The artificial intelligence and machine learning in lung cancer immunotherapy

Gao, Qing; Yang, Luyu; Lu, Mingjun; Jin, Renjing; Ye, Huan; Ma, Teng

doi:10.1186/s13045-023-01456-y

Journal of Hematology & Oncology

Table 1 Machine learning algorithm predicts PD-L1, TMB, TME in lung cancer

From: The artificial intelligence and machine learning in lung cancer immunotherapy

Omics	Category	Task	Secondary task	Algorithm	Year	Description
Radiomics	PD-L1	Expression	Prognosis	RF	2020 [22]	Extracting image features from CT images to predict PD-L1 expression level and progression risk
Radiomics	PD-L1	Expression		SResCNN	2021 [14]	Using SResCNN to analyze PET/CT images and clinical data, using DLS score to predict PD-L1 expression
Radiomics	PD-L1	Expression		Logistic regression, RF	2020 [25]	Extracting features from CT, PET, and PET/CT images to model and predict the positive and high expression of PD-L1 simultaneously
Radiomics	PD-L1	Expression	Survive	DL	2020 [5]	Using deep learning to find CT image features to distinguish TMB expression and to predict survival in patients treated with ICIs
Pathomics	PD-L1/TMB	Expression	Treatment	ML	2023 [29]	Extraction of the tumor, mesenchymal, and TIL counts from HE-stained images for modeling and assessment of TMB and PD-L1 expression levels and efficacy prediction
Multi-omics	PD-L1	Treatment		ML	2022[30]	Combining sequencing data, IHC images, demographic data and laboratory data to predict the efficacy of immunotherapy
Multi-omics	PD-L1	Expression	Pneumonia	LCI-RPV	2023 [20]	The LCI-RPV model was developed to predict the ratio of PD-L1 expression to pneumonia by collecting CT images, CD274 counts and PD-L1 mRNA expression data
Multi-omics	TMB	Expression		ML	2022 [31]	Combining genomic and epigenetic data to predict TMB
Radiomics	TME	Prognosis	Treatment	ML	2020 [39]	Extracting PET/CT image features to ⁺distinguish groups who benefit from immunotherapy
Radiomics	TME	Expression	Treatment	ML	2022 [37]	Predicting TME by modeling PET/CT image features with CD8⁺T expression data to predict the immune status
Radiomics	TME	Expression	Prognosis	ML	2022 [38]	Extracting pGGO features from CT images combined with associated risk genes modelling to predict TME
Pathomics	TME/TIL	Expression	Prognosis	CNN	2018 [40]	Use CNN to analyze HE images in the database, model and predict TME and OS
Pathomics	TIL	Expression	Prognosis	CNN	2022 [41]	Development of I-score to predict clinical risk using CNN analysis of CD3⁺ T cell and CD8⁺T cell densities in WSI images
Pathomics	TME	Prognosis		CNN	2020 [42]	Improved boundary recognition for WSI images, extraction of spatial features modeling prognosis
Pathomics	TIL	Prognosis		Lunit SCOPE IO	2022 [43]	Segmentation and quantification of WSI images to build the model Lunit SCOPE IO analysis TIL
Multi-omics	TIL	Prognosis		Unsupervised clustering	2022 [44]	Extraction of TIME, patient survival data, SMG and CNV modeling to analyze TIL
Multi-omics	TME	Prognosis	Treatment	ML	2022 [45]	Screening gene combinations and modelling to predict OS and efficacy
Multi-omics	TME	Expression		K-means, SVM	2022 [46]	Screening, modeling, and predicting TIME of gene profiles using K-means and SVM

PD-L1 Programmed Death Ligand 1, TMB Tumor Mutation Burden, TME Tumor Microenvironment, CT Computer Tomography, RF Random Forests, SResCNN Small Residual Product Network, LightGBM Light Gradient Boosting Machine, DL Deep Learning, ML Machine Learning, ICI Immune Checkpoint Inhibitor, WSI Whole Slide Image, TIL Tumor Infiltrating Lymphocyte, CNN Convolutional Neural Networks, SVM Support Vector Machine, SMG Significantly Mutated Gene, CNV Copy Number Variation, TIME Tumor Immune Microenvironment, OS Overall Survival, pGGO Pure Ground-Glass Opacity

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ISSN: 1756-8722

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