Elif Gündoğdu

1. A triple standard treatment protocol is implemented in locally advanced rectal cancer (RCa). This treatment protocol includes chemoradiotherapy, total mesorectal excision, and chemotherapy (Meix...

Artificial intelligence (AI)–based decision support systems are increasingly explored in surgical oncology. However, their concordance with multidisciplinary tumor board (MTB) decisions, particularly in complex gastrointestinal malignancies, remains insufficiently characterized. This retrospective observational study included 47 patients who underwent oncological surgery and were discussed at a multidisciplinary tumor board during an eighteen-month period. For each patient, concordance between MTB-established treatment decisions and subsequent AI-generated recommendations (ChatGPT-based) was assessed. Concordance was categorized as discordant (0), partially concordant (1), or fully concordant (2). Discordant cases were further analyzed across predefined domains, including staging discrepancies, resectability assessment, interpretation of metastatic disease burden, and treatment sequencing. Full concordance was observed in 18 cases (38.3%), partial concordance in 22 cases (46.8%), and discordance in 7 cases (14.9%). Overall, 85.1% of cases demonstrated at least partial concordance. Discordance was more frequent among male patients (5 of 19; 26.3%) compared with female patients (2 of 28; 7.1%). Qualitative analysis revealed that discordance most commonly arose from differences in resectability assessment and staging interpretation. AI recommendations tended to favor broader surgical candidacy, whereas MTB decisions more frequently excluded surgery based on nuanced clinical and contextual factors. AI-generated recommendations show substantial overlap with MTB decisions in most cases but diverge at critical surgical decision points, particularly regarding resectability and staging. These findings suggest that AI may serve as a complementary decision-support tool rather than a substitute for multidisciplinary clinical judgment for oncological surgery care.

This study aimed to evaluate the predictive value of osteosarcopenia for the Peritoneal Cancer Index (PCI) and completeness of cytoreduction (CC score) in patients with peritoneal carcinomatosis. Additionally, the study investigated the impact of osteosarcopenia on major postoperative complications. A total of 80 patients who underwent cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) between January 2015 and October 2024 were retrospectively analyzed. Patients were divided into two groups based on the presence of osteosarcopenia. Demographic characteristics, PCI and CC scores, nutritional and inflammatory markers, and postoperative outcomes were compared. Major postoperative complications were defined as Clavien–Dindo grade ≥3. Independent predictors of major complications were identified using multivariable logistic regression analysis. Osteosarcopenia was detected in 22 patients (27.5%). The proportion of patients with PCI ≥5 was significantly higher in the osteosarcopenia group compared with those without osteosarcopenia (86.4% vs. 62.1%; p = 0.041). Nutritional and inflammatory markers, including PNI, CALLY and LCR, were significantly lower in patients with osteosarcopenia (p=0.031; p=0.044; p=0.019, respectively). Major postoperative complications occurred more frequently in the osteosarcopenia group (36.4% vs. 22.4%; p = 0.004). In multivariate analysis, osteosarcopenia (OR 2.45; p=0.021), PCI ≥5 (OR 3.00; p=0.018), and CC-1 cytoreduction (OR 2.00; p=0.043) emerged as independent predictors of major complications.Osteosarcopenia is associated with higher PCI values and serves as an independent risk factor for major postoperative complications. These results suggest that osteosarcopenia can be considered a useful biomarker in preoperative risk stratification and surgical decision-making for patients undergoing CRS and HIPEC.

Purpose: Chronic liver disease (CLD) is a significant health issue, and detection is crucial for effective treatment. This study aimed to develop a deep learning based convolutional neural network (DeepCNN) to differentiate CLD from non-CLD patients using magnetic resonance imaging (MRI) images without segmentation, enhancing diagnostic accuracy and supporting timely intervention. Material and methods: A retrospective study was conducted using MRI data from 184 patients collected between 2018 and 2024, totaling 1112 images (460 normal, 652 CLD). Various MRI sequences, including axial T1, T2, and coronal, were used. The images were preprocessed with resizing, augmentation, and normalization techniques. The DeepCNN model was trained and compared against traditional machine learning (ML) algorithms, including logistic regression, k-nearest neighbor, support vector machines, and random forest. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Results: The DeepCNN model achieved a 93% accuracy and an F1-score of 0.939. Precision and recall for CLD classification were 97% and 98%, respectively. In comparison, traditional ML algorithms performed with accuracies ranging from 72.31% to 83.16%, with random forest achieving the highest. The DeepCNN model significantly outperformed these methods, demonstrating its strength in medical image classification. Using axial-only images reduced accuracy to 86%, showing that coronal views contribute valuable information. Limitation of data constrained learning. Conclusions: The DeepCNN model provides superior accuracy in diagnosing CLD compared to traditional ML me­thods, using MRI images without segmentation. This approach offers a practical solution for improving CLD detection and paves the way for future enhancements using attention mechanisms and advanced deep learning architectures.

