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BackgroundRadiomics details signal intensity through radiomic features (RFs). We aimed to biologically profile clots in acute ischemic stroke (AIS) using radiomics on non-contrast computed tomography (NCCT).MethodsTen clots retrieved following mechanical thrombectomy were imaged using micro-computed tomography (micro-CT) and subsequently histologically analyzed. Micro-CT slides were matched to histological cuts to identify red blood cells (RBCs), fibrin, white blood cells (WBCs) and calcium. 3D Slicer was used to isolate these components in micro-CT, and RFs were extracted. Multivariate logistic regression was performed to identify RFs associated with clots biological components. Spearman’s rank correlation was used to correlate micro-CT RFs with percentage of composition. The ten clots were localized in NCCT images obtained at admission and RFs were extracted. Micro-CT and NCCT RFs were then correlated. Receiver operating characteristic (ROC) analysis was conducted to retrieve optimal thresholds for RFs in NCCT. Finally, a large dataset of NCCT images from AIS etiologies were analyzed through radiomics.ResultsMicro-CT Total Energy (TE) (OR: 1.35, 95% CI: 1.20–1.54, P= <.001) and Large Dependence High Gray Level Emphasis (LDHGLE) (OR: 1.18, 95% CI: 1.07–1.32, P= 0.01) were associated with RBCs. These features were correlated to histological cuts with 70% of RBCs (Rho 0.654 and Rho 0.721, respectively), NCCT TE and LDHGLE from clots with >70% RBCs per histology (Rho 0.687 and 0.657, respectively). Remaining features were not correlated with their histological percentage of composition. NCCT TE and LDHGLE were sensitive (67%, 67% respectively) and specific (71%,86% respectively) for assessing >70% RBC composition in NCCT. A total of 145 NCCT images including 50/145 cardioembolic, 45/145 large artery atherosclerosis (LAA) and 50/145 cryptogenic clots were analyzed using TE and LDHGLE in NCCT. Radiomics analysis revealed that 36/50 (72%) of cardioembolic, 13/45 (29%) of LAA and 25/50 (50%) of cryptogenic clots were mainly composed of RBCs.ConclusionsRadiomics is promising to assess clots RBCs composition. Cardioembolic clots might be predominantly composed by RBCs.DisclosuresA. Gudino: None. N. Shenoy: None. C. Dier: None. D. Cifuentes: None. R. Calle: None. P. Martinez: None. E. Sagues: None. C. Aamot: None. E. Samaniego: None.

IntroductionMultiple radiomics-based models have been proposed for glioma grading with different magnetic resonance imaging sequences, models, and features.Aim of StudyGiven the heterogeneity and rapid expansion of radiomics for glioma grading, we aimed to better define the overall performance of these different techniques.MethodsWe conducted a systematic review of the literature and a meta-analysis of studies reporting on radiomics for glioma-grade prediction. A comprehensive literature search of the databases PubMed, Ovid MEDLINE, and Ovid EMBASE was designed and conducted by an experienced librarian with input from the authors. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ2 test was performed to assess the heterogeneity.ResultsOverall SEN and SPE for differentiation between low-grade glioma (LGG) and high-grade glioma (HGG) were 91% and 84%, respectively. As for the discrimination task between WHO grade III and WHO grade IV, the overall SEN was 89% and the overall SPE was 81%. There is a better trend for modern non-linear classifiers while textural features are the most used and the best-performing (28.6%).ConclusionThe current diagnostic performance of radiomics for glioma grading is higher for the LGGs vs. HGGs discrimination task than the WHO grade III vs. IV task, both in terms of SEN and SPE. In the forthcoming years, we expect even more precise models, especially for the LGGs vs. HGGs categorization.Disclosure of InterestNothing to disclose

