Endovascular treatment of a pancreatic artery pseudoaneurysm with arterioportal fistula in chronic pancreatitis: A case report.

Arterioportal fistula (APF) combined with a visceral artery pseudoaneurysm is an exceptionally rare and critical vascular disorder of the abdominal viscera, with pseudoaneurysm rupture being potentially fatal and severe APF leading to portal hypertension, both of which necessitate immediate intervention. An 87-year-old woman with a history of pancreatitis presented with upper abdomen and back pain. Laboratory tests revealed elevated amylase levels and severe anemia. A computed tomography (CT) scan showed a large dorsal pancreatic artery (DPA) pseudoaneurysm with a fistula to the main portal vein. Given her advanced age, surgery was deemed high-risk, and endovascular treatment was selected. Transcatheter arterial embolization was successfully performed using coils to embolize the DPA pseudoaneurysm. A follow-up CT 1 week postprocedure confirmed the absence of a pseudoaneurysm and no further progression of anemia.

Artificial Intelligence in Obstetric and Gynecological MR Imaging.

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.

The Utility of Apparent Water Diffusion Coefficient Maps for Evaluating the Presence of Myometrial Invasion in Patients with Endometrial Cancer.

PURPOSE: To assess the utility of apparent diffusion coefficient maps (ADC) for diagnosing myometrial invasion (MI) in endometrial cancer (EC). METHODS: This retrospective study included 164 patients (mean age, 56 years; range, 25-89 years) who underwent preoperative MRI for EC with <1/2 MI or no MI between April 2016 and July 2023. Five sequences were evaluated: T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), ADC, dynamic contrast-enhanced T1-weighted imaging (DCE-T1WI), and contrast-enhanced T1WI (CE-T1WI). Three experienced radiologists independently assessed the sequences for MI. For ADC, MI was determined if the endometrial-myometrial junction-tumor boundary had disappeared. Additionally, the assessment of MI was performed using the combination of T2WI, DWI, and ADC, as well as T2WI, DCE-T1WI, and CE-T1WI. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for the presence of MI were calculated and compared between the sequences and combinations. Inter-reader agreement was assessed using kappa (κ) statistics. RESULTS: The sensitivity of ADC was significantly higher than T2WI (P < 0.001) and DCE-T1WI (P = 0.018) for one reader and significantly higher than CE-T1WI (P = 0.045 and 0.043) for two readers. The specificity of ADC was significantly lower than T2WI (P = 0.015 and < 0.001) and CE-T1WI (P = 0.031 and 0.01) for two readers and significantly lower than DCE-T1WI (P = 0.031) for one reader. The AUC of ADC was significantly higher than T2WI (P = 0.048) and DCE-T1WI (P = 0.049) for one reader. The combination including ADC showed higher positive predictive value for all three readers compared to any sequence or combination including contrast enhancement. Additionally, ADC demonstrated the highest agreement rates. CONCLUSION: ADC had high sensitivity for MI and the highest agreement rate among all sequences. Thus, this sequence, combined with other sequences, can be crucial for a comprehensive evaluation of MI.

Diagnostic Imaging Performance of Dual-Energy Computed Tomography Compared with Conventional Computed Tomography and Magnetic Resonance Imaging for Uterine Cervical Cancer.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI ( p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI ( p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading.

Diagnostic imaging analysis to differentiate struma ovarii from mucinous carcinomas, encompassing T2*-based imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging.

OBJECTIVES: To clarify the differences between struma ovarii (SO) and mucinous carcinomas (MC) on CT and MRI, including T2*-based images, diffusion-weighted images (DWI), and time-intensity curve (TIC) patterns, which have not been previously reported. METHODS: We retrospectively compared the presence of low intensity on T2-weighted and T2*-based images, high intensity on T1-weighted images, hyperattenuation on non-contrast CT, TIC pattern, T2 ratio, T1 ratio, CT value, and apparent diffusion coefficient (ADC) value in 15 patients with SO and 27 patients with MC. RESULTS: SO exhibited a significantly higher frequency of low intensity on T2-weighted and T2*-based images, and hyperattenuation on non-contrast CT than MC (P < .001, <.001, and .006, respectively). The T2 ratios and CT attenuation of the locules were also significantly different (P < .001, and .006, respectively). In SO, sites of low intensity on T2-weighted and T2*-based images and sites of hyperattenuation on CT images always coincided. Regarding the TIC pattern, most SO showed a high-risk pattern, with a significant difference (P = .003). The ADC values of SO were significantly lower, and only one case of SO showed high signal intensity on DWI. CONCLUSIONS: SO were more frequently with low intensity on T2-weighted and T2*-based images, and hyperattenuation on non-contrast CT, and showed high-risk TIC patterns without diffusion restriction. ADVANCES IN KNOWLEDGE: SO shows a high-risk TIC pattern but can be specifically diagnosed in combination with the lack of diffusion restriction and loculi with marked hypointensity on T2-weighted and T2*-based images consistent with hyperattenuation on non-contrast CT.

