Evidence map›Paper›PMID 38222341›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023

Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.

Luoting Zhuang, Vedrana Ivezic, Jeffrey Feng, Chushu Shen, Ashwath Radhachandran, Vivek Sant, Maitraya Patel, Rinat Masamed, Corey Arnold, William Speier

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Luoting ZhuangMedical Informatics Home Area, University of California, Los Angeles, CA, USA.
Vedrana IvezicMedical Informatics Home Area, University of California, Los Angeles, CA, USA.
Jeffrey FengMedical Informatics Home Area, University of California, Los Angeles, CA, USA.
Chushu ShenDepartment of Bioengineering, University of California, Los Angeles, CA, USA.
Ashwath RadhachandranDepartment of Bioengineering, University of California, Los Angeles, CA, USA.
Vivek SantSection of Endocrine Surgery, Department of Surgery, University of California, Los Angeles, CA, USA.
Maitraya PatelDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Rinat MasamedDepartment of Radiological Sciences, University of California, Los Angeles, CA, USA.
Corey ArnoldMedical Informatics Home Area, University of California, Los Angeles, CA, USA.
William SpeierMedical Informatics Home Area, University of California, Los Angeles, CA, USA.

Funding

Predicting the Presence of Clinically Significant Thyroid Cancer using Ultrasound ImagingR21EB030691 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SPEIER, WILLIAM F · 2021 to 2023
$586k
NIBIB NIH HHS R21 EB030691
6 · The paper itself

Abstract

For patients with thyroid nodules, the ability to detect and diagnose a malignant nodule is the key to creating an appropriate treatment plan. However, assessments of ultrasound images do not accurately represent malignancy, and often require a biopsy to confirm the diagnosis. Deep learning techniques can classify thyroid nodules from ultrasound images, but current methods depend on manually annotated nodule segmentations. Furthermore, the heterogeneity in the level of magnification across ultrasound images presents a significant obstacle to existing methods. We developed a multi-scale, attention-based multiple-instance learning model which fuses both global and local features of different ultrasound frames to achieve patient-level malignancy classification. Our model demonstrates improved performance with an AUROC of 0.785 (p<0.05) and AUPRC of 0.539, significantly surpassing the baseline model trained on clinical features with an AUROC of 0.667 and AUPRC of 0.444. Improved classification performance better triages the need for biopsy.

Indexed as

Thyroid NeoplasmsThyroid NoduleBiopsyHumansUltrasonography

Identifiers

PMID38222341
PMCPMC10785838

What OpenQuestion holds

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Registered trials

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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.