Objective To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.
Design Population based cohort study.
Setting QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.
Participants 141765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.
Main outcome measures Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.
Results During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21688 breast cancer related deaths and 11454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20367 breast cancer related deaths occurred during a total of 688564.81 person years. The crude breast cancer mortality rate was 295.79 per 10000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox models random effects meta-analysis pooled estimate for Harrells C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrells C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrells C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.
Conclusion In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.
Clinical prediction models already support medical decision making in breast cancer by providing individualised estimations of risk. Tools such as PREDICT Breast1 or the Nottingham Prognostic Index23 are used in patients with early stage, surgically treated breast cancer for prognostication and selection of post-surgical treatment. Such tools are, however, inherently limited to treatment specific subgroups of patients. Accurate estimation of mortality risk after diagnosis across all patients with breast cancer of any stage may be clinically useful for stratifying follow-up, counselling patients about their expected prognosis, or identifying high risk individuals suitable for clinical trials.4
The scope for machine learning approaches in clinical prediction modelling has attracted considerable interest.56789 Some have posited that these flexible approaches might be more suitable for capturing non-linear associations, or for handling higher order interactions without explicit programming.10 Others have raised concerns about model transparency,1112 interpretability,13 risk of algorithmic bias exacerbating extant health inequalities,14 quality of evaluation and reporting,15 ability to handle rare events16 or censoring,17 and appropriateness of comparisons11 to regression based methods.18 Indeed, systematic reviews have shown no inherent benefit of machine learning approaches over appropriate statistical models in low dimensional clinical settings.18 As no a priori method exists to predict which modelling approach may yield the most useful clinical prediction model for a given scenario, frameworks that appropriately compare different models can be used.
Owing to the risks of harm from suboptimal medical decision making, clinical prediction models should be comprehensively evaluated for performance and utility,19 and, if widespread clinical use is intended, heterogeneity in model performance across relevant patient groups should be explored.20 Given developments in treatment for breast cancer over time, with associated temporal falls in mortality, another key consideration is the transportability of risk modelsnot just across regions and subpopulations but also across time periods.21 Although such dataset shift22 is a common issue with any algorithm sought to be deployed prospectively, this is not routinely explored. Robust evaluation is necessary but is non-uniform in the modelling of breast cancer prognostication.23 A systematic review identified 58 papers that assessed prognostic models for breast cancer,24 but only one study assessed clinical effectiveness by means of a simplistic approach measuring the accuracy of classifying patients into high or low risk groups. A more recent systematic review25 appraised 922 breast cancer prediction models using PROBAST (prediction model risk of bias assessment tool)26 and found that most of the clinical prediction models are poorly reported, show methodological flaws, or are at high risk of bias. Of the 27 models deemed to be at low risk of bias, only one was intended to estimate the risks of breast cancer related mortality in women with disease of any stage.27 However, this small study of 287 women using data from a single health department in Spain had methodological limitations, including possibly insufficient data to fit a model (see supplementary table 1) and uncertain transportability to other settings. Therefore, no reliable prediction model exists to provide accurate risk assessment of mortality in women with breast cancer of any stage. Although we refer to women throughout, this is based on self-reported female sex, which may include some individuals who do not identify as female.
We aimed to develop a clinically useful prediction model to reliably estimate the risks of breast cancer specific mortality in any woman with a diagnosis of breast cancer, in line with modern best practice. Utilising data from 141675 women with invasive breast cancer diagnosed between 2000 and 2020 in England from a population representative, national linked electronic healthcare record database, this study comparatively developed and evaluated clinical prediction models using a combination of analysis methods within an internal-external validation strategy.2829 We sought to identify and compare the best performing methods for model discrimination, calibration, and clinical utility across all stages of breast cancer.
We evaluated four model building approaches: two regression methods (Cox proportional hazards and competing risks regression) and two machine learning methods (XGBoost and neural networks). The prediction horizon was 10 year risk of breast cancer related death from date of diagnosis. The study was conducted in accordance with our protocol30 and is reported consistent with the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines.31
Assuming 100 candidate predictor parameters, an annual mortality rate of 0.024 after diagnosis,32 and a conservative 15% of the maximal Cox-Snell R2, we estimated that the minimum sample size for fitting the regression models was 10080, with 1452 events, and 14.52 events for each predictor parameter.3334 No standard method exists to estimate minimum sample size for our machine learning models of interestsome evidence, albeit on binary outcome data, suggests that some machine learning methods may require much more data.35
The QResearch database was used to identify an open cohort of women aged 20 years and older (no upper age limit) at time of diagnosis of any invasive breast cancer between 1 January 2000 and 31 December 2020 in England. QResearch has collected data from more than 1500 general practices in the United Kingdom since 1989 and comprises individual level linkage across general practice data, NHS Digitals Hospital Episode Statistics, the national cancer registry, and the Office for National Statistics death registry.
