Evidence map›Paper›PMID 42528688›Full record

ArticleFrontiers in oncology2026

Advancing breast cancer diagnosis: a combined approach using deep learning-reconstructed diffusion-weighted imaging and Synthetic MRI.

Wanjun Xia, Qichang Fu, Kaiyu Wang, Lin Li, Anning Sai, Yong Zhang

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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Wanjun XiaDepartment of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Qichang FuDepartment of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Kaiyu WangMR Research China, GE Healthcare, Beijing, China.
Lin LiDepartment of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Anning SaiDepartment of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yong ZhangDepartment of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is the most common cause of malignancy in women, excluding skin cancer, leading to increased interest in novel non-invasive imaging techniques that eliminate the need for contrast agents. Objective: This study aims to establish a deep learning-reconstructed diffusion-weighted imaging (DL-DWI) model combined with Synthetic MRI and to evaluate its diagnostic value for breast cancer in this proof of concept study. Methods: A total of 111 patients with pathologically confirmed breast lesions (39 benign, 72 malignant) were enrolled, and all patients underwent DL-DWI and synthetic MRI scans. Quantitative parameters including ADC, DL-ADC, T1, T2 and PD were compared between benign and malignant groups using rank sum tests. ROC curve analysis was used to assess the diagnostic efficacy of single and combined models, and DeLong's test was applied for pairwise AUC comparison. Results: Both DL-ADC (AUC = 0.993; 95% CI: 0.955-1.000) and conventional ADC (AUC = 0.990; 95% CI: 0.949-1.000) demonstrated excellent and comparable diagnostic accuracy. The DL-ADC derived from DL-DWI demonstrated excellent diagnostic accuracy (AUC = 0.993), comparable to conventional ADC (AUC = 0.990). A model combining Synthetic MRI with DL-DWI yielded an AUC of 0.995. However, DeLong's test revealed no statistically significant differences in AUC between any pair of models (all p > 0.05): Synthetic MRI + DL-DWI vs. DL-DWI alone (p=0.45), Synthetic MRI + DL-DWI vs. DWI alone (p=0.54), and DL-DWI vs. DWI alone (p=0.67). Conclusion: In this proof of concept study, standalone DL-DWI achieves high accuracy in differentiating benign and malignant in breast lesions meeting the study size criteria. Adding synthetic MRI brings no significant diagnostic improvement, indicating DL-ADC is the dominant diagnostic biomarker. This contrast-free deep learning-based MRI approach has potential as a contrast free protocol and should be further evaluated in a larger clinical trial comparing contrast-enhanced breast MRI with this novel technique.

Indexed as

breast cancerdeep learning reconstructiondifferential diagnosisdiffusion-weighted imagingsynthetic MRI

Identifiers

PMID42528688
PMCPMC13414729

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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.