CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer

"Application of CT radiomics in brain metastasis of lung cancer: A systematic review and meta-analysis"

PURPOSE

This study aimed to systematically assess the quality and performance of computed tomography (CT) radiomics studies in predicting brain metastasis (BM) among patients with lung cancer.

METHODS

The PubMed, Embase and Web of Science were searched for studies predicting BM in patients with lung cancer using CT-based radiomics features. Information regarding patients, imaging, and radiomics analysis was extracted from eligible studies. We assessed the quality of included studies using the Radiomics Quality Scoring (RQS) tool and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A meta-analysis of studies regarding the prediction of BM in patients with lung cancer was performed.

RESULTS

Thirteen studies were identified, with sample sizes ranging from 75 to 602. The mean RQS of the studies was 12 (range 9-16), and the corresponding percentage of the score was 33.55 % (range 25.00-44.44 %). Four studies (30.8 %) were considered as low risk of bias, while the remaining nine studies (69.2 %) were considered to have unclear risks. The meta-analysis included twelve studies. The pooled sensitivity, specificity and Area Under the Curve (AUC) value with 95 % confidence intervals were 0.75 [0.69, 0.80], 0.76 [0.68, 0.82], and 0.81 [0.77-0.84], respectively.

CONCLUSION

CT radiomics-based models show promising results as a non-invasive method to predict BM in lung cancer patients. However, multicenter and prospective studies are warranted to enhance the stability and acceptance of radiomics.

Computed tomography-based radiomics and clinical-genetic features for brain metastasis prediction in patients with stage III/IV epidermal growth factor receptor-mutant non-small-cell lung cancer

PURPOSE

To evaluate the value of computed tomography (CT)-based radiomics combined with clinical-genetic features in predicting brain metastasis in patients with stage III/IV epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC).

METHODS

The study included 147 eligible patients treated at our institution between January 2018 and May 2021. Patients were randomly divided into two cohorts for model training (n = 102) and validation (n = 45). Radiomics features were extracted from the chest CT images before treatment, and a radiomics signature was constructed using the Least Absolute Shrinkage and Selection Operator regression. Kaplan-Meier survival analysis was used to describe the differences in brain metastasis-free survival (BM-FS) risk. A clinical-genetic model was developed using Cox regression analysis. Radiomics, genetic, and combined prediction models were constructed, and their predictive performances were evaluated by the concordance index (C-index).

RESULTS

Patients with a low radiomics score had significantly longer BM-FS than those with a high radiomics score in both the training (p < 0.0001) and the validation (p = 0.0016) cohorts. The C-indices of the nomogram, which combined the radiomics signature and N stage, overall stage, third-generation tyrosine kinase inhibitor treatment, and EGFR mutation status, were 0.886 (95% confidence interval [CI] 0.823-0.949) and 0.811 (95% CI 0.719-0.903) in the training and validation cohorts, respectively. The combined model achieved a higher discrimination and clinical utility than the single prediction models.

CONCLUSIONS

The combined radiomics-genetic model could be used to predict BM-FS in stage III/IV NSCLC patients with EGFR mutations.

MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis

AIM

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases.

MATERIALS AND METHODS

117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models.

RESULTS

Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI).

CONCLUSION

The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung

OBJECTIVE

To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma.

METHODS

Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness.

RESULTS

In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group).

CONCLUSIONS

Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model

BACKGROUND

Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model.

METHODS

This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model.

RESULTS

The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05).

CONCLUSIONS

Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

18F-FDG PET/CT radiomics predicts brain metastasis in I-IIIA resected Non-Small cell lung cancer

OBJECTIVE

To establish ¹⁸F-FDG PET/CT radiomics model for predicting brain metastasis in non-small cell lung cancer (NSCLC) patients.

METHODS

This research comprised 203 NSCLC patients who had received surgical therapy at two institutions. To identify independent predictive factors of brain metastasis, metabolic indicators, CT features, and clinical features were investigated. A prediction model was established by incorporating radiomics signature and clinicopathological risk variables. The suggested model's performance was assessed from the perspective of discrimination, calibration, and clinical application.

RESULTS

The C-indices of the PET/CT radiomics model in the training, internal validation, and external validation cohorts were 0.911, 0.825 and 0.800, respectively. According to the multivariate analysis, neuron-specific enolase (NSE) and air bronchogram were independent risk factors for brain metastasis (BM). Furthermore, the combined model integrating radiomics and clinicopathological characteristics related to brain metastasis performed better in terms of prediction, with C-indices of 0.927, 0.861, and 0.860 in the training, internal validation, and external validation cohorts, respectively. The decision curve analysis (DCA) suggested that the PET/CT nomogram was clinically beneficial.

