Comparison of clinical and radiological characteristics of inflammatory and non-inflammatory Rathke cleft cysts.

PURPOSE: Rathke cleft cysts are commonly encountered sellar lesions, and their inflammation induces symptoms and recurrence. Cyst wall enhancement is related to inflammation; however, its range and frequency have not yet been investigated. This study aimed to investigate the clinical and radiological differences between inflammatory and non-inflammatory Rathke cleft cysts. METHODS: Forty-one patients who underwent cyst decompression surgery for Rathke's cleft cysts between January 2008 and July 2022 were retrospectively analyzed. Based on the pathological reports, patients were divided into inflammatory and non-inflammatory groups. Clinical assessments, endocrinological evaluations, cyst content analysis, and imaging metrics (mean computed tomographic value, maximum diameter, mean apparent diffusion coefficient [ADC] value, and qualitative features) were analyzed. Receiver operating characteristic curve analysis was performed, to determine ADC cutoff values, for differentiating inflammatory group from non-inflammatory group. RESULTS: Totally, 21 and 20 cases were categorized into the inflammatory and non-inflammatory groups, respectively. The inflammatory group displayed a higher incidence of central diabetes insipidus (arginine vasopressin deficiency) (p = 0.04), turbid cyst content (p = 0.03), significantly lower mean ADC values (p = 0.04), and more extensive circumferential wall enhancement on magnetic resonance imaging (MRI) (p < 0.001). In the inflammatory group, all cases revealed circumferential wall enhancement, with some exhibiting thick wall enhancement. There were no significant differences in other radiological features. The ADC cutoff value for differentiating the two groups was 1.57 × 10-3 mm2/s, showing a sensitivity of 81.3% and specificity of 66.7% CONCLUSION: Inflammatory Rathke cleft cysts tended to show a higher incidence of central diabetes insipidus and turbid cyst content. Radiologically, they exhibited lower mean ADC values and greater circumferential wall enhancement on MRI.

ChatGPT's diagnostic performance based on textual vs. visual information compared to radiologists' diagnostic performance in musculoskeletal radiology.

OBJECTIVES: To compare the diagnostic accuracy of Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT-4 with vision (GPT-4V) based ChatGPT, and radiologists in musculoskeletal radiology. MATERIALS AND METHODS: We included 106 "Test Yourself" cases from Skeletal Radiology between January 2014 and September 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Two radiologists (a radiology resident and a board-certified radiologist) independently provided diagnoses for all cases. The diagnostic accuracy rates were determined based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT-4-based ChatGPT significantly outperformed GPT-4V-based ChatGPT (p < 0.001) with accuracy rates of 43% (46/106) and 8% (9/106), respectively. The radiology resident and the board-certified radiologist achieved accuracy rates of 41% (43/106) and 53% (56/106). The diagnostic accuracy of GPT-4-based ChatGPT was comparable to that of the radiology resident, but was lower than that of the board-certified radiologist although the differences were not significant (p = 0.78 and 0.22, respectively). The diagnostic accuracy of GPT-4V-based ChatGPT was significantly lower than those of both radiologists (p < 0.001 and < 0.001, respectively). CONCLUSION: GPT-4-based ChatGPT demonstrated significantly higher diagnostic accuracy than GPT-4V-based ChatGPT. While GPT-4-based ChatGPT's diagnostic performance was comparable to radiology residents, it did not reach the performance level of board-certified radiologists in musculoskeletal radiology. CLINICAL RELEVANCE STATEMENT: GPT-4-based ChatGPT outperformed GPT-4V-based ChatGPT and was comparable to radiology residents, but it did not reach the level of board-certified radiologists in musculoskeletal radiology. Radiologists should comprehend ChatGPT's current performance as a diagnostic tool for optimal utilization. KEY POINTS: This study compared the diagnostic performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists in musculoskeletal radiology. GPT-4-based ChatGPT was comparable to radiology residents, but did not reach the level of board-certified radiologists. When utilizing ChatGPT, it is crucial to input appropriate descriptions of imaging findings rather than the images.

A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan.

