Evidence map›Paper›PMID 39678423›Full record

ArticleGland surgery2024

Combined model integrating clinical, radiomics, BRAF

Lichang Zhong, Lin Shi, Jinyu Lai, Yuhong Hu, Liping Gu

Abstract read
In one paragraph

Article in Gland surgery, 2024. 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.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
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

5 authors.

Lichang ZhongDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University, Shanghai Institute of Ultrasound in Medicine, Shanghai, China.ORCID https://orcid.org/0000-0002-6701-7419
Lin ShiDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University, Shanghai Institute of Ultrasound in Medicine, Shanghai, China.
Jinyu LaiDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University, Shanghai Institute of Ultrasound in Medicine, Shanghai, China.
Yuhong HuDepartment of Ultrasound in Medicine, Tinglin Hospital, Tongji Medical Group, Shanghai, China.
Liping GuDepartment of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Medical College of Shanghai Jiao Tong University, Shanghai Institute of Ultrasound in Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The management of thyroid nodules diagnosed as Bethesda III by fine-needle aspiration presents certain challenges, and there is an urgent need for a non-invasive and accurate method for early identification of the benign or malignant nature of Bethesda III nodules. Our objective is to develop and validate a clinical-radiomics nomogram based on preoperative ultrasound (US) images and clinical features, for predicting the malignancy of thyroid nodules with indeterminate cytology (Bethesda III). Methods: Between June 2017 and June 2022, we conducted a retrospective study on 274 patients with surgically confirmed indeterminate cytology (Bethesda III) across two separate medical centers in Shanghai. The training and internal validation sets were comprised of 136 and 58 patients, respectively, all sourced from Shanghai's Sixth People's Hospital. To facilitate external test, a further 80 patients were selected from Tinglin Hospital. Utilizing preoperative US data, we obtained imaging markers for radiomic features. After feature selection, we developed a comprehensive diagnostic model to evaluate the predictive value for Bethesda III benign and malignant cases. The model's diagnostic accuracy, calibration, and clinical applicability were systematically assessed. Results: The results showed that the prediction model, which integrated US radiomics, and clinical risk features, exhibited superior stability in distinguishing between benign and malignant indeterminate thyroid nodules (Bethesda III). In the external test set, the area under the curve (AUC) was 0.824 [95% confidence interval (CI): 0.718-0.929], and the accuracy, sensitivity, specificity, precision, and recall were 0.775, 0.731, 0.796, 0.633, and 0.731, respectively. Conclusions: An integrated model, utilizing US radiomics and clinical risk features, effectively discriminates between benign and malignant indeterminate thyroid nodules (Bethesda III), thereby minimizing the need for unnecessary diagnostic surgeries and subsequent complications.

Indexed as

convolutional neural networkdeep transfer learningIndeterminate thyroid nodulemachine learningradiomics

Identifiers

PMID39678423
PMCPMC11635559

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.