External Validation of Adjuvant! Online Breast Cancer Prognosis Tool. Prioritising Recommendations for Improvement (original) (raw)
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British journal of cancer, 2009
Adjuvant! Online is an internet-based computer programme providing 10-year prognosis predictions for early breast cancer patients. It was developed in the United States, has been successfully validated in Canada, and is used in the United Kingdom and elsewhere. This study investigates the performance of Adjuvant! in a cohort of patients from the United Kingdom. Data on the prognostic factors and management of 1065 women with early breast cancer diagnosed consecutively at the Churchill Hospital in Oxford between 1986 and 1996 were entered into Adjuvant! to generate predictions of overall survival (OS), breast cancer-specific survival (BCSS), and event-free survival (EFS) at 10 years. Such predictions were compared with the observed 10-year outcomes of these patients. For the whole cohort, Adjuvant! significantly overestimated OS (by 5.54%, P<0.001), BCSS (by 4.53%, P<0.001), and EFS (by 3.51%, P=0.001). For OS and BCSS, overestimation persisted across most demographic, patholog...
British Journal of Cancer, 2016
Background: Limited data are available on the prognostic performance of Adjuvant! Online (AOL) and Nottingham Prognostic Index (NPI) in young breast cancer patients. Methods: This multicentre hospital-based retrospective cohort study included young (p40 years) and older (55-60 years) breast cancer patients treated from January 2000 to December 2004 at four large Belgian and Italian institutions. Predicted 10-year overall survival (OS) and disease-free survival (DFS) using AOL and 10-year OS using NPI were calculated for every patient. Tools ability to predict outcomes (i.e., calibration) and their discriminatory accuracy was assessed. Results: The study included 1283 patients, 376 young and 907 older women. Adjuvant! Online accurately predicted 10-year OS (absolute difference: 0.7%; P ¼ 0.37) in young cohort, but overestimated 10-year DFS by 7.7% (P ¼ 0.003). In older cohort, AOL significantly underestimated both 10-year OS and DFS by 7.2% (Po0.001) and 3.2% (P ¼ 0.04), respectively. Nottingham Prognostic Index significantly underestimated 10-year OS in both young (8.5%; Po0.001) and older (4.0%; Po0.001) cohorts. Adjuvant! Online and NPI had comparable discriminatory accuracy. Conclusions: In young breast cancer patients, AOL is a reliable tool in predicting OS at 10 years but not DFS, whereas the performance of NPI is sub-optimal.
Validation of the online PREDICT tool in a cohort of early breast cancer in Brazil
Brazilian Journal of Medical and Biological Research, 2022
PREDICT is a tool designed to estimate the benefits of adjuvant therapy and the overall survival of women with early breast cancer. The model uses clinical, histological, and immunohistochemical variables. This study aimed to evaluate the model's performance in a Brazilian population. We assessed the discrimination and calibration of the PREDICT model to estimate overall survival (OS) in five and ten years of follow-up in a cohort of 873 women with early breast cancer diagnosed from January 2001 to December 2016. A total of 743 patients had estrogen receptor (ER)-positive and 130 had ER-negative tumors. The area under the receiver operating characteristic (ROC) curve (AUC) for discrimination was 0.72 (95%CI: 0.66-0.78) at five years and 0.67 (95%CI: 0.61-0.72) at ten years for women with ER-positive tumors. The AUC was 0.72 (95%CI: 0.62-0.81) at five years and 0.67 (95%CI: 0.54-0.77) at ten years for women with ER-negative tumors. The predicted survival in ER-positive tumors was 91.0% (95%CI: 90.2-91.6%) at five years and 79.3% (95%CI: 77.7-81.0%) at ten years, and the observed survival 90.7% (95%CI: 88.6-92.9%) and 77.2% (95%CI: 73.4-81.4%), respectively. The predicted survival in ER-negative tumors was 84.5% (95%CI: 82.5-86.6%) at five years and 75.0% (95%CI: 71.6-78.5%) at ten years, and the observed survival 76.3% (95%CI: 69.1-84.3%) and 67.9% (95%CI: 58.6-78.6%), respectively. In conclusion, PREDICT was accurate to estimate OS in women with ER-positive tumors and overestimated the OS in women with ER-negative tumors.
P2-12-20: Adjuvant! Online Is Overoptimistic in Predicting Survival of Asian Breast Cancer Patients
Cancer Research, 2011
Background Adjuvant! Online is a free web-based tool which predicts 10-year breast cancer outcomes and efficacy of adjuvant therapy in patients with breast cancer. As its prognostic performance has only been validated in high income Caucasian populations, the model was validated in a middle income Asian setting. Material and Methods: Within the University Malaya Hospital-Based Breast Cancer Registry, all 631 women receiving standard surgery for invasive non-metastatic breast cancer between 1993 and 2000 were identified. Calibration of Adjuvant! Online was evaluated by comparing the predicted 10-year overall survival with observed 10-year survival. Discrimination of the model was tested by receiver operating characteristic (ROC) analysis. Results: For the entire cohort, Adjuvant! Online predicted 10 year survival (70.3%) was significantly higher than the observed 10 years survival (63.6 %) by a difference of 6.7% (95%CI: 3.0%- 10.4%). The model was especially overoptimistic in women ...
