Cox regression analysis. Follow-up was complete until January 1, 2014. A total of 37,278 women was included in the analysis. LRRs or SP tumours were diagnosed in 890 (2,4%) and 897 (2,4%) respectively. Longer DFI was strongly and independently related to an improved survival after a LRR (long versus short: HR 0.65, 95% CI 0.48–0.88; medium versus short HR 0.81, 95% CI 0.65–1.01). Other factors related to improved survival after LRR were younger age (<70 years) and surgical removal of the recurrence. No significant association was found between DFI and survival after SP tumours. This is the first study to explore the association between the DFI and survival after recurrence in a nationwide population-based cancer registry. The DFI before a LRR is an independent prognostic factor for survival, with a longer DFI predicting better prognosi

Patterns and risk of first and subsequent recurrences in women within ten years after primary invasive breast cancer

Background: Previous studies suggest a distinct pattern and a number of predictive factors for breast cancer recurrence. However, only few studies include data on recurrence site and no study provides data regarding second and third breast cancer recurrence after local and regional recurrence. The aim of this study was to analyse the occurrence, timing and predictive factors of first and subsequent local (LR), regional (RR) or distant (DM) recurrence during the first 10 years after treatment for primary invasive breast cancer in women. Methods: Women with stage I-III invasive breast cancer diagnosed in 2003 and treated with curative intent were selected from the Netherlands Cancer Registry (N = 9797). Median follow-up was 10 years. Multivariable cox proportional hazards regression was used to model the hazard of recurrence over time for site-specific first recurrence and for subsequent recurrences after LR or RR. Predictive factors were identified for first and for subsequent recurrences. All tests were two-sided and probability values of 2 cm, grade III and negative ER were predictive factors for first RR and tumour size >2 cm, grade II or III, increasing number of involved lymph nodes and negative progesterone-receptor (PR) status were predictive factors for first DM. After a LR 109/379 patients (28.7%) developed subsequent recurrence: 11 patients had another LR (2.9%), 13 patients had RR (3.4%) and 85 patients (22.4%) had DM. Median time to second recurrence was 1.1 year (IQR 0.3–2.5 year). Tumour size >2 cm, grade III, primary tumour histology (other vs invasive ductal), >3 positive lymph nodes and negative PR-status were predictive factors for a second recurrence after LR. After a first RR 79/156 patients (50.6%) developed subsequent recurrence: 8 patients had LR (5.1%), 3 patients had RR (1.9%) and 68 patients (43.6%) had DM. Median time to second recurrence was 1.1 year (IQR 0.5–2.1 year). In multivariable analysis, no predictive factor for a second recurrence after RR was identified. After previous LR or RR a third subsequent recurrence occurred in 18 patients (9.6%). Conclusions: The pattern of first recurrence was similar for LR, RR and DM. To improve personalized follow-up, predictive factors could be taken into account. However, this study showed no explicit predictive factor for site specific recurrence and subsequent recurrences after LR and RR. Future studies that take treatment characteristics into account are needed

Patterns and risk of first and subsequent recurrences in women within ten years after primary invasive breast cancer

