scholarly journals Machine Learning to Guide the use of Adjuvant Therapies for Breast Cancer

2020 ◽  
Author(s):  
Ahmed Alaa ◽  
Deepti Gurdasani ◽  
Adrian Harris ◽  
Jem Rashbass ◽  
Mihaela van der Schaar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Adjuvant Therapy ◽  
Large Scale ◽  
External Validation ◽  
The United States ◽  
Cancer Registration ◽  
Survival Prediction ◽  
Therapeutic Decisions ◽  
Improved Accuracy

Abstract Accurate prediction of the individualized survival benefit of adjuvant therapy is key to making informed therapeutic decisions for patients with early invasive breast cancer. Here, we use a state-of the-art automated and interpretable machine learning algorithm to develop a breast cancer prognostication and treatment benefit prediction model — Adjutorium — using data from large-scale cohorts of nearly 1 million women captured in the national cancer registries of the United Kingdom and the United States. We trained and internally validated the Adjutorium model on 395,862 patients from the UK National Cancer Registration and Analysis Service (NCRAS); we then externally validated the model among 571,635 patients from the US Surveillance, Epidemiology, and End Results (SEER) Program. Adjutorium exhibited significantly improved accuracy compared to the major prognostic tool in current clinical use (PREDICT v2.1) in both internal and external validation (AUC-ROC for 5-year survival prediction in NCRAS was 0.835, 95% CI: 0.833–0.837 and 0.755, 95% CI: 0.753–0.757 for Adjutorium and PREDICT v2.1. In SEER, the AUC-ROC performance was 0.815, 95% CI: 0.813–0.817 and 0.775, 95% CI: 0.772–0.778 for Adjutorium and PREDICT v2.1, respectively). Importantly, our model substantially improved accuracy in specific subgroups known to be under-served by existing models. Adjutorium is currently implemented as a web-based decision support tool (vanderschaar-lab.com/adjutorium/) to aid decisions on adjuvant therapy in women with early breast cancer, and can be publicly accessed by patients and clinicians worldwide.

scholarly journals Estimating nitrogen and phosphorus concentrations in streams and rivers across the contiguous United States: a machine learning framework

2019 ◽  
Author(s):  
Longzhu Shen ◽  
Giuseppe Amatulli ◽  
Tushar Sethi ◽  
Peter Raymond ◽  
Sami Domisch
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
External Validation ◽  
Anthropogenic Activity ◽  
Spatial And Temporal Variability ◽  
Nitrogen And Phosphorus ◽  
Learning Framework ◽  
Environmental Models ◽  
Improved Accuracy

Nitrogen (N) and Phosphorus (P) are essential nutrients for life processes in water bodies but in excessive quantities, they are a significant source of aquatic pollution. Eutrophication has now become widespread due to such an imbalance, and is largely attributed to anthropogenic activity. In view of this phenomenon, we present a new dataset and statistical method for estimating and mapping elemental and compound con- centrations of N and P at a resolution of 30 arc-seconds (∼1 km) for the conterminous US. The model is based on a Random Forest (RF) machine learning algorithm that was fitted with environmental variables and seasonal N and P concentration observations from 230,000 stations spanning across US stream networks. Accounting for spatial and temporal variability offers improved accuracy in the analysis of N and P cycles. The algorithm has been validated with an internal and external validation procedure that is able to explain 70-83% of the variance in the model. The dataset is ready for use as input in a variety of environmental models and analyses, and the methodological framework can be applied to large-scale studies on N and P pollution, which include water quality, species distribution and water ecology research worldwide.

scholarly journals Estimating nitrogen and phosphorus concentrations in streams and rivers across the contiguous United States: a machine learning framework

2019 ◽  
Author(s):  
Longzhu Shen ◽  
Giuseppe Amatulli ◽  
Tushar Sethi ◽  
Peter Raymond ◽  
Sami Domisch
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
External Validation ◽  
Anthropogenic Activity ◽  
Spatial And Temporal Variability ◽  
Nitrogen And Phosphorus ◽  
Learning Framework ◽  
Environmental Models ◽  
Improved Accuracy

Nitrogen (N) and Phosphorus (P) are essential nutrients for life processes in water bodies but in excessive quantities, they are a significant source of aquatic pollution. Eutrophication has now become widespread due to such an imbalance, and is largely attributed to anthropogenic activity. In view of this phenomenon, we present a new dataset and statistical method for estimating and mapping elemental and compound con- centrations of N and P at a resolution of 30 arc-seconds (∼1 km) for the conterminous US. The model is based on a Random Forest (RF) machine learning algorithm that was fitted with environmental variables and seasonal N and P concentration observations from 230,000 stations spanning across US stream networks. Accounting for spatial and temporal variability offers improved accuracy in the analysis of N and P cycles. The algorithm has been validated with an internal and external validation procedure that is able to explain 70-83% of the variance in the model. The dataset is ready for use as input in a variety of environmental models and analyses, and the methodological framework can be applied to large-scale studies on N and P pollution, which include water quality, species distribution and water ecology research worldwide.

