Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Gal, Jocelyn; Bailleux, Caroline; Chardin, David; Pourcher, Thierry; Gilhodes, Julia; Jing, Lun; Guigonis, Jean-Marie; Ferrero, Jean-­Marc; Milano, Gérard; Mograbi, Baharia; Brest, Patrick; Château, Y.; Humbert, Olivier; Chamorey, Emmanuel

doi:10.1016/j.csbj.2020.05.021

Cited by 26 publications

(24 citation statements)

References 98 publications

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“…Although many forecasting models for predicting outcomes after breast cancer surgery have been proposed in recent years, models for predicting recurrence within 10 years after breast cancer surgery have had major shortcomings: (1) recently proposed forecasting models have lower prediction accuracy compared to conventional models [ 6 , 7 ], (2) proposed forecasting models require use of health insurance claims data, which may be unavailable for real-time use in clinical settings [ 8 , 9 ], and (3) predictions of postoperative recurrence after breast surgery do not consider demographic characteristics, clinical characteristics, quality of care and preoperative health-related quality of life [ 10 , 11 ]. Successful applications of statistical data mining and machine learning methods have been demonstrated in the medical field [ 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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

Lou

Hou

Chang

et al. 2020

Cancers

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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.

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Section: Introductionmentioning

confidence: 99%

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

Lou

Hou

Chang

et al. 2020

Cancers

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“…As acknowledged by [ 4 ], these findings suggest that a novel classification for PBC should rely on subgroups with a meaningful phenotypic differentiation and should be robust to different unsupervised techniques. The recognition of a meaningful phenomapping should be made collegially, i.e., based on the results of a set of methods.…”

Section: Discussionmentioning

confidence: 90%

“…Further, at the gene expression level, five main subgroups have been identified and combining gene expression with copy number data further refined breast cancer into integrated subgroups with different genomic and transcriptomic profiles and prognosis [ 3 ]. However, heterogeneity persists in biological features within PBC subtypes, highlighting the need to improve the taxonomy [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

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Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Ferro

Bottigliengo

Gregori

et al. 2021

JPM

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Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.

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“…On the other hand, recent years have witnessed an unprecedented development in the use of machine learning (ML) in various biotechnology, biomedicine, medical imaging and healthcare applications [ 27 – 30 ]. Supervised ML tools can be utilized to build predictive models involve the implementation of statistical means for learning and predicting disease status, either by including or excluding the polymorphisms genotypes [ 5 , 31 – 33 ]. The following are popular ML algorithms that were evaluated in the current research to predict T2DM and dyslipidemia based on the clinical parameters, demographic and polymorphism data: random forest (RF) [ 34 – 36 ]; naïve Bayesian (NB) [ 37 – 40 ]; eXtreme Gradient Boosting (XGBoost) [ 41 – 43 ]; k-nearest neighbors (kNN) [ 44 – 46 ], support vector machine (SVM) [ 47 , 48 ]; probabilistic neural networks (PNN) [ 49 – 53 ]; multilayer perceptron (MLP) [ 54 , 55 ]; adaptive boosting (AdaBoost) [ 56 , 57 ]; gradient boost [ 58 , 59 ]; and K-star (K*) [ 60 , 61 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

et al. 2021

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CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.

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Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Cited by 26 publications

References 98 publications

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

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

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

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