CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment

Early recovery of leukocyte subsets is associated with favorable progression-free survival in patients with inoperable stage II/III NSCLC after multimodal treatment: a prospective explorative study

BACKGROUND

We explored the dynamic changes of major leukocyte subsets during definitive treatment of patients with inoperable stage II/III NSCLC lung cancer and correlated it to survival to identify subpopulations associated with maximal patient benefit.

METHODS

We analyzed peripheral blood of 20 patients, either treated with thoracic radiotherapy (RT), concurrent chemo-radiotherapy (cCRT), or cCRT with additional immune-checkpoint inhibition therapy. Peripheral blood of 20 patients was collected at 9 timepoints before, during, and up to 1 year post treatment and analyzed by multi-color flow cytometry. Statistical analysis was conducted for leukocyte subpopulations, IL-6, progression-free survival (PFS) and overall survival (OS).

RESULTS

Increase of absolute lymphocyte counts (ALC) after the end of RT until 6 months thereafter was a predictor of PFS. Baseline lymphocyte counts showed no significant correlation to PFS or OS. Early recovery of absolute counts (AC) at 3 weeks after RT, total CD3 + T-cells, and CD8 + cytotoxic T-cells distinguished those patients with favorable PFS (≥ 12 months) from all other patients. Discriminant analysis identified B-cells, neutrophil-lymphocyte-ratio (NLR), CD4 + T-helper-cells, and NK-cells as predictors of favorable PFS. High variability in IL-6 plasma concentration of consecutive measurements within 6 months after the end of RT correlated negatively with PFS.

CONCLUSION

Our results suggest that two parameters commonly assessed in clinical routine can be used to predict patient outcome. These are: early increase in CD8 + T-cell lymphocyte count and variability in IL-6 plasma concentration, that are correlated to patients with favorable, respectively, poor outcome after definitive therapy independent of treatment regimen.

Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma

Estimation of the age of epidural hematoma (EDH) is a challenge in clinical forensic medicine, and this issue has yet to be conclusively resolved. The advantages of objectivity and non-invasiveness make computing tomography (CT) imaging an potential diagnostic method for EDH in living individuals. Recently, radiomics, the extraction hidden information from medical images, has emerged as a promising method for constructing predictive models. The aim of this study is to explore the feasibility and applicability of CT-based radiomics in predicting the timing of EDH injuries in surviving victims. A cohort of 95 EDH cases with definite injured time (within 12 h since injury) was selected. Clinical characteristics (age, gender, injury time, bleeding location, bleeding volume, and fracture) were recorded. The datasets were divided randomly into training and test cohorts. LIFEx software was used to segment the hematoma area in the CT and extract radiomic features. Machine learning algorithms were applied for features selection and model building. Twenty-three features were selected to calculate the Radscore, a key metric in our analysis. Utilizing this Radscore in conjunction with the time since injury, we constructed an Ordinary Least Squared (OLS) model. Our validation study has shown that mean absolute error (MAE) of the test cohort was 2.42 h, indicating a high degree of accuracy. In order to enhance the accuracy of prediction, the dataset was divided into unstable phase, occurring within the first 5 h post injury, and the stable phases. The Random Forest algorithm presented a significant divergence in predictive performance between the two phases, achieving an area under the curve (AUC) of 0.79, with an accuracy of 75.86%. The MAE of the regression model was 1.05 h for the unstable phase, and 1.23 h for the stable phase. Our findings underscore the potential of CT-based radiomics to offer a novel, convenient, and efficient approach to dating EDH, promising to illuminate new avenues in the field of medical diagnostics.

Multi-omics models for predicting prognosis in non-small cell lung cancer patients following chemotherapy and radiotherapy: A multi-center study

BACKGROUND AND PURPOSE

Quantifying tumor heterogeneity from various dimensions is crucial for precise treatment. This study aimed to develop and validate multi-omics models based on the computed tomography images, pathological images, dose and clinical information to predict treatment response and overall survival of non-small cell lung cancer (NSCLC) patients undergoing chemotherapy and radiotherapy.

MATERIALS AND METHODS

This retrospective study included 220 NSCLC patients from three centers. Following feature extraction and selection, single-omics and multi-omics models were built for treatment response and overall survival prediction. The performance of treatment response models was evaluated using the area under the curve (AUC) and box plots. For overall survival analysis, the model's evaluation included AUC, concordance index (C-index), Kaplan-Meier curves, and calibration curves. Shapley values were used to assess the contribution of different features to multi-omics models.