The outcome for this study was breast cancer related mortality within 10 years from the date of a diagnosis of invasive breast cancer. We defined the diagnosis of invasive breast cancer as the presence of breast cancer related Read/Systemised Nomenclature of Medicine Clinical Terms (SNOMED) codes in general practice records, breast cancer related ICD-10 (international classification of diseases, 10th revision) codes in Hospital Episode Statistics data, or as a patient with breast cancer in the cancer registry (stage >0; whichever occurred first). The outcome, breast cancer death, was defined as the presence of relevant ICD-10 codes as any cause of death (primary or contributory) on death certificates from the ONS register. We excluded women with recorded carcinoma in situ only diagnoses as these are non-obligate precursor lesions and present distinct clinical considerations.36 Clinical codes used to define predictors and outcomes are available in the QResearch code group library (https://www.qresearch.org/data/qcode-group-library/). Follow-up time was calculated from the first recorded date of breast cancer diagnosis (earliest recorded on any of the linked datasets) to the earliest of breast cancer related death, other cause of death, or censoring (reached end of study period, left the registered general practice, or the practice stopped contributing to QResearch). The status at last follow-up depended on the modelling framework (ie, Cox proportional hazards or competing risks framework). The maximum follow-up was truncated to 10 years, in line with the model prediction horizon. Supplementary table 2 shows ascertainment of breast cancer diagnoses across the linked datasets.
Individual participant data were extracted on the candidate predictor parameters listed in Box 1, as well as geographical region, auxiliary variables (breast cancer treatments), and dates of events of interest. Candidate predictors were based on evidence from the clinical, epidemiological, or prediction model literature.12337383940 The most recently recorded values before or at the time of breast cancer diagnosis were used with no time restriction. Data were available from the cancer registry about cancer treatment within one year of diagnosis (eg, chemotherapy) but without any corresponding date. The intended model implementation (prediction time) would be at the breast cancer multidisciplinary team meeting or similar clinical setting, following initial diagnostic investigations and staging. To avoid information leakage, and since we did not seek model treatment selection within a causal framework,41 breast cancer treatment variables were not included as predictors.
Age at breast cancer diagnosiscontinuous or fractional polynomial
Townsend deprivation score at cohort entrycontinuous or fractional polynomial
Body mass index (most recently recorded before breast cancer diagnosis)continuous or fractional polynomial
Cancer stage at diagnosis (ordinal: I, II, III, IV)
Differentiation (categorical: well differentiated, moderately differentiated, poorly or undifferentiated)
Oestrogen receptor status (binary: positive or negative)
Progesterone receptor status (binary: positive or negative)
Human epidermal growth factor receptor 2 (HER2) status (binary: positive or negative)
Route to diagnosis (categorical: emergency presentation, inpatient elective, other, screen detected, two week wait)
Comorbidities or medical history on general practice or Hospital Episodes Statistics data (recorded before or at entry to cohort; categorical unless stated otherwise):
Ischaemic heart disease
Type 1 diabetes mellitus
Type 2 diabetes mellitus
Chronic liver disease or cirrhosis
Systemic lupus erythematosus
Chronic kidney disease (ordinal: none or stage 2, stage 3, stage 4, stage 5)
Family history of breast cancer (categorical: recorded in general practice or Hospital Episodes Statistics data, before or at entry to cohort)
Drug use (before breast cancer diagnosis):
Hormone replacement therapy
Selective serotonin reuptake inhibitor
Monoamine oxidase inhibitor
Oral contraceptive pill
Angiotensin converting enzyme inhibitor
Renin-angiotensin aldosterone antagonists
Age (fractional polynomial terms)family history of breast cancer
Ethnicityage (fractional polynomial terms)
Fractional polynomial42 terms for the continuous variables age at diagnosis, Townsend deprivation score, and body mass index (BMI) at diagnosis were identified in the complete data. This was done separately for the Cox and competing risks regression models, with a maximum of two powers permitted.
Multiple imputation with chained equations was used to impute missing data for BMI, ethnicity, Townsend deprivation score, smoking status, cancer stage at diagnosis, cancer grade at diagnosis, HER2 status, oestrogen receptor status, and progesterone receptor status under the missing at random assumption.4344 The imputation model contained all other candidate predictors, the endpoint indicator, breast cancer treatment variables, the Nelson-Aalen cumulative hazard estimate,45 and the period of cohort entry (period 1=1 January 2000-31 December 2009; period 2=1 January 2010-31 December 2020). The natural logarithm of BMI was used in imputation for normality, with imputed values exponentiated back to the regular scale for modelling. We generated 50 imputations and used these in all model fitting and evaluation steps. Although missing data were observed in the linked datasets used for model development, in the intended use setting (ie, risk estimation at breast cancer multidisciplinary team after a medical history has been taken), the predictors would be expected to be available for all patients.