scholarly journals Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marco Pellegrini
Keyword(s):  
Breast Cancer ◽  
Adjuvant Therapy ◽  
Cancer Survival ◽  
State Of The Art ◽  
Survival Prediction ◽  
Tumor Removal ◽  
Tumor Relapse ◽  
Heterogeneous Nature ◽  
Improved Accuracy ◽  
New Machine

AbstractFor a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The ’coherent voting communities’ metaphor provides a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence. The method we propose is quite flexible and applicable to other types of cancer.

scholarly journals SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii203-ii203
Author(s):  
Alexander Hulsbergen ◽  
Yu Tung Lo ◽  
Vasileios Kavouridis ◽  
John Phillips ◽  
Timothy Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
External Validation ◽  
Superior Performance ◽  
Prognostic Models ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Adaptive Boosting

Abstract INTRODUCTION Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms. METHODS An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained and assessed for performance. Moreover, an ensemble model was created incorporating random forest, adaptive boosting, gradient boosting, and logistic regression algorithms. Five-fold cross validation was used for hyperparameter tuning. Model performance was assessed using area under the receiver-operating curve (AUC) and calibration and was compared against the diagnosis-specific graded prognostic assessment (ds-GPA); the most established prognostic model in BMs. RESULTS The ensemble model showed superior performance with an AUC of 0.81 in the hold-out test set, a calibration slope of 1.14, and a calibration intercept of -0.08, outperforming the ds-GPA (AUC 0.68). Patients were stratified into high-, medium- and low-risk groups for death at 6 months; these strata strongly predicted both 6-months and longitudinal overall survival (p < 0.001). CONCLUSIONS We developed and internally validated an ensemble ML model that accurately predicts 6-month survival after neurosurgical resection for BM, outperforms the most established model in the literature, and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

scholarly journals Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3817
Author(s):  
Shi-Jer Lou ◽  
Ming-Feng Hou ◽  
Hong-Tai Chang ◽  
Chong-Chi Chiu ◽  
Hao-Hsien Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
External Validation ◽  
Model Development ◽  
Cancer Surgery ◽  
Machine Learning Algorithms ◽  
Breast Cancer Surgery ◽  
Training Dataset

No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.

scholarly journals Interpretable Machine Learning Algorithm Reveals Novel Gut Microbiome Features in Predicting Type 2 Diabetes

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1559-1559
Author(s):  
Wanglong Gou ◽  
Chu-Wen Ling ◽  
Yan He ◽  
Zengliang Jiang ◽  
Yuanqing Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Gut Microbiome ◽  
Large Scale ◽  
Validation Cohort ◽  
Learning Algorithm ◽  
External Validation ◽  
Positive Association ◽  
Interpretable Machine Learning

Abstract Objectives The gut microbiome-type 2 diabetes (T2D) relationship among human cohorts have been controversial. We hypothesized that this limitation could be addressed by integrating the cutting-edge interpretable machine learning framework and large-scale human cohort studies. Methods 3 independent cohorts with &gt;9000 participants were included in this study. We proposed a new machine learning-based analytic framework — using LightGBM to infer the relationship between incorporated features and T2D, and SHapley Additive explanation(SHAP) to identified microbiome features associated with the risk of T2D. We then generated a microbiome risk score (MRS) integrating the threshold and direction of the identified microbiome features to predict T2D risk. Results We finally identified 15 microbiome features (two of them are indicators of microbial diversity, others are taxa-related features) associated with the risk of T2D. The identified T2D-related gut microbiome features showed superior T2D prediction accuracy compared to host genetics or traditional risk factors. Furthermore, we found that the MRS (per unit change in MRS) consistently showed positive association with T2D risk in the discovery cohort (RR 1.28, 95%CI 1.23-1.33), external validation cohort 1 (RR 1.23, 95%CI 1.13-1.34) and external validation cohort 2 (GGMP, RR 1.12, 95%CI 1.06-1.18). The MRS could also predict future glucose increment. We subsequently identified dietary and lifestyle factors which could prospectively modulate the microbiome features, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Conclusions Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiome features may serve as potential drug targets for T2D in future. Funding Sources This study was funded by National Natural Science Foundation of China (81903316, 81773416), Westlake University (101396021801) and the 5010 Program for Clinical Researches (2007032) of the Sun Yat-sen University (Guangzhou, China).

Survey of breast cancer patients concerning their knowledge and expectations of adjuvant therapy.

