Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Li, Yawei; Wu, Xin; Yang, Ping; Jiang, Guoqian; Luo, Yuan

doi:10.1016/j.gpb.2022.11.003

Cited by 70 publications

(27 citation statements)

References 195 publications

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“…16,17,22,23 This is precisely where the prowess of machine learning (ML) algorithms becomes evident, having already demonstrated remarkable accomplishments across various cancer categories. [32][33][34][35] A proportion of ASCC patients undergoing curative CRT still require salvage abdominoperineal resection (APR) due to inadequate treatment response or local recurrence. 36,37 In this context, the integration of radiomics and ML from outset imaging may play a significant role in stratifying patients who are at higher risk of requiring salvage APR.…”

Section: Discussionmentioning

confidence: 99%

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Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

Temperley,

O’Sullivan,

Waters

et al. 2023

The American Surgeon™

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Introduction The management of anal cancer relies on clinical and histopathological features for treatment decisions. In recent years, the field of radiomics, which involves the extraction and analysis of quantitative imaging features, has shown promise in improving management of pelvic cancers. The aim of this study was to evaluate the current application of radiomics in the management of anal cancer. Methods A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed randomized and non-randomized trials investigating the use of radiomics to predict post-operative recurrence in anal cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Results The systematic review identified a total of nine studies, with 589 patients examined. There were three main outcomes assessed in included studies: recurrence (6 studies), progression-free survival (2 studies), and prediction of human papillomavirus (HPV) status (1 study). Radiomics-based risk stratification models were found to provide valuable insights into treatment response and patient outcomes, with all developed signatures demonstrating at least modest accuracy (range: .68-1.0) in predicting their primary outcome. Conclusion Radiomics has emerged as a promising tool in the management of anal cancer. It offers the potential for improved risk stratification, treatment planning, and response assessment, thereby guiding personalized therapeutic approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

Temperley,

O’Sullivan,

Waters

et al. 2023

The American Surgeon™

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show abstract

“…Among them, the ML method has been widely used in cancer prognosis research, such as laryngeal cancer, lung cancer, breast cancer, kidney cancer, malignant pleural mesothelioma, etc. [7,[10][11][12][13] . Indeed, it effectively offers accurate prognoses based on the cancer sample data [14] .…”

Section: Introductionmentioning

confidence: 99%

Construction of Prediction Model of Early Glottic Cancer Based on machine learning

Zhao,

Zhi,

Zheng

et al. 2024

Preprint

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Objective To explore the clinical applicability of the diagnosis of early glottic cancer based on machine learning (ML) combined with narrow-band imaging (NBI). Methods Chi-square test and multivariate logistic regression analysis were used to explore clinical and laryngoscopic features that could potentially predict early glottic cancer. Afterward, three classical ML methods, namely random forest (RF), support vector machine（SVM), and decision tree (DT), were combined with NBI endoscopic images to identify risk factors related to glottic cancer and to construct and compare the predictive models. The patients were randomly divided into a training group and a test group. In the training set, RF, DT, and SVM were used to construct a predictive model to distinguish between benign and malignant laryngeal lesions, and the test set was used to evaluate the predictive value of the model. Results According to the comparative modelling results, the RF‑based model was found to predict more accurately than other methods and have a significant predominance over others.The accuracy, precision, recall, F1 index, and AUC value of the RF model were 0.96, 0.0.90, 1.00, 0.95. The ROC curve analysis results (overall accuracy) indicate the RF model (AUC = 0.97) had the highest score among others. Conclusion RF, DT, and SVM were used to construct a predictive model for early glottic cancer, and the RF model outperformed the other models.

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“…Machine learning algorithms can be categorized into supervised learning, unsupervised learning, and reinforcement learning [30], and choosing the appropriate algorithm based on the problem and data type is crucial. Machine learning has a wide range of applications in life sciences, particularly in oncology, where it assists in tumor diagnosis, predicting treatment outcomes, aiding physicians in formulating personalized treatment plans, and estimating patient survival rates [31]. For instance, machine learning can help identify tumor types, locations, and characteristics by analyzing imaging data [32]; predict tumor growth trends and specific treatment methods by analyzing patients' genomic or clinical data, facilitating personalized treatment [33]; and in feature selection and data mining, identify and select the most informative features in big data for more accurate tumor prediction, diagnosis, and treatment [34].…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Transcriptomic, hdWGCNA, and Machine Learning reveal the Role of DYNLL1 in Lung Adenocarcinoma within Epithelial Cells

Wu,

Liu,

Yang

et al. 2023

Preprint

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Background: Lung cancer has the highest morbidity and mortality worldwide. Lung adenocarcinoma (LUAD) is the most common histological subtype and tends to be diagnosed at advanced stages with poor prognosis. Tumor heterogeneity and lack of prognostic biomarkers pose significant challenges for personalized therapy. Single-cell sequencing provides new opportunities to characterize intratumor heterogeneity and identify prognostic markers for early diagnosis or treatment target. Methods: In this study, we analyzed single cell RNA sequencing data of 11 normal lung tissue samples and 11 LUAD samples from the GEO dataset GSE131907. High dimensional Weighted gene co-expression network analysis (hdWGCNA) was applied to the single-cell expression profile of epithelial cells to construct gene co-expression modules. Subsequently, a 13-gene prognostic signature was established by integrating 207 models with 15 machine learning algorithms in a cross validation framework. The correlation of the hub gene DYNLL1 with prognosis, clinical characteristics and biological pathways were comprehensively analyzed. Furthermore, loss-of-function assays by siRNA knockdown of DYNLL1 were performed to examine its functional role in LUAD cell lines. Results: Significant differences in immune cell type distributions were observed between normal and tumor tissues, indicating important immune dysregulations during LUAD development. hdWGCNA identified 27 modules in LUAD epithelial cells potentially associated with tumorigenesis and disease outcomes. The 13-gene prognostic model effectively stratified patients into high- and low-risk groups with distinct survival. DYNLL1 was identified as an independent prognostic biomarker and potential therapeutic target. High expression of DYNLL1 positively correlated with advanced tumor stage and poorer survival. Knockdown of DYNLL1 expression suppressed cancer cell proliferation and invasion capacities. Conclusions: Our study provided in-depth characterization of LUAD at single-cell resolution, identified key gene modules and prognostic biomarkers, and suggested DYNLL1 as a candidate prognostic predictor and therapeutic target, pending further verification in clinical settings. Keywords: Lung adenocarcinoma、scRNA_seq、hdWGCNA、machine learning 、Prognostic biomarker

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Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Cited by 70 publications

References 195 publications

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

Construction of Prediction Model of Early Glottic Cancer Based on machine learning

Single-Cell Transcriptomic, hdWGCNA, and Machine Learning reveal the Role of DYNLL1 in Lung Adenocarcinoma within Epithelial Cells

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