Models were fit to the entire cohort and then evaluated using internal-external cross validation,28 which involved splitting the dataset by geographical region (n=10) and time period (see figure 1 for summary). For the internal-external cross validation, we recalculated follow-up so that those women who entered the study during the first study decade and survived into the second study period had their follow-up truncated (and status assigned accordingly) at 31 December 2009. This was to emulate two wholly temporally distinct datasets, both with maximum follow-up of 10 years, for the purposes of estimating temporal transportability of the models.
Summary of internal-external cross validation framework used to evaluate model performance for several metrics, and transportability
For the approach using Cox proportional hazards modelling, we treated other (non-breast cancer) deaths as censored. A full Cox model was fitted using all candidate predictor parameters. Model fitting was performed in each imputed dataset and the results combined using Rubins rules, and then this pooled model was used as the basis for predictor selection. We selected binary or multilevel categorical predictors associated with exponentiated coefficients >1.1 or <0.9 (at P<0.01) for inclusion, and interactions and continuous variables were selected if associated with P<0.01. Then these were used to refit the final Cox model. The predictor selection approach benefits from starting with a full, plausible, maximally complex model,46 and then considers both the clinical and the statistical magnitude of predictors to select a parsimonious model while making use of multiply imputed data.4748 This approach has been used in previous clinical prediction modelling studies using QResearch.495051 Clustered standard errors were used to account for clustering of participants within individual general practices in the database.
Deaths from other, non-breast cancer related causes represent a competing risk and in this framework were handled accordingly.30 We repeated the fractional polynomial term selection and predictor selection processes for the competing risks models owing to potential differential associations between predictors and risk or functional forms thereof. A full model was fit with all candidate predictors, with the same magnitude and significance rule used to select the final predictors.
The competing risks model was developed using jack-knife pseudovalues for the Aalen-Johansen cumulative incidence function at 10 years as the outcome variable52the pseudovalues were calculated for the overall cohort (for fitting the model) and then separately in the data from period 1 and from period 2 for the purposes of internal-external cross validation. These values are a marginal (pseudo) probability that can then be used in a regression model to predict individuals probabilities conditional on the observed predictor values. Pseudovalues for the cumulative incidence function at 10 years were regressed on the predictor parameters in a generalised linear model with a complementary log-log link function525354 and robust standard errors to account for the non-independence of pseudovalues. The resultant coefficients are statistically similar to those of the Fine-Gray model5254 but computationally less burdensome to obtain, and permit direct modelling of probabilities.
All fitting and evaluation of the Cox and competing risks regression models occurred in each separate imputed dataset, with Rubins rules used to pool coefficients and standard errors across all imputations.55
The XGBoost and neural network approaches were adapted to handle right censored data in the setting of competing risks by using the jack-knife pseudovalues for the cumulative incidence function at 10 years as a continuous outcome variable. The same predictor parameters as selected for the competing risks regression model were used for the purposes of benchmarking. The XGBoost model used untransformed values for continuous predictors, but these were minimum-maximum scaled (constrained between 0 and 1) for the neural network. We converted categorical variables with more than two levels to dummy variables for both machine learning approaches.
We fit the XGBoost and neural network models to the entire available cohort and used bayesian optimisation56 with fivefold cross validation to identify the optimal configuration of hyperparameters to minimise the root mean squared error between observed pseudovalues and model predictions. Fifty iterations of bayesian optimisation were used, with the expected improvement acquisition function.
For the XGBoost model, we used bayesian optimisation to tune the number of boosting rounds, learning rate (eta), tree depth, subsample fraction, regularisation parameters (alpha gamma, and lambda), and column sampling fractions (per tree, per level). We used the squared error regression option as the objective, and the root mean squared error as the evaluation metric.
To permit modelling of higher order interactions in this tabular dataset, we used a feed forward artificial neural network approach with fully connected dense layers: the model architecture comprised an input layer of 26 nodes (ie, number of predictor parameters), rectified linear unit activation functions in each hidden layer, and a single linear activation output node to generate predictions for the pseudovalues of the cumulative incidence function. The Adam optimiser was used,57 with the initial learning rate, number of hidden layers, number of nodes in each hidden layer, and number of training epochs tuned using bayesian optimisation. If the loss function had plateaued for three epochs, we halved the learning rate, with early stopping after five epochs if the loss function had not reduced by 0.0001. The loss function was the root mean squared error between observed and predicted pseudovalues due to the continuous nature of the target variable.58
After identification of the optimal hyperparameter configurations, we fit the models accordingly to the entirety of the cohort data. We then assessed the performance of these models using the internal-external cross validation strategythis resembled that for the regression models but with the addition of a hyperparameter tuning component (fig 1). During each iteration of internal-external cross validation, we used bayesian optimisation with fivefold cross validation to identify the optimal hyperparameters for the model fitted to the development data from period 1, which we then tested on the held-out period 2 data. This therefore constituted a form of nested cross validation.59
As the XGBoost and neural network models do not constitute a linear set of parameters and do not have standard errors (therefore not able to be pooled using Rubins rules), we used a stacked imputation strategy. The 50 imputed datasets were stacked to form a single, long dataset, which enabled us to use the same full data as for the regression models, avoiding suboptimal approaches such as complete case analysis or single imputation. For model evaluation after internal-external cross validation, we used approaches based on Rubins rules,55 with performance estimates calculated in each separate imputed dataset using the internal-external cross validation generated individual predictions, and then the estimates were pooled.