CONCLUSIONS

A predictive algorithm based on PET/CT imaging information and clinicopathological features may accurately predict the probability of brain metastasis in NSCLC patients following surgery. This presented doctors with a unique technique for screening NSCLC patients at high risk of brain metastasis.

The Association of Gross Tumor Volume and Its Radiomics Features with Brain Metastases Development in Patients with Radically Treated Stage III Non-Small Cell Lung Cancer

PURPOSE

To identify clinical risk factors, including gross tumor volume (GTV) and radiomics features, for developing brain metastases (BM) in patients with radically treated stage III non-small cell lung cancer (NSCLC).

METHODS

Clinical data and planning CT scans for thoracic radiotherapy were retrieved from patients with radically treated stage III NSCLC. Radiomics features were extracted from the GTV, primary lung tumor (GTVp), and involved lymph nodes (GTVn), separately. Competing risk analysis was used to develop models (clinical, radiomics, and combined model). LASSO regression was performed to select radiomics features and train models. Area under the receiver operating characteristic curves (AUC-ROC) and calibration were performed to assess the models' performance.

RESULTS

Three-hundred-ten patients were eligible and 52 (16.8%) developed BM. Three clinical variables (age, NSCLC subtype, and GTVn) and five radiomics features from each radiomics model were significantly associated with BM. Radiomic features measuring tumor heterogeneity were the most relevant. The AUCs and calibration curves of the models showed that the GTVn radiomics model had the best performance (AUC: 0.74; 95% CI: 0.71-0.86; sensitivity: 84%; specificity: 61%; positive predictive value [PPV]: 29%; negative predictive value [NPV]: 95%; accuracy: 65%).

CONCLUSION

Age, NSCLC subtype, and GTVn were significant risk factors for BM. GTVn radiomics features provided higher predictive value than GTVp and GTV for BM development. GTVp and GTVn should be separated in clinical and research practice.

A "Seed-and-Soil" Radiomics Model Predicts Brain Metastasis Development in Lung Cancer: Implications for Risk-Stratified Prophylactic Cranial Irradiation

Introduction: Brain is a major site of metastasis for lung cancer, and effective therapy for developed brain metastasis (BM) is limited. Prophylactic cranial irradiation (PCI) has been shown to reduce BM rate and improve survival in small cell lung cancer, but this result was not replicated in unselected non-small cell lung cancer (NSCLC) and had the risk of inducing neurocognitive dysfunctions. We aimed to develop a radiomics BM prediction model for BM risk stratification in NSCLC patients. Methods: 256 NSCLC patients with no BM at baseline brain magnetic resonance imaging (MRI) were selected; 128 patients developed BM within three years after diagnosis and 128 remained BM-free. For radiomics analysis, both the BM and non-BM groups were randomly distributed into training and testing datasets at an 70%:30% ratio. Both brain MRI (representing the soil) and chest computed tomography (CT, representing the seed) radiomic features were extracted to develop the BM prediction models. We first developed the radiomic models using the training dataset (89 non-BM and 90 BM cases) and subsequently validated the models in the testing dataset (39 non-BM and 38 BM cases). A radiomics BM score (RadBM score) was generated, and BM-free survival were compared between RadBM score-high and RadBM score-low groups. Results: The radiomics model developed from baseline brain MRI features alone can predict BM development in NSCLC patients. A fusion model integrating brain MRI features with primary tumor CT features (seed-and-soil model) provided synergetic effect and was more efficient in predicting BM (areas under the receiver operating characteristic curve 0.84 (95% confidence interval: 0.80−0.89) and 0.80 (95% confidence interval: 0.71−0.88) in the training and testing datasets, respectively). BM-free survival was significantly shorter in the RadBM score-high group versus the RadBM score-low group (Log-rank, p < 0.001). Hazard ratios for BM were 1.056 (95% confidence interval: 1.044−1.068) per 0.01 increment in RadBM score. Cumulative BM rates at three years were 75.8% and 24.2% for the RadBM score-high and RadBM score-low groups, respectively. Only 1.2% (7/565) of the BM lesions were located within the hippocampal avoidance region. Conclusion: The results demonstrated that intrinsic features of a non-metastatic brain exert a significant impact on BM development, which is first-in-class in metastasis prediction studies. A radiomics BM prediction model utilizing both primary tumor and pre-metastatic brain features might provide a useful tool for individualized PCI administration in NSCLC patients more prone to develop BM.

Feature selection methods and predictive models in CT lung cancer radiomics

Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.

Predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT for brain metastases in patients with locally advanced non-small-cell lung cancer

OBJECTIVES

Brain metastases (BMs) are a major cause leading to the failure of treatment management for non-small-cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate the predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT measured with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for brain metastases (BMs) development in patients with locally advanced non-small-cell lung cancer (NSCLC) after treatment.