Introduction: In chronic liver disease (CLD) and liver cirrhosis (LC), assessing hepatic function and disease severity is crucial for patient management. This study aimed to evaluate the relationship between platelet-albumin-bilirubin (PALBI) grade and aspartate aminotransferase/platelet ratio index (APRI) with the functional liver imaging score (FLIS) and liver-to-kidney enhancement ratio (LKER) using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced hepatobiliary phase (HBP) magnetic resonance imaging (MRI). Methods: After applying exclusion criteria, 86 patients with CLD or LC who underwent Gd-EOB-DTPA-enhanced MRI between January 2018 and October 2023 were included. APRI and PALBI grades were calculated from laboratory data. FLIS was determined as the sum of three HBP imaging features (liver parenchymal enhancement, biliary excretion, and portal vein sign), with each scoring 0–2. LKER was calculated by dividing liver signal intensity by kidney intensity using region of interest (ROI) measurements. Spearman’s correlation was used to assess relationships between the variables. Results: APRI showed a weak negative correlation with both FLIS (r = –0.327, p = 0.02) and LKER (r = –0.308, p = 0.004). PALBI showed a moderate negative correlation with FLIS (r = –0.495, p = 0.001) and LKER (r = –0.554, p = 0.0001). Discussion: FLIS and LKER moderately correlated with PALBI and weakly with APRI. LKER may be a more practical tool due to its quantitative nature. Despite limitations, combining imaging and lab-based scores could enhance liver function assessment. Conclusion: FLIS and LKER can validate, rather than predict or exclude, liver dysfunction in CLD and LC.

Radiological Evaluation in Endocrine Diseases of The Pancreas - Türk Radyoloji Seminerleri

Hyperthermic intraperitoneal chemotherapy (HIPEC) combined with cytoreductive surgery (CRS) has become an increasingly preferred treatment for women with ovarian cancer presenting with peritoneal involvement. However, due to the high morbidity of HIPEC, accurate preoperative patient selection is essential. This study aimed to evaluate the prognostic value of immunonutritional indices—CALLY (C-reactive protein, albumin, lymphocyte) and Prognostic Nutritional Index (PNI)—in predicting early mortality and recurrence in patients undergoing CRS and HIPEC. A retrospective analysis was conducted on 49 female patients. Low CALLY (

Purpose: The aim of the study is to demonstrate the prognostic impact of preoperative nutritional status indicators, including prognostic nutritional index (PNI), hemoglobin-albumin-lymphocyte-platelet (HALP) score, sarcopenia index (SI), and bone mineral density (BMD) values, on postoperative complications and survival in patients with resectable pancreatic cancer (PC). Materials and methods: The medical data and computed tomography (CT) images of 85 patients who undergone surgery for PC between January 2017 and 2023 were evaluated retrospectively. Patients were grouped according to the presence or absence of sarcopenia and osteoporosis, high and low PNI and HALP scores. The groups were compared in terms of the complication development rate, 30- and 90-day mortality, and 5-years overall survival (OS). Sarcopenia and osteoporosis were determined from CT images (sarcopenia index used for sarcopenia, bone mineral density for osteoporosis). Results: Except from OS (p < 0.0001), no differences were found between sarcopenic and non-sarcopenic groups in terms of postoperative complications, 30- and 90-day mortality (p = 0.775, p = 0.704, p = 0.196, respectively). There were no differences between the groups with and without osteoporosis in terms of the presence of postoperative complications, 30- and 90-day mortality, and OS (p = 0.770, p = 0.608, p = 0.196, p = 0.09, respectively) as low and high HALP score groups (p = 0.236, p = 0.696, p = 0.299, p = 0.45, respectively). Except from a 30-day mortality (p = 0.03), no differences were found between low and high PNI groups in terms of postoperative complications, 90-day mortality, and OS (p = 0.82, p = 0.09, p = 0.18, respectively). Conclusion: PNI may be used as prognostic data for early postoperative mortality, while sarcopenia may be indicative of 5-year OS in patients with resectable PC. Our results suggest that providing nutritional support may potentially improve prognosis. Future studies, in which other factors effective in prognosis are evaluated together with nutritional status, will show more information on this subject.