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Objectives <p>This study aimed to develop and validate a novel strain elastography (SE) radiomics nomogram for diagnosing breast cancer (BC) by analyzing intratumoral and peritumoral regions.</p> Methods <p>A cohort of 322 patients, comprising 217 from hospital #1 (06/2021–05/2023) and 105 from hospital #2 (06/2022–05/2023) with breast lesions, was enrolled. Radiomic features were extracted from intratumoral and peritumoral (0–1&#xa0;mm, 1–2&#xa0;mm, 2–3&#xa0;mm) regions on strain elastography images. Significant features were selected using Mann–Whitney U test, Spearman's correlation coefficient, and LASSO logistic regression. A radiomic model was constructed utilizing these features, followed by the development of a radiomic nomogram integrating optimal features.</p> Results <p>The intratumoral radiomic model exhibited an area under the receiver operating characteristic curve (AUC) of 0.774 (95% CI: 0.626–0.922) in the internal testing set. Combining peritumoral radiomics, the intratumoral &amp; peritumoral_0–1&#xa0;mm radiomic model emerged as the optimal model with an AUC of 0.884 (95% CI: 0.766–0.998) in the internal testing set, signifying improved BC identification. The optimal model demonstrated an AUC of 0.841 (95% CI: 0.762–0.920) in the external testing set, indicating robustness and generalization.</p> Conclusions <p>The radiomic model incorporating intratumoral &amp; peritumoral_0–1&#xa0;mm radiomic features shows promise in diagnosing BC, aiding in devising effective clinical treatment strategies.</p>

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Multi-center strain elastography radiomics for breast cancer diagnosis: integrating intratumoral and peritumoral regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月2日

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  • Guoqiu Li,
  • Qiaoying Li,
  • Huaiyu Wu,
  • Zhibin Huang,
  • Hongtian Tian,
  • Keen Yang,
  • Jing Chen,
  • Jinfeng Xu,
  • Lijun Yuan,
  • Fajin Dong

摘要

Objectives

This study aimed to develop and validate a novel strain elastography (SE) radiomics nomogram for diagnosing breast cancer (BC) by analyzing intratumoral and peritumoral regions.

Methods

A cohort of 322 patients, comprising 217 from hospital #1 (06/2021–05/2023) and 105 from hospital #2 (06/2022–05/2023) with breast lesions, was enrolled. Radiomic features were extracted from intratumoral and peritumoral (0–1 mm, 1–2 mm, 2–3 mm) regions on strain elastography images. Significant features were selected using Mann–Whitney U test, Spearman's correlation coefficient, and LASSO logistic regression. A radiomic model was constructed utilizing these features, followed by the development of a radiomic nomogram integrating optimal features.

Results

The intratumoral radiomic model exhibited an area under the receiver operating characteristic curve (AUC) of 0.774 (95% CI: 0.626–0.922) in the internal testing set. Combining peritumoral radiomics, the intratumoral & peritumoral_0–1 mm radiomic model emerged as the optimal model with an AUC of 0.884 (95% CI: 0.766–0.998) in the internal testing set, signifying improved BC identification. The optimal model demonstrated an AUC of 0.841 (95% CI: 0.762–0.920) in the external testing set, indicating robustness and generalization.

Conclusions

The radiomic model incorporating intratumoral & peritumoral_0–1 mm radiomic features shows promise in diagnosing BC, aiding in devising effective clinical treatment strategies.