Predicted risks when using the Cox model can be derived by combining the linear predictor with the baseline hazard function using the equation: predicted event probability=1Stexp(X) where St is the baseline survival function calculated at 10 years, and X is the individuals linear predictor. For internal-external cross validation, we estimated baseline survival functions separately in each imputation in the period 1 data (continuous predictors centred at the mean, binary predictors set to zero), with results pooled across imputations in accordance with Rubins rules.55 We estimated the final models baseline function similarly but using the full cohort data.
Probabilistic predictions for the competing risks regression model were directly calculated using the following transformation of the linear predictors (X, which included a constant term): predicted event probability=1exp(exp(X)).
As the XGBoost and neural network approaches modelled the pseudovalues directly, we handled the generated predictions as probabilities (conditional on the predictor values). As pseudovalues are not restricted to lie between 0 and 1, we clipped the XGBoost and neural network model predictions to be between 0 and 1 to represent predicted probabilities for model evaluation.
Discrimination was assessed using Harrells C index,60 calculated at 10 years and taking censoring into accountthis used inverse probability of censoring weights for competing risks regression, XGBoost, and neural networks given their competing risks formulation.61 Calibration was summarised in terms of the calibration slope and calibration-in-the-large.6263 Region level results for these metrics were computed during internal-external cross validation and pooled using random effects meta-analysis20 with the Hartung-Knapp-Sidik-Jonkmann method64 to provide an estimate of each metric with a 95% confidence interval, and with a 95% prediction interval. The prediction interval estimates the range of model performance on application to a distinct dataset.20 We also computed these metrics by ethnicity, 10 year age groups, and cancer stage (I-IV) using the pooled, individual level predictions.
Using the individual level predictions from all models, we generated smoothed calibration plots to assess alignment of observed and predicted risks across the spectrum of predicted risks. We generated these using a running smoother through individual risk predictions, and observed individual pseudovalues65 for the Kaplan-Meier failure function (Cox model) or cumulative incidence function (all other models).
Meta-regression following Hartung-Knapp-Sidik-Jonkmann random effects models were used to calculate measures of I2 and R2 to assess the extent to which inter-regional heterogeneity in discrimination and calibration metrics could be attributable to regional variation in age, BMI (standard deviation thereof), mean deprivation score, and ethnic diversity (percentage of people of non-white ethnicity).20 These region level characteristics were estimated using the data from period 2.
We compared the models for clinical utility using decision curve analysis.66 This analysis assesses the trade-off between the benefits of true positives (breast cancer deaths) and the potential harms that may arise from false positives across a range of threshold probabilities. Each model was compared using the two default scenarios of treat all or treat none, with the mean model prediction used for each individual across all imputations. This approach implicitly takes into account both discrimination and calibration and also extends model evaluation to consider the ramifications on clinical decision making.67 The competing risk of other, non-breast-cancer death was taken into account. Decision curves were plotted overall, and by cancer stage to explore potential utility for all breast cancers.
Predictions generated from the Cox proportional hazards model and other, competing risks approaches have different interpretations, owing to their differential handling of competing events and their modelling of hazard functions with distinct statistical properties.
Data processing, multiple imputation, regression modelling, and evaluation of internal-external cross validation results utilised Stata (version 17). Machine learning modelling was performed in R 4.0.1 (xgboost, keras, and ParBayesianOptimization packages), with an NVIDIA Tesla V100 used for graphical processing unit support. Analysis code is available in repository https://github.com/AshDF91/Breast-cancer-prognosis.
Two people who survived breast cancer were involved in discussions about the scope of the project, candidate predictors, importance of research questions, and co-creation of lay summaries before submitting the project for approval. This project was also presented at an Oxfordshire based breast cancer support group to obtain qualitative feedback on the studys aims and face validity or plausibility of candidate predictors, and to discuss the acceptability of clinical risk models to guide stratified breast cancer care.
A total of 141765 women aged between 20 and 97 years at date of breast cancer diagnosis were included in the study. During the entirety of follow-up (median 4.16 (interquartile range 1.76-8.26) years), there were 21688 breast cancer related deaths and 11454 deaths from other causes. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20367 breast cancer related deaths occurred during a total of 688564.81 person years. The crude mortality rate was 295.79 per 10000 person years (95% confidence interval 291.75 to 299.88). Supplementary figure 1 presents ethnic group specific mortality curves. Table 1 shows the baseline characteristics of the cohort overall and separately by decade defined subcohort.