RESULTS

Multi-omics models consistently exhibited superior discriminative ability compared to single-omics models in predicting both treatment response and overall survival. For treatment response, the three all-modality models achieved AUC values of 0.87, 0.91, and 0.82 in the external validation set, respectively. In overall survival analysis, the three all-modality models demonstrated AUC values and C-index of 0.73/0.72, 0.80/0.77, 0.79/0.78 in the external validation set, respectively.

CONCLUSION

Multi-omics prediction models demonstrated superior predictive ability with robustness and interpretability. By predicting treatment response and overall survival in NSCLC patients, these models have the potential to assist clinician optimizing treatment plans, supporting individualized treatment strategies, improving the tumor control probability and prolonging the patients' survival.

Whole lung radiomic features are associated with overall survival in patients with locally advanced non-small cell lung cancer treated with definitive radiotherapy

BACKGROUND

Several studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.

METHODS

A total of 661 patients with LA-NSCLC treated with definitive radiotherapy in combination with chemotherapy were enrolled in this study, with 292 patients in the training set, 57 patients from the same hospital from January to December 2017 as an independent test set (test-set-1), 83 patients from a multi-institutional prospective clinical trial data set (RTOG0617) as test-set-2, and 229 patients from a Dutch radiotherapy center as test-set-3. Tumor-based radiomic features and whole lung-based radiomic features were extracted from primary tumor and whole lungs (excluding the primary tumor) delineations in planning CT images. Feature selection of radiomic features was done by the least absolute shrinkage (LASSO) method embedded with a Cox proportional hazards (CPH) model with 5-fold cross-internal validation, with 1000 bootstrap samples. Radiomics prognostic scores (RS) were calculated by CPH regression based on selected features. Three models based on a tumor RS, and a lung RS separately and their combinations were constructed. The Harrell concordance index (C-index) and calibration curves were used to evaluate the discrimination and calibration performance. Patients were stratified into high and low risk groups based on median RS, and a log-rank test was performed.

RESULTS

The discrimination ability of lung- and tumor-based radiomics model was similar in terms of C-index, 0.69 vs. 0.68 in training set, 0.68 vs. 0.66 in test-set-1, 0.61 vs. 0.62 in test-set-2, 0.65 vs. 0.64 in test-set-3. The combination of tumor- and lung-based radiomics model performed best, with C-index of 0.71 in training set, 0.70 in test-set-1, 0.69 in test-set-2, and 0.68 in test-set-3. The calibration curve showed good agreement between predicted values and actual values. Patients were well stratified in training set, test-set-1 and test-set-3. In test-set-2, it was only whole lung-based RS that could stratify patients well and tumor-based RS performed bad.

CONCLUSION

Lung- and tumor-based radiomic features have the power to predict OS in LA-NSCLC. The combination of tumor- and lung-based radiomic features can achieve optimal performance.

Radiomic-based biomarkers: Transforming age and body composition metrics into personalized age-informed indices

Chronological age has been the standard for quantifying the aging process. While it is simple to quantify it cannot fully discern the biological variability of aging between individuals. The growing body of interest in this variability of human aging has led to the introduction of new biomarkers to operationalize biological age. The inclusion of body composition may provide additional value to biological aging as a prediction and estimation factor of individual health outcomes. Diagnostic images based on radiomic techniques such as Computed Tomography contain an untapped wealth of patient-specific data that remain inaccessible to healthcare providers. These images are beneficial for collecting information from body composition that adds precision and granularity when compared to traditional measures. This information can subsequently be aggregated to construct models for changes in the human body associated with aging. In addition, aging leads to a natural decline in the best parameter of drug dosing in older adults, glomerular filtration rate. Since the conventional models of kidney function are correlated with age and body composition, the radiomic biomarkers representing age-related changes in body composition may also serve as potential new imaging biomarkers of kidney function for personalized dosing. Our review introduces potential radiomic biomarkers as measures of body composition change targeting the aging processes. As a functional example, we have hypothesized an age-related model of radiomics as a covariate of kidney function to improve personalized dosing. Future research focusing on evaluating this hypothesis in human subject studies is acknowledged.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma

BACKGROUND

Concurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways.

METHODS

Overall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing.

RESULTS

The DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79-0·92) in the training cohort and 0.84 (95% CI 0.75-0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36-0.80], P = 0.002; 0.44 [0.28-0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24-0.88], P = 0.008; 0.30 [0.14-0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells.

