Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)

A rare case report of low-grade endometrial sarcoma: A surgical tale from Himalayas

INTRODUCTION

In the context of female genital tract malignancy, uterine sarcoma is considered the rarest form of the disease. Despite the inert nature of low-grade endometrial sarcoma, they must be meticulously diagnosed on time, with an exact grading of the severity and staging of the disease, which further guides the treatment modality and prognosis.

CASE SUMMARY

A married Asian female without any significant past medical and surgical history complained of abdominal distension and discomfort, which was progressive in nature, for which a radiological assessment was made that showed features suggestive of endometrial sarcoma. Total abdominal hysterectomy with sapingoopherectomy was done without any perioperative complications. Histology further confirmed the diagnosis. Post-operatively, the patient had an unremarkable hospital stay and was discharged home.

DISCUSSION

Endometrial stromal sarcoma is one of the rare malignant entities presenting usually in late adult females, but sometimes it can present at an earlier age as well. Abdominal masses in females, although usually overlooked as benign, can sometimes be associated with a malignant picture. Low-grade endometrial sarcomas have been seen to masquerade other minor benign cases, such as leiomyoma. Despite the rarity of such malignant conditions, diagnosis and management are rather straightforward, and post-operative patient prognosis has been found to be rewarding.

CONCLUSION

Among the uterine sarcoma cases, endometrial sarcoma comes under the malignant disease of the least occurrence. Compared to other malignant conditions, these patients present with minor symptoms like discomfort, which may go unchecked. The major factor that should be noted is the on-time diagnosis and appropriate choice of treatment modality. Overall, despite a minute prevalence and difficult diagnosis, the prognosis of the patient is rather good.

MedViT: A robust vision transformer for generalized medical image classification

Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attacks since inaccurate diagnosis could lead to disastrous consequences in the safety realm. In this study, we propose a highly robust yet efficient CNN-Transformer hybrid model which is equipped with the locality of CNNs as well as the global connectivity of vision Transformers. To mitigate the high quadratic complexity of the self-attention mechanism while jointly attending to information in various representation subspaces, we construct our attention mechanism by means of an efficient convolution operation. Moreover, to alleviate the fragility of our Transformer model against adversarial attacks, we attempt to learn smoother decision boundaries. To this end, we augment the shape information of an image in the high-level feature space by permuting the feature mean and variance within mini-batches. With less computational complexity, our proposed hybrid model demonstrates its high robustness and generalization ability compared to the state-of-the-art studies on a large-scale collection of standardized MedMNIST-2D datasets.

Advances in the Preoperative Identification of Uterine Sarcoma

Simple Summary

As a lethal malignant tumor, uterine sarcomas lack specific diagnostic criteria due to their similar presentation with uterine fibroids, clinicians are prone to make the wrong diagnosis or adopt incorrect treatment methods, which leads to rapid tumor progression and increased metastatic propensity. In recent years, with the improvement of medical level and awareness of uterine sarcoma, more and more studies have proposed new methods for preoperative differentiation of uterine sarcoma and uterine fibroids. This review outlines the up-to-date knowledge about preoperative differentiation of uterine sarcoma and uterine fibroids, including laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies, and provides recommendations for future research.

Abstract

Uterine sarcomas are rare malignant tumors of the uterus with a high degree of malignancy. Their clinical manifestations, imaging examination findings, and laboratory test results overlap with those of uterine fibroids. No reliable diagnostic criteria can distinguish uterine sarcomas from other uterine tumors, and the final diagnosis is usually only made after surgery based on histopathological evaluation. Conservative or minimally invasive treatment of patients with uterine sarcomas misdiagnosed preoperatively as uterine fibroids will shorten patient survival. Herein, we will summarize recent advances in the preoperative diagnosis of uterine sarcomas, including epidemiology and clinical manifestations, laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies.

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.