Summary characteristics of final study cohort overall and separated into temporally distinct subcohorts used in internal-external cross validation. Values are number (column percentage) unless stated otherwise
After the cohort was split by decade of cohort entry and follow-up was truncated for the purposes of internal-external cross validation, 7551 breast cancer related deaths occurred in period 1 during a total of 211006.95 person years of follow-up (crude mortality rate 357.96 per 10000 person years (95% confidence interval 349.87 to 366.02)). In the period 2 data, 8808 breast cancer related deaths occurred during a total of 297066.74 person years of follow-up, with a lower crude mortality rate of 296.50 per 10000 person years (290.37 to 302.76) observed.
We selected non-linear fractional polynomial terms for age and BMI (see supplementary figure 2). The final Cox model after predictor selection is presented as exponentiated coefficients in figure 2 for transparency, with the full model detailed in supplementary table 3. Model performance across all ethnic groups is summarised in supplementary table 4: discrimination ranged between a Harrells C index of 0.794 (95% confidence interval 0.691 to 0.896) in Bangladeshi women to 0.931 (0.839 to 1.000) in Chinese women, but the low numbers of event counts in smaller ethnic groups (eg, Chinese) meant that overall calibration indices were imprecisely estimated for some.
Final Cox proportional hazards model predicting 10 year risk of breast cancer mortality, presented as its exponentiated coefficients (hazard ratios with 95% confidence intervals). Model contains fractional polynomial terms for age (0.5, 2) and body mass index (2, 2), but these are not plotted owing to reasons of scale. Model also includes a baseline survival term (not plottedthe full model as coefficients is presented in the supplementary file). ACE=angiotensin converting enzyme; CI=confidence interval; CKD=chronic kidney disease; ER=oestrogen receptor; GP=general practitioner; HER2= human epidermal growth factor receptor 2; HRT=hormone replacement therapy; PR=progesterone receptor; RAA=renin-angiotensin aldosterone; SSRI=selective serotonin reuptake inhibitor
Overall, the Cox models random effects meta-analysis pooled estimate for Harrells C index was the highest of any model, at 0.858 (95% confidence interval 0.853 to 0.864, 95% prediction interval 0.843 to 0.873). A small degree of miscalibration occurred on summary metrics, with a meta-analysis pooled estimate for the calibration slope of 1.108 (95% confidence interval 1.079 to 1.138, 95% prediction interval 1.034 to 1.182) (table 2). Figure 3, figure 4, and figure 5 show the meta-analysis pooling of performance metrics across regions. Smoothed calibration plots showed generally good alignment of observed and predicted risks across the entire spectrum of predicted risks, albeit with some minor over-prediction (fig 6).
Summary performance metrics for all four models, estimated using random effects meta-analysis after internal-external cross validation.
Results from internal-external cross validation of Cox proportional hazards model for Harrells C index. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval
Results from internal-external cross validation of Cox proportional hazards model for calibration slope. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval
Results from internal-external cross validation of Cox proportional hazards model for calibration-in-the-large. Plots display region level performance metric estimates and 95% confidence intervals (diamonds with lines), and an overall pooled estimate obtained using random effects meta-analysis and 95% confidence interval (lowest diamond) and 95% prediction interval (line through lowest diamond). CI=confidence interval
Calibration of the four models tested. Top row shows the alignment between predicted and observed risks for all models with smoothed calibration plots. Bottom row summarises the distribution of predicted risks from each model as histograms
Regional differences in the Harrells C index were relatively slight. None of the inter-region heterogeneity observed for discrimination (I2=53.14%) and calibration (I2=42.35%) appeared to be attributable to regional variation in any of the sociodemographic factors examined (table 3). The model discriminated well across cancer stages, but discriminative capability decreased with increasing stage; moderate variation was observed in calibration across cancer stage groups (supplementary table 9).
Random effects meta-regression of relative contributions of regional variation in age, body mass index, deprivation, and non-white ethnicity on inter-regional differences in performance metrics after internal-external cross validation
Similar fractional polynomial terms were selected for age and BMI in the competing risks regression model (see supplementary figure 2), and predictor selection yielded a model with fewer predictors than the Cox model. The competing risks regression model is presented as exponentiated coefficients in figure 7, with the full model (including constant term) detailed in supplementary table 5. Ethnic group specific discrimination and overall calibration metrics are detailed in supplementary table 4the model generally performed well across ethnic groups, with similar discrimination, but there was some overt miscalibration on summary metricsalthough some metrics were estimated imprecisely owing to small event counts in some ethnic groups.