CONCLUSION

The DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.

The prognostic analysis and a machine-learning based disease-specific survival state model in pulmonary large-cell neuroendocrine carcinomas

Background

Pulmonary large-cell neuroendocrine carcinoma (PLCNEC) is a rare and highly malignant lung cancer. Due to the paucity of data from clinical studies, its clinical characteristics and treatment remain controversial. The present study explored factors influencing the prognosis and survival outcomes of patients with PLCNEC and developed a dependable prognostic model using machine learning.

Methods

The clinical data of PLCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020. A total of 2,897 PLCNEC patients were enrolled and univariate and multivariate Cox regression analyses were performed to explore independent prognostic factors for disease-specific survival (DSS). Ten machine learning algorithms were utilized to predict the 2-year survival. The clinicopathological data collected from The First Affiliated Hospital of Sun Yat-sen University between 2010 and 2022 were used to test the trained machine.

Results

Sex [hazard ratio (HR) 1.168, 95% confidence interval (CI): 1.063-1.284], age (HR 1.262, 95% CI: 1.144-1.391), surgery (HR 0.481, 95% CI: 0.413-0.559), chemotherapy (HR 0.450, 95% CI: 0.404-0.501), bone metastasis (HR 1.284, 95% CI: 1.124-1.466), brain metastasis (HR 1.167, 95% CI: 1.023-1.331), liver metastasis (HR 1.223, 95% CI: 1.069-1.399), American Joint Committee on Cancer-Node (AJCC-N), and tumor stage were independent prognostic factors. The gradient boosting decision tree (GBDT) performed better than other models, with an F1-score of 0.791 and an area under the curve of 0.831.

Conclusions

Male, age ≥65 years, distant metastasis to the bone, liver, and brain are associated with a worse prognosis in PLCNEC patients, while surgery and chemotherapy are associated with improved prognosis. GBDT showed promising performance in predicting 2-year survival, which can serve as a valuable reference for clinical diagnosis and treatment of PLCNEC.

Radiogenomics models for predicting prognosis in locally advanced non-small cell lung cancer patients undergoing definitive chemoradiotherapy

Background

Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients.

Methods

The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index).

Results

The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).

Conclusions

The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images

Introduction

Radiation therapy (RT) is one of the primary treatment options for early-stage non-small cell lung cancer (ES-NSCLC). Therefore, accurately predicting the overall survival (OS) rate following radiotherapy is crucial for implementing personalized treatment strategies. This work aims to develop a dual-radiomics (DR) model to (1) predict 3-year OS in ES-NSCLC patients receiving RT using pre-treatment CT images, and (2) provide explanations between feature importanceand model prediction performance.

Methods

The publicly available TCIA Lung1 dataset with 132 ES-NSCLC patients received RT were studied: 89/43 patients in the under/over 3-year OS group. For each patient, two types of radiomic features were examined: 56 handcrafted radiomic features (HRFs) extracted within gross tumor volume, and 512 image deep features (IDFs) extracted using a pre-trained U-Net encoder. They were combined as inputs to an explainable boosting machine (EBM) model for OS prediction. The EBM's mean absolute scores for HRFs and IDFs were used as feature importance explanations. To evaluate identified feature importance, the DR model was compared with EBM using either (1) key or (2) non-key feature type only. Comparison studies with other models, including supporting vector machine (SVM) and random forest (RF), were also included. The performance was evaluated by the area under the receiver operating characteristic curve (AUCROC), accuracy, sensitivity, and specificity with a 100-fold Monte Carlo cross-validation.

Results

The DR model showed highestperformance in predicting 3-year OS (AUCROC=0.81 ± 0.04), and EBM scores suggested that IDFs showed significantly greater importance (normalized mean score=0.0019) than HRFs (score=0.0008). The comparison studies showed that EBM with key feature type (IDFs-only demonstrated comparable AUCROC results (0.81 ± 0.04), while EBM with non-key feature type (HRFs-only) showed limited AUCROC (0.64 ± 0.10). The results suggested that feature importance score identified by EBM is highly correlated with OS prediction performance. Both SVM and RF models were unable to explain key feature type while showing limited overall AUCROC=0.66 ± 0.07 and 0.77 ± 0.06, respectively. Accuracy, sensitivity, and specificity showed a similar trend.