Comparison of Benign, Borderline, and Malignant Ovarian Seromucinous Neoplasms on MR Imaging.

PURPOSE: This study aimed to compare MRI findings among benign, borderline, and malignant ovarian seromucinous neoplasms. METHODS: We retrospectively analyzed MRI data from 24 patients with ovarian seromucinous neoplasms-seven benign, thirteen borderline, and six malignant. The parameters evaluated included age, tumour size, morphology, number, height, apparent diffusion coefficient (ADC) values, T2 ratios, time-intensity curve (TIC) descriptors, and TIC patterns of the mural nodules. Additionally, we examined the T2 and T1 ratios of the cyst contents, tumour markers, and the presence of endometriosis. We used statistical tests, including the Kruskal-Wallis and Fisher-Freeman-Halton exact tests, to compare these parameters among the three aforementioned groups. RESULTS: The cases showed papillary architecture with internal branching in 57% of benign, 92% of borderline, and 17% of malignant cases. Three or fewer mural nodules were seen in 57% of benign, 8% of borderline, and 17% of malignant cases. Compared to benign and borderline tumours, mural nodules of malignant neoplasms had significantly increased height (P = 0.015 and 0.011, respectively), lower means ADC values (P = 0.003 and 0.035, respectively). The mural nodules in malignant cases also demonstrated significantly lower T2 ratios than those in the benign cases (P = 0.045). Most neoplasms displayed an intermediate-risk TIC pattern, including 80% benign, 83% borderline, and 60% malignant neoplasms, and no significant differences were observed. CONCLUSION: Most benign and borderline tumours exhibited a papillary architecture with an internal branching pattern, whereas this feature was less common in malignant neoplasms. Additionally, benign tumours had fewer mural nodules compared to borderline tumours. Malignant neoplasms were characterized by mural nodules with increased height and lower ADC values than those in benign and borderline tumours. Interestingly, all three groups predominantly exhibited an intermediate-risk TIC pattern, emphasizing the complexity of diagnosing seromucinous neoplasms using MRI.

Comparison of preoperative diagnostic performance between dual-energy CT, conventional CT, and MRI in endometrial cancer.

Purpose: To compare the diagnostic performance of virtual monoenergetic imaging (VMI), computed tomography (CT), and magnetic resonance imaging (MRI) in patients with endometrial cancer (EC). Material and methods: This retrospective study analysed 45 EC patients (mean age: 62 years, range: 44-84 years) undergoing contrast-enhanced CT with dual-energy CT (DECT) and MRI between September 2021 and October 2022. Dual-energy CT generated conventional CT (C-CT) and 40 keV VMI. Quantitative analysis compared contrast-to-noise ratio (CNR) of tumour to myometrium between C-CT and VMI. Qualitative assessment by 5 radiologists compared C-CT, VMI, and MRI for myometrial invasion (MI), cervical invasion, and lymph node metastasis. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated and compared for each diagnostic parameter. Results: Virtual monoenergetic imaging showed significantly higher CNR than C-CT (p < 0.001) and a higher sensitivity for MI than C-CT (p = 0.027) and MRI (p = 0.011) but lower specificity than MRI (p = 0.018). C-CT had a higher sensitivity and AUC for cervical invasion than MRI (p = 0.018 and 0.004, respectively). Conclusions: The study found no significant superiority of MRI over CT across all diagnostic parameters. VMI demonstrated heightened sensitivity for MI, and C-CT showed greater sensitivity and AUC for cervical invasion than MRI. This suggests that combining VMI with C-CT holds promise as a comprehensive preoperative staging tool for EC when MRI cannot be performed.

Imaging approaches for the diagnosis of genetic diseases affecting the female reproductive organs and beyond.