Final competing risks regression model predicting 10 year risk of breast cancer mortality, presented as its exponentiated coefficients (subdistribution hazard ratios with 95% confidence intervals). Model contains fractional polynomial terms for age (1, 2) and body mass index (2, 2), but these are not plotted owing to reasons of scale. Model also includes an intercept term (not plottedsee supplementary file for full model as coefficients). CI=confidence interval; ER=oestrogen receptor; GP=general practitioner; HER2=human epidermal growth factor receptor 2; HRT=hormone replacement therapy; PR=progesterone receptor
The random effects meta-analysis pooled Harrells C index was 0.849 (95% confidence interval 0.839 to 0.859, 95% prediction interval 0.821 to 0.876). Some evidence suggested systematic miscalibration overallthat is, a pooled calibration slope of 1.160 (95% confidence interval 1.064 to 1.255, 95% prediction interval 0.872 to 1.447). Smoothed calibration plots showed underestimation of risk at the highest predicted values (eg, predicted risk >40%, fig 6). Supplementary figure 3 displays regional performance metrics.
An estimated 41.33% of the regional variation in the Harrells C index for the competing risks regression model was attributable to inter-regional case mix (table 3); ethnic diversity was the leading sociodemographic factor associated therewith (table 3). For calibration, the I2 from the full meta-regression model was 56.68%, with regional variation in age, deprivation, and ethnic diversity associated therewith. Similar to the Cox model, discrimination tended to decrease with increasing cancer stage (supplementary table 9).
Table 4 summarises the selected hyperparameter configuration for the final XGBoost model. The discrimination of this model appeared acceptable overall,68 albeit lower than for both regression models (table 2; supplementary figure 4), with a meta-analysis pooled Harrells C index of 0.821 (95% confidence interval 0.813 to 0.828, 95% prediction interval 0.805 to 0.837). Pooled calibration metrics suggested some mild systemic miscalibrationfor example, the meta-analysis pooled calibration slope was 1.084 (95% confidence interval 1.003 to 1.165, 95% prediction interval 0.842 to 1.326). Calibration plots showed miscalibration across much of the predicted risk spectrum (fig 6), with overestimation in those with predicted risks <0.4 (most of the individuals) before mixed underestimation and overestimation in the patients at highest risk. Discrimination and calibration were poor for stage IV tumours (see supplementary table 9). Regarding regional variation in performance metrics as a result of differences between regions, most of the variation in calibration was attributable to ethnic diversity, followed by regional differences in age (table 3).
Description of machine learning model architectures and hyperparameters tuning performed
Table 4 summarises the selected hyperparameter configuration for the final neural network. This model performed better than XGBoost for overall discriminationthe meta-analysis pooled Harrells C index was 0.847 (95% confidence interval 0.835 to 0.858, 95% prediction interval 0.816 to 0.878, table 2 and supplementary figure 5). Post-internal-external cross validation pooled estimates of summary calibration metrics suggested no systemic miscalibration overall, such as a calibration slope of 1.037 (95% confidence interval 0.910 to 1.165), but heterogeneity was more noticeable across region, manifesting in the wide 95% prediction interval (slope: 0.624 to 1.451), and smoothed calibration plots showed a complex pattern of miscalibration (fig 6). Meta-regression estimated that the leading factor associated with inter-regional variation in discrimination and calibration metrics was regional differences in ethnic diversity (table 3).
Both the XGBoost and neural network approaches showed erratic calibration across cancer stage groups, especially major miscalibration in stage III and IV tumours, such as a slope for the neural network of 0.126 (95% confidence interval 0.005 to 0.247) in stage IV tumours (see supplementary table 9). Overall decision curves showed that when accounting for competing risks, net benefit was generally better for the regression models, and the neural network had lowest clinical utility; when not accounting for competing risks, the regression models had higher net benefit across the threshold probabilities examined (fig 8). Lastly, the clinical utility of the machine learning models was variable across tumour stages, such as null or negative net benefit compared with the scenarios of treat all for stage IV tumours (see supplementary figure 6).
Decision curves to assess clinical utility (net benefit) of using each model. Top plot accounts for the competing risk of other cause mortality. Bottom plot does not account for competing risks
Table 5 illustrates the predictions obtained using the Cox and competing risks regression models for different sample scenarios. When relevant, these are compared with predictions for the same clinical scenarios from PREDICT Breast and the Adjutorium model (obtained using their web calculators: https://breast.predict.nhs.uk/ and https://adjutorium-breastcancer.herokuapp.com).
Risk predictions from Cox and competing risks regression models developed in this study for illustrative clinical scenarios, compared where relevant with PREDICT and Adjutorium*
This study developed and evaluated four models to estimate 10 year risk of breast cancer death after diagnosis of invasive breast cancer of any stage. Although the regression approaches yielded models that discriminated well and were associated with favourable net benefit overall, the machine learning approaches yielded models that performed less uniformly. For example, the XGBoost and neural network models were associated with negative net benefit at some thresholds in stage I tumours, were miscalibrated in stage III and IV tumours, and exhibited complex miscalibration across the spectrum of predicted risks.