2021
Background: Accurate prediction of survival is crucial for both physicians and women with breast cancer to enable clinical decision making on appropriate treatments. The currently available survival prediction tools were developed based on demographic and clinical data obtained from specific populations and may underestimate or overestimate the survival of women with breast cancer in China. Objective: This study aims to develop and validate a prognostic app to predict the overall survival of women with breast cancer in China. Methods: Nine-year (January 2009-December 2017) clinical data of women with breast cancer who received surgery and adjuvant therapy from 2 hospitals in Xiamen were collected and matched against the death data from the Xiamen Center of Disease Control and Prevention. All samples were randomly divided (7:3 ratio) into a training set for model construction and a test set for model external validation. Multivariable Cox regression analysis was used to construct a survival prediction model. The model performance was evaluated by receiver operating characteristic (ROC) curve and Brier score. Finally, by running the survival prediction model in the app background thread, the prognostic app, called iCanPredict, was developed for women with breast cancer in China. Results: A total of 1592 samples were included for data analysis. The training set comprised 1114 individuals and the test set comprised 478 individuals. Age at diagnosis, clinical stage, molecular classification, operative type, axillary lymph node dissection, chemotherapy, and endocrine therapy were incorporated into the model, where age at diagnosis (hazard ratio [HR] 1.031, 95% CI 1.011-1.051; P=.002), clinical stage (HR 3.044, 95% CI 2.347-3.928; P<.001), and endocrine therapy (HR 0.592, 95% CI 0.384-0.914; P=.02) significantly influenced the survival of women with breast cancer. The operative type (P=.81) and the other 4 variables (molecular classification [P=.91], breast reconstruction [P=.36], axillary lymph node dissection [P=.32], and chemotherapy [P=.84]) were not significant. The ROC curve of the training set showed that the model exhibited good discrimination for predicting 1-(area under the curve [AUC] 0.802, 95% CI 0.713-0.892), 5-(AUC 0.813, 95% CI 0.760-0.865), and 10-year (AUC 0.740, 95% CI 0.672-0.808) overall survival. The Brier scores at 1, 5, and 10 years after diagnosis were 0.005, 0.055, and 0.103 in the training set, respectively, and were less than 0.25, indicating good predictive ability. The test set externally validated
British journal of cancer, 2015
Background:Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level.Methods:We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction.Results:Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71-0.77) for the Cancermath.net model and 0.72 (95% CI 0.69-0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the su...
Risk prediction models of breast cancer: a systematic review of model performances
Breast Cancer Research and Treatment, 2012
The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.
JOURNAL OF ASSOCIATED MEDICAL SCIENCES (Online), 2024
Background: Many studies employed machine learning (ML) to forecast the prognosis of breast cancer (BC) patients and discovered that the ML model showed high individualized forecasting ability. Breast cancer is the most frequent kind of carcinoma in women globally and ranks as the leading cause of death in women. Objectives: This study intends to use the Surveillance, Epidemiology, and End Results dataset to categorize breast carcinoma cases' alive and dead conditions. Deep learning and machine learning have been extensively utilized in clinical studies to address various categorization problems due to their ability to manage massive data sets in an organized manner. Pre-processing the data allows it to be visualized and analyzed for making critical choices. This study describes a realistic machine learning-based strategy for categorizing the SEER breast cancer dataset. Materials and methods: We employed classification and machine learning algorithms to classify breast cancer mortality. Four well-known classification ML algorithms were employed in this study. To identify risk factors, we employed multivariate analysis using the data set. Results: The decision tree performed the best accuracy (0.914) among all the models. T4 stage (β=1.4, p<0.001, OR=4.22, 95% CI (2.06-8.64), N2 stage (β=0.39, p=0.008, OR= 1.49, 95% CI (1.111-1.997) found to be major risk factors for breast cancer mortality using multivariate analysis. Conclusion: The significant prognostic variables affecting the breast carcinoma survival rates reported in the current research are relevant and might be turned into decision support systems in the medical realm.
Adjuvant! Online is overoptimistic in predicting survival of Asian breast cancer patients
BACKGROUND: Adjuvant! Online is a free web-based tool which predicts 10-year breast cancer outcomes and the efficacy of adjuvant therapy in patients with breast cancer. As its prognostic performance has only been validated in high income Caucasian populations, we validated the model in a middle income Asian setting. METHODS: Within the University Malaya Hospital-Based Breast Cancer Registry, all 631 women who were surgically treated for invasive non-metastatic breast cancer between 1993 and 2000 were identified. The discriminative performance of Adjuvant! Online was tested using receiver operating characteristic (ROC) analysis. Calibration of the model was evaluated by comparing predicted 10-year overall survival with observed 10-year survival. FINDINGS: Adjuvant! Online was fairly capable in discriminating between good and poor survivors, as attested by the area under ROC curve of 0.73 (95% Confidence Interval: 0.69-0.77). Overall, Adjuvant! Online predicted 10 year survival (70.3%) was significantly higher than the observed 10 year survival (63.6%, difference of 6.7%; 95% CI: 3.0-10.4%). The model was especially overoptimistic in women under 40 years and in women of Malay ethnicity, where survival was overestimated by approximately 20% (95% CI: 9.8-29.8%) and 15% (95% CI: 5.3-24.5%) respectively. INTERPRETATION: Even though Adjuvant! Online is capable of discriminating between good and poor survivors, it systematically overestimates survival. These findings suggest that the model requires adaptation prior to use in Asian settings.