Computed Tomography Radiomic Features Research Articles

A CT-Based Clinical-Radiomics Nomogram for Predicting the Overall Survival to TACE Combined with Camrelizumab and Apatinib in Patients with Advanced Hepatocellular Carcinoma

ObjectivesTo construct a computed tomography (CT)-based clinical-radiomics nomogram for estimating overall survival (OS) in advanced hepatocellular carcinoma (HCC) patients receiving transcatheter arterial chemoembolization (TACE) in combination with camrelizumab and apatinib. MethodsA retrospective recruitment of 150 patients with clinically or pathologically confirmed HCC was conducted, followed by their division into training cohort (n=105) and test cohort (n=45). To generate the radiomics score (Rad-score), a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Subsequently, a clinical-radiomics nomogram was constructed using the Rad-score combined with independent clinical prognostic factors, followed by assessments of its calibration, discrimination, reclassification, and clinical utility. ResultsFive CT radiomics features were selected. The Rad-score showed a significant correlation with OS (P < 0.001). The clinical-radiomics nomogram demonstrated superior performance in estimating OS, with a concordance index (C-index) of 0.840, compared to the radiomics nomogram (C-index: 0.817) and the clinical nomogram (C-index: 0.661). It also exhibited high 1-year and 2-year area under the curves of 0.936 and 0.946, respectively. Additionally, the clinical-radiomics nomogram markedly enhanced classification accuracy for OS outcomes, as evidenced by net reclassification improvement and integrated discrimination improvement. Decision curve analysis confirmed its clinical utility. ConclusionA CT-based clinical-radiomics nomogram exhibits strong potential for predicting OS in advanced HCC patients undergoing TACE combined with camrelizumab and apatinib.

Interpretable artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis

Abstract Background The concurrent presence of transthyretin amyloid cardiomyopathy (ATTR-CM) in candidates for transcatheter aortic valve implantation (TAVI) with severe aortic stenosis (AS) is frequently observed. Objective This study aims to develop a multimodal artificial intelligence-based model for ATTR-CM detection in patients with severe AS. Methods A total of 263 prospectively enrolled (57% males, 83±4.6 years) patients with severe AS were enrolled. All underwent DPD-scintigraphy, and coexisting ATTR-CM was confirmed in 27 (10.3%). 4D-cardiac computed tomography (CT), electrocardiogram, echocardiography, angiography, clinical, and laboratory data were assessed. Specifically, in the 4D-CT, LV myocardial radiomics features as well as global- and segmental myocardial strain. All features underwent z-score normalization, and those with high correlation were eliminated. Different feature selection and machine learning (ML) techniques were used for classification in single- and multimodality scenarios. Eighty percent of the dataset was used for pre-processing, feature extraction, and tuning, with the remaining 20% for evaluating the final model. The model's effectiveness was assessed with accuracy, F1 score, and ROC-AUC across 100 random seeds to establish a reliable classifier. The SHAP (SHapley Additive exPlanations) model is used to interpret the output of ML models. Results Among the different algorithms used, Logistic Regression offers the most effective detection of ATTR-CM, surpassing other models with AUC scores of 0.85. CT strain (AUC: 0.85), Radiomics (AUC: 0.76), and clinical features (AUC: 0.70) alone and in combination yielded the highest performance (i.e., AUC: &amp;gt;70), while the remaining modalities had AUC scores ranging from 0.52 to 0.56. Conclusion The study shows that AI is effective for non-invasively detecting ATTR-CM in severe aortic stenosis patients, offering a potential alternative to costly DPD-scintigraphy or invasive biopsy.

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.

