Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer

Liu, Yang; Pelckmans, Kristiaan

doi:10.7763/ijmlc.2014.v6.459

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Lately, several machine learning (ML) algorithms have been developed to overcome the shortcomings of statistical models, such as high dimensionality and nonlinearities (43)(44)(45). Of these, gradient boosted algorithms are used in several works, often in combination with various feature selection techniques, with satisfactory performances (43,(46)(47)(48)(49)(50). Considering specific applications for NSCLC, a systematic review and metanalysis by Kothari et al has recently provided a state-of-art representation of radiomics for this subset of patients (33).…”

mentioning

confidence: 99%

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe

Isaksson

Zaffaroni

et al. 2022

Transl Lung Cancer Res

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Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.Methods: Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded.Cox PH with LASSO regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low-and high-volume lesions.Results: Radiomic models significantly outperformed models built on only clinical variables and volume.However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models.Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low-and high-volume models performed significantly better with the inclusion of *, affiliation at the time of the study.

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mentioning

confidence: 99%

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe

Isaksson

Zaffaroni

et al. 2022

Transl Lung Cancer Res

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“…In total, 15 variables were selected for model development (Multimedia Appendix 2). The XGBoost decision tree algorithm was used to estimate the hazard ratio, and hyperparameters were obtained using Bayesian optimization and cross-validation [18]. The likelihood of disease progression or death within a 5-year period was estimated using the equation ŷ(t, X) = 1 -[S 0 (t)] hr(X) , where, t denotes the observed period, X denotes the selected variables, S 0 (t) denotes a population-level baseline survival function, and hr() denotes the hazard ratio outputted by the model, respectively.…”

Section: Discussionmentioning

confidence: 99%

Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Luo¹,

Deng²,

Liu³

et al. 2020

JMIR Med Inform

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Background Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. Objective We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. Methods This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. Results The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. Conclusions Our prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

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“…Another 6 gene expression data sets are available online from work by Yang and colleagues [ 48 ]. We have named them GE1 to GE6 for easier rendering of labels in our figures.…”

Section: Methodsmentioning

confidence: 99%

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

et al. 2022

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Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics; these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

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Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer

Abstract: Abstract-Cox

Cited by 8 publications

References 28 publications

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

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