A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Wang, Yueying; Liu, Shuai; Wang, Zhao; Fan, Yongchang; Huang, Jingxuan; Huang, Lan; Li, Zhijun; Li, Xinwei; Jin, Mengdi; Yu, Qiong; Zhou, Fengfeng

doi:10.3390/medicina57020099

Cited by 17 publications

(18 citation statements)

References 58 publications

(31 reference statements)

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“…The data sources for the studies were varied, with most utilizing information from EHRs or other clinical databases. Within these studies, the majority only included structured data in their models (47, 48, 49, 50, 58, 59, 60, 61, 62) while a few incorporated both structured and unstructured data from these resources (45, 43, 44, 63). Another source of data was surveys, questionnaires, or interviews, where again the majority of studies used only structured data in their models (46, 53, 54, 51, 57), with only one study applying NLP methods and used both structured and unstructured data (64).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of the included studies employed only machine learning techniques (15/22, 68.2%) to uncover gender disparities. These included traditional machine learning methods such as Support vector machine (SVM) (49, 50, 51), classification tree analysis including random forest (53, 58, 60), XGBoost (59) and C4.5 (47), regression models RIDGE (46) and logistic regression (59, 55) or other regression models, such as Multivariate adaptive regression splines (MARS) (57). Other studies used artificial neural networks, including self-organizing maps (SOM) (48, 61), and AutoCM (54).…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies explored the role of sex-specific differences in the creation of classification or prediction models for a variety of conditions including, Alzheimer’s disease (48), autism(51), cancer (59), cardiovascular disease (47, 60, 63), musculoskeletal disorders (57), substance use (44,55, 58), mental illness (43, 52, 62, 64) obesity/physical activity (41, 54) and tinnitus (46). Within these studies, nine developed separate models for males and females finding these models were better able to discriminate sex-specific determinants of the trait of interest.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Golder

OConnor

Wang

et al. 2022

Preprint

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A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Golder

OConnor

Wang

et al. 2022

Preprint

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“…SVM is a generally effective learning technique in light of measurable learning theory. Nevertheless, these procedures are expensive and recognize cellular dysfunction in the lungs at its highest levels [ 22 , 23 ], owing to the likelihood of tolerance to be exceptionally low. Previous identification of malignant growths is cooperative in fully recovering from the infection.…”

Section: Introductionmentioning

confidence: 99%

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

et al. 2023

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In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.

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“…Nevertheless, this invasive procedure can lead to some associated side effects, as it can be painful and risky for the patient. Recently, blood-based screening has been used to detect early lung cancer diagnostic biomarkers [8]. However, despite being less invasive than biopsy, this technique is still bothersome for the patient.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

et al. 2021

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The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

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A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Cited by 17 publications

References 58 publications

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

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