There is a current lack of data pertaining to the potential link between gout flares and dual-energy computed tomography radiomic features. This study aimed to construct and validate a comprehensive dual-energy computed tomography-based radiomics model for differentiating patients with and without gout flares. The analysis included 200 patients, of whom 150 were confirmed to have experienced at least one flare in the past 12months; the remaining 50 patients did not experience flares. The radiomic features of the tophi at the bilateral first metatarsophalangeal joints were extracted and analyzed. Optimal radiomic features were selected using the least absolute shrinkage and selection operator method, and logistic regression analysis was used to screen clinical characteristics and establish a clinical model. The optimal radiomic features were then combined with the identified independent clinical variables to develop a comprehensive model. The performances of the radiomic, clinical, and comprehensive models were evaluated using receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. Four radiomic features distinguished patients with at least one flare from those without flares and were used to establish the radiomic model. Disease duration and hypertension were independent factors that differentiated flare occurrences. The radiomic, clinical, and comprehensive models showed favorable discrimination, with areas under the receiver operating characteristic curves of 0.76 (95% CI, 0.69-0.83), 0.72(95% CI, 0.63-0.80), and 0.79(95% CI, 0.73-0.86), respectively. The calibration curves (P > 0.05) showed that the differentiated values of the comprehensive model agreed well with the actual values. Decision curve analysis demonstrated that the comprehensive model achieved higher net clinical benefits than the use of either the radiomic or clinical model alone. The results of this study suggest that a radiomics model can distinguish patients with and without gout flares. Our proposed clinical radiomics nomogram can increase the efficacy of differentiating flare occurrence, which may facilitate the clinical decision-making process.

DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.

Glycolysis proved to have a prognostic value in lung cancer; however, to identify glycolysis-related genomic markers is expensive and challenging. This study aimed at identifying glycolysis-related computed tomography (CT) radiomics features to develop a deep-learning prognostic model for non-small cell lung cancer (NSCLC). The study included 274 NSCLC patients from cohorts of The Second Affiliated Hospital of Soochow University (SZ; n=64), the Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), and the Gene Expression Omnibus dataset (n=136). Initially, the glycolysis enrichment scores were evaluated using a single-sample gene set enrichment analysis, and the cut-off values were optimized to investigate the prognostic potential of glycolysis genes. Radiomic features were then extracted using LIFEx software. The least absolute reduction and selection operator (LASSO) algorithm was employed to determine the glycolytic CT radiomics features. A deep-learning prognostic model was constructed by integrating CT radiomics and clinical features. The biological functions of the model were analyzed by incorporating RNA sequencing data. Kaplan-Meier curves indicated that elevated glycolysis levels were associated with poorer survival outcomes. The LASSO algorithm identified 11 radiomic features that were then selected for inclusion in the deep-learning model. They have shown significant discrimination capability in assessing glycolysis status, achieving an area under the curve value of 0.8442. The glycolysis-based radiomics deep-learning model was named the DeepGR model. This model was able to effectively predict the clinical outcomes of NSCLC patients with AUCs of 0.8760 and 0.8259 in the SZ and TCGA cohorts, respectively. High-risk DeepGR scores were strongly associated with poor overall survival, resting memory CD4+ T cells, and a high response to programmed cell death protein 1 immunotherapy. The DeepGR model effectively predicted the prognosis of NSCLC patients.

A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms

Objective To evaluate the effectiveness of a machine learning based on computed tomography (CT) radiomics to distinguish nontuberculous mycobacterial pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB). Methods In this retrospective analysis, medical records of 99 individuals afflicted with NTM-PD and 285 individuals with PTB in Zhejiang Chinese and Western Medicine Integrated Hospital were examined. Random numbers generated by a computer were utilized to stratify the study cohort, with 80% designated as the training cohort and 20% as the validation cohort. A total of 2153 radiomics features were extracted using Python (Pyradiomics package) to analyse the CT characteristics of the large disease areas. The identification of significant factors was conducted through the least absolute shrinkage and selection operator (LASSO) regression. The following four supervised learning classifier models were developed: random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGBoost). For assessment and comparison of the predictive performance among these models, receiver-operating characteristic (ROC) curves and the areas under the ROC curves (AUCs) were employed. Results The Student’s t-test, Levene test, and LASSO algorithm collectively selected 23 optimal features. ROC analysis was then conducted, with the respective AUC values of the XGBoost, LR, SVM, and RF models recorded to be 1, 0.9044, 0.8868, and 0.7982 in the training cohort. In the validation cohort, the respective AUC values of the XGBoost, LR, SVM, and RF models were 0.8358, 0.8085, 0.87739, and 0.7759. The DeLong test results noted the lack of remarkable variation across the models. Conclusion The CT radiomics features can help distinguish between NTM-PD and PTB. Among the four classifiers, SVM showed a stable performance in effectively identifying these two diseases.

An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.

