ArticleJournal of healthcare engineering2021
Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- Auxiliary meta-learning strategy for cancer recognition: leveraging external data and optimized feature mapping.BMC cancer · 2025Article
- Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI.The British journal of radiology · 2024Article
- Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.Journal of clinical medicine · 2024Review
- Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer.Scientific reports · 2023Article
- A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.Diagnostics (Basel, Switzerland) · 2023Article
- Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical Analysis of 137 Cases of Ovarian Tumors in Pregnancy.Journal of oncology · 2022Article
- Enhancing clinical decision-making in endometrial cancer through deep learning technology: A review of current research.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to investigate the intelligent recognition of radiomics based on the convolutional neural network (CNN) in predicting endometrial cancer (EC). In this study, 158 patients with EC in hospital were selected as the research objects and divided into a training group and a test group. All the patients underwent magnetic resonance imaging (MRI) before surgery. Based on the CNN, the imaging model of EC prediction was constructed according to the characteristics. Besides, the comprehensive prediction model was established through the clinical information and imaging parameters. The results showed that the area under the working characteristic curve (AUC) of the radiomics model and comprehensive prediction model was 0.897 and 0.913 in the training group, respectively. In addition, the AUC of the radiomics model was 0.889 in the test group and that of the comprehensive prediction model was 0.897. The comprehensive prediction model was established through specific imaging parameters and clinical pathological information, and its prediction performance was good, indicating that radiomics parameters could be applied as noninvasive markers to predict EC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.