Background/Objectives: The long-term sequelae of COVID-19 pneumonia, particularly the persistence of imaging abnormalities and their relationship to clinical symptoms, remain unclear. While the acute radiologic patterns are well-documented, the transition to chronic pulmonary changes—and their implications for long COVID symptoms—require systematic investigation. Methods: Our study included 93 patients with moderate to severe COVID-19 pneumonia who were admitted to Istanbul Medical Faculty Hospital, each having one follow-up CT scan over a ten-month period. Two thoracic radiologists independently calculated semi-quantitative initial chest CT scores to evaluate lung involvement in pneumonia (0–5 per lobe, total score 0–25). Two radiologists and one pulmonologist retrospectively examined the persistence of follow-up imaging findings, interpreting them alongside the relevant clinical and laboratory data. Additionally, in a subcohort (n = 46), mid-term (5–7 months) and long-term (≥10 months) scans were compared to assess temporal trajectories. Results: Among the 93 patients with long-term follow-up imaging, non-fibrotic changes persisted in 34 scans (36.6%), while fibrotic-like changes were observed in 70 scans (75.3%). The most common persistent non-fibrotic changes were heterogeneous attenuation (29%, n = 27) and ground-glass opacities (17.2%, n = 16), and the persistent fibrotic-like changes were pleuroparenchymal bands or linear atelectasis (58%, n = 54), fine reticulation (52.6%, n = 49), and subpleural curvilinear lines (34.4%, n = 32). Both persistent non-fibrotic and fibrotic-like changes were statistically correlated with the initial CT score (p < 0.001), LDH (p < 0.001), and ferritin levels (p = 0.008 and p = 0.003, respectively). Fatigue (p = 0.025) and chest pain (p < 0.001) were reported more frequently in patients with persistent non-fibrotic changes, while chest pain (p = 0.033) was reported more frequently among those with persistent fibrotic-like changes. Among the 46 patients who underwent both mid- and long-term follow-up imaging, 47.2% of those with non-fibrotic changes (17 out of 36) and 10% of those with fibrotic-like changes (4 out of 40) exhibited regression over the long term. Conclusions: Initial imaging and laboratory findings may indicate persistent imaging findings related to long-term sequelae of COVID-19 pneumonia. Many of these persistent imaging abnormalities, particularly non-fibrotic changes seen in the mid-term, tend to lessen over the long term. A correlation exists between persistent imaging findings and clinical outcomes of long COVID-19, underscoring the need for further research.

(1) Acute pancreatitis (AP) is a medical emergency associated with high mortality rates. Early and accurate prognosis assessment during admission is crucial for optimizing patient management and outcomes. This study seeks to develop robust radiomics-based machine learning (ML) models to classify the severity of AP using contrast-enhanced computed tomography (CECT) scans. (2) Methods: A retrospective cohort of 287 AP patients with CECT scans was analyzed, and clinical data were collected within 72 h of admission. Patients were classified as mild or moderate/severe based on the Revised Atlanta classification. Two radiologists manually segmented the pancreas and peripancreatic regions on CECT scans, and 234 radiomic features were extracted. The performance of the ML algorithms was compared with that of traditional scoring systems, including Ranson and Glasgow-Imrie scores. (3) Results: Traditional severity scoring systems produced AUC values of 0.593 (Ranson, Admission), 0.696 (Ranson, 48 h), 0.677 (Ranson, Cumulative), and 0.663 (Glasgow-Imrie). Using LASSO regression, 12 radiomic features were selected for the ML classifiers. Among these, the best-performing ML classifier achieved an AUC of 0.876 in the training set and 0.777 in the test set. (4) Conclusions: Radiomics-based ML classifiers significantly enhanced the prediction of AP severity in patients undergoing CECT scans within 72 h of admission, outperforming traditional severity scoring systems. This research is the first to successfully predict prognosis by analyzing radiomic features from both pancreatic and peripancreatic tissues using multiple ML algorithms applied to early CECT images.

Purpose: Distinguishing high-risk intraductal papillary mucinous neoplasms (IPMNs) from low-risk lesions remains a clinical challenge, often resulting in unnecessary procedures due to limited specificity of current methods. While radiomics and deep learning (DL) have been explored for pancreatic cancer, cyst-level malignancy risk stratification of IPMNs remains untapped. Methods: Our multi-institutional assessed the feasibility of AI for predicting IPMN dysplasia grade using cyst-level image features using 359 T2-weighted (T2W) MRI images from seven centers. We developed and compared 2D and 3D radiomics-only, DL-only, and radiomics-DL fusion models using expert radiologist scoring as a baseline reference. Model performance was evaluated using held-out test data. Results: The radiomics-DL fusion model showed the highest discriminatory ability on the test set AUC of 69.2%, outperforming the radiomics-only model, AUC of 66.5%. Expert accuracy varied widely from 37.4% to 66.7%, and the inter-rater agreement varied as well with weighted Cohen’s kappa coefficients of 0.33–0.67. Conclusion: The fusion model, which combines DL with radiomics features from routine T2W MRI, shows promise for objective, cyst-level risk stratification of IPMNs in a multi-center cohort, outperforming radiomics-only models and nearly matching expert radiologists using only T2W and T1-weighted (T1W) sequences. While performance requires improvement for standalone clinical use, this approach offers a scalable, non-invasive method to potentially improve diagnostic accuracy and reduce unnecessary surgical interventions.

This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 MBC, 78 non-metaplastic TNBC) who underwent standardized breast magnetic resonance imaging (MRI), which included T1-weighted contrast-enhanced (T1W-CE) and short-tau inversion recovery (STIR) sequences. Two radiologists performed ground truth lesion segmentation, verified by a senior radiologist. We extracted 214 radiomic features (using PyRadiomics) and used least absolute shrinkage and selection operator (LASSO) regression for feature selection. Seven machine learning classifiers were thoroughly evaluated using five-fold cross-validation, with performance assessed through ROC analysis and accuracy metrics. The combined T1W-CE and STIR analysis demonstrated superior diagnostic performance for distinguishing MBC from non-metaplastic TNBC (AUC = 0.845; accuracy = 81%) compared with either sequence alone (T1W only AUC = 0.805; accuracy = 80%; STIR only AUC:0.768; accuracy = 77%). Multisequence MRI radiomics can reliably distinguish between MBC and TNBC at the time of initial diagnosis. This could potentially facilitate the selection of more appropriate treatments and help avoid ineffective chemotherapy for MBC patients.