Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Abstract

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

Keywords: Feature extraction; Imaging dataset; Immunotherapy; Omics dataset; Prediction.

Figure 1

Applications of ML model in lung cancer We presented an overview of ML methodologies for different aspects of lung cancer therapies, including CAD from imaging datasets, lung cancer early detection based on sequencing technologies, data integration and biomarker extraction from multi-omics datasets, treatment response and prognosis prediction, and immunotherapy studies. ML, machine learning; IC50, half-maximal inhibitory concentration; HLA, human leukocyte antigen; CT, computed tomography; MALDI, matrix-assisted laser desorption/ionization; DL, deep learning; cfDNA, cell-free DNA; CAD, computer-aided diagnosis; CNV, copy number variation; RECIST, Response Evaluation Criteria in Solid Tumors; TIL, tumor-infiltrating lymphocyte.

Figure 2

Feature-based CAD and DL-based CAD systems Differences in the development process of feature-based CAD systems and CNN-based CAD systems. Compared with feature-based CAD systems, the DL-based CAD systems can automatically retrieve and extract intrinsic features of a suspicious nodule. CNN, convolutional neural network; LR, logistic regression; SVM, support vector machine; RF, random forest.

Figure 3

Omics analysis in lung cancer studies Different sequencing techniques allow for the simultaneous measurement of multiple molecular features of a biological sample. To improve efficiency and reduce overfitting, statistical and ML tools perform differential analysis or feature selection. Further ML models concatenate the obtained omics features with clinical features as input for lung cancer diagnostic/prognostic prediction. DEG, differentially expressed gene; RFE, recursive feature elimination; UAF, univariate association filtering.

Figure 4

Diagram of ML applications in treatment response and survival prediction