This review aims to provide an overview of neoplastic lesions associated with genetic diseases affecting the female reproductive organs. It seeks to enhance our understanding of the radiological aspects in diagnosing genetic diseases including hereditary breast and ovarian cancer syndromes, Lynch syndrome, Peutz-Jeghers syndrome, nevoid basal cell carcinoma syndrome, and Swyer syndrome, and explores the patterns and mechanisms of inheritance that require elucidation. Additionally, we discuss the imaging characteristics of lesions occurring in other regions due to the same genetic diseases.

Imaging findings in inflammatory disease of the genital organs.

This review focuses on inflammatory diseases of female and male genital organs and discusses their epidemiology, pathogenesis, clinical presentation, and imaging findings. The female section covers pelvic inflammatory disease (PID) primarily caused by sexually transmitted infections (STIs) that affect the uterus, fallopian tubes, and ovaries. Unusual causes such as actinomycosis and tuberculosis have also been explored. The male section delves into infections affecting the vas deferens, epididymis, testes, prostate, and seminal vesicles. Uncommon causes such as tuberculosis, and Zinner syndrome have also been discussed. In addition, this review highlights other conditions that mimic male genital tract infections such as vasculitis, IgG4-related diseases, and sarcoidosis. Accurate diagnosis and appropriate management of these inflammatory diseases are essential for preventing serious complications and infertility. Imaging modalities such as ultrasound, magnetic resonance imaging, and computed tomography play a crucial role in diagnosis. Understanding the diverse etiologies and imaging findings is vital for the effective management of inflammatory diseases of the genital organs.

Differences in the position of endometriosis-associated and non-associated ovarian cancer relative to the uterus.

BACKGROUND: Preoperative assessment of the histological type of ovarian cancer is essential to determine the appropriate treatment strategy. Tumor location may be helpful in this regard. The purpose of this study was to compare the position of endometriosis-associated (EAOCs) and non-associated (non-EAOCs) ovarian cancer relative to the uterus using MRI. METHODS: This retrospective study included patients with pathologically confirmed malignant epithelial ovarian tumors who underwent MRI at our hospital between January 2015 and January 2023. T2-weighted images of the sagittal and axial sections of the long axis of the uterine body were used for the analysis. Three blinded experienced radiologists independently interpreted the images and assessed whether the ovarian tumor was attached to the uterus, and the angle between the uterus and the tumor was measured. The presence of attachment and the measured angles were compared for each histology. In addition, the angles between EAOCs, including endometrioid carcinomas (ECs) and clear cell carcinomas (CCCs), were compared with non-EAOCs. RESULTS: In total, 184 women (mean age, 56 years; age range, 20-91 years) were evaluated. High-grade serous carcinomas (HGSCs) were significantly smaller than the others and had significantly less uterine attachment than CCCs (p < 0.01 for all readers). According to the mean of the measured angles, CCCs were positioned significantly more posteriorly than HGSCs and mucinous carcinomas (p < 0.02), and EAOCs were positioned significantly more posteriorly to the uterus than non-EAOCs (p < 0.01). CONCLUSION: HGSCs are often not attached to the uterus, and EAOCs are positioned more posteriorly to the uterus than non-EAOCs. CRITICAL RELEVANCE STATEMENT: High-grade serous carcinomas were often not attached to the uterus, and endometriosis-associated ovarian cancers were positioned more posteriorly to the uterus than non-endometriosis-associated ovarian cancers. KEY POINTS: • The position of the ovarian tumor can be determined using MRI. • High-grade serous carcinomas had less attachment to the uterus. • Endometriosis-associated cancers were positioned more posteriorly to the uterus. • The location of ovarian tumors is helpful in estimating histology.

Triple modal treatment comprising with proton beam radiation, hyperthermia, and gemcitabine/nab-paclitaxel for locally advanced pancreatic cancer: a phase I/II study protocol (TT-LAP trial).