Background: Previous studies suggest a distinct pattern and a number of predictive factors for breast cancer recurrence. However, only few studies include data on recurrence site and no study provides data regarding second and third breast cancer recurrence after local and regional recurrence. The aim of this study was to analyse the occurrence, timing and predictive factors of first and subsequent local (LR), regional (RR) or distant (DM) recurrence during the first 10 years after treatment for primary invasive breast cancer in women. Methods: Women with stage I-III invasive breast cancer diagnosed in 2003 and treated with curative intent were selected from the Netherlands Cancer Registry (N = 9797). Median follow-up was 10 years. Multivariable cox proportional hazards regression was used to model the hazard of recurrence over time for site-specific first recurrence and for subsequent recurrences after LR or RR. Predictive factors were identified for first and for subsequent recurrences. All tests were two-sided and probability values of &lt;0.05 were considered statistically significant. Results: In total 379 patients had LR, 156 patients had RR and 1412 patients had DM as first recurrence. The risk of first recurrence was highest around 2 years post-diagnosis (HR 0.040 95% CI 0.036–0.044) with a similar pattern for LR, RR and DM. Multivariable analysis showed that lower age and negative estrogen-receptor (ER) status were predictive factors for first LR. Tumour size &gt;2 cm, grade III and negative ER were predictive factors for first RR and tumour size &gt;2 cm, grade II or III, increasing number of involved lymph nodes and negative progesterone-receptor (PR) status were predictive factors for first DM. After a LR 109/379 patients (28.7%) developed subsequent recurrence: 11 patients had another LR (2.9%), 13 patients had RR (3.4%) and 85 patients (22.4%) had DM. Median time to second recurrence was 1.1 year (IQR 0.3–2.5 year). Tumour size &gt;2 cm, grade III, primary tumour histology (other vs invasive ductal), &gt;3 positive lymph nodes and negative PR-status were predictive factors for a second recurrence after LR. After a first RR 79/156 patients (50.6%) developed subsequent recurrence: 8 patients had LR (5.1%), 3 patients had RR (1.9%) and 68 patients (43.6%) had DM. Median time to second recurrence was 1.1 year (IQR 0.5–2.1 year). In multivariable analysis, no predictive factor for a second recurrence after RR was identified. After previous LR or RR a third subsequent recurrence occurred in 18 patients (9.6%). Conclusions: The pattern of first recurrence was similar for LR, RR and DM. To improve personalized follow-up, predictive factors could be taken into account. However, this study showed no explicit predictive factor for site specific recurrence and subsequent recurrences after LR and RR. Future studies that take treatment characteristics into account are needed

Personalisation of breast cancer follow-up: a time-dependent prognostic nomogram for the estimation of annual risk of locoregional recurrence in early breast cancer patients

The objective of this study was to develop and validate a time-dependent logistic regression model for prediction of locoregional recurrence (LRR) of breast cancer and a web-based nomogram for clinical decision support. Women first diagnosed with early breast cancer between 2003 and 2006 in all Dutch hospitals were selected from the Netherlands Cancer Registry (n = 37,230). In the first 5 years following primary breast cancer treatment, 950 (2.6 %) patients developed a LRR as first event. Risk factors were determined using logistic regression and the risks were calculated per year, conditional on not being diagnosed with recurrence in the previous year. Discrimination and calibration were assessed. Bootstrapping was used for internal validation. Data on primary tumours diagnosed between 2007 and 2008 in 43 Dutch hospitals were used for external validation of the performance of the nomogram (n = 12,308). The final model included the variables grade, size, multifocality, and nodal involvement of the primary tumour, and whether patients were treated with radio-, chemo- or hormone therapy. The index cohort showed an area under the ROC curve of 0.84, 0.77, 0.70, 0.73 and 0.62, respectively, per subsequent year after primary treatment. Model predictions were well calibrated. Estimates in the validation cohort did not differ significantly from the index cohort. The results were incorporated in a web-based nomogram (http://​www.​utwente.​nl/​mira/​influence). This validated nomogram can be used as an instrument to identify patients with a low or high risk of LRR who might benefit from a less or more intensive follow-up after breast cancer and to aid clinical decision making for personalised follow-up

Mind your data:Privacy and legal matters in eHealth

The health care sector can benefit considerably from developments in digital technology. Consequently, eHealth applications are rapidly increasing in number and sophistication. For successful development and implementation of eHealth, it is paramount to guarantee the privacy and safety of patients and their collected data. At the same time, anonymized data that are collected through eHealth could be used in the development of innovative and personalized diagnostic, prognostic, and treatment tools. To address the needs of researchers, health care providers, and eHealth developers for more information and practical tools to handle privacy and legal matters in eHealth, the Dutch national Digital Society Research Programme organized the "Mind Your Data: Privacy and Legal Matters in eHealth" conference. In this paper, we share the key take home messages from the conference based on the following five tradeoffs: (1) privacy versus independence, (2) informed consent versus convenience, (3) clinical research versus clinical routine data, (4) responsibility and standardization, and (5) privacy versus solidarity

