Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its... Show moreBackground: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment.Methods: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds.Results: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors. Conclusions: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset. Show less
McLernon, D.J.; Giardiello, D.; Calster, B. van; Wynants, L.; Geloven, N. van; Smeden, M. van; ... ; STRATOS Initiative 2022
Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time... Show moreRisk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event ") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker.The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation.The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models. Show less
Background: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model ... Show moreBackground: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.Methods: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.Results: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%Pl 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/ expected ratio at 10 years of 0.92 (95%Pl 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.Conclusions: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging. Show less
Women with breast cancer often wonder whether they should have their other breast removed as well, to prevent a potential tumor from developing there. The exact risks vary significantly per person.... Show moreWomen with breast cancer often wonder whether they should have their other breast removed as well, to prevent a potential tumor from developing there. The exact risks vary significantly per person. We used information about patients, breast cancer characteristics and treatments, and rare and common genetic variant correlated with a higher or lower risk of developing breast cancer in the other breast in large datasets to develop and validate statistical models to predict each patient’s risk of developing a tumor. We investigated whether and how these models might be clinically useful to better inform patients and physicians to tailor clinical decision making about potential strategies to prevent or early detect a tumor in the opposite breast. We discussed statistical aspects about model development and validation, and we provided frameworks about how to develop and assess prediction performance of risk prediction models using motivating examples in breast cancer. Show less
Geloven, N. van; Giardiello, D.; Bonneville, E.F.; Teece, L.; Ramspek, C.L.; Smeden, M. van; ... ; STRATOS Initiative 2022
Background: The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment... Show moreBackground: The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics.Methods: Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities.Results: A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29 -32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%.Conclusions: Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers. (C) 2022 The Author(s). Published by Elsevier Ltd. Show less
Geloven, N. van; Giardiello, D.; Bonneville, E.F.; Teece, L.; Ramspek, C.L.; Smeden, M. van; ... ; Steyerberg, E. 2022
Thorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes... Show moreThorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes is a competing risk. Model performance measures must account for such competing events. In this article, we present a comprehensive yet accessible overview of performance measures for this competing eventsetting, including the calculation and interpretation of statistical measures for calibration, discrimination, overall prediction error, and clinical usefulness by decision curve analysis. All methods are illustrated for patients with breast cancer, with publicly available data and R code. Show less
Plas-Krijgsman, W.G. van der; Giardiello, D.; Putter, H.; Steyerberg, E.W.; Bastiaannet, E.; Stiggelbout, A.M.; ... ; Glas, N.A. de 2021
Background Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate... Show moreBackground Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged >= 65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment.Methods We selected surgically treated patients with early invasive breast cancer (stage I-III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots.Findings We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74.8 years (range 65-98) in the development cohort and 76.0 years (70-101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0.76 (95% CI 0.75-0.76) for overall mortality, 0.76 (0.76-0.77) for recurrence, and 0.75 (0.74-0.75) for other-cause mortality.Interpretation The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd. Show less
Seijen, M. van; Lips, E.H.; Fu, L.P.; Giardiello, D.; Duijnhoven, F. van; Munck, L. de; ... ; Grand Challenge Precision Consorti 2021
Background Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact... Show moreBackground Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort. Methods The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989-2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models. Results The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9-32.6) after BCS compared to 18.2% (95% CI 16.3-20.3) following BCS + RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)(age < 50) 3.2 (95% CI 1.6-6.6); HRage >= 50 3.6 (95% CI 2.6-4.8) and for iIBC: HRage<50 2.1 (95% CI 1.4-3.2); HRage >= 50 4.3 (95% CI 3.0-6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HRage < 50 0.7 (95% CI 0.3-1.5); HRage >= 50 0.7 (95% CI 0.4-1.3) and for iIBC: HRage < 50 0.6 (95% CI 0.4-0.9); HRage >= 50 1.2 (95% CI 0.9-1.6)). Conclusion RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter. Show less
Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the... Show morePrevious research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies. Show less
We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage... Show moreWe aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I-III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10-year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04-1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82-0.92). In patients diagnosed >= 2011, the HR for invasive CBC was 1.38 (95% CI = 1.35-1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46-3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50-0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients. Show less
Background Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in... Show moreBackground Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Methods We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Results The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. Conclusions Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making. Show less
Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop... Show moreBackground: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.Methods: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.Results: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.Conclusions: We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging. Show less
Sobral-Leite, M.; Salomon, I.; Opdam, M.; Kruger, D.T.; Beelen, K.J.; Noort, V. van der; ... ; Kok, M. 2019
Introduction The presence of tumor-infiltrating lymphocytes (TILs) is correlated with good prognosis and outcome after (immuno)therapy in triple-negative and HER2-positive breast cancer. However,... Show moreIntroduction The presence of tumor-infiltrating lymphocytes (TILs) is correlated with good prognosis and outcome after (immuno)therapy in triple-negative and HER2-positive breast cancer. However, the role of TILs in luminal breast cancer is less clear. Emerging evidence has now demonstrated that genetic aberrations in malignant cells influence the immune landscape of tumors. Phosphatidylinositol 3-kinase (PI3K) is the most common altered pathway in ER-positive breast cancer. It is unknown whether changes in the PI3K pathway result in a different composition of the breast tumor microenvironment. Here we present the retrospective analysis of a prospective randomized trial in ER-positive breast cancer on the prognostic and predictive value of specific tumor-associated lymphocytes in the context of PI3K alterations. Methods We included 563 ER-positive tumors from a multicenter trial for stage I to III postmenopausal breast cancer patients, who were randomized to tamoxifen or no adjuvant therapy. The amount of CD8-, CD4-, and FOXP3-positive cells was evaluated by immunohistochemistry and quantified by imaging-analysis software. We analyzed the associations between PIK3CA hotspot mutations, PTEN expression, phosphorylated proteins of the PI3K and MAPK pathway (p-AKT, p-ERK1/2, p-4EBP1, p-p70S6K), and recurrence-free interval after adjuvant tamoxifen or no adjuvant treatment. Results CD8-positive lymphocytes were significantly more abundant in PIK3CA-mutated tumors (OR = 1.65; 95% CI 1.03-2.68). While CD4 and FOXP3 were not significantly associated with prognosis, patients with tumors classified as CD8-high had increased risk of recurrence (HR = 1.98; 95% CI 1.14-3.41; multivariable model including PIK3CA status, treatment arm, and other standard clinicopathological variables). Lymphocytes were more often present in tumors with increased PI3K downstream phosphorylation. This was most pronounced for FOXP3-positive cells. Conclusion These exploratory analyses of a prospective trial in luminal breast cancer suggest high CD8 infiltration is associated with unfavorable outcome and that PI3K pathway alterations might be associated with the composition of the tumor microenvironment. Show less