BACKGROUND: Locally advanced pancreatic ductal adenocarcinoma (PDAC), accounting for about 30% of PDAC patients, is difficult to cure by radical resection or systemic chemotherapy alone. A multidisciplinary strategy is required and our TT-LAP trial aims to evaluate whether triple-modal treatment with proton beam therapy (PBT), hyperthermia, and gemcitabine plus nab-paclitaxel is a safe and synergistically effective treatment for patients with locally advanced PDAC. METHODS: This trial is an interventional, open-label, non-randomized, single-center, single-arm phase I/II clinical trial organized and sponsored by the University of Tsukuba. Eligible patients who are diagnosed with locally advanced pancreatic cancer, including both borderline resectable (BR) and unresectable locally advanced (UR-LA) patients, and selected according to the inclusion and exclusion criteria will receive triple-modal treatment consisting of chemotherapy, hyperthermia, and proton beam radiation. Treatment induction will include 2 cycles of chemotherapy (gemcitabine plus nab-paclitaxel), proton beam therapy, and 6 total sessions of hyperthermia therapy. The initial 5 patients will move to phase II after adverse events are verified by a monitoring committee and safety is ensured. The primary endpoint is 2-year survival rate while secondary endpoints include adverse event rate, treatment completion rate, response rate, progression-free survival, overall survival, resection rate, pathologic response rate, and R0 (no pathologic cancer remnants) rate. The target sample size is set at 30 cases. DISCUSSION: The TT-LAP trial is the first to evaluate the safety and effectiveness (phases1/2) of triple-modal treatment comprised of proton beam therapy, hyperthermia, and gemcitabine/nab-paclitaxel for locally advanced pancreatic cancer. ETHICS AND DISSEMINATION: This protocol was approved by the Tsukuba University Clinical Research Review Board (reference number TCRB22-007). Results will be analyzed after study recruitment and follow-up are completed. Results will be presented at international meetings of interest in pancreatic cancer plus gastrointestinal, hepatobiliary, and pancreatic surgeries and published in peer-reviewed journals. TRIAL REGISTRATION: Japan Registry of Clinical Trials, jRCTs031220160. Registered 24 th June 2022, https://jrct.niph.go.jp/en-latest-detail/jRCTs031220160 .

Mucinous tumors arising from ovarian teratomas as another source of pseudomyxoma peritoneii: MR findings comparison with ovarian metastases from appendiceal mucinous tumors.

Objective: The origin of pseudomyxoma peritoneii (PMP) has been established as low-grade appendiceal mucinous tumors (AMT). However, intestinal-type ovarian mucinous tumors are known as another source of PMP. Recently, it is advocated that ovarian mucinous tumors causing PMP originates from teratomas. However, AMTs are often too small to detect on imaging; then, differentiating metastatic ovarian tumors of AMT from ovarian teratoma-associated mucinous tumors (OTAMT) is important. Therefore, this study investigates the MR characteristics of OTAMT compared to the ovarian metastasis of AMT. Methods: MR findings of six pathologically confirmed OTAMT were retrospectively analyzed compared to ovarian metastases of low-grade appendiceal mucinous neoplasms (LAMN). We studied the existence of PMP, uni- or bilateral disease, the maximum diameter of ovarian masses, the number of loculi, a variety of sizes and signal intensity of each content, the existence of the solid part, fat, calcification within the mass, and appendiceal diameters. All the findings were statistically analyzed using the Mann-Whitney test. Results: Four of the six OTAMT showed PMP. OTAMT showed unilateral disease, had a larger diameter, more frequent intratumoral fat, smaller appendiceal diameter than those in AMT, and they were statistically significant (p < .05). On the other hand, the number, variety of size, signal intensity of loculi, and the solid part, calcification within the mass did not differ from each other. Conclusion: Both OTAMT and ovarian metastasis of AMT appeared as multilocular cystic masses with relatively uniform signal and size of loculi. However, a larger unilateral disease with intratumoral fat and smaller size of the appendix may suggest OTAMT. Advances in knowledge: OTAMT can be another source of PMP, as AMT. MR characteristics of OTAMT were very similar to ovarian metastases of AMT; however, in cases with PMP combined with fat-containing multilocular cystic ovarian mass, we can diagnose them as OTAMT, not PMP caused by AMT.

A case of Pseudo-Meigs' syndrome due to Brenner tumor.

A 65-year-old woman with suspected ascites-associated abdominal distention had elevated CA125 levels. Contrast-enhanced computed tomography to search for the cause of the ascites showed bilateral pleural effusions, ascites, and an ovarian tumor. On magnetic resonance imaging the tumor exhibited a lobulated structure and markedly low signal intensity on both T1- and T2-weighted imaging, with no restrictions on diffusion-weighted images. Progressive enhancement was observed at tumor margins. Meigs syndrome due to fibroma was suspected, and total hysterectomy, bilateral salpingo-oophorectomy, and partial omentectomy were performed. Postoperatively, the pleural effusion and ascites resolved promptly without specific treatment. On pathological examination, the ovarian tumor was diagnosed as a benign Brenner tumor with scattered nests of transitional epithelium within a large amount of stroma. Based on the clinical course, the patient was diagnosed with pseudo-Meigs' syndrome due to a Brenner tumor.