BACKGROUND: Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function is unknown. We aimed to estimate two major pulmonary functions from chest x-rays. METHODS: In this retrospective model development and validation study, we trained, validated, and externally tested a deep learning-based artificial intelligence (AI) model to estimate forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) from chest x-rays. We included consecutively collected results of spirometry and any associated chest x-rays that had been obtained between July 1, 2003, and Dec 31, 2021, from five institutions in Japan (labelled institutions A-E). Eligible x-rays had been acquired within 14 days of spirometry and were labelled with the FVC and FEV1. X-rays from three institutions (A-C) were used for training, validation, and internal testing, with the testing dataset being independent of the training and validation datasets, and then x-rays from the two other institutions (D and E) were used for independent external testing. Performance for estimating FVC and FEV1 was evaluated by calculating the Pearson's correlation coefficient (r), intraclass correlation coefficient (ICC), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) compared with the results of spirometry. FINDINGS: We included 141 734 x-ray and spirometry pairs from 81 902 patients from the five institutions. The training, validation, and internal test datasets included 134 307 x-rays from 75 768 patients (37 718 [50%] female, 38 050 [50%] male; mean age 56 years [SD 18]), and the external test datasets included 2137 x-rays from 1861 patients (742 [40%] female, 1119 [60%] male; mean age 65 years [SD 17]) from institution D and 5290 x-rays from 4273 patients (1972 [46%] female, 2301 [54%] male; mean age 63 years [SD 17]) from institution E. External testing for FVC yielded r values of 0·91 (99% CI 0·90-0·92) for institution D and 0·90 (0·89-0·91) for institution E, ICC of 0·91 (99% CI 0·90-0·92) and 0·89 (0·88-0·90), MSE of 0·17 L2 (99% CI 0·15-0·19) and 0·17 L2 (0·16-0·19), RMSE of 0·41 L (99% CI 0·39-0·43) and 0·41 L (0·39-0·43), and MAE of 0·31 L (99% CI 0·29-0·32) and 0·31 L (0·30-0·32). External testing for FEV1 yielded r values of 0·91 (99% CI 0·90-0·92) for institution D and 0·91 (0·90-0·91) for institution E, ICC of 0·90 (99% CI 0·89-0·91) and 0·90 (0·90-0·91), MSE of 0·13 L2 (99% CI 0·12-0·15) and 0·11 L2 (0·10-0·12), RMSE of 0·37 L (99% CI 0·35-0·38) and 0·33 L (0·32-0·35), and MAE of 0·28 L (99% CI 0·27-0·29) and 0·25 L (0·25-0·26). INTERPRETATION: This deep learning model allowed estimation of FVC and FEV1 from chest x-rays, showing high agreement with spirometry. The model offers an alternative to spirometry for assessing pulmonary function, which is especially useful for patients who are unable to undergo spirometry, and might enhance the customisation of CT imaging protocols based on insights gained from chest x-rays, improving the diagnosis and management of lung diseases. Future studies should investigate the performance of this AI model in combination with clinical information to enable more appropriate and targeted use. FUNDING: None.

Diagnostic accuracy of vision-language models on Japanese diagnostic radiology, nuclear medicine, and interventional radiology specialty board examinations.

PURPOSE: The performance of vision-language models (VLMs) with image interpretation capabilities, such as GPT-4 omni (GPT-4o), GPT-4 vision (GPT-4V), and Claude-3, has not been compared and remains unexplored in specialized radiological fields, including nuclear medicine and interventional radiology. This study aimed to evaluate and compare the diagnostic accuracy of various VLMs, including GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus, using Japanese diagnostic radiology, nuclear medicine, and interventional radiology (JDR, JNM, and JIR, respectively) board certification tests. MATERIALS AND METHODS: In total, 383 questions from the JDR test (358 images), 300 from the JNM test (92 images), and 322 from the JIR test (96 images) from 2019 to 2023 were consecutively collected. The accuracy rates of the GPT-4 + GPT-4V, GPT-4o, Claude-3 Sonnet, and Claude-3 Opus were calculated for all questions or questions with images. The accuracy rates of the VLMs were compared using McNemar's test. RESULTS: GPT-4o demonstrated the highest accuracy rates across all evaluations with the JDR (all questions, 49%; questions with images, 48%), JNM (all questions, 64%; questions with images, 59%), and JIR tests (all questions, 43%; questions with images, 34%), followed by Claude-3 Opus with the JDR (all questions, 40%; questions with images, 38%), JNM (all questions, 42%; questions with images, 43%), and JIR tests (all questions, 40%; questions with images, 30%). For all questions, McNemar's test showed that GPT-4o significantly outperformed the other VLMs (all P < 0.007), except for Claude-3 Opus in the JIR test. For questions with images, GPT-4o outperformed the other VLMs in the JDR and JNM tests (all P < 0.001), except Claude-3 Opus in the JNM test. CONCLUSION: The GPT-4o had the highest success rates for questions with images and all questions from the JDR, JNM, and JIR board certification tests.