PURPOSE 25% of patients with Crohn’s disease (CD) develop severe stricturing disease which is non-responsive to standard-of-care medication. Early and non-invasive determination of the extent of inflammation and fibrosis within the stricture via CT enterography (CTE) could facilitate the selection of targeted therapy or earlier surgical resection to improve patient outcomes; but currently there is no validated and reliable approach for this differentiation. We present initial results for machine-reader evaluation of severe inflammation and fibrosis in CD strictures via quantitative radiomic features and expert radiologist scoring on CTE. METHODS AND MATERIALS IRB approved, retrospective, single center study. 100 patients (n=66 for discovery; n=34 for hold-out validation) confirmed with stricturing CD on histopathology and CTE within 15 weeks of surgery. Histopathological Stenosis Therapy & Research (STAR) scoring of specimens (range 0-100, scores > 50 =severe) used as reference standard for inflammation and fibrosis each. An expert radiologist annotated the resected strictures on CTE and provided a global assessment of inflammation and chronic non-inflammatory findings (fibrosis) using a 0-100 visual analog score (VAS). Radiomics features to capture severe inflammation and fibrosis were separately extracted from the annotated strictures. Radiomics models and VAS scores evaluated against pathology-defined severe inflammation and fibrosis, via ROC analysis. RESULTS Two distinct sets of radiomic features capturing textural heterogeneity (patterns, wavelets, local entropy) within strictures were significantly associated (p<0.01) with severe inflammation and severe fibrosis; across both discovery (AUC=0.69, 0.69) and hold-out validation (AUCs =0.72,0.67). Radiological VAS had an AUC=0.64 for identifying severe inflammation and AUC =0.62 for identifying severe fibrosis (Figure 1). Clinical variables including sex, age, Montreal classification and stricture type were not significantly associated with severe inflammation or fibrosis, across discovery and validation groups (Table 1). CONCLUSIONS Radiomic analysis shows improved performance in identifying severe inflammation and severe fibrosis in CD strictures on CTE compared to radiological visual assessment scoring. Supplementing radiological visual assessment with quantitative radiomics could enable more accurate phenotyping of CD strictures potentially improving outcomes by personalizing treatment pathways.

BackgroundClots biological composition could affect the first pass effect (FPE) in mechanical thrombectomy (MT). We aimed to biologically profile clots on non-computed tomography (NCCT) through radiomics to assess FPE.MethodsTen clots were retrieved following MT and imaged with micro-Computed tomography (micro-CT) and histologically analyzed. Micro-CT slides were paired with histological cuts. Red blood cells (RBCs), fibrin, white blood cells were identified and matched on micro-CT. 3D Slicer was used to isolate the aforementioned elements and radiomics features (RFs) were retrieved. Multivariate logistic regression was conducted to identify RFs associated to these components. Spearman’s rank correlation was used to correlate Micro-CT RFs with percentage of biological composition. The ten clots were identified in NCCT and NCCT RFs were retrieved. Similarly, micro-CT and NCCT RFs were then correlated. Receiver operating characteristic (ROC) sensitivity (SN) and specificity (SP) analysis was conducted to retrieve optimal thresholds for RFs associated to biological components in NCCT. Moreover, a large set of NCCT images of clots retrieved after MT were biologically evaluated through radiomics. Finally, a logistic regression was conducted to find an association between clots biological composition and rates of FPE. FPE was defined as modified treatment in cerebral infarction (mTICI) more or equal than 2c on first MT attempt.ResultsTotal Energy (TE) (OR: 1.35, 95% CI: 1.20–1.54, P= <.001) and Large Dependence High Gray Level Emphasis (LDHGLE) (OR: 1.18, 95% CI: 1.07–1.32, P= 0.01) were associated with RBCs in micro-CT. Additionally, TE and LDHGLE were correlated with histological slides with > 70% of RBCs (Rho 0.654 and Rho 0.721, respectively) and NCCT TE and LDHGLE (Rho 0.687 and Rho 0.657, respectively) of clots that had > 70% of RBCs per histology. No association was found between RFs and remaining clots components. ROC analysis showed that TE and LDHGLE were sensitive (67% and 67%, respectively) and specific (71% and 86%, respectively) to identify RBCs clot composition higher than 70% in NCCT. TE and LDHGLE thresholds were applied in 150 NCCT images of clots showing that 76/150 (51%) of clots had RBCs as main component. FPE was achieved in 54/150 (36%) of these cases. The presence of more than 70% of RBCs among clots were associated with higher odds of achieving FPE (OR: 2.1, 95% CI: 1.5–4.1, P= 0.001)ConclusionRadiomics hold promise to analyze rates of FPE considering clots RBCs biological composition.Abstract P-032 Figure 1DisclosuresA. Gudino: None. C. Dier: None. N. Shenoy: None. D. Cifuentes: None. R. Calle: None. P. Martinez: None. E. Sagues: None. C. Aamot: None. E. Samaniego: None.

