Table 3.

Characteristics of different studies on the use of AI in outcome prediction and response to treatment, including strengths and limitations.

Authors	Country	Imaging Modality	Patient Number	Study Nature	AI System	Validation	Main Theme	Strengths	Limitations
Aerts et al. [104]	USA	CT	47	Prospective	Prognostic radiomics signature	Yes	Radiomic data could define a response phenotype for NSCLC patients treated with Gefitinib therapy	Strong associations	- Limited sample size - Only 11 independent radiomic features
Gevaert et al. [87]	USA	CT	186		Predictive radiogenomics decision model		Association between ground glass opacity and the presence of EGFR mutations		Need for validations
Zhao et al. [88]	China	CT	637		Predictive radiomics model	Yes	Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status	Sample size	- Different CT scanning parameters - Single center study
Yamamoto et al. [89]	USA	CT	172	Retrospective	Predictive radiogenomics model	Yes	ALK+ tumors have a CT radiophenotype that distinguishes them from tumors with other NSCLC molecular phenotypes	Multi-institutional, international study cohort	- Limited sample - No treatment response validation
Song et al. [90]	China	CT	937	Retrospective	Three blocks deep learning neural network		DLM trained by both CT images and clinicopathological information could effectively predict the ALK fusion status and treatment response		- Small size of the ALK-target therapy cohort (n = 91)
Chang et al. [92]	China	PET/CT	526	Prospective	Three predictive radiomics models		PET/CT-clinical model has a significant advantage to predict the ALK mutation status		- Images acquired and processed in the same way - Single medical center
Wei et al. [93]	China	CT	134	Prospective	Predictive radiomics signature model via binary logistic regression model		The radiomics model (21 features) was superior to clinical model in predicting the efficacy of chemotherapy in patients with SCLC
Bourbonne et al. [96]	France		167	Retrospective	Three predictive radiomics models via neural network training		In patients with lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs in predicting APT and LPT
Jiang et al. [99]	China	PET/CT	399		Predictive radiomics models via logistic regression and random forest classifiers	Yes, five-fold cross-validation	Imaging-derived signatures could classify expression rate of specific PD-L1 type		- Stage IV NSCLC patients composed a very small proportion - PET/CT data were obtained in clinical routine through two different manufacture-derived machines with different scanning parameters
Yoon et al. [100]	South Korea	CT	153	Retrospective	Two predictive radiomics model via multivariate logistic regression		Quantitative CT radiomic features can help predict PD-L1 expression		- Patients were identified only from those having PD-L1 testing results - Proposed prediction model did not undergo external validation - PD-L1 test lacks universal reference standards
Khorrami et al. [112]	USA	CT	139	Prospective	Machine learning-based radiomics texture features (DelRADx)	Yes	DelRADx features were (1) predictive of response to ICI therapy, (2) prognostic of improved overall survival, and (3) associated with TIL density on corresponding diagnostic biopsy samples	- Validation in two independent test sets - Radiomic features extracted also from the annular perinodular regions	- The sizes of cohorts, both for discovery and validation, were relatively small - Radiomic feature expressions might be sensitive to lesion annotation accuracy
Trebeschi et al. [102]	Netherlands	CT	203		Machine learning-based radiomics model	Yes	Higher levels of surface-area-to-volume ratio in nonresponding lesions in both cancers suggest that more compact and spherical profiles are associated with better response	Individual lesion-based approach, avoiding the issue of mixed response	Need for validation in larger cohorts
Grove et al. [105]	USA	CT	109	Retrospective	Predictive CT-based features: convexity morphological feature and		Quantitative imaging biomarkers can be used as an additional diagnostic tool in management of lung adenocarcinomas.	Development of imaging features that were descriptive and reproducible using retrospectively acquired clinical scans	- Cohort sample sizes - The two cohorts are likely not comparable (different overall survival trend)
Tang et al. [106]	USA	CT	190	Retrospective	Immunopathology-informed model (IPIM)	Yes	First radiomics model to leverage immunopathology features (CD3+ cell density and percent tumor cell PDL1 expression) to obtain immune-informed radiomics model yielded subtypes associated with OS		- Conducted at a single institution
Wang et al. [107]	USA/China	CT	18232	Prospective	Fully automated artificial intelligence system (FAIS)	Yes	FAIS learned to identify patients with an EGFR mutation who are at high risk of having TKI resistance		- Other genes are relevant to targeted therapy (e.g., ALK, KRAS) - Combined method (whole lung + tumor-based) wasn’t studied
Jiao et al. [108]	USA	CT	421		Convolutional AE DL model with three layers of CNNs		Integrating DL radiomics models and CTC counts improves patient stratification in predicting recurrence outcomes for patients treated with SBRT for ES-NSCLC