The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease. In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center. Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods. The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

BackgroundChronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning.MethodsThe retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier.Results104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87.ConclusionsThe proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

ObjectiveMitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma.Materials and methods246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status.ResultsThe classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913–0.921) in the training set and 0.858 (95% CI 0.841–0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927–0.934), 0.915 (95% CI 0.912–0.919), and 0.901 (95% CI 0.900–0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859–0.882), 0.860 (95% CI 0.852–0.872), and 0.820 (95% CI 0.813–0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively.ConclusionsCECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study

BackgroundThe existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients.MethodsThe data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram.ResultsIn total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values.ConclusionsIn this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma

Background & aimsThe present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model.MethodsWe recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison.ResultsSeven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973–0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835– 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively).ConclusionCT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Improving the Reproducibility of Computed Tomography Radiomic Features Using an Enhanced Hierarchical Feature Synthesis Network

Improving the Reproducibility of Computed Tomography Radiomic Features Using an Enhanced Hierarchical Feature Synthesis Network

Impact of slice thickness on reproducibility of CT radiomic features of lung tumors

Background Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology. There is scarcity of research that how ST affects variability of RF in LC. The present study helps in identifying the specific RF categories affected by variations in ST and provides valuable insights for researchers and clinicians working with RF in the field of LC.Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors. Methods This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm STwas reconstructed retrospectively. RF were extracted from the CECT thorax images of both ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin’s Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses. Results Out of 107 RF, 66 (61.6%) exhibited a statistically significant distinction (p&lt;0.05) when comparing two ST and while 41 (38.3%) RF did not show significant distinction (p&gt;0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility. Conclusions The ST had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

Development and validation of a radiomics nomogram for diagnosis of malignant pleural effusion

ObjectiveWe aimed to develop a radiomics nomogram based on computed tomography (CT) scan features and high-throughput radiomics features for diagnosis of malignant pleural effusion (MPE).MethodsIn this study, 507 eligible patients with PE (207 malignant and 300 benign) were collected retrospectively. Patients were divided into training (n = 355) and validation cohorts (n = 152). Radiomics features were extracted from initial unenhanced CT images. CT scan features of PE were also collected. We used the variance threshold algorithm and least absolute shrinkage and selection operator (LASSO) to select optimal features to build a radiomics model for predicting the nature of PE. Univariate and multivariable logistic regression analyzes were used to identify significant independent factors associated with MPE, which were then included in the radiomics nomogram.ResultsA total of four CT features were retained as significant independent factors, including massive PE, obstructive atelectasis or pneumonia, pleural thickening > 10 mm, and pulmonary nodules and/or masses. The radiomics nomogram constructed from 13 radiomics parameters and four CT features showed good predictive efficacy in training cohort [area under the curve (AUC) = 0.926, 95% CI 0.894, 0.951] and validation cohort (AUC = 0.916, 95% CI 0.860, 0.955). The calibration curve and decision curve analysis showed that the nomogram helped differentiate MPE from benign pleural effusion (BPE) in clinical practice.ConclusionThis study presents a nomogram model incorporating CT scan features and radiomics features to help physicians differentiate MPE from BPE.

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures

PurposeTo develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs).Materials and methods128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model.ResultsFor mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846.ConclusionMachine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.

Impact of slice thickness on reproducibility of CT radiomic features of lung tumors

Background: Radiomics, a field of research, relies on the theory that quantified characteristics from radiographic images would reflect underlying pathophysiology. Lung cancer continues to stand as one of the prevalent and well-known forms of cancer, causing mortality. The slice thickness (ST) of computed tomography (CT) images would be key concern regarding generalizability of radiomic features (RF) results in oncology. There is scarcity of research that has delved into how ST affects variability of RF in lung tumors. Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors. Methods: This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm ST, and was reconstructed retrospectively. RF were extracted from the CECT thorax images of 5-mm and 2-mm ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin’s Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses. Results: Out of 107 RF extracted, 66 (61.6%) exhibited a statistically significant distinction (p&lt;0.05) when comparing two slice thicknesses and while 41 (38.3%) RF did not show significant distinction (p&gt;0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility. Conclusions: The slice thickness had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.