Data set terminology of deep learning in medicine: a historical review and recommendation.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

Embolization with or without portal vein stenting for bleeding ectopic jejunal varices in hepatopetal portal collaterals due to extrahepatic portal vein occlusion or stenosis after hepatobiliary and pancreatic surgery.

PURPOSE: To evaluate the efficacy and safety of embolization with or without portal vein stenting for bleeding ectopic jejunal varices in the hepatopetal portal collateral due to extrahepatic portal vein occlusion or stenosis after hepatobiliary and pancreatic surgery. MATERIALS AND METHODS: This study included consecutive patients who underwent embolization for bleeding ectopic jejunal varices in the hepatopetal collateral due to extrahepatic portal vein occlusion or stenosis after hepatobiliary and pancreatic surgery between September 2012 and December 2020. The safety, technical and clinical success rates (no re-bleeding within 1 month) and re-bleeding-free survival after the first therapy and overall survival were assessed. RESULTS: Fourteen sessions in 11 patients were included. Four patients (7 sessions) underwent variceal embolization only, and the remaining seven patients (7 sessions) underwent portal vein stenting and variceal embolization. Technical success was achieved in all 14 sessions (100%). Clinical success was achieved in 13 of 14 sessions (92.9%). No treatment-related serious complications including liver failure were observed. One-year and 2-year re-bleeding-free survival rate after the first endovascular therapy in all 11 patients was 90.9 and 60.6%, respectively. Two patients who experienced re-bleeding had repeat embolization treatment. There was no significant difference in re-bleeding-free survival after endovascular therapy between the combination with stenting and embolization group and the embolization-only group (p = 0.13). CONCLUSION: Embolization with or without portal vein stenting of bleeding ectopic jejunal varices in the hepatopetal portal collateral due to extrahepatic portal vein occlusion or stenosis after hepatobiliary and pancreatic surgery can be considered a safe, effective, and repeatable therapy for long-term hemostasis of uncontrollable bleeding.

A rat-based preclinical platform facilitating transcatheter hepatic arterial infusion in immunodeficient rats with liver xenografts of patient-derived pancreatic ductal adenocarcinoma.

Liver metastases from pancreatic ductal adenocarcinoma (PDAC) are highly fatal. A rat-based patient-derived tumor xenograft (PDX) model is available for transcatheter therapy. This study aimed to create an immunodeficient rat model with liver xenografts of patient-derived primary PDAC and evaluate efficacy of hepatic arterial infusion chemotherapy with cisplatin in this model. Three patient-derived PDACs were transplanted into the livers of 21 rats each (totally, 63 rats), randomly assigned into hepatic arterial infusion, systemic venous infusion, and control groups (n = 7 each) four weeks post-implantation. Computed tomography evaluated tumor volumes before and four weeks after treatment. Post-euthanasia, resected tumor specimens underwent histopathological examination. A liver-implanted PDAC PDX rat model was established in all 63 rats, with first CT identifying all tumors. Four weeks post-treatment, arterial infusion groups exhibited significantly smaller tumor volumes than controls for all three tumors on second CT. Xenograft tumors histologically maintained adenocarcinoma features compared to original patient tumors. Ki67 expression was significantly lower in arterial infusion groups than in the other two for the three tumors, indicating reduced tumor growth in PDX rats. A liver-implanted PDAC PDX rat model was established as a rat-based preclinical platform. Arterial cisplatin infusion chemotherapy represents a potential therapy for PDAC liver metastasis.

Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.

