Appendiceal intussusception is a rare condition characterized by the invagination of the appendix into the base of the cecum. In some cases, this condition can lead to obstruction, ischemia, and perforation. It is more common in elderly patients, particularly women, and is often associated with the presence of a lesion, benign or malignant, acting as a “lead point.” This case report details the emergency management of a 54-year-old female patient with severe abdominal pain, nausea, and vomiting. The physical examination was unremarkable, as were the laboratory tests. However, ultrasound revealed a small amount of fluid in the pelvis, leading to further investigation with a CT scan which showed appendiceal intussusception with significant wall edema, fluid in the surrounding fatty tissue, and reactive lymph nodes. The patient then underwent an operation that confirmed an intussuscepted appendix showing signs of ischemia. Histopathology showed the presence of an endometrioma acting as the “lead point”. This case report showcases the crucial role of diagnostic imaging, which is superior to clinical examination and laboratory tests when diagnosing these patients. Diagnostic imaging, coupled with careful differential diagnosis, is vital to distinguishing benign conditions from malignancy and ensuring timely and appropriate intervention. Early diagnosis and surgical intervention are essential to prevent life-threatening complications such as gangrene and perforation and exclude malignancy in adult patients.

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Radiomics and artificial intelligence (AI) have emerged as promising tools for quantitative imaging analysis and precision staging. This study aimed to evaluate the ability of an AI-based radiomics model to preoperatively predict tumor (T) and nodal (N) stage, lymphovascular invasion (LVI), and postoperative complications in patients with early-stage NSCLC. Material and Methods: This retrospective study included 51 consecutive patients who underwent anatomical lobectomy with systematic lymph node dissection between 2019 and 2024, at the Clinic for Thoracic Surgery of the University Clinical Center of Serbia. Quantitative imaging features were extracted from preoperative CT scans using the Lesion Scout with Auto ID module (syngo.via VB50 MM, Siemens Healthineers). Radiomics and clinical predictors were analyzed using regularized logistic regression (LASSO) with five-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, precision, and F1 score, and calibration was evaluated using the Hosmer–Lemeshow test. Groups were compared using parametric and non-parametric tests. Correlation between the variables was assessed using Spearman’s rank correlation coefficient. All p-values less than 0.05 were considered significant. Results: The AI-based model showed excellent performance for predicting the T component (training AUC = 0.89; test AUC = 0.86; F1 = 0.81) and acceptable calibration (p = 0.41). Nodal metastasis (OR = 0.108; 95% CI: 0.011–1.069; p = 0.057) and LVI (OR = 0.519; 95% CI: 0.139–1.937; p = 0.329) were not significantly predicted. Emphysema was identified as a significant independent predictor of postoperative complications (χ² = 5.13; p = 0.024). Conclusions: The AI-driven radiomics model demonstrated strong predictive ability for the T component and identified emphysema as a clinically relevant predictor of postoperative complications.

This case represents a relatively rare localization of a gastrointestinal stromal tumor involving the distal esophagus with a very unusual mode of metastatic spread to the lymph nodes and bone structures. Diagnostic follow-up showed the effect of tyrosine kinase inhibitor therapy on metastatic lesions, highlighting the necessity of comprehensive diagnostic evaluation in patients with atypical presentations and contributing to the improvement of therapeutic strategies in complex clinical scenarios.