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

Clinical and MRI Characteristics of Uterine Cervical Adenocarcinoma: Its Variants and Mimics

Adenocarcinoma currently accounts for 10–25% of all uterine cervical carcinomas and has a variety of histopathological subtypes. Among them, mucinous carcinoma gastric type is not associated with high-risk human papillomavirus (HPV) infection and a poor prognosis, while villoglandular carcinoma has an association with high-risk HPV infection and a good prognosis. They show relatively characteristic imaging findings which can be suggested by magnetic resonance imaging (MRI), though the former is sometimes difficult to be distinguished from lobular endocervical glandular hyperplasia. Various kinds of other tumors including squamous cell carcinoma should be also differentiated on MRI, while it is currently difficult to distinguish them on MRI, and HPV screening and pathological confirmation are usually necessary for definite diagnosis and further patient management

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

Abstract 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. Graphical abstrac

Development of Gamification-Based E-Learning on Web Design Topic

Conventional learnings have given way to methods aided by technological advances. The approach known as Student-Centered Learning (SCL) is the foundation of e-learning. Furthermore, SCL demands students to be self-motivated to complete courses and engage with the offered learning content. Programming is one of the most important subjects to study independently and with the aid of an instructor. The need for graduates with programming and computational skills in the field of web design continues to receive widespread attention from the industry. However, problems such as low student engagement are often encountered in learning about programming concepts and syntax. Gamification is an instructional approach to facilitate learning and boost motivation through game elements, mechanics, and thinking. Therefore, this research aims to discuss the development of Gamification-based e-learning to increase student engagement and motivation, especially in HTML and CSS web design. This system was tested with 3 methods, including unit, system, and user testing. In user testing, this study involved 264 students from computer backgrounds to validate the system. The results show that the e-learning system can perform well according to the expected specifications

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

Background: This study aimed to compare deep learning with radiologists&rsquo; 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&ndash;0.85, specificity of 0.77&ndash;0.92, accuracy of 0.81&ndash;0.87, and an AUC of 0.83&ndash;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

Urachal mucinous adenocarcinoma in the pelvic wall mimicking endometriosis

We report the case of a 30-year-old woman who complained of a painful palpable mass. Magnetic resonance imaging revealed an ill-defined mass approximately 8 cm in diameter with internal microcytic components. The mass diffusely involved the subcutaneous tissues, the muscles of the pelvic wall, and urinary bladder via a postoperative scar and resembled endometriosis. The histopathologic diagnosis was mucinous adenocarcinoma arisen from the urachal remnant. This is a very rare case of urachal adenocarcinoma arising mainly in the pelvic wall and mimicking endometriosis on MRI. Keywords: Endometriosis, Scar, Magnetic resonance, Tumor, Urachu

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