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.

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.

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.

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.

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments.

BACKGROUND: This study aimed to compare deep learning with radiologists' assessments for diagnosing ovarian carcinoma using MRI. METHODS: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. RESULTS: The CNN of each sequence had a sensitivity of 0.77-0.85, specificity of 0.77-0.92, accuracy of 0.81-0.87, and an AUC of 0.83-0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). CONCLUSION: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI.

Pelvic arterial embolisation with cyanoacrylate during caesarean hysterectomy for placenta accreta.

PURPOSE: To compare n-butyl cyanoacrylate (NBCA) and gelatine sponge (GS) as embolic materials for prophylactic pelvic arterial embolisation during caesarean hysterectomy for placenta accreta spectrum (PAS). MATERIAL AND METHODS: This retrospective study comprised 12 women (age range, 23-42 years; mean, 34.1 years) who underwent caesarean hysterectomy for PAS. Following caesarean delivery, bilateral uterine and non-uterine parasitic arteries were embolized with GS in the first four cases (GS group) and primarily with NBCA mixed with iodized oil in the subsequent eight cases (NBCA group). Procedure time for embolisation and hysterectomy and total blood loss were compared between the two groups using Welch's t-test. RESULTS: Although procedure time for embolisation tended to be longer in the NBCA group than in the GS group (111 ± 47 min versus 71 ± 32 min, p=.11), that for hysterectomy was significantly reduced in the NBCA group when compared to the GS group (158 ± 42 min versus 236 ± 39 min, p=.02). Total blood loss was significantly lower in the NBCA group than in the GS group (1375 ± 565 mL versus 2668 ± 587 mL, p=.01). CONCLUSION: Procedure time for hysterectomy and total blood loss during caesarean hysterectomy can be reduced by using NBCA instead of GS in prophylactic pelvic arterial embolisation for PAS.

Preoperative Diagnosis of a Torsioned Accessory Spleen.

We report a case of a 12-year-old boy with an accessory spleen torsion. He presented with left-sided abdominal pain after trauma. A 4 cm oval mass without contrast enhancement was detected on contrast-enhanced computed tomography (CT), and ultrasound (US) showed a 4 cm oval mass below the spleen. The mass mainly consisted of high echoes similar to the spleen; the central part showed irregularly low echoes. Subsequent follow-up daily US examinations showed gradual expansion of the central low echoes with conspicuous hyperechoic dots. Discontinuation of the branch from the splenic artery to the mass was observed, both, on US and CT. These findings led to the diagnosis of a hemorrhagic infarct caused by torsion of the accessory spleen. Laparoscopy showed adherence of the accessory spleen to the omentum and colon by twisting four times around its axis. It was resected and confirmed the diagnosis of a torsioned accessory spleen.

Risk factors for venous thromboembolism induced by prolonged bed rest during interstitial brachytherapy for gynecological cancer: a retrospective study.

BACKGROUND: Screening and management of venous thromboembolism (VTE) after surgery is important in preventing sublethal VTE. However, the risk factors for VTE during interstitial brachytherapy (ISBT) remain unknown, and appropriate screening and management strategies are yet to be established. Therefore, this study aimed to evaluate the risk factors for VTE resulting from requisite bed rest during ISBT for gynecologic cancers. METHODS: We retrospectively analyzed 47 patients. For patients without definitive preceding radiotherapy, whole pelvic irradiation (30-50 Gy) followed by ISBT of 12-30 Gy/2-5 fx/1-3 days was administered to CTV D90. For patients with preceding radiotherapy, 36-42 Gy/6-7 fx/3-4 days was delivered by ISBT alone. The supine position was required during ISBT. D-dimer (DD) was measured at initial presentation, 1 week before ISBT, pre-ISBT, on the day of, and the day following needle removal. Patients were divided into three groups according to the risk of VTE and were managed accordingly; Group 1: DD was not detected (negative) before ISBT, Group 2: VTE was not detected on venous ultrasound imaging, although DD was positive before ISBT, and Group 3: VTE was detected (positive) before ISBT. An intermittent pneumatic compression device was used during ISBT; for the patients without VTE before ISBT. Heparin or oral anticoagulants were administered to patients with VTE before ISBT. RESULTS: Overall, the median values of DD pre-ISBT, on the day of, and on the day following needle removal were 1.0 (0.4-5.8), 1.1 (0.5-88.9), and 1.5 (0.7-40.6) µg/mL, respectively. After ISBT, no patients had deep vein thrombosis (DVT) in groups 1 and 2. In group 3, 7 of 14 patients experienced worsening of VTE but remained asymptomatic. In univariate analysis, DVT diagnosed before ISBT, Caprini score ≥ 7, and difference in DD values between pre-ISBT and the day of or the day following needle removal ≥ 1 were associated with the incidence or worsening of VTE. CONCLUSION: DD should be measured before and after ISBT to detect the incidence or worsening of VTE in patients with DVT. The Caprini score may help in the prediction of VTE during or after ISBT.

Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists.

PURPOSE: To compare deep learning with radiologists when diagnosing uterine cervical cancer on a single T2-weighted image. METHODS: This study included 418 patients (age range, 21-91 years; mean, 50.2 years) who underwent magnetic resonance imaging (MRI) between June 2013 and May 2020. We included 177 patients with pathologically confirmed cervical cancer and 241 non-cancer patients. Sagittal T2-weighted images were used for analysis. A deep learning model using convolutional neural networks (DCNN), called Xception architecture, was trained with 50 epochs using 488 images from 117 cancer patients and 509 images from 181 non-cancer patients. It was tested with 60 images for 60 cancer and 60 non-cancer patients. Three blinded experienced radiologists also interpreted these 120 images independently. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were compared between the DCNN model and radiologists. RESULTS: The DCNN model and the radiologists had a sensitivity of 0.883 and 0.783-0.867, a specificity of 0.933 and 0.917-0.950, and an accuracy of 0.908 and 0.867-0.892, respectively. The DCNN model had an equal to, or better, diagnostic performance than the radiologists (AUC = 0.932, and p for accuracy = 0.272-0.62). CONCLUSION: Deep learning provided diagnostic performance equivalent to experienced radiologists when diagnosing cervical cancer on a single T2-weighted image.

Ovarian and non-ovarian teratomas: a wide spectrum of features.

Teratoma is a germ cell tumor (GCT) derived from stem cells of the early embryo and the germ line. Teratoma is the most common neoplasm of the ovaries and is usually diagnosed easily using imagings by detecting fat components. However, there are various histopathological types and the imaging findings differ according to the type. Teratoma usually occurs in the gonads or in the midline due to migration of primordial germ cells during development. The clinical course of teratomas depends on the age of the patient, histological type, and anatomical site. Sometimes teratomas show unusual manifestations, such as mature teratoma without demonstrable fat components, torsion, rupture, growing teratoma syndrome, anti-N-methyl-D-aspartate receptor encephalitis, and autoimmune hemolytic anemia. For all of these reasons, teratomas demonstrate a wide spectrum of imaging features and radiologists should be familiar with these variabilities. The present article aims to introduce a model encompassing types of GCTs based on their developmental potential, and to review several histopathological types in various anatomical sites and unusual manifestations of teratomas, with representative imaging findings.

Carcinosarcoma of the ovary: MR and clinical findings compared with high-grade serous carcinoma.

PURPOSE: To clarify imaging and clinical characteristics of ovarian carcinosarcoma (CS) compared with high-grade serous carcinoma (HGSC). METHODS: We retrospectively reviewed MR imagings of 12 patients with CS and 30 patients with HGSC and evaluated tumor size, shape, appearance, nature of cystic and solid components, hemorrhage, and necrosis. Age, premenopausal or postmenopausal, histologic subtype, presence of endometriosis, tumor markers, and stage were also evaluated. These parameters were compared using the Mann-Whitney U test and the chi-square test/Fisher's exact test. RESULTS: The mean size of CSs was 13.6 cm, and significantly larger than that of HGSCs (mean 9.0 cm, p = 0.022). The stained-glass appearance (67% vs. 23%, p = 0.013), hemorrhage (100% vs. 50%, p = 0.003), necrosis (75.0% vs. 13%, p = 0.000), and endometriosis (33% vs. 7%, p = 0.012) were significantly more common in CSs. The postmenopausal ratio of CSs was 100% and significantly higher than that of HGSCs (70.0%, p = 0.041). Among the tumor makers, only CA-125 was significantly lower in CSs than in HGSCs (mean 715.1 U/ml vs. 1677.1 U/ml, p = 0.009). The stage distribution was similar and was not significantly different. CONCLUSION: CSs formed larger masses, and the stained-glass appearance, hemorrhage, and necrosis were more frequently observed in CSs.