METHODS

Forty-seven patients with stage IIB-IIIC NSCLC who underwent baseline 18F-FDG PET/CT examinations were retrospectively reviewed. The maximum standardized uptake value (SUVmax), MTV, and TLG of the primary tumor (SUVmaxT, MTVT, and TLGT), metastatic lymph nodes (SUVmaxN, MTVN, and TLGN), and whole-body tumors (SUVmaxWB, MTVWB, and TLGWB) were measured. The optimal cut-off values of PET parameters to predict brain metastasis-free survival were obtained using Receiver operating characteristic (ROC) analysis, and the predictive value of clinical variables and PET parameters were evaluated using Cox proportional hazards regression analysis.

RESULTS

The median follow-up duration was 25.0 months for surviving patients, and 13 patients (27.7%) developed BM. The optimal cut-off values were 21.1 mL and 150.0 g for MTVT and TLGT, 20.0, 10.9 mL and 55.6 g for SUVmaxN, MTVN and TLGN, and 27.9, 27.4 mL and 161.0 g for SUVmaxWB, MTVWB and TLGWB, respectively. In the Cox proportional hazards models, the risk of BM was significantly associated with MTVN and MTVWB or TLGN and TLGWB after adjusting for histological cell type, N stage, SUVmaxN, and SUVmaxWB.

CONCLUSIONS

Baseline metabolic tumor burden (MTV and TLG) evaluated from the level of metastatic lymph nodes and whole-body tumors are significant predictive factors for BM development in patients with locally advanced NSCLC.

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction:

Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC.

Methods:

Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC.

Results:

In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively).

Conclusion:

CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.

Application of Artificial Intelligence in Lung Cancer

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient's prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer

Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC).

Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who were diagnosed with BM at baseline examination (n = 16) or within 1 year’s follow-up (n = 14), and BM− were those without BM followed up for at least 1 year (n = 47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model.

Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736–0.921), and when combined with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845–0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram.

Conclusion: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within 1 year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients.

Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma

Background

To develop and validate a radiomics model using computed tomography (CT) images acquired from the first diagnosis to estimate the status of occult brain metastases (BM) in patients with stage IV lung adenocarcinoma (LADC).

Methods

One hundred and ninety-three patients who were first diagnosed with stage IV LADC were enrolled and divided into a training cohort (n=135) and a validation cohort (n=58). Then, 725 radiomic features were extracted from contoured primary tumor volumes of LADCs. Intra- and interobserver reliabilities were calculated, and the least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Subsequently, a radiomics signature (Rad-Score) was built. To improve performance, a nomogram incorporating a radiomics signature and an independent clinical predictor was developed. Finally, the established signature and nomogram were assessed using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Both empirical and α-binomial model-based ROCs and PRCs were plotted, and the area under the curve (AUC) and average precision (AP) of ROCs and PRCs were calculated and compared.

Results

A radiomics signature and Rad-Score were constructed using eight radiomic features, and these had significant correlations with occult BM status. A nomogram was developed by incorporating a Rad-Score and the primary tumor location. The nomogram yielded an optimal AUC of 0.911 [95% confidence interval (CI): 0.903–0.919] and an AP of 0.885 (95% CI: 0.876–0.894) in the training cohort, and an AUC of 0.873 (95% CI: 0.866–0.80) and an AP of 0.827 (95% CI: 0.820–0.834) in the validation cohort using α-binomial model-based method. The calibration curve demonstrated that the nomogram showed high agreement between the actual occult BM probability and predicted by the nomogram (P=0.427).

Conclusions

The nomogram incorporating a radiomics signature and a clinical risk factor achieved optimal performance after holistic assessment using unbiased indexes for diagnosing occult BM of patients who were first diagnosed with stage IV LADC.

Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Simple Summary

Despite recent advancements in lung cancer treatment, individuals with lung cancer have a dismal 5-year survival rate of only 15%. In patients with non-small cell lung cancer (NSCLC), medical images have lately been employed as a valuable marker for predicting overall survival. The primary goal of this study was to develop a risk score based on computed tomography (CT) based radiomics feature signatures that may be used to predict survival in NSCLC patients. After analyzing 577 NSCLC patients from two data sets, we discovered that the risk score model’s prediction ability as a prognostic indicator was superior to other clinical indicators (age, stage, and gender), and the possibility of patient risk stratification with survival was evaluated using a risk score representation of 10 radiomics signatures. According to this study, the risk score generated using CT-based radiomics signatures promises to predict overall survival in NSCLC patients.

Abstract

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.