Evidence map›Paper›PMID 37681026›Full record

ArticleFrontiers in oncology2023

Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer.

Wen-Jie Lu, Lin Mao, Jin Li, Liang-Yan OuYang, Jia-Yao Chen, Shi-Yan Chen, Yun-Yong Lin, Yi-Wen Wu, Shao-Na Chen, Shao-Dong Qiu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

7 citing papers in PubMed, 5 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Wen-Jie LuDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Lin MaoDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jin LiDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Liang-Yan OuYangDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jia-Yao ChenDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shi-Yan ChenDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Yun-Yong LinDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Yi-Wen WuDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shao-Na ChenDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shao-Dong QiuDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Fei ChenDepartment of Ultrasound, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Second Affiliated Hospital of Guangzhou Medical University · CNGuangzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To develop and validate a three-dimensional ultrasound (3D US) radiomics nomogram for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid cancer (PTC). Methods: This retrospective study included 168 patients with surgically proven PTC (non-ETE, n = 90; ETE, n = 78) who were divided into training (n = 117) and validation (n = 51) cohorts by a random stratified sampling strategy. The regions of interest (ROIs) were obtained manually from 3D US images. A larger number of radiomic features were automatically extracted. Finally, a nomogram was built, incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were performed to validate the capability of the nomogram on both the training and validation sets. The nomogram models were compared with conventional US models. The DeLong test was adopted to compare different ROC curves. Results: The area under the receiver operating characteristic curve (AUC) of the radiologist was 0.67 [95% confidence interval (CI), 0.580-0.757] in the training cohort and 0.62 (95% CI, 0.467-0.746) in the validation cohort. Sixteen features from 3D US images were used to build the radiomics signature. The radiomics nomogram, which incorporated the radiomics signature, tumor location, and tumor size showed good calibration and discrimination in the training cohort (AUC, 0.810; 95% CI, 0.727-0.876) and the validation cohort (AUC, 0.798; 95% CI, 0.662-0.897). The result suggested that the diagnostic efficiency of the 3D US-based radiomics nomogram was better than that of the radiologist and it had a favorable discriminate performance with a higher AUC (DeLong test: p < 0.05). Conclusions: The 3D US-based radiomics signature nomogram, a noninvasive preoperative prediction method that incorporates tumor location and tumor size, presented more advantages over radiologist-reported ETE statuses for PTC.

Indexed as

extrathyroidal extension (ETE)nomogrampapillary thyroid cancer (PTC)radiomicsthree-dimensional ultrasound

Identifiers

PMID37681026
PMCPMC10482087
OpenAlexW4386086270

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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.