PURPOSE: To compare the diagnostic performance among Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT­4 with vision (GPT-4V) based ChatGPT, and radiologists in challenging neuroradiology cases. METHODS: We collected 32 consecutive "Freiburg Neuropathology Case Conference" cases from the journal Clinical Neuroradiology between March 2016 and December 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case. Six radiologists (three radiology residents and three board-certified radiologists) independently reviewed all cases and provided diagnoses. ChatGPT and radiologists' diagnostic accuracy rates were evaluated based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. RESULTS: GPT­4 and GPT-4V-based ChatGPTs achieved accuracy rates of 22% (7/32) and 16% (5/32), respectively. Radiologists achieved the following accuracy rates: three radiology residents 28% (9/32), 31% (10/32), and 28% (9/32); and three board-certified radiologists 38% (12/32), 47% (15/32), and 44% (14/32). GPT-4-based ChatGPT's diagnostic accuracy was lower than each radiologist, although not significantly (all p > 0.07). GPT-4V-based ChatGPT's diagnostic accuracy was also lower than each radiologist and significantly lower than two board-certified radiologists (p = 0.02 and 0.03) (not significant for radiology residents and one board-certified radiologist [all p > 0.09]). CONCLUSION: While GPT-4-based ChatGPT demonstrated relatively higher diagnostic performance than GPT-4V-based ChatGPT, the diagnostic performance of GPT­4 and GPT-4V-based ChatGPTs did not reach the performance level of either radiology residents or board-certified radiologists in challenging neuroradiology cases.

Development and Validation of Artificial Intelligence-Based Algorithms for Predicting the Segments Debulked by Rotational Atherectomy Using Intravascular Ultrasound Images.

We develop and evaluate an artificial intelligence (AI)-based algorithm that uses pre-rotation atherectomy (RA) intravascular ultrasound (IVUS) images to automatically predict regions debulked by RA. A total of 2106 IVUS cross-sections from 60 patients with de novo severely calcified coronary lesions who underwent IVUS-guided RA were consecutively collected. The 2 identical IVUS images of pre- and post-RA were merged, and the orientations of the debulked segments identified in the merged images were marked on the outer circle of each IVUS image. The AI model was developed based on ResNet (deep residual learning for image recognition). The architecture connected 36 fully connected layers, each corresponding to 1 of the 36 orientations segmented every 10°, to a single feature extractor. In each cross-sectional analysis, our AI model achieved an average sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 81%, 72%, 46%, 90%, and 75%, respectively. In conclusion, the AI-based algorithm can use information from pre-RA IVUS images to accurately predict regions debulked by RA and will assist interventional cardiologists in determining the treatment strategies for severely calcified coronary lesions.

Percutaneous transhepatic sclerotherapy for ascending colonic varices due to left-sided portal hypertension.

Left-sided portal hypertension (LSPH) causes varices and splenomegaly due to splenic vein issues. Colonic varices are rare and lack standardized treatment. We report the successful treatment of colonic varices caused by LSPH, by addressing both the afferent and efferent veins. A 70-year-old man with distal cholangiocarcinoma had surgery without splenic vein resection, leading to proximal splenic vein stenosis and varices at multiple locations. Percutaneous transhepatic splenic venography revealed that collateral veins flowed into the ascending colonic varices and returned to the portal vein. Complete thrombosis of the varices was achieved by injecting sclerosants and placing coils in both the afferent and efferent veins. The procedure was safe and effective, with no variceal recurrence. This approach provides a minimally invasive option for treating colonic varices associated with LSPH.

Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.

BACKGROUND: Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another. PURPOSE: This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging. DATA SOURCES: PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images. STUDY SELECTION: This review collected 102 works published between April 2017 and August 2023. DATA ANALYSIS: Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test. DATA SYNTHESIS: Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions. LIMITATIONS: Our review is limited by the study design and model heterogeneity. CONCLUSIONS: Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.

A case of percutaneous deep pelvic abscess drainage using CT fluoroscopic guided cranio-caudal puncture technique.