Fibrolipomatous hamartoma with macrodactyly and carpal tunnel syndrome.

A 26-year-old man presented with congenital left thumb macrodactyly. MRI showed diffuse enlargement of the left median nerve from the wrist to the digits, with particularly severe swelling of the thumb; US revealed the median nerve swelling comprised enlarged hypoechoic nerve bundles and increased hyperechoic areas around the nerve bundles. These typical cable-like and spaghetti-like appearances led to the diagnosis of fibrolipomatous hamartoma (FLH). Only debulking was performed for cosmetic reasons and enlarged nerves contiguous to the skin of the distal phalanx were cauterized and dissected. The diagnosis of FLH was confirmed pathologically. FLH is a rare, congenital disorder characterized by anomalous overgrowth of fibroadipose tissue between and around nerve bundles. Patients with median nerve involvement often present with carpal tunnel syndrome. Once its characteristic image is obtained, biopsy is not necessary.

A case of renal involvement in juvenile xanthogranulomatosis.

Juvenile xanthogranuloma (JXG) is a type of non-Langerhans cell histiocytosis that rarely involves other than the skin. Here, we present detailed ultrasound (US) findings, including a contrast study, of a rare JXD renal lesion. A 42-year-old woman with JXG had chronic kidney disease. Ultrasound showed multiple cystic masses with fine internal septa in both kidneys. Contrast-enhanced US revealed early staining and late washout consistent with the internal septa inside the masses and led us to suspect cystic renal cell carcinomas in both kidneys. Left nephrectomy was performed for diagnostic purposes. Microscopic examination revealed a foamy component with Touton-type giant cells by histiocytosis; CD68 and S100 were positive, and CD1a was negative, leading the diagnosis of JXD. The US findings of extracutaneous lesions on JXA are variable and can be cystic, and when arising in the kidney may resemble cystic renal cell carcinoma.

Comparing Characteristics of Pelvic High-grade Serous Carcinomas with and without Breast Cancer Gene Variants on MR Imaging.

PURPOSE: To compare MRI findings of high-grade serous carcinoma (HGSC) with and without breast cancer (BRCA) gene variants to explore the feasibility of MRI as a genetic predictor. METHODS: We retrospectively reviewed MRI data from 16 patients with BRCA variant-positive (11 patients of BRCA1 and 5 patients of BRCA2 variant-positive) and 32 patients with BRCA variant-negative HGSCs and evaluated tumor size, appearance, nature of solid components, apparent diffusion coefficient (ADC) value, time-intensity curve, several dynamic contrast-enhanced curve descriptors, and nature of peritoneal metastasis. Age, primary site, tumor stage, bilaterality, presence of lymph node metastasis, presence of peritoneal metastasis, and tumor markers were also compared between the groups with the Mann-Whitney U and chi-square tests. RESULTS: The mean tumor size of BRCA variant-positive HGSCs was 9.6 cm, and that of variant-negative HGSCs was 6.8 cm, with no significant difference (P = 0.241). No significant difference was found between BRCA variant-positive and negative HGSCs in other evaluated factors, except for age (mean age, 53 years old; range, 32-78 years old for BRCA variant-positive and mean age, 61 years old; range, 44-80 years old for BRCA variant-negative, P = 0.033). Comparing BRCA1 variant-positive and BRCA2 variant-positive HGSCs, BRCA1 variant-positive HGSCs were larger (P = 0.040), had greater Max enhancement (P = 0.013), Area under the curve (P = 0.013), and CA125 (P = 0.038), and had a higher frequency of lymph node metastasis (P = 0.049), with significance. CONCLUSION: There was no significant difference in the MRI findings between patients with HGSCs with and without BRCA variants. Although studied in small numbers, BRCA1 variant-positive HGSCs were larger and more enhanced than BRCA2 variant-positive HGSCs with higher CA125 and more frequent lymph node metastases, and may represent more aggressive features.

Differentiation of carcinosarcoma from endometrial carcinoma on magnetic resonance imaging using deep learning.

