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Article Citation - WoS: 6Citation - Scopus: 5Prognostic Value of Metabolic Parameters on Baseline 18f-Fdg Pet/Ct in Small Cell Lung Cancer(Edizioni Minerva Medica, 2022) Araz, Mine; Soydal, Cigdem; Ozkan, Elgin; Sen, Elif; Nak, Demet; Kucuk, Ozlem N.; Kir, K. MetinBACKGROUND: Maximum standardized uptake value (SUVmax) is the primary quantitave parameter given in 18F-FDG PET/CT reports. Calculations derived from three dimensional metabolic volumetric images have been proposed to be more successful than SUVmax alone in prognostification with a lower interobserver variability in many cancers. We aimed to determine the prognostic value of metabolic parameters derived from 18F-FDG PET/CT studies in small cell lung cancer (SCLC) patient population with a long follow-up time. METHODS: In this study, 38 consecutive SCLC patients (34M, 4F, age:65.76 +/- 8.18 years) who were referred to 18F-FDG PET/CT for staging between October 2006-January 2011 were included. SUVmax, SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. Overall survival (OS) was calculated from the date of the initial PET/CT to death from any cause. Survival tables were obtained and Kaplan Meier curves were reconstructed. Mantel-Cox regression analysis was performed in order to investigate if any of these parameters have an effect on survival along with other clinical risk factors. RESULTS: Median SUVmax, SUVmean, SUVpeak, MTV, TLG and LDH values were calculated as 13.9 g/dL, 6.4 g/dL,10.69 g/dL, 147 cm(3), 1898.52 and 375U/L respectively. Median follow-up was 761.23 +/- 873.21 days (25.37 months, range:110-3338 days). Since basal 18F-FDG PET/CT scans, all patients were lost in the follow-up except for two patients. MTV was a significant prognostic factor in SCLC patients. Estimated mean survival times were 261.0 +/- 45.6 (95% CI: 171.6-350.3) days in patients with MTV value above the calculated median 147, and 577.0 +/- 124.0 (95% CI: 333.7-820.2) days in patients with MTV<147. The difference was statistically significant with a P=0.037. CONCLUSIONS: Baseline whole body MTV reflecting total tumor load is a prognostic index in SCLC. SUV is insufficient to predict prognosis.Article Citation - WoS: 1Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review(MDPI, 2025) Ayhan, Cagri; Mekhaeil, Marina; Channawi, Rita; Ozcan, Alp Eren; Akargul, Elif; Deger, Atakan; Soliman, OsamaAcute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models-particularly ensemble algorithms and biomarker-integrated approaches-have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI's growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics-enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia-showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling-potentially through the development of digital twins-will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA.