Background. Interstitial Lung Disease (ILD) is a frequent manifestation of Sjögren’s Disease (SjD), associated with significant morbidity and mortality. However, data on the prevalence of progressive-ILD in SjD remain scarce, and biomarkers for predicting progression are lacking. Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) is a validated CT-based software that quantifies ILD patterns such as ground-glass opacities, reticulation, honeycombing, and low attenuation areas. CALIPER-derived parameters correlate with pulmonary function tests (PFT) and outcomes in IPF and other autoimmune ILDs, but their role in SjD-ILD is unexplored. Our aim was to assess the prognostic value of CALIPER-derived parameters in SjD-ILD patients   Materials and Methods. SjD patients (2016 ACR/EULAR criteria) with HRCT-confirmed ILD followed from Jan-2018 to Sep-2023 were retrospectively included if >/=1 HRCT was suitable for CALIPER analysis. Clinical, laboratory and PFT data were collected at baseline and follow-up. Progressive-fibrosing ILD (PF-ILD) was defined (ATS criteria) by >/=2 of: worsening symptoms, FVC decline >/=5% or DLCO >/=10%, or radiological progression within 1 year. CALIPER-derived ILD% (sum of ground-glass, reticular, honeycombing) and VRS% (vascular-related structures) were calculated. Visual ILD extent (Warrick score) and pattern were assessed by a thoracic radiologist   Results. Twenty-three patients (F:M=18:5, mean age 66.2±9.5 yrs, mean follow-up 6.1±5 yrs) were enrolled. ILD patterns included NSIP (n=14), UIP (n=6), and NSIP+OP (n=3). Dyspnea and cough occurred in 21 and 19 patients respectively. Median baseline FVC and DLCO were 83% and 64%. Seventeen patients received immunosuppressants; none received antifibrotics. Seven required long-term oxygen (LTO2) and 7 developed PF-ILD. Baseline ILD% and VRS% demonstrated a moderate-strong correlation with FCV% and DLCO%, visual ILD quantification (Warrick score) and between them (table 1). VRS%, Reticulation%, and Honeycombing% were significantly higher in UIP (p=.021, .038, .047 respectively). On univariate regression analysis, VRS% (OR 3.2, 95%CI 1.1-9.1, p=.03) and ILD% (OR 1.13, 95%CI 1.01-1.3, p=.05) were associated with LTO2. According to ROC analysis, VRS% had an AUC of 0.813 (p=.019) with an optimal cut-off of 3.8% yielding a sensitivity of 71.4% and a specificity of 87.5% in predicting LTO2. ILD% had an AUC of 0.777 (p=.038) with a cut-off of 11.6% resulting in a 71.4% sensitivity and a 75% specificity in predicting LTO2. On univariate analysis VRS% (OR 2.8, 95%CI 1-8, p=.05) was associated with a PF-ILD behavior in the next year, with an AUC of 0.85 (p=.023) and an optimal cut-off of 3.8% resulting in a 83.3% sensitivity and 80% specificity.   Conclusions. CALIPER-derived parameters correlate strongly with lung function and predict adverse outcomes in SjD-ILD. Automated HRCT analysis offers promising digital biomarkers for ILD assessment and risk stratification. Integrating these metrics with clinical and functional data could support early identification of progressive-ILD and guide therapy. Further validation in larger cohorts is warranted.

The journal retracts the article titled “A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET” [...]

BACKGROUND Differentiating fibrotic from inflammatory strictures in Crohn’s disease can be challenging, but it is important for treatment decisions. Artificial intelligence (AI) models applied to magnetic resonance enterography (MRE) and computed tomography enterography (CTE) may improve noninvasive fibrosis assessment. We performed a systematic review and meta-analysis of histology-referenced studies evaluating the performance of these models for fibrosis. METHODS A systematic search was performed in PubMed and Embase from inception through August 2025 for human studies that evaluated AI or radiomics models using MRE or CTE to classify fibrosis in Crohn’s disease. Diagnostic performance was assessed by pooling area under the ROC curve (AUC) values across studies using a random-effects model. We also examined results by imaging modality and validation type, and performed sensitivity analyses to assess the influence of individual studies. RESULTS Four studies that included 698 bowel segments from 576 patients met our inclusion criteria (Table 1). Two studies utilized MRE with internal validation, while two used CTE with external validation. The pooled AUC for fibrosis classification was 0.85 (95% CI, 0.79-0.89; I2 = 84%) (Figure 1). Subgroup analysis showed AUCs of 0.87 (95% CI, 0.65-0.96) for MRE studies and 0.83 (95% CI, 0.80-0.85) for CTE studies. Sensitivity analysis excluding individual studies showed stable pooled values. Inflammation classification was assessed in only one study, which reported an AUC of 0.67. CONCLUSION AI models applied to MRE or CTE show moderate-to-high accuracy for detecting histologic fibrosis in Crohn’s disease strictures. Performance varies based on modality and validation type, with heterogeneity largely driven by study design and overlap between imaging approach and validation strategy. Larger multicenter studies with standardized protocols are needed before use in clinical practice.

Background Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. Method FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. Results The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell’s consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. Conclusion The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.