Purpose: To verify whether deep learning can be used to differentiate between carcinosarcomas (CSs) and endometrial carcinomas (ECs) using several magnetic resonance imaging (MRI) sequences. Material and methods: This retrospective study included 52 patients with CS and 279 patients with EC. A deep-learning model that uses convolutional neural networks (CNN) was trained with 572 T2-weighted images (T2WI) from 42 patients, 488 apparent diffusion coefficient of water maps from 33 patients, and 539 fat-saturated contrast-enhanced T1-weighted images from 40 patients with CS, as well as 1612 images from 223 patients with EC for each sequence. These were tested with 9-10 images of 9-10 patients with CS and 56 images of 56 patients with EC for each sequence, respectively. Three experienced radiologists independently interpreted these test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for each sequence were compared between the CNN models and the radiologists. Results: The CNN model of each sequence had sensitivity 0.89-0.93, specificity 0.44-0.70, accuracy 0.83-0.89, and AUC 0.80-0.94. It also showed an equivalent or better diagnostic performance than the 3 readers (sensitivity 0.43-0.91, specificity 0.30-0.78, accuracy 0.45-0.88, and AUC 0.49-0.92). The CNN model displayed the highest diagnostic performance on T2WI (sensitivity 0.93, specificity 0.70, accuracy 0.89, and AUC 0.94). Conclusions: Deep learning provided diagnostic performance comparable to or better than experienced radiologists when distinguishing between CS and EC on MRI.

Diagnosing gangrenous cholecystitis on computed tomography using deep learning: A preliminary study.

Aim: To compare deep learning and experienced physicians in diagnosing gangrenous cholecystitis using computed tomography images and explore the feasibility of diagnostic assistance for acute cholecystitis requiring emergency surgery. Methods: This retrospective study included 25 patients with pathologically confirmed gangrenous cholecystitis and 129 patients with noncomplicated acute cholecystitis who underwent computed tomography between 2016 and 2021 at two institutions. All available computed tomography images at the time of the initial diagnosis were used for the analysis. A deep learning model based on a convolutional neural network was trained using 1,517 images of 112 patients (18 patients with gangrenous cholecystitis and 94 patients with acute cholecystitis) and tested with 68 images of 42 patients (seven patients with gangrenous cholecystitis and 35 patients with acute cholecystitis). Three blinded, experienced physicians independently interpreted the test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were compared between the convolutional neural network and the reviewers. Results: The convolutional neural network (sensitivity, 0.70; 95% confidence interval [CI], 0.44-0.87, specificity, 0.93; 95% CI, 0.88-0.96, accuracy, 0.89; 95% CI, 0.81-0.95, area under the receiver operating characteristic curve, 0.84; 95% CI, 0.68-1.00) had achieved a better diagnostic performance than the reviewers (ex. sensitivity, 0.55; 95% CI, 0.30-0.77, specificity, 0.67; 95% CI, 0.62-0.71, accuracy, 0.65; 95% CI, 0.57-0.72, area under the receiver operating characteristic curve, 0.63; 95% CI, 0.44-0.82; P = 0.048 for area under the receiver operating characteristic curve versus convolutional neural network). Conclusions: Deep learning had a better diagnostic performance than experienced reviewers in diagnosing gangrenous cholecystitis and has potential applicability for assisting in identifying indications for emergency surgery in the future.

Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases.

PURPOSE: To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection. METHOD: This retrospective study used TS CT comprising bony landmark detection, bone segmentation with a multi-atlas-based method, and spatial registration of two images by a log-domain diffeomorphic Demons algorithm. Paired current and past CT images of 50 patients without vertebral metastasis, recorded during June 2011-September 2016, were included as training data. A deep learning-based method estimated registration errors and suppressed false positives. Thereafter, paired CT images of 40 cancer patients with newly developed vertebral metastases and 40 control patients without vertebral metastases were evaluated. Six board-certified radiologists and five radiology residents independently interpreted 80 paired CT images with and without TS CT. RESULTS: Records of 40 patients in the metastasis group (median age: 64.5 years; 20 males) and 40 patients in the control group (median age: 64.0 years; 20 males) were evaluated. With TS CT, the overall figure of merit (FOM) of the board-certified radiologist and resident groups improved from 0.848 to 0.876 (p = 0.01) and from 0.752 to 0.799 (p = 0.02), respectively. The sub-analysis focusing on attenuation changes in lesions revealed that the FOM of osteoblastic lesions significantly improved in both the board-certified radiologist and resident groups using TS CT. The sub-analysis focusing on lesion location showed that the FOM of the resident group significantly improved in the vertebral arch (p = 0.04). CONCLUSIONS: TS CT was effective in detecting bone metastasis by both board-certified radiologists and radiology residents.

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists.

PURPOSE: To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. METHODS: This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. RESULTS: The AUC of the CNNs pertaining to the single and combined image sets were 0.88-0.95 and 0.87-0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88-0.95. CONCLUSION: CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.