Evidence map›Paper›PMID 39496537›Full record

ArticleAcademic radiology2025

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

Xiao Chen, Yang Zhang, Jiejie Zhou, Yong Pan, Hanghui Xu, Ying Shen, Guoquan Cao, Min-Ying Su, Meihao Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

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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

9 authors.

Xiao ChenDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.).
Yang ZhangDepartment of Radiological Sciences, University of California, Irvine, CA (Y.Z., J.Z., M-Y.S.); Department of Radiation Oncology, University of California, Irvine, CA (Y.Z.).
Jiejie ZhouDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.); Department of Radiological Sciences, University of California, Irvine, CA (Y.Z., J.Z., M-Y.S.).
Yong PanDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.).
Hanghui XuZhuji People's Hospital of Zhejiang Province, China (H.X.).
Ying ShenDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.).
Guoquan CaoDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.).
Min-Ying SuDepartment of Radiological Sciences, University of California, Irvine, CA (Y.Z., J.Z., M-Y.S.); Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan (M-Y.S.). Electronic address: msu@uci.edu.
Meihao WangDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China (X.C., J.Z., Y.P., Y.S., G.C., M.W.); Key Laboratory of Intelligent Medical Imaging of Wenzhou, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Institute of Aging, Wenzhou Medical University, Wenzhou, China (M.W.).

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
NCI NIH HHS P30 CA062203
6 · The paper itself

Abstract

RATIONALE AND

objectivesDetection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by radiomics for classification. MATERIALS AND

methods500 cases with AD on DBT reports were identified; the earlier 292 cases for training, and the later 208 cases for testing. The DL Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to automatically localize abnormalities and generate a region of interest (ROI), which was put into the radiomics model to estimate the malignancy probability for constructing ROC curves. Radiologists delineated ROI manually for comparison. Cases were categorized into pure AD and AD associated with other features, including mass, regional high-density, and calcifications. The ROC curves were compared using the DeLong test.

resultsThe overall malignancy rate was 57% (285/500). Of them, 267 cases were classified as pure AD, and the malignancy rate (106/267 = 39.7%) was significantly lower compared to AD cases associated with other features (179/233 = 76.8%, p < 0.01). In the testing set, the diagnostic AUC was 0.82 when using the manual ROI and 0.84 when using the DL-generated ROI. In the more challenging pure AD cases, DL-generated ROI yielded an AUC of 0.77, significantly lower than 0.86 for AD associated with other features.

conclusionDL could detect AD on DBT, and the diagnostic performance was comparable to manual ROI. The strategy worked for pure AD, but the performance was worse than that for AD with other features.

Indexed as

Breast NeoplasmsDeep LearningMammographyRadiographic Image Interpretation, Computer-AssistedAdultAgedAlgorithmsFemaleHumansMiddle AgedRadiomicsRetrospective StudiesSensitivity and SpecificityArchitectural distortionBreast cancerDeep learningDigital breast tomosynthesisRadiomics

Identifiers

PMID39496537
PMCPMC11875907

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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.