Evidence map›Paper›PMID 35652118›Full record

ArticleRadiology. Artificial intelligence2022

Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System Using US Cine-Clip Images.

Rikiya Yamashita, Tara Kapoor, Minhaj Nur Alam, Alfiia Galimzianova, Saad Ali Syed, Mete Ugur Akdogan, Emel Alkim, Andrew Louis Wentland, Nikhil Madhuripan, Daniel Goff and 5 more

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PubMed Full text at PMC Publisher

In one paragraph

Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it

0cells of the map it votes in

9citing papers in PubMed

2.1field-weighted citation impact, top 12% 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

9 citing papers in PubMed, 17 citations in OpenAlex.

Deep Learning Based on B-Mode and Color Doppler Ultrasound for Differentiation of Primary Thyroid Lymphoma and Hashimoto's Thyroiditis: A Retrospective Single-Center Study.Diagnostics (Basel, Switzerland) · 2026
Article
The value of a nomogram model combining ultrasonic features and blood inflammatory indicators in differentiating benign and malignant thyroid nodules.Medicine · 2026
Article
Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026
Article
Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026
Article
A Multimodal Large Language Model as an End-to-End Classifier of Thyroid Nodule Malignancy Risk: Usability Study.JMIR formative research · 2025
Article
A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules.NPJ digital medicine · 2025
Article
A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging.Bioengineering (Basel, Switzerland) · 2024
Article
Fully-Automatic Detection and Diagnosis System for Thyroid Nodules Based on Ultrasound Video Sequences by Artificial Intelligence.Journal of multidisciplinary healthcare · 2024
Article
Malignancy Rates in Thyroid Imaging Reporting and Data System Category 3 (TI-RADS 3) Thyroid Nodules: A Retrospective Study.Cureus · 2023
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

15 authors at 1 institution in 1 country.

Rikiya YamashitaDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID https://orcid.org/0000-0002-2686-2333

Tara KapoorDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0001-5218-3329

Minhaj Nur AlamDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0003-3095-2232

Alfiia GalimzianovaDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Saad Ali SyedDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Mete Ugur AkdoganDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0003-1520-1481

Emel AlkimDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Andrew Louis WentlandDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0003-1736-8218

Nikhil MadhuripanDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0002-3690-5261

Daniel GoffDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID https://orcid.org/0000-0003-2109-1696

Victoria BarbeeDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Natasha Diba SheybaniDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID 0000-0002-1137-058X

Hersh SagreiyaDepartments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.

Daniel L Rubin *Departments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID https://orcid.org/0000-0001-5057-4369

Terry S Desser *Departments of Biomedical Data Science (R.Y., T.K., M.N.A., A.G., M.U.A., E.A., N.D.S., H.S., D.L.R.) and Radiology (S.A.S., A.L.W., N.M., D.G., V.B., D.L.R., T.S.D.), Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.ORCID https://orcid.org/0000-0001-7983-9738

Stanford University · US

Funding

STANFORD CANCER IMAGING TRAINING (SCIT) PROGRAMT32CA009695 · NCI · STANFORD UNIVERSITY · PI BRUCE L DANIEL, Jeremy Dahl · 1993 to 2026

$7.7M

Focused Ultrasound and Multifunctional Nanoparticle Vaccines as Adjuvant Strategies for Cancer ImmunotherapyK00CA234954 · NCI · STANFORD UNIVERSITY · PI SHEYBANI, NATASHA DIBA · 2020 to 2021

$183k

NCI NIH HHS K00 CA234954NCI NIH HHS T32 CA009695

6 · The paper itself

Abstract

Purpose: To develop a deep learning-based risk stratification system for thyroid nodules using US cine images. Materials and Methods: In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS)-structured radiology reports were evaluated. A deep learning-based system that exploits the cine images obtained during three-dimensional volumetric thyroid scans and outputs malignancy risk was developed and compared, using fivefold cross-validation, against a two-dimensional (2D) deep learning-based model (Static-2DCNN), a radiomics-based model using cine images (Cine-Radiomics), and the ACR TI-RADS level, with histopathologic diagnosis as ground truth. The system was used to revise the ACR TI-RADS recommendation, and its diagnostic performance was compared against the original ACR TI-RADS. Results: The system achieved higher average area under the receiver operating characteristic curve (AUC, 0.88) than Static-2DCNN (0.72, Conclusion: The risk stratification system using US cine images had higher diagnostic performance than prior models and improved specificity of ACR TI-RADS when used to revise ACR TI-RADS recommendation.

Indexed as

Abdomen/GIComputer Applications–3DConvolutional Neural Network (CNN)DiagnosisHead/NeckNeural NetworksOncologySupervised LearningThyroidTransfer LearningUS

Identifiers

PMID35652118

DOI10.1148/ryai.210174

PMCPMC9152684

OpenAlexW4280641512

What OpenQuestion holds

Textmetadata

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.