Evidence map›Paper›PMID 39882442›Full record

ArticleFrontiers in oncology2024

MRI-based intratumoral and peritumoral radiomics for assessing deep myometrial invasion in patients with early-stage endometrioid adenocarcinoma.

Jing Yang, Yang Liu, Xiaolong Liu, Yaoxin Wang, Xianhong Wang, Conghui Ai, Qiu Bi, Ying Zhao

Abstract read
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Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Jing Yang *Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
Yang Liu *Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xiaolong Liu *Department of Medical Imaging, The People's Hospital of Puer, The Affiliated Hospital of Kunming University of Science and Technology, Puer, Yunnan, China.
Yaoxin WangDepartment of Radiology, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.
Xianhong WangThe Affiliated Hospital of Kunming University of Science and Technology, School of Clinical Medicine, Kunming, Yunnan, China.
Conghui Ai *Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, Yunnan, China.
Qiu Bi *Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
Ying Zhao *Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the effectiveness of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics models for predicting deep myometrial invasion (DMI) of early-stage endometrioid adenocarcinoma (EAC). Methods: The data of 459 EAC patients from three centers were retrospectively collected. Radiomics features were extracted separately from the intratumoral and peritumoral regions expanded by 0 mm, 5 mm, and 10 mm on unimodal and multimodal MRI. Then, various radiomics models were developed and validated, and the optimal model was confirmed. Integrated models were constructed by ensemble and stacking algorithms based on the above radiomics models. The models' performance was evaluated using the area under the curve (AUC). Results: The multimodal MRI-based radiomics model, which included both intratumoral and peritumoral regions expanded by 5 mm, was the optimal radiomics model, with an AUC of 0.74 in the validation group. When the same integrated algorithm was utilized, the integrated models with 5-mm expansion presented higher AUCs than those with 0-mm and 10-mm expansion in the validation group. The performance of the stacking model and ensemble model with 5-mm expansion was similar, and their AUCs were 0.74 and 0.75, respectively. Conclusion: The multimodal radiomics model from the intratumoral and peritumoral regions expanded by 5 mm has the potential to improve the performance for detecting DMI of early-stage EAC. The integrated models are of little value in increasing the prediction.

Indexed as

deep myometrial invasionendometrial carcinomaMRIperitumoralradiomics

Identifiers

PMID39882442
PMCPMC11774896

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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.