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Purpose: Follow-up guidelines barely diverge from a one-size-fits-all approach, even though the risk of recurrence differs per patient. However, the personalization of breast cancer care improves outcomes for patients. This study explores the variation in follow-up pathways in the Netherlands using real-world data to determine guideline adherence and the gap between daily practice and risk-based surveillance, to demonstrate the benefits of personalized risk-based surveillance compared with usual care., Methods: Patients with stage I-III invasive breast cancer who received surgical treatment in a general hospital between 2005 and 2020 were selected from the Netherlands Cancer Registry and included all imaging activities during follow-up from hospital-based electronic health records. Process analysis techniques were used to map patients and activities to investigate the real-world utilisation of resources and identify the opportunities for improvement. The INFLUENCE 2.0 nomogram was used for risk prediction of recurrence., Results: In the period between 2005 and 2020, 3478 patients were included with a mean follow-up of 4.9 years. In the first 12 months following treatment, patients visited the hospital between 1 and 5 times (mean 1.3, IQR 1-1) and received between 1 and 9 imaging activities (mean 1.7, IQR 1-2). Mammogram was the prevailing imaging modality, accounting for 70% of imaging activities. Patients with a low predicted risk of recurrence visited the hospital more often., Conclusions: Deviations from the guideline were not in line with the risk of recurrence and revealed a large gap, indicating that it is hard for clinicians to accurately estimate this risk and therefore objective risk predictions could bridge this gap., (© 2024. The Author(s).)

Introduction: 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., Methods: 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., Results: 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., Conclusion: 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., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: To systematically review the currently available prediction models that may support treatment decision-making in breast cancer., Study Design and Setting: Literature was systematically searched to identify studies reporting on development of prediction models aiming to support breast cancer treatment decision-making, published between January 2010 and December 2020. Quality and risk of bias were assessed using the Prediction model Risk Of Bias (ROB) Assessment Tool (PROBAST)., Results: After screening 20,460 studies, 534 studies were included, reporting on 922 models. The 922 models predicted: mortality (n = 417 45%), recurrence (n = 217, 24%), lymph node involvement (n = 141, 15%), adverse events (n = 58, 6%), treatment response (n = 56, 6%), or other outcomes (n = 33, 4%). In total, 285 models (31%) lacked a complete description of the final model and could not be applied to new patients. Most models (n = 878, 95%) were considered to contain high ROB., Conclusion: A substantial overlap in predictor variables and outcomes between the models was observed. Most models were not reported according to established reporting guidelines or showed methodological flaws during the development and/or validation of the model. Further development of prediction models with thorough quality and validity assessment is an essential first step for future clinical application., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual's risk development over time would allow assessing not just who to screen but also when to screen. This paper illustrates the value of personalised, time-dependent risk predictions to optimize risk-based screening schemes., Methods: In a simulation analysis, two different time-dependent risk-based screening approaches are compared to another risk-based, but time-independent approach regarding their impact on screening efficiency. For this purpose, 81 scenarios featuring 5000 patients with five consecutive annual risk estimations for a hypothetical disease D are simulated, using different parameters to model disease progression and risk distribution. This simulation analysis is validated using a real-world clinical case study based on German breast cancer patients and the INFLUENCE-nomogram for locoregional breast cancer recurrence., Results: If individual risk estimations were used to personalise screening for a disease D aiming at detecting a 90% of curable cases, more than 20% of screening examinations could be avoided relative to a conventional uninformed approach, depending on the simulated scenario. Whereas an individual but time-independent approach is associated with acceptable saving potentials in case of a relatively homogenous risk distribution, the time-dependent approaches are superior when the complexity of a scenario increases. With slowly progressing diseases, risk-accumulation over time needs to be considered to achieve the highest screening efficiency on population level, for rapidly progressing diseases, an interval-specific approach is superior. The possible benefits of time-dependent risk-based screening were confirmed in the real-world clinical case study., Conclusions: Appropriate approaches to use time-dependent risk predictions may considerably enhance screening efficiency on individual and population level. Therefore, predicting risk development over time should be supported by future prediction tools and be incorporated in decision algorithms., (© 2022. The Author(s).)