Study strengths include the use of linked primary and secondary healthcare datasets for case ascertainment, identification of clinical diagnoses using accurately coded data, and avoidance of selection and recall biases. Use of centralised national mortality registries was beneficial for ascertainment of the endpoint and competing events. Our methodology enabled the adaptation of machine learning models to handle time-to-event data with competing risks and inclusion of multiple imputation so that all models benefitted from maximal available information, and the internal-external cross validation framework28 permitted robust assessment of model performance and heterogeneity across time, place, and population groups.
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- Microchip Launches the MPLAB Machine Learning Development Suite for 8-, 16-, 32-Bit MCUs and MPUs - Hackster.io - September 11th, 2023 [September 11th, 2023]
- Learning and predicting the unknown class using evidential deep learning | Scientific Reports - Nature.com - September 11th, 2023 [September 11th, 2023]
- Why Consider Python for Machine Learning and AI? - Analytics Insight - September 11th, 2023 [September 11th, 2023]
- Rise Of The Machine LearningDeep Fakes Could Threaten Our Democracy - Forbes - September 11th, 2023 [September 11th, 2023]
- What is the future of machine learning? - TechTarget - September 11th, 2023 [September 11th, 2023]
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- New Rice Continuing Studies course to explore generative AI ... - Rice News - September 11th, 2023 [September 11th, 2023]
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- Machine learning helps identify metabolic biomarkers that could ... - News-Medical.Net - September 11th, 2023 [September 11th, 2023]
- Amazons Rajeev Rastogi on AI and Machine Learning revolutionising workplace trends - People Matters - September 11th, 2023 [September 11th, 2023]
- Updates on Multitask learning part1(Machine Learning) | by ... - Medium - September 11th, 2023 [September 11th, 2023]
- Google Research Explores: Can AI Feedback Replace Human Input for Effective Reinforcement Learning in Large Language Models? - MarkTechPost - September 11th, 2023 [September 11th, 2023]
- PhD Candidate in Machine Learning in Neurology job with ... - Times Higher Education - September 11th, 2023 [September 11th, 2023]
- Revolutionizing Drug Development with Machine Learning to ... - Cryptopolitan - September 11th, 2023 [September 11th, 2023]
- Prediction of lung papillary adenocarcinoma-specific survival using ... - Nature.com - September 11th, 2023 [September 11th, 2023]
- Seismologists use deep learning to forecast earthquakes - University of California - September 11th, 2023 [September 11th, 2023]
- Stay ahead of the game: The promise of AI for supply chain ... - Washington State Hospital Association - September 11th, 2023 [September 11th, 2023]
- Predicting Stone-Free Status of Percutaneous Nephrolithotomy ... - Dove Medical Press - September 11th, 2023 [September 11th, 2023]
- What is Image Annotation, and Why is it Important in Machine ... - Ground Report - September 11th, 2023 [September 11th, 2023]
- Detection of diabetic patients in people with normal fasting glucose ... - BMC Medicine - September 11th, 2023 [September 11th, 2023]
- Addressing gaps in data on drinking water quality through data ... - Nature.com - September 11th, 2023 [September 11th, 2023]
- Artificial Intelligence: Transforming Healthcare, Cybersecurity, and Communications - Forbes - September 4th, 2023 [September 4th, 2023]
- Machine learning for chemistry: Basics and applications - Phys.org - September 4th, 2023 [September 4th, 2023]
- Harnessing deep learning for population genetic inference - Nature.com - September 4th, 2023 [September 4th, 2023]
- How Apple is already using machine learning and AI in iOS - AppleInsider - September 4th, 2023 [September 4th, 2023]
- Some Experiences Integrating Machine Learning with Vision and ... - Quality Magazine - September 4th, 2023 [September 4th, 2023]
- Here's Why GPUs Are Deep Learning's Best Friend - Hackaday - September 4th, 2023 [September 4th, 2023]
- UWMadison part of effort to advance fusion energy with machine ... - University of Wisconsin-Madison - September 4th, 2023 [September 4th, 2023]
- Machine learning tool simplifies one of the most widely used reactions in the pharmaceutical industry - Phys.org - September 4th, 2023 [September 4th, 2023]
- Revolutionizing Drug Development Through Artificial Intelligence ... - Pharmacy Times - September 4th, 2023 [September 4th, 2023]
- Money, markets and machine learning: Unpacking the risks of adversarial AI - The Hill - September 4th, 2023 [September 4th, 2023]
- Open source in machine learning: experts weigh in on the future - CryptoTvplus - September 4th, 2023 [September 4th, 2023]
