Evidence map›Paper›PMID 41216220›Full record

ArticleInternational journal of women's health2025

Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification.

Omar H Abu-Azzam, Amer Mahmoud Sindiani, Salem Alhatamleh, Mohammad Amin, Hamad Yahia Abu Mhanna, Rola Madain, Hanan Fawaz Akhdar, Hasan Gharaibeh, Omar F Altal, Eman Hussein Alshdaifat and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Omar H Abu-AzzamDepartment of Obstetrics and Gynecology, Faculty of Medicine, Mutah University, Al-Karak, Jordan.
Amer Mahmoud SindianiDepartment of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.ORCID 0000-0001-5115-7372
Salem AlhatamlehComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan.
Mohammad AminComputer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan.ORCID 0009-0009-9044-8178
Hamad Yahia Abu MhannaDepartment of Medical Imaging, Faculty of Allied Medical Sciences, Isra University, Amman, Jordan.ORCID 0000-0001-6629-3297
Rola MadainDepartment of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.ORCID 0009-0005-7674-895X
Hanan Fawaz AkhdarPhysics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.ORCID 0000-0001-5144-7169
Hasan GharaibehArtificial Intelligence and Data Innovation Office, King Hussein Cancer Center, Amman, Jordan.
Omar F AltalDepartment of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Eman Hussein AlshdaifatDepartment of Obstetrics and Gynecology, Faculty of Medicine, Yarmouk University, Irbid, Jordan.ORCID 0000-0003-2096-3165
Tarfah Majed AlinadPhysics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Fatimah MaasheyPhysics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Ahmad NasayrehArtificial Intelligence and Data Innovation Office, King Hussein Cancer Center, Amman, Jordan.
Ayah BashkamiDepartment of Medical Laboratory Sciences, Al Balqa Applied University, Salt, Jordan.
Latifah AlghulayqahPhysics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: This study aimed to evaluate and compare the diagnostic performance of computed tomography (CT) and magnetic resonance imaging (MRI) in the detection of endometrial cancer, using a deep learning approach. Methods: Two endometrial image sets were obtained from King Abdullah University Hospital: the KAUH Endometrial Cancer MRI dataset (KAUH-ECMD) and the KAUH Endometrial Cancer CT dataset (KAUH-ECCTD), collected from 300 patients aged between 22 and 85. A hybrid deep learning model combining ResNet50 and Vision Transformer (ViT) was applied to classify the images into three categories: benign, malignant, and normal. Results: The proposed ViTNet model achieved an accuracy of 90.24% in detecting endometrial cancer using MRI images and 86.99% using CT images. The MRI-based approach demonstrated superior diagnostic performance in detecting endometrial cancer compared to CT-based classification. Conclusion: Deep learning models utilizing MRI and CT images demonstrate high accuracy in classifying endometrial cases. MRI in particular shows promise in supporting diagnostic workflows. Future work will focus on further validating the model's ability to evaluate depth of invasion and other prognostic features.

Indexed as

computed tomographydeep learningendometrial neoplasmsmagnetic resonance imagingradiomics

Identifiers

PMID41216220
PMCPMC12596896

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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.