1998 ◽  
Vol 16 (2) ◽  
pp. 515-521 ◽  
Author(s):  
P M Ravdin ◽  
I A Siminoff ◽  
J A Harvey
Keyword(s):  
Breast Cancer ◽  
Adjuvant Therapy ◽  
Quantitative Estimate ◽  
Breast Cancer Survivors ◽  
Treatment Guidelines ◽  
Recurrence Risk ◽  
The United States ◽  
Breast Cancer Patients ◽  
Net Benefit ◽  
Quantitative Estimates

PURPOSE A survey of breast cancer survivors in the United States was conducted to define what they had been told about their prognosis and the value of adjuvant therapy, what they estimated their prognosis to be with and without adjuvant therapy, and what level of improvement they would have found minimally worthwhile. MATERIALS AND METHODS Survey questionnaires were mailed to individual members and member organizations of the National Alliance of Breast Cancer Organizations (NABCO). Questionnaires were returned anonymously in prepaid mailers. Five hundred sixty-two women responded. Of these, the 318 women who received adjuvant chemotherapy were included in this analysis. RESULTS Only 39% of the women recalled receiving quantitative estimates of their prognosis, and only 31% of women received a quantitative estimate both with and without adjuvant therapy. Sixty-eight percent of the women were able to provide a quantitative estimate for their outcome at 5 years both with and without adjuvant therapy. From these estimates, we calculated that the median estimated proportional risk reduction for recurrence that women thought they had achieved was 79%. Women were asked what degree of absolute benefit they would have found acceptable. The median acceptable extension of life expectancy was 3 to 6 months, and acceptable reduction in recurrence risk was 0.5% to 1.0%. However, there was considerable variation, with 27% of women not accepting less than 1 year and 26% not accepting a less than 5% reduction in recurrence risk. CONCLUSION In general, American women in the surveyed population (1) do not recall being provided quantitative estimates of outcome during the process of making decisions about adjuvant therapy, (2) overestimate the value of their therapy, and (3) often will accept remarkably low degrees of net benefit. Overall, these observations can be used to support the argument that improvements in doctor/patient communication may be important to truly informed decision-making, and that flexibility for individual patients' preferences should not be superseded by rigid treatment guidelines.

scholarly journals Breast Cancer Surgery 10-Year Survival Prediction by Machine Learning: A Large Prospective Cohort Study

Biology ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 47
Author(s):  
Shi-Jer Lou ◽  
Ming-Feng Hou ◽  
Hong-Tai Chang ◽  
Hao-Hsien Lee ◽  
Chong-Chi Chiu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Summary Score ◽  
Cancer Surgery ◽  
Machine Learning Algorithms ◽  
Breast Cancer Surgery ◽  
Support Vector ◽  
Survival Prediction ◽  
Sf 36

Machine learning algorithms have proven to be effective for predicting survival after surgery, but their use for predicting 10-year survival after breast cancer surgery has not yet been discussed. This study compares the accuracy of predicting 10-year survival after breast cancer surgery in the following five models: a deep neural network (DNN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC) and Cox regression (COX), and to optimize the weighting of significant predictors. The subjects recruited for this study were breast cancer patients who had received breast cancer surgery (ICD-9 cm 174–174.9) at one of three southern Taiwan medical centers during the 3-year period from June 2007, to June 2010. The registry data for the patients were randomly allocated to three datasets, one for training (n = 824), one for testing (n = 177), and one for validation (n = 177). Prediction performance comparisons revealed that all performance indices for the DNN model were significantly (p < 0.001) higher than in the other forecasting models. Notably, the best predictor of 10-year survival after breast cancer surgery was the preoperative Physical Component Summary score on the SF-36. The next best predictors were the preoperative Mental Component Summary score on the SF-36, postoperative recurrence, and tumor stage. The deep-learning DNN model is the most clinically useful method to predict and to identify risk factors for 10-year survival after breast cancer surgery. Future research should explore designs for two-level or multi-level models that provide information on the contextual effects of the risk factors on breast cancer survival.

A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners

2019 ◽  
Vol 63 (3) ◽  
pp. 435-447
Author(s):  
Mohsen Salehi ◽  
Jafar Razmara ◽  
Shahriar Lotfi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Cancer Patients ◽  
Cancer Incidence ◽  
The United States ◽  
Machine Learning Techniques ◽  
Breast Cancer Patients ◽  
Stacked Generalization ◽  
Learning Techniques

Abstract Breast cancer survivability has always been an important and challenging issue for researchers. Different methods have been utilized mostly based on machine learning techniques for prediction of survivability among cancer patients. The most comprehensive available database of cancer incidence is SEER in the United States, which has been frequently used for different research purposes. In this paper, a new data mining has been performed on the SEER database in order to investigate the ability of machine learning techniques for survivability prediction of breast cancer patients. To this end, the data related to breast cancer incidence have been preprocessed to remove unusable records from the dataset. In sequel, two machine learning techniques were developed based on the Multi-Layer Perceptron (MLP) learner machine including MLP stacked generalization and mixture of MLP-experts to make predictions over the database. The machines have been evaluated using K-fold cross-validation technique. The evaluation of the predictors revealed an accuracy of 84.32% and 83.86% by the mixture of MLP-experts and MLP stacked generalization methods, respectively. This indicates that the predictors can be significantly used for survivability prediction suggesting time- and cost-effective treatment for breast cancer patients.

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