AIMC Topic: Uterine Neoplasms
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Radiomics and artificial intelligence in malignant uterine body cancers: Protocol for a systematic review.
PloS one
Jun 8, 2022
INTRODUCTION: Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is hindered by the complex classification of patients into risk classes. This problem could be simplifie...
Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas.
European radiology
May 18, 2022
OBJECTIVES: To explore the feasibility and effectiveness of machine learning (ML) based on multiparametric magnetic resonance imaging (mp-MRI) features extracted from transfer learning combined with clinical parameters to differentiate uterine sarcom...
Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches.
Computers in biology and medicine
Jul 24, 2021
Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can damage the surrounding tissues and cause the formation of cancerous cells. In the USA, according to the...
Tricky Diagnosis and Robot-laparoscopic Surgical Approach to Disseminated Peritoneal Leiomyomatosis.
Journal of minimally invasive gynecology
Apr 18, 2021
STUDY OBJECTIVE: To show the challenging diagnosis of, and safe robotic surgical approach to, a rare case of disseminated peritoneal leiomyomatosis (DPL).
Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors.
Gynecologic oncology
Apr 16, 2021
OBJECTIVE: To develop and evaluate the performance of a radiomics and machine learning model applied to ultrasound (US) images in predicting the risk of malignancy of a uterine mesenchymal lesion.
Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy.
PloS one
Mar 31, 2021
Endometrial cancer is a ubiquitous gynecological disease with increasing global incidence. Therefore, despite the lack of an established screening technique to date, early diagnosis of endometrial cancer assumes critical importance. This paper presen...
Comparison of operative time between robotic and laparoscopic myomectomy for removal of numerous myomas.
The international journal of medical robotics + computer assisted surgery : MRCAS
Sep 8, 2020
BACKGROUND: We hypothesized that the total operative time of robot myomectomy (RM) is shorter than that of laparoscopic myomectomy (LM) in cases where numerous myomas are removed, due to the faster suturing time of the robotic system. To verify this,...
Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization.
Journal of vascular and interventional radiology : JVIR
May 4, 2020
PURPOSE: To develop and validate a deep learning model based on routine magnetic resonance (MR) imaging obtained before uterine fibroid embolization to predict procedure outcome.
Uterine Artery Embolization
Adult
Middle Aged
Image Interpretation, Computer-Assisted
Humans
Diagnosis, Computer-Assisted
Treatment Outcome
Clinical Decision-Making
Observer Variation
Deep Learning
Magnetic Resonance Imaging
Retrospective Studies
Female
Philadelphia
Predictive Value of Tests
Reproducibility of Results
Leiomyoma
Uterine Neoplasms
Aged
A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method.
Scientific reports
May 4, 2020
This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were inc...
Middle Aged
Diagnosis, Computer-Assisted
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Decision Trees
Predictive Value of Tests
Uterine Neoplasms
Magnetic Resonance Spectroscopy
Female
Reproducibility of Results
Adult
Sensitivity and Specificity
Humans
Machine Learning
Algorithms
Leiomyoma
Premenopause
Myometrium
Clinical calculator predictive of chemotherapy benefit in stage 1A uterine papillary serous cancers.
Gynecologic oncology
Nov 30, 2019
OBJECTIVE: Determine the utility of a clinical calculator to predict the benefit of chemotherapy in stage IA uterine papillary serous cancer (UPSC).
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