A 52-year-old male patient presented with complaints of abdominal and back pain. CT revealed a deep pelvic abscess extending into the anterior sacral space. Since puncture via the conventional transgluteal approach cannot reach a deep abscess, percutaneous pelvic abscess drainage was performed under CT fluoroscopy using the cranio-caudal puncture technique. The cranio-caudal puncture requires needle insertion perpendicular to the CT cross-section. This method advances the CT gantry deeper than the needle tip and follows the CT cross-section with the needle tip. This series of images and movements continues until the needle reaches the target. The procedure was successful without complications, the abscess was reduced in size, and blood test data improved. The cranio-caudal puncture technique provides an alternative for the drainage of deep pelvic abscesses that avoids the complications associated with gluteal muscle puncture. Percutaneous drainage of pelvic abscesses under CT fluoroscopy-guided cranio-caudal puncture offers a safe option as a puncture route for deep pelvic abscesses.

Evaluation of cranial nerve involvement in chordomas and chondrosarcomas: a retrospective imaging study.

PURPOSE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). CONCLUSION: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

Deep learning-based diffusion tensor image generation model: a proof-of-concept study.

This study created an image-to-image translation model that synthesizes diffusion tensor images (DTI) from conventional diffusion weighted images, and validated the similarities between the original and synthetic DTI. Thirty-two healthy volunteers were prospectively recruited. DTI and DWI were obtained with six and three directions of the motion probing gradient (MPG), respectively. The identical imaging plane was paired for the image-to-image translation model that synthesized one direction of the MPG from DWI. This process was repeated six times in the respective MPG directions. Regions of interest (ROIs) in the lentiform nucleus, thalamus, posterior limb of the internal capsule, posterior thalamic radiation, and splenium of the corpus callosum were created and applied to maps derived from the original and synthetic DTI. The mean values and signal-to-noise ratio (SNR) of the original and synthetic maps for each ROI were compared. The Bland-Altman plot between the original and synthetic data was evaluated. Although the test dataset showed a larger standard deviation of all values and lower SNR in the synthetic data than in the original data, the Bland-Altman plots showed each plot localizing in a similar distribution. Synthetic DTI could be generated from conventional DWI with an image-to-image translation model.

A case of recurrent laryngeal nerve paralysis caused by radiofrequency ablation for mediastinal recurrence of lung cancer.

Radiofrequency ablation (RFA) has emerged as a potent therapeutic modality for tumor treatment, and offers benefits such as reduced recovery time and minimal damage to nearby tissues. However, RFA is not devoid of complications, notably nerve damage during intrathoracic lesion treatments, which can significantly impact patients' quality of life. This report describes the unique case of a 71-year-old male who experienced hoarseness attributed to injury to the recurrent nerve after RFA for a locally recurrent lung cancer lesion in the mediastinum near the aortic arch. Although RFA has the advantages of a minimally invasive nature and positive outcomes, its risk of nerve injury, specifically in the thoracic region, highlights the need for improved techniques and preventive measures.

Correlation between Pancreatic Fat Deposition and Metabolic Syndrome: Relationships with Location in the Pancreas and Sex.

Objective To investigate the correlation between pancreatic fat deposition and metabolic syndrome (MetS) parameters, focusing on the locations of fat deposition in the pancreas and sex differences. Methods Degrees of fat deposition in the head, body, and tail of the pancreas were evaluated using computed tomography (CT). We examined the relationships between pancreatic fat deposition and the age, body mass index (BMI), visceral and subcutaneous fat, serum lipid profiles, hepatic steatosis, diabetes mellitus (DM), and hypertension (HTN). Results In this retrospective study, greater fat deposition was associated with a higher BMI, visceral and subcutaneous fat accumulation, and hepatic steatosis, with the pancreatic head showing the strongest correlation. Correlations of pancreatic fat deposition with the BMI and visceral and subcutaneous fat accumulation were stronger in females than in males, while correlations with hepatic steatosis were stronger in males than in females. In addition, a multivariate analysis did not suggest a direct causal relationship between pancreatic fat deposition and DM and HTN, but there was a significant correlation between pancreatic fat deposition in the pancreatic head and visceral fat area. Conclusion Pancreatic fat deposition, as evaluated by CT, especially in the part of the pancreatic head adjacent to the ampulla of Vater, is a sensitive indicator of MetS. The correlations between pancreatic fat deposition and MetS parameters tended to be stronger in females than in males. These results may help further elucidate the pathophysiology of MetS and provide opportunities for its diagnosis.