Discussion

In conclusion, a DR model was successfully developed to predict ES-NSCLC OS based on pre-treatment CT images. The results suggested that the feature importance from DR model is highly correlated to the model prediction power.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Optimizing lung cancer radiation therapy: A systematic review of multifactorial risk assessment for radiation-induced lung toxicity

BACKGROUND

Radiation therapy (RT) is essential in treating advanced lung cancer, but may lead to radiation pneumonitis (RP). This systematic review investigates the use of pulmonary function tests (PFT) and other parameters to predict and mitigate RP, thereby improving RT planning.

METHODS

A systematic review sifted through PubMed and on BioMed Central, targeting articles from September 2005 to December 2022 containing the keywords: Lung Cancer, Radiotherapy, and pulmonary function test.

RESULTS

From 1153 articles, 80 were included. RP was assessed using CTCAEv.4 in 30 % of these. Six studies evaluated post-RT quality of life in lung cancer patients, reporting no decline. Patients with RP and chronic obstructive pulmonary disease (COPD) generally exhibited poorer overall survival. Notably, forced expiratory volume in one second (FEV1) and diffusing capacity of the lung for carbon monoxide (DLCO) declined 24 months post-RT, while forced vital capacity (FVC) stayed stable. In the majority of studies, age over 60, tumors located in the lower part of the lung, and low FEV1 before RT were associated with a higher risk of RP. Dosimetric factors (V5, V20, MLD) and metabolic imaging emerged as significant predictors of RP risk. A clinical checklist blending patient and tumor characteristics, PFT results, and dosimetric criteria was proposed for assessing RP risk before RT.

CONCLUSION

The review reveals the multifactorial nature of RP development following RT in lung cancer. This approach should guide individualized management and calls for a prospective study to validate these findings and enhance RP prevention strategies.

Revolutionizing radiation therapy: the role of AI in clinical practice

This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist's perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.

Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study

BACKGROUND

It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses.

OBJECTIVE

This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling.

METHODS

A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created.

RESULTS

Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC.

CONCLUSION

The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.

Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study

Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Artificial intelligence predicts lung cancer radiotherapy response: A meta-analysis

BACKGROUND

Artificial intelligence (AI) technology has clustered patients based on clinical features into sub-clusters to stratify high-risk and low-risk groups to predict outcomes in lung cancer after radiotherapy and has gained much more attention in recent years. Given that the conclusions vary considerably, this meta-analysis was conducted to investigate the combined predictive effect of AI models on lung cancer.

METHODS

This study was performed according to PRISMA guidelines. PubMed, ISI Web of Science, and Embase databases were searched for relevant literature. Outcomes, including overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and local control (LC), were predicted using AI models in patients with lung cancer after radiotherapy, and were used to calculate the pooled effect. Quality, heterogeneity, and publication bias of the included studies were also evaluated.

RESULTS

Eighteen articles with 4719 patients were eligible for this meta-analysis. The combined hazard ratios (HRs) of the included studies for OS, LC, PFS, and DFS of lung cancer patients were 2.55 (95 % confidence interval (CI) = 1.73-3.76), 2.45 (95 % CI = 0.78-7.64), 3.84 (95 % CI = 2.20-6.68), and 2.66 (95 % CI = 0.96-7.34), respectively. The combined area under the receiver operating characteristics curve (AUC) of the included articles on OS and LC in patients with lung cancer was 0.75 (95 % CI = 0.67-0.84), and 0.80 (95%CI = 0.0.68-0.95), respectively.

CONCLUSION

The clinical feasibility of predicting outcomes using AI models after radiotherapy in patients with lung cancer was demonstrated. Large-scale, prospective, multicenter studies should be conducted to more accurately predict the outcomes in patients with lung cancer.

The artificial intelligence and machine learning in lung cancer immunotherapy

Since the past decades, more lung cancer patients have been experiencing lasting benefits from immunotherapy. It is imperative to accurately and intelligently select appropriate patients for immunotherapy or predict the immunotherapy efficacy. In recent years, machine learning (ML)-based artificial intelligence (AI) was developed in the area of medical-industrial convergence. AI can help model and predict medical information. A growing number of studies have combined radiology, pathology, genomics, proteomics data in order to predict the expression levels of programmed death-ligand 1 (PD-L1), tumor mutation burden (TMB) and tumor microenvironment (TME) in cancer patients or predict the likelihood of immunotherapy benefits and side effects. Finally, with the advancement of AI and ML, it is believed that "digital biopsy" can replace the traditional single assessment method to benefit more cancer patients and help clinical decision-making in the future. In this review, the applications of AI in PD-L1/TMB prediction, TME prediction and lung cancer immunotherapy are discussed.