Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC
Abstract
:Simple Summary
Abstract
1. Introduction
2. Artificial Intelligence (AI)
2.1. AI-Driven Radiomics
- Medical image acquisition using the proper modality (CT, PET/CT, other);
- Acquired image preprocessing, using noise reduction, image resizing, and contrast enhancement, to improve data quality;
- Segmentation of tissue in order to define the region of interest (ROI);
- Quantitative features extraction, including tumor size, shape, texture, and signal intensities, which can reflect the lesion’s malignant potential as well as its heterogeneity;
- Relevant features selection for improved subsequent analyses;
- Extracted features normalization in datasets to eliminate inconsistencies among different imaging techniques or protocols;
- AI-based model development;
- AI-based model (internal or external) validation in independent datasets to assess its performance and generalizability;
- Correlation of model predictions (radiomic features) with patients’ data (clinical outcomes); and
- Integration, validation, and refinement of validated radiomics within clinical workflows [38].
2.2. AI-Driven Pathomics
3. Discussion
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Reference | Author, Year | AI-Based Methodology | Model Features | Target Variable | Patient Population | Training Cohort | Validation Cohort | Best Performance |
---|---|---|---|---|---|---|---|---|
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[45] | Li et al., 2019 | Logistic regression (LG) | CT radiomics | EGFR 19del and L858R | NSCLC | N = 236 | N = 76 | 0.7925 (19del); 0.775 (L858R) |
[46] | Yang et al., 2022 | Least absolute shrinkage and selection operator (LASSO) regression model | CT radiomics | T790M mutation | EGFR-mutated adNSCLC post-progression on 1st line with 1st or 2nd generation EGFR TKI | N = 186 | N = 74 | 0.71 |
[47] | Yang et al., 2020 | LASSO regression model | CT radiomics | Response to EGFR-TKI | EGFR-mutated adNSCLC, clinical stage IIIB-IV, under 1st line with EGFR-TKI | N = 253 | N/A | 0.7268 (unenhanced phase); 0.7793 (arterial phase); 0.9104 (venous phase) |
[48] | Wang et al., 2022 | LASSO regression model | CT radiomics | EGFR genotype; Response to EGFR-TKI | NSCLC, stage I-IV from 9 cohorts (7 retrospective Chinese cohorts, The Cancer Imaging Archive cohort, and a prospective Chinese cohort) | Ν = 5645 (EGFR genotype and thick CT); N = 4782 (EGFR genotype and thin CT); N = 490 (Response to EGFR-TKI) | N = 3364 (EGFR genotype and thick CT); N = 6528 (EGFR genotype and thin CT); N = 110 (Response to EGFR-TKI) | 0.755–0.770 (EGFR genotype and thick CT); 0.748–0.797 (EGFR genotype and thin CT); Genotype predicted by the fully automated AI-system (FAIS) combined with clinical factors (FAIS-C model) was significantly associated with EGFR-TKI prognosis (p < 0.05) |
[49] | Hao et al., 2022 | LASSO regression model | CT radiomics, clinical and radiographic (CT) features | ALK rearrangement | In situ and invasive adNSCLC, stage I-IV | N = 154 | N = 39 | 0.914 (CT image and clinical features-based ML model); 0.89 (CT image-based ML model); 0.735 (clinical features) |
[50] | Chang et al., 2021 | LASSO regression model | PET/CT radiomics | ALK rearrangement | adNSCLC, stage I-IV | N = 367 | N = 159 | 0.88 |
[51] | Shao et al., 2022 | Multi-label multi-task deep learning (MMDL) system | CT radiomics | Various driver mutations; PD-L1 tumor proportion score (TPS) ≥ 50% | NSCLC | N = 876 | N = 110 | 0.796 (EGFR); 0.867 (ALK); 0.680 (BRAF); 0.816 (KRAS); 0.912 (PD-L1 TPS ≥ 50%) |
[52] | Jiang et al., 2021 | ML-based models, including random forest, decision tree, LG, AdaBoost, Gaussian process, and support vector machine | CT radiomics | PD-L1 expression | Resected adNSCLC | N = 91 | N = 34 | 0.85 (CT-based hand-crafted radiomic signature); 0.61 (radiomics-nomogram model); 0.38 (clinical model) |
[53] | Tian et al., 2021 | Deep convolutional neural network | CT radiomics | PD-L1 TPS ≥ 50% | NSCLC, stage IIIB-IV | N = 750 | N = 96 | 0.78 (training cohort); 0.71 (validation cohort); 0.76 (test cohort) |
[54] | Wang et al., 2022 | DL | CT radiomics | PD-L1 expression | NSCLC | N = 908 | N = 227 | 0.950 (TPS < 1%); 0.934 (TPS: 1–49%); 0.946 (TPS ≥ 50%) |