Objectives: An important aim of follow-up after primary breast cancer treatment is early detection of locoregional recurrences (LRR). This study compares 2 personalized follow-up scheme simulations based on LRR risk predictions provided by a time-dependent prognostic model for breast cancer LRR and quantifies their possible follow-up efficiency., Methods: Surgically treated early patients with breast cancer between 2003 and 2008 were selected from the Netherlands Cancer Registry. The INFLUENCE nomogram was used to estimate the 5-year annual LRR. Applying 2 thresholds, they were defined according to Youden's J-statistic and a predefined follow-up sensitivity of 95%, respectively. These patient's risk estimations served as the basis for scheduling follow-up visits; 2 personalized follow-up schemes were simulated. The number of potentially saved follow-up visits and corresponding cost savings for each follow-up scheme were compared with the current Dutch breast cancer guideline recommendation and the observed utilization of follow-up on a training and testing cohort., Results: Using LRR risk-predictions for 30 379 Dutch patients with breast cancer from 2003 to 2006 (training cohort), 2 thresholds were calculated. The threshold according to Youden's approach yielded a follow-up sensitivity of 62.5% and a potential saving of 62.1% of follow-up visits and €24.8 million in 5 years. When the threshold corresponding to 95% follow-up sensitivity was used, 17% of follow-up visits and €7 million were saved compared with the guidelines. Similar results were obtained by applying these thresholds to the testing cohort of 11 462 patients from 2007 to 2008. Compared with the observed utilization of follow-up, the potential cost-savings decline moderately., Conclusions: Personalized follow-up schemes based on the INFLUENCE nomogram's individual risk estimations for breast cancer LRR could decrease the number of follow-up visits if one accepts a limited risk of delayed LRR detection., (Copyright © 2020 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform ( https://www.evidencio.com ) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation., Methods: Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation., Results: Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods., Conclusions: This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Purpose: The estrogen (ER), progesterone (PR), and HER2 status are essential in guiding treatment decisions in breast cancer patients. In daily life, the ER/PR/HER2 status is expected to be commonly tested twice, i.e., at diagnosis using material from tumor needle biopsies, and after tumor resection using full tumor tissue material. This study explored the discordance of ER/PR/HER2 between tumor needle biopsies and full tumor resection material using real-world patient-level data from Dutch breast cancer patients., Methods: Pathology reports of 11,054 breast cancer patients were derived from PALGA (Dutch Pathology Registry). Discordance was calculated for multiple combinations of the ER/PR/HER2 receptor status. The influence of patient and tumor characteristics on the probability of having discordant test results was analyzed using multiple logistic regression models (separately for ER, PR and HER2)., Results: For 1279 patients (14.4%), at least one of the receptors (ER/PR/HER2) was determined on both biopsy and tumor tissue material. The majority had concordant test results for ER (n = 916; 94.8%), PR (n = 1170; 86.7%), and HER2 (n = 881; 98.1%). Patients having an ER- and HER2-positive but PR-negative biopsy classification, BR grade III, and < 10% tumor tissue remaining after neoadjuvant therapy (NAT) have the highest probability of ER discordant test results (OR 4.991; p = 83.31%). The probability of discordance in PR is based on different sets of patient and tumor characteristics. Potential cost savings from omitting multiple tests if concordance can be perfectly predicted can be up to €205,000 yearly., Conclusions: Double testing of ER/PR/HER2 is less common than expected. Discordance in ER/PR/HER2 test results between tumor needle biopsy taken at the time of diagnosis and tumor resection material is very low, especially in patients not receiving any form of neoadjuvant therapy. These results imply that a substantial number of tests can potentially be omitted in specific subgroups of breast cancer patients.

Purpose: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making., Methods: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability., Results: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%., Conclusions: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery., (Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

Reviews on circulating biomarkers in breast cancer usually focus on one single biomarker or a selective group of biomarkers. An overview summarizing the discovery and evaluation of all blood-based biomarkers in metastatic breast cancer is lacking. This systematic review aims to identify the available evidence of known blood-based biomarkers in metastatic breast cancer, regarding their clinical utility and state-of-the-art position in the validation process. The initial search yielded 1078 original studies, of which 420 were assessed for eligibility. A total of 320 studies were included in the final synthesis. A Development, Evaluation and Application Chart (DEAC) of all biomarkers was developed. Most studies focus on identifying new biomarkers and search for relations between these biomarkers and traditional molecular characteristics. Biomarkers are usually investigated in only one study (68.8%). Only 9.8% of all biomarkers was investigated in more than five studies. Circulating tumor cells, gene expression within tumor cells and the concentration of secreted proteins are the most frequently investigated biomarkers in liquid biopsies. However, there is a lack of studies focusing on identifying the clinical utility of these biomarkers, by which the additional value still seems to be limited according to the investigated evidence.

Background: Accurate preoperative localization of nonpalpable breast cancer is essential to achieve complete resection. Radioguided occult lesion localization (ROLL) has been introduced as an alternative for wire-guided localization (WGL). Although efficacy of ROLL has been established in a randomized controlled trial, cost-effectiveness of ROLL compared with WGL is not yet known. The objective of this study was to determine whether ROLL has acceptable cost-effectiveness compared with WGL., Methods: An economic evaluation was performed along with a randomized controlled trial (ClinicalTrials.gov, No. NCT00539474). Women (>18 years) with histologically proven nonpalpable breast cancer and eligible for breast conserving treatment with sentinel node procedure were randomized to ROLL (n = 162) or WGL (n = 152). Empirical data on direct medical costs were collected, and changes in quality of life were measured over a 6-month period. Bootstrapping was used to assess uncertainty in cost-effectiveness estimates, and sensitivity of the results to the missing data approach was investigated., Results: In total, 314 patients with 316 invasive breast cancers were enrolled. On average ROLL required the same time as WGL for the surgical procedure (119 vs 118 min), resulted in a 7 % higher reinterventions risk, and 13 % more complications. Quality of life effects were similar (difference 0.00 QALYs 95 % CI (-0.04-0.05). Total costs were also similar for ROLL and WGL (+26 per patient 95 % CI -250-311)., Conclusion: ROLL is comparable to WGL with respect to both costs and quality of life effects as measured with the EQ5D and will therefore not lead to more cost-effective medical care.