Evidence map›Paper›PMID 38848168›Full record

ArticleCancer biomarkers : section A of Disease markers2025

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules.

Axel H Masquelin, Nick Cheney, Raúl San José Estépar, Jason H T Bates, C Matthew Kinsey

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PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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5 · Who and what money

Authors and funding

5 authors.

Axel H MasquelinElectrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA.ORCID 0000-0002-9412-0390
Nick CheneyComputer Science, University of Vermont, Burlington, VT, USA.
Raúl San José EstéparDepartment of Radiology, Brigham and Women's Hospital, Somerville, MA, USA.
Jason H T BatesDepartment of Medicine, College of Medicine, University of Vermont, Burlington, VT, USA.
C Matthew KinseyDepartment of Medicine, Pulmonary and Critical Care, College of Medicine, University of Vermont, Burlington, VT, USA.

Funding

CT Characteristics of Emphysema for Evaluation of Pulmonary NodulesK23HL133476 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI KINSEY, CHARLES MATTHEW · 2016 to 2021
$933k
Improving the Generalizability of Deep Neural Networks by Teaching them Lung Cancer PathophysiologyF99CA274713 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI MASQUELIN, AXEL HERVE PATRICK · 2022 to 2022
$33k
Leveraging prior knowledge to classify Indeterminate Lung Nodules in CT images using Deep Neural NetworksF31CA268908 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI MASQUELIN, AXEL HERVE PATRICK · 2022 to 2022
$18k
NCI NIH HHS F31 CA268908NCI NIH HHS F99 CA274713NHLBI NIH HHS K23 HL133476
6 · The paper itself

Abstract

BACKGROUNDContinued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images.OBJECTIVEIn this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules.METHODSWe selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included.RESULTSThe Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80

Indexed as

Deep LearningLung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle Agedconvolutional neural networksfeature attributionlow-dose computed tomographyLung cancer

Identifiers

PMID38848168
PMCPMC12809699

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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.