Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival

Hsu, Jason C.; Nguyen, Phung‐Anh; Phuc, Phan Thanh; Lo, Tsai-Chih; Hsu, Min‐Huei; Hsieh, Min‐Shu; Le, Nguyen Quoc Khanh; Cheng, Chi‐Tsun; Chang, Tzu Hao; Chen, Cheng-Yu

doi:10.3390/cancers14225562

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…However, their results may have been limited by the use of only clinical variables to train the model. Hsu et al (2022) used an institutional dataset and variables, including demographics, comorbidities, medications, laboratory values, and genetic test results, to develop nine models to predict NSCLC survival. Their best performing model, an artificial neural network, achieved an AUC of 0.89.…”

Section: Variables Used In Development Of Lung Cancer Survival Modelsmentioning

confidence: 99%

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

Didier,

Nigro,

Noori

et al. 2024

Front. Artif. Intell.

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IntroductionMachine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models.ResultsThe literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models.ConclusionMachine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies.

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Section: Variables Used In Development Of Lung Cancer Survival Modelsmentioning

confidence: 99%

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

Didier,

Nigro,

Noori

et al. 2024

Front. Artif. Intell.

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“…The Taipei Medical University Clinical Research Database (TMUCRD) gathers data from multiple centers and sources of various data types. It systematically collects both structured and unstructured data from three affiliated hospitals: Taipei Medical University Hospital, Wanfang Hospital, and Shuangho Hospital (9)(10)(11). The National Health Insurance database in Taiwan has a gap of 2 years in the dissemination of data for research purposes.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Hien,

Tsai,

Chang

et al. 2024

Front. Med.

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BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

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A deep learning approach for overall survival prediction in lung cancer with missing values

Caruso,

Guarrasi,

Ramella

et al. 2024

Computer Methods and Programs in Biomedicine

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Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival

Cited by 9 publications

References 29 publications

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

A deep learning approach for overall survival prediction in lung cancer with missing values

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