- Machine-Learning Tool Sorts Tics From Non-Tics on Video - Medscape - September 4th, 2023 [September 4th, 2023]
- Machine learning and thought, climate impact on health, Alzheimer's ... - Virginia Tech - September 4th, 2023 [September 4th, 2023]
- Machine Learning Regularization Explained With Examples - TechTarget - September 4th, 2023 [September 4th, 2023]
- Advanced Space-led Team Applying Machine Learning to Detect ... - Space Ref - September 4th, 2023 [September 4th, 2023]
- The challenges of reinforcement learning from human feedback (RLHF) - TechTalks - September 4th, 2023 [September 4th, 2023]
- Optimization of therapeutic antibodies for reduced self-association ... - Nature.com - September 4th, 2023 [September 4th, 2023]
- Bayer Is Rapidly Expanding Its Footprint With Artificial Intelligence - Forbes - September 4th, 2023 [September 4th, 2023]
- Machine Learning, Numerical Simulation Integrated To Estimate ... - Society of Petroleum Engineers - September 4th, 2023 [September 4th, 2023]
- 3 Up-and-Coming Machine Learning Stocks to Put on Your Must ... - InvestorPlace - September 4th, 2023 [September 4th, 2023]
- UW-Madison: Cancer diagnosis and treatment could get a boost ... - University of Wisconsin System - September 4th, 2023 [September 4th, 2023]
- Seismologists use deep learning to forecast earthquakes - University of California, Santa Cruz - September 4th, 2023 [September 4th, 2023]
- How Can Hybrid Machine Learning Techniques Help With Effective ... - Dataconomy - September 4th, 2023 [September 4th, 2023]
- Smarter AI: Choosing the Best Path to Optimal Deep Learning - SciTechDaily - September 4th, 2023 [September 4th, 2023]
- This AI Paper Identifies Popular Dynamics in Behavioral and Physiological Smartphone Authentication and their Performance with Various Deep Learning... - September 4th, 2023 [September 4th, 2023]
- Electronic health records and stratified psychiatry: bridge to ... - Nature.com - September 4th, 2023 [September 4th, 2023]
- This AI Research Paper Presents a Comprehensive Survey of Deep Learning for Visual Localization and Mapping - MarkTechPost - September 4th, 2023 [September 4th, 2023]
- Working with Undirected graphs in Machine Learning part2 - Medium - September 4th, 2023 [September 4th, 2023]
- Generative AI at an inflection point: What's next for real-world ... - VentureBeat - September 4th, 2023 [September 4th, 2023]
- Best use cases of t-SNE 2023 part2(Machine Learning) - Medium - August 26th, 2023 [August 26th, 2023]
- Using AI technologies for effective document processing ... - Data Science Central - August 26th, 2023 [August 26th, 2023]
- The Dawn of Intelligence - Embracing AI's Rise and What It Means ... - TechiExpert.com - August 26th, 2023 [August 26th, 2023]
- The TRIPOD-P reporting guideline for improving the integrity and ... - Nature.com - August 22nd, 2023 [August 22nd, 2023]
- What does the future of learning look like? Faculty and students ... - Lumina Foundation - August 22nd, 2023 [August 22nd, 2023]
- $130+ Billion In-Vitro Diagnostics Markets - Global Forecast to 2030 ... - Business Wire - August 22nd, 2023 [August 22nd, 2023]
- How sure is sure? Incorporating human error into machine learning - University of Cambridge news - August 11th, 2023 [August 11th, 2023]
- The 3 Best Machine Learning Stocks to Buy in August - InvestorPlace - August 11th, 2023 [August 11th, 2023]
- Meet Rebecca Gorman, whose company, Aligned AI, is trying to match up human values with machine learning - Fortune - August 11th, 2023 [August 11th, 2023]
- Machine-Learning-Artificial-Intelligence-And-Big-Data-in-the-New ... - Anesthesiology News - August 11th, 2023 [August 11th, 2023]
- Best of artificial intelligence, machine learning will be deployed at Air India; airline is not just anoth - The Economic Times - August 11th, 2023 [August 11th, 2023]
- Machine learning for improved clinical management of cancers of ... - Nature.com - August 11th, 2023 [August 11th, 2023]
- Machine learning model could enable targeted gene therapies for ... - The Hub at Johns Hopkins - August 11th, 2023 [August 11th, 2023]
- Protiviti Achieves AI and Machine Learning in Microsoft Azure ... - PR Newswire - August 11th, 2023 [August 11th, 2023]
- University of North Florida Launches Artificial Intelligence & Machine ... - Fagen wasanni - August 11th, 2023 [August 11th, 2023]
- Guiding Vaccine Development with Machine Learning - HS Today - HSToday - August 11th, 2023 [August 11th, 2023]
- Machine learning and metagenomics reveal shared antimicrobial ... - Nature.com - August 11th, 2023 [August 11th, 2023]
- The Role of Artificial Intelligence and Machine Learning in ... - Fagen wasanni - August 11th, 2023 [August 11th, 2023]
- Unveiling the Power of Classification and Regression in Machine ... - Medium - August 11th, 2023 [August 11th, 2023]
- How to Use Machine Learning to Scale HR Processes - The HR Director Magazine - August 11th, 2023 [August 11th, 2023]