CT Radiomics Features of Abdominal Adipose and Muscle Tissues Can Predict the Postoperative Early Recurrence of Hepatocellular Carcinoma

To investigate the potential of computed tomography radiomics features extracted from abdominal adipose and muscle in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after surgery. This retrospective study enrolled 252 patients with HCC who underwent curative resection from twoindependent institutions. In the training cohort of 178 patients from institution A, radiomics signatures extracted from abdominal visceral adipose, subcutaneous adipose, and muscle were applied to establish the radiomics score using the least absolute shrinkage and selection operator regression. Using multivariable Cox regression analysis, two models were developed: one incorporated preoperative variables, and the other incorporated both pre- and postoperative variables. The external validation of the twomodels was conducted at institution B with 74 patients. The preoperative model incorporated tumor size, alpha-fetoprotein, body mass index, and radiomics score, whereas the postoperative model additionally integrated Edmondson-Steiner grade on the basis of the aforementioned parameters. In both cohorts, both models demonstrated superior performance to traditional staging systems and corresponding clinical models (P<0.01), with time-dependent area under the curveexceeding 0.81 and concordance indices exceeding 0.72. Furthermore, the two models exhibited lower prediction errors (integrated Brier score < 0.19), well-calibrated calibration curves, and greater net clinical benefits. Finally, the two radiomics-based models facilitated risk stratification by accurately distinguishing the high-, intermediate-, and low-risk groups for ER (P<0.01). Statistical models integrating the radiomics data of abdominal adipose and muscle can provide accurate and reliable predictions of postoperative ER for patients with HCC.

Using CT Radiomics and Machine Learning to Distinguish Radiotherapy vs. Immune Checkpoint Inhibitor-Induced Pneumonitis in Non-Small Cell Lung Cancer Patients

Both radiotherapy (RT) and immune checkpoint inhibitors (ICIs) may induce pneumonitis which is difficult to distinguish clinically. We tested the utility of computed tomography (CT) in identifying the cause of pneumonitis in patients with non-small cell lung cancer (NSCLC) who received RT, ICIs and RT combined with ICIs (RT+ICIs). In this study, we analyzed the clinical characteristics and CT morphology of 130 NSCLC patients who developed pneumonitis after receipt of ICIs only (n = 50), thoracic RT only (n = 50) (ICIs only + RT only, the training cohort, n = 100), and RT+ICIs (the test cohort, n = 30). CT radiomics features were extracted using 3D-Slicer software and selected using LASSO regression for training a random forest (RF) classifier and a linear discriminant analysis (LDA) classifier to discern pneumonitis etiology. Patients who received RT or ICIs only were defined as the training cohort since the cause of their pneumonitis was readily identifiable. Patients who received RT+ICIs were defined as the test cohort and the cause of their pneumonitis were determined by clinicians as the ground truth. In the study, the median time from the initiation of treatment to pneumonitis was 1.9 months in RT+ICIs patients, which was shorter than 2.5 months in RT only and 2.7 months in ICIs only patients. In addition, the patients in RT+ICIs group have more high grade (grade 3-4) pneumonitis compared to patients in ICIs only or RT only group (p<0.05). Pneumonitis after the combined therapy was not a simple superposition mode of RP and CIP, resulting in the distinct characteristics of both RT and ICIs-related pneumonitis. (1) Some cases resembled RP with sharp borders in the ipsilateral lung and others resembled the bilateral distribution of typical CIP, while some patients exhibit an intermediate morphology; (2) Some pneumonitis occurred in the areas of low and medium doses of radiation rather than the high-dose radiation areas similar to RP; (3) Some pneumonitis was initially limited to the ipsilateral lung that had received radiation and then developed in bilateral lungs. The RF classifier showed favorable discrimination between CIP and RP with an area under the receiver operating curve (AUC) of 0.859 (95% CI: 0.7884-0.9292) in the training cohort and 0.851 (95% CI: 0.6998-1) in the test cohort. The simplified LDA classifier consisting of 7 LASSO-selected features achieved an AUC of 0.881 (95% CI: 0.815-0.947) in the training cohort and 0.842 (95% CI: 0.686-0.997) in the test cohort. RT and ICIs-related pneumonitis exhibit distinct clinical characteristics and morphologic features on CT. CT radiomics-based classifiers could provide a noninvasive method to identify the predominant etiology in NSCLC patients who developed pneumonitis after thoracic RT and ICIs.

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients.

Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.