Development of machine learning models to predict cancer-related fatigue in Dutch breast cancer survivors up to 15 years after diagnosis

Purpose: To prevent (chronic) cancer-related fatigue (CRF) after breast cancer, it is important to identify survivors at risk on time. In literature, factors related to CRF are identified, but not often linked to individual risks. Therefore, our aim was to predict individual risks for developing CRF.Methods: Two pre-existing datasets were used. The Nivel-Primary Care Database and the Netherlands Cancer Registry (NCR) formed the Primary Secondary Cancer Care Registry (PSCCR). NCR data with Patient Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship (PROFILES) data resulted in the PSCCR-PROFILES dataset. Predictors were patient, tumor and treatment characteristics, and pre-diagnosis health. Fatigue was GP-reported (PSCCR) or patient-reported (PSCCR-PROFILES). Machine learning models were developed, and performances compared using the C-statistic.Results: In PSCCR, 2224/12813 (17%) experienced fatigue up to 7.6 ± 4.4 years after diagnosis. In PSCCR-PROFILES, 254 (65%) of 390 patients reported fatigue 3.4 ± 1.4 years after diagnosis. For both, models predicted fatigue poorly with best C-statistics of 0.561 ± 0.006 (PSCCR) and 0.669 ± 0.040 (PSCCR-PROFILES).Conclusion: Fatigue (GP-reported or patient-reported) could not be predicted accurately using available data of the PSCCR and PSCCR-PROFILES datasets.Implications for Cancer Survivors: CRF is a common but underreported problem after breast cancer. We aimed to develop a model that could identify individuals with a high risk of developing CRF, ideally to help them prevent (chronic) CRF. As our models had poor predictive abilities, they cannot be used for this purpose yet. Adding patient-reported data as predictor could lead to improved results. Until then, awareness for CRF stays crucial

Evaluating the Age-Based Recommendations for Long-Term Follow-Up in Breast Cancer

Background: After 5 years of annual follow-up following breast cancer, Dutch guidelines are age based: annual follow-up for women &lt;60 years, 60–75 years biennial, and none for &gt;75 years. We determined how the risk of recurrence corresponds to these consensus-based recommendations and to the risk of primary breast cancer in the general screening population. Subjects, Materials, and Methods: Women with early-stage breast cancer in 2003/2005 were selected from the Netherlands Cancer Registry (n = 18,568). Cumulative incidence functions were estimated for follow-up years 5–10 for locoregional recurrences (LRRs) and second primary tumors (SPs). Risks were compared with the screening population without history of breast cancer. Alternative cutoffs for age were determined by log-rank tests. Results: The cumulative risk for LRR/SP was lower in women &lt;60 years (5.9%, 95% confidence interval [CI] 5.3–6.6) who are under annual follow-up than for women 60–75 (6.3%, 95% CI 5.6–7.1) receiving biennial visits. All risks were higher than the 5-year risk of a primary tumor in the screening population (ranging from 1.4% to 1.9%). Age cutoffs &lt;50, 50–69, and &gt; 69 revealed better risk differentiation and would provide more risk-based schedules. Still, other factors, including systemic treatments, had an even greater impact on recurrence risks. Conclusion: The current consensus-based recommendations use suboptimal age cutoffs. The proposed alternative cutoffs will lead to a more balanced risk-based follow-up and thereby more efficient allocation of resources. However, more factors should be taken into account for truly individualizing follow-up based on risk for recurrence. Implications for Practice: The current age-based recommendations for breast cancer follow-up after 5 years are suboptimal and do not reflect the actual risk of recurrent disease. This results in situations in which women with higher risks actually receive less follow-up than those with a lower risk of recurrence. Alternative cutoffs could be a start toward risk-based follow-up and thereby more efficient allocation of resources. However, age, or any single risk factor, is not able to capture the risk differences and therefore is not sufficient for determining follow-up. More risk factors should be taken into account for truly individualizing follow-up based on the risk for recurrence.</p