[55] | Mu et al., 2021 | Small-residual-convolutional-network (SResCNN) | PET/CT radiomics | PD-L1 expression; Response to ICI | NSCLC, stage I-IV from 5 cohorts (bi-institutional; China and Florida) | N = 284 (PD-L1 expression); N = 177 (Response to ICI) | N = 116 (PD-L1 expression); N = 35 (Response to ICI) | 0.82 (PD-L1 expression); c-indexes of 0.7–0.87 for the combination of Deep-Learned score (DLS) with clinical characteristics (response to ICI) |
[56] | Jiang et al., 2020 | LASSO regression model | CT, PET, and PET/CT radiomics | PD-L1 expression | NSCLC, stage I-IV | N = 266 | N = 133 | 0.97, 0.61, and 0.97 (PD-L1 > 1%, CT, PET, and PET/CT radiomics, respectively); 0.80, 0.65, and 0.77 (PD-L1 > 50%, CT, PET, and PET/CT radiomics, respectively) |
[57] | Trebeschi et al., 2019 | Gene set enrichment analysis (GSEA) | CT radiomics | Response to PD-1 ICI | Advanced NSCLC under anti-PD1 therapy | N = 123 | N = 262 | 0.79 |
[58] | Gong et al., 2022 | Support vector machine (SVM) classifier | Delta radiomics | Response to ICI | NSCLC, clinical stage III–IV, under immunotherapy alone | N = 93 | N = 131 | 0.82–0.87 |
[59] | Ramella et al., 2018 | Random forest classifier | CT radiomics | Response to concurrent chemoradiation (cCRT) | NSCLC, stage III, treated with cCRT | N = 91 | N/A | 0.82 |
[60] | Sun et al., 2018 | Linear elastic-net ML model | CT radiomics | CD8 gene expression; tumor immune phenotype | LC | N = 30 | N = 119 (CD8 gene expression); N = 100 (tumor immune phenotype) | 0.67 (CD8 gene expression); 0.76 (tumor immune phenotype) |
[61] | Sun et al., 2020 | N/A | CT radiomics | CD8 gene expression as a predictive biomarker of response to ICI + RT | Advanced NSCLC | N = 14 | N/A | 0.63 |
[62] | Mu et al., 2020 | LASSO regression model | PET/CT radiomics | TME image features to predict response to ICI | NSCLC, stage IIIB–IV | N = 99 | N = 95 | 0.86 (training cohort); 0.83 (retrospective test cohort); 0.81 (prospective test cohort) |
Reference | Author, Year | AI-Based Methodology | Model Features | Target Variable | Dataset Source | Training Cohort | Validation Cohort | Best Performance |
---|---|---|---|---|---|---|---|---|
[76] | Coudray et al., 2018 | Deep convolutional neural network (CNN) | Pathomics | Molecular classification | The Cancer Genome Atlas (TCGA) database | N = 1144 whole slide images | N = 490 whole slide images | 0.97 (adNSCLC, sqNSCLC, and healthy tissue discrimination); 0.733–0.856 (molecular classification) |
[77] | Mayer et al., 2022 | Advanced CNN | Pathomics | ALK and ROS1 fusion identification | NSCLC patients (single institution) | N = 162 | N = 72 | Sensitivity: 100% (for both genes); Specificity: 100% (for ALK fusion) and 98.6% (for ROS1 fusion) |
[78] | Ren et al., 2023 | DL | Pathomics | Diagnosis and gene alteration prediction | Pleural effusion cell block whole-slide images (single institution) | N = 410 | N/A | 0.932 (diagnosis); 0.869 (ALK fusion); 0.804 (KRAS mutation); 0.644 (EGFR mutation); 0.774 (no alterations) |
[79] | Rakaee et al., 2023 | QuPath v.0.2.3 (supervised ML algorithm) | Pathomics | Tumor-infiltrating lymphocytes (TILs), TMB, and PD-L1 as predictive biomarkers of ICI | ICI-treated, advanced-stage NSCLC | N = 284 | N = 97 | 0.70 (PD-L1/TMB); 0.56 (PD-L1/TILs); 0.52 (PD-L1); 0.77 (TILs, in PD-L1 TPS < 1%); 0.65 (TMB, in PD-L1 TPS < 1%) |
[80] | Hondelink et al., 2022 | 4 separate CNNs | Pathomics | PD-L1 expression | NSCLC | N = 60 | N = 139 | 79% concordance with the reference score |
[81] | Nibid et al., 2023 | 5 separate CNNs | Pathomics | Response to cCRT | NSCLC, stage IIIA/IIIB under cCRT | N = 33 | N = 2 | TPr = 0.75; TNr = 90.1 |
[82] | Lin et al., 2022 | ML | Pathomics | CD3+ T-cell and CD8+ T-cell density in TME and its prognostic value | NSCLC patients who underwent upfront surgery | N = 145 | N = 180 | DFS HR: 0.57 for the high l-score (p = 0.022) |
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Fiste, O.; Gkiozos, I.; Charpidou, A.; Syrigos, N.K. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers 2024, 16, 831. https://doi.org/10.3390/cancers16040831
Fiste O, Gkiozos I, Charpidou A, Syrigos NK. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers. 2024; 16(4):831. https://doi.org/10.3390/cancers16040831
Chicago/Turabian StyleFiste, Oraianthi, Ioannis Gkiozos, Andriani Charpidou, and Nikolaos K. Syrigos. 2024. "Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC" Cancers 16, no. 4: 831. https://doi.org/10.3390/cancers16040831
APA StyleFiste, O., Gkiozos, I., Charpidou, A., & Syrigos, N. K. (2024). Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers, 16(4), 831. https://doi.org/10.3390/cancers16040831