Publications by authors named "Xuji Jiang"
Article Synopsis
- - Endometrial cancer (EC) is on the rise among women, highlighting the urgent need for early detection and better treatment options to improve survival rates.
- - Traditional diagnostic methods like ultrasound, MRI, and histopathology are crucial but can be slow and prone to human error due to heavy reliance on expert analysis.
- - Deep learning (DL) in computer vision is emerging as a transformative tool in medical imaging, showing great promise for improving the accuracy of EC diagnosis and patient prognosis while addressing current challenges and future development opportunities.
Background: In the realm of endometrial cancer (EC) therapeutics and prognostic assessments, lymph nodes' status is paramount. The sentinel lymph node (SLN) detection, recognized for its reliability, has been progressively adopted as a standard procedure, posing a compelling alternative to conventional systematic lymphadenectomy. However, there remains a lack of agreement on the most effective choice of tracers for this procedure.
View Article and Find Full Text PDFBackground: The primary objective of this paper was to assess and analyze the top 100 most cited articles currently cited in studies of fertility-sparing treatments for cervical cancer.
Methods: Searching the Web of Science Core Collection database for the top 100 most cited articles on fertility-sparing treatments for cervical cancer, different aspects of the articles were analyzed, including countries, journals, institutions, authors, keywords and topics.
Results: The search was conducted up to August 2023, and the number of citations for the top 100 articles ranged from 19 to 212.
Article Synopsis
- Twenty-five percent of cervical cancers are endocervical adenocarcinomas (EACs), characterized by a diverse range of tumors, which can be difficult to classify using current histopathological methods.
- A new deep learning tool called Silva3-AI was created to automatically analyze histopathologic images and classify Silva patterns accurately, developed from data of 202 EAC patients and later tested on an additional 161 patients from various medical centers.
- Silva3-AI demonstrated high accuracy in pattern classification, achieving scores comparable to experienced pathologists, and also provided visualization techniques, allowing for better understanding of tumor microenvironment variation.
Background: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM.
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