Development of a non-invasive diagnostic model for high-risk esophageal varices based on radiomics of spleen CT.

PURPOSE: To evaluate the diagnostic performance of radiomics models derived from multi-phase spleen CT for high-risk esophageal varices (HREV) in cirrhotic patients. METHODS: We retrospectively selected cirrhotic patients with esophageal varices from two hospitals from September 2019 to September 2023. Patients underwent non-contrast and contrast-enhanced CT scans and were categorized into HREV and non-HREV groups based on endoscopic evaluations. Radiomics features were extracted from spleen CT images in non-contrast, arterial, and portal venous phases, with feature selection via lasso regression and Pearson's correlation. Ten machine learning models were developed to diagnose HREV, evaluated by area under the curve (AUC). The AUC values of the three groups of models were statistically compared by the Kruskal-Wallis H test and Bonferroni-corrected Mann-Whitney U test. A p-value less than 0.05 was considered statistically significant. RESULTS: Among 233 patients, 11, 6, and 11 features were selected from non-contrast, arterial, and portal venous phases, respectively. Significant differences in AUC values were observed across phases (p < 0.05), and the arterial phase models showed the highest AUC values. The best model in arterial phase was the logical regression model, whose AUC value was 0.85, sensitivity was 83.3%, specificity was 80% and F1 score was 0.81. CONCLUSION: Radiomics models based on spleen CT, especially the arterial phase models, demonstrate high diagnostic accuracy for HREV, offering the potential for early detection and intervention.

Predicting High-Risk Esophageal Varices in Cirrhosis: A Multi-Parameter Splenic CT Study.

RATIONALE AND OBJECTIVES: To explore the value of splenic hemodynamic parameters from low-dose one-stop dual-energy and perfusion CT (LD-DE&PCT) in non-invasively predicting high-risk esophageal varices (HREV) in cirrhotic patients. METHODS: We retrospectively analyzed cirrhotic patients diagnosed with esophageal varices (EV) through clinical, laboratory, imaging, and endoscopic examinations from September 2021 to December 2023 in our hospital. All patients underwent LD-DE&PCT to acquire splenic iodine concentration and perfusion parameters. Radiation dose was recorded. Patients were classified into non-HREV and HREV groups based on endoscopy. Univariate and multivariate logistic regression analysis were performed, and the prediction model for HREV was constructed. P < 0.05 was considered statistically significant. RESULTS: Univariate analysis revealed that significant differences were found in portal iodine concentration (PIC), blood flow (BF), permeability surface (PS), spleen volume (V-S), total iodine concentration (TIC), and total blood volume of the spleen (BV-S) between groups. TIC demonstrated the highest predictive value with an area under the curve (AUC) value of 0.87. Multivariate analysis showed that PIC, PS, and BV-S were independent risk factors for HREV. The logistic regression model for predicting HREV had an AUC of 0.93. The total radiation dose was 20.66 ± 4.07 mSv. CONCLUSION: Splenic hemodynamic parameters obtained from LD-DE&PCT can non-invasively and accurately assess the hemodynamic status of the spleen in cirrhotic patients with EV and predict the occurrence of HREV. Despite the retrospective study design and limited sample size of this study, these findings deserve further validation through prospective studies with larger cohorts.

Application of Radiomics in the Efficacy Evaluation of Transarterial Chemoembolization for Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: This meta-analysis was aimed at evaluating the predictive value of radiomics in the context of transarterial chemoembolization (TACE) therapeutic response (TR) for hepatocellular carcinoma (HCC) and patients' survival status (SS) and providing favorable evidence for clinical application. MATERIALS AND METHODS: We searched for literature in which radiomics was applied to assess the TR of TACE for HCC and the affected patients' survival status across PubMed, Embase, Cochrane Library and Web of Science until Jul 12, 2023. The quality of included literature was evaluated using a radiomics quality score (RQS) approach, and a meta-analysis was conducted using Stata15.0. RESULTS: Twenty-four studies were included in the analysis. The meta-analysis revealed that the overall concordance-index (C-index) based on radiomics for predicting the TR and SS with TACE was 0.85 and 0.78, respectively. The combined radiomics-clinical model provided the best performance in evaluating the TR and SS associated with TACE. The C-index was 0.93 and 0.88 for TR and 0.84 and 0.80 for SS, in the training and validation sets, respectively. These values were higher than the 0.87 and 0.79 for TR and 0.79 and 0.70 for SS, respectively with the radiomics model, and 0.71 and 0.66 for TR and 0.72 and 0.66 for SS, respectively with the clinical model. CONCLUSION: The radiomics prediction model for the efficacy of TACE in HCC showed a satisfactory prediction performance. The combined radiomics-clinic prediction model had the best performance.

Developing a Nomogram to Stratify Intracranial Solitary Fibrous Tumor Recurrence.

RATIONALE AND OBJECTIVES: To develop a nomogram to stratify tumor recurrence (TR) in intracranial solitary fibrous tumors (ISFTs) based on the clinical, radiological, and pathological features. MATERIALS AND METHODS: A total of 215 patients from Beijing Tiantan Hospital, Capital Medical University and 48 patients from Lanzhou University Second Hospital, diagnosed with ISFT based on histopathological findings, were included. The patients were randomly divided into training and test cohorts at a ratio of 8:2. Information regarding clinical, radiological, and histopathological features, and the clinical outcomes was retrospectively analyzed. Univariate and multivariate analyses were performed using the Cox proportional hazard model for TR in the training cohort. A nomogram incorporating the independent risk factors was developed in the training cohort and validated in the test cohort. Its predictive performance was analyzed using the Harrell C-index. Decision curve analysis (DCA) was used to evaluate the net clinical benefit. RESULTS: The Harrell C-indices for TR at 3 and 5 years were 0.845 (0.578-0.944) and 0.807 (0.612-0.901) for the test cohort, respectively. In the test cohort, the nomogram provided a net clinical benefit in the DCA over the TR scheme or non-TR scheme. Although postoperative radiotherapy (PORT) was useful for TR prevention, high doses (≥46 Gy) were not superior to lower doses in prolonging the progression-free survival. CONCLUSION: The nomogram obtained in our study had a good predictive performance and could be used for ISFT patients.

Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor.

BACKGROUND: Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE: To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE: Retrospective. POPULATION: Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE: 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT: Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS: Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS: The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION: The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.