Evidence map›Paper›PMID 34722287›Full record

ArticleFrontiers in oncology2021

Nomogram Combining Radiomics With the American College of Radiology Thyroid Imaging Reporting and Data System Can Improve Predictive Performance for Malignant Thyroid Nodules.

Xingzhi Huang, Zhenghua Wu, Aiyun Zhou, Xiang Min, Qi Qi, Cheng Zhang, Songli Chen, Pan Xu

Abstract read
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

8 authors.

Xingzhi HuangDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Zhenghua WuDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Aiyun ZhouDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Xiang MinDepartment of Head and Neck Otolaryngology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Qi QiDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Cheng ZhangDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Songli ChenDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Pan XuDepartment of Ultrasonography, The First Affiliated Hospital of Nanchang University, Nanchang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and validate a nomogram combining radiomics of B-mode ultrasound (BMUS) images and the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) for predicting malignant thyroid nodules and improving the performance of the guideline.

methodA total of 451 thyroid nodules referred for surgery and proven pathologically at an academic referral center from January 2019 to September 2020 were retrospectively collected and randomly assigned to training and validation cohorts (7:3 ratio). A nomogram was developed through combining the BMUS radiomics score (Rad-Score) with ACR TI-RADS score (ACR-Score) in the training cohort; the performance of the nomogram was assessed with respect to discrimination, calibration, and clinical application in the validation and entire cohorts.

resultsThe ACR-Rad nomogram showed good calibration and yielded an AUC of 0.877 (95% CI 0.836-0.919) in the training cohort and 0.864 (95% CI 0.799-0.931) in the validation cohort, which were significantly better than the ACR-Score model (

conclusionThe ACR-Rad nomogram has favorable value in predicting malignant thyroid nodules and improving performance of the ACR TI-RADS for senior and junior radiologists.

Indexed as

nomogrampredictionradiomicsthyroid noduleultrasound

Identifiers

PMID34722287
PMCPMC8550451

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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.