人工智能辅助影像学评价乳腺癌患者新辅助化疗效果

张楠; 张亚芳; 周琪

doi:10.19732/j.cnki.2096-6210.2026.02.006

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人工智能辅助影像学评价乳腺癌患者新辅助化疗效果

论著 | 更新时间：2026-05-07

- 人工智能辅助影像学评价乳腺癌患者新辅助化疗效果
- Evaluation of neoadjuvant chemotherapy response for breast cancer with artificial intelligence assisted imaging ZHANG Nan¹, ZHANG Yafang², ZHOU　Qi³
- 肿瘤影像学 2026年35卷第2期页码：264-272
- 作者机构：
  
  1.唐山市人民医院乳腺外科，河北唐山 063000
  2.唐山市人民医院核磁室，河北唐山 063000
  3.唐山市人民医院乳腺三科，河北唐山 063000
- 作者简介：
  
  张　楠（ORCID: 0009-00004-7708-3673），本科，主治医师，E-mail: qgqo64@sina.com。
- 基金信息：
  
  河北省医学科学研究课题计划(20251218)
- DOI：10.19732/j.cnki.2096-6210.2026.02.006
  中图分类号： R737.9;R445.2
- 收稿：2025-11-27，
  
  修回：2026-01-17，
  
  纸质出版：2026-04-28
- 稿件说明：
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张　楠, 张亚芳, 周　琪. 人工智能辅助影像学评价乳腺癌患者新辅助化疗效果［J］. 肿瘤影像学, 2026, 35(2): 264-272.

ZHANG N, ZHANG Y F, ZHOU Q. Citation:Evaluation of neoadjuvant chemotherapy for breast cancer with artificial intelligence assisted imaging［J］. Oncoradiology, 2026, 35(2): 264-272.
张　楠, 张亚芳, 周　琪. 人工智能辅助影像学评价乳腺癌患者新辅助化疗效果［J］. 肿瘤影像学, 2026, 35(2): 264-272. DOI： 10.19732/j.cnki.2096-6210.2026.02.006.

ZHANG N, ZHANG Y F, ZHOU Q. Citation:Evaluation of neoadjuvant chemotherapy for breast cancer with artificial intelligence assisted imaging［J］. Oncoradiology, 2026, 35(2): 264-272. DOI： 10.19732/j.cnki.2096-6210.2026.02.006.

摘要

目的

探讨人工智能辅助影像学在乳腺癌新辅助化疗（neoadjuvant chemotherapy，NAC）效果评价中的应用价值。

方法

从公共数据集中下载乳腺癌患者的临床资料进行回顾性分析，按照2∶1比例随机分为训练集和验证集。另回顾并分析2023年3月—2025年5月于唐山市人民医院接受治疗的乳腺癌患者的临床资料作为测试集。所有患者术前均行磁共振成像检查，且术后均有明确的病理学检查结果。利用分层成像维度累加结合锚定注意力框和不同深度学习模型（convnext、efficientnet、swin、vit）、不同机器学习算法（SVM、Ranger）组合构建8个机器学习模型，基于训练集数据筛选最佳机器学习模型。使用验证集对最佳机器学习模型进行优化，并利用测试集评价该模型预测乳腺癌NAC效果的效能。

结果

本研究共构建8个机器学习模型，其中基于CropNor-Substract-convnext-Ranger的机器学习模型预测乳腺癌NAC效果的灵敏度、特异度、准确度、曲线下面积均高于其他类型机器学习模型；使用验证集对CropNor-Substract-convnext-Ranger机器学习模型进行优化，优化终止时交叉熵损失为1.012，灵敏度为90.91%、特异度为92.67%、准确度为91.97%；CropNor-Substract-convnext-Ranger机器学习模型预测测试集乳腺癌NAC效果的灵敏度为90.48%、特异度为92.81%、准确度为91.86%、曲线下面积为0.924，其与病理学检查结果的一致性Kappa指数为0.832（

0.001）。

结论

CropNor-Substract-convnext-Ranger机器学习模型预测乳腺癌NAC效果的效能较好，具有较好的临床应用价值。

Abstract

Objective

To explore the application value of artificial intelligence assisted imaging in the evaluation of neoadjuvant chemotherapy (NAC) response for breast cancer.

Methods

The clinical data of breast cancer patients downloaded from the public data set were retrospectively analyzed

and they were randomly divided into a training set and a validation set according to the 2∶1 ratio. In addition

the clinical data of breast cancer patients treated in Tangshan People's Hospital from March 2023 to May 2025 were analyzed retrospectively as the test set. All patients underwent magnetic resonance imaging examination before surgery

and all patients had definitive postoperative pathological results. Eight machine learning models were constructed by combining layered imaging dimension accumulation with anchored attention boxes and different deep learning models (convnext

efficientnet

swin

and vit) and different machine learning algorithms (SVM and Ranger)

and the best machine learning model was selected based on the training set data. The best machine learning model was optimized using the validation set

and the effectiveness of the model in predicting the curative effect of breast cancer NAC was evaluated using the test set.

Results

Eight machine learning models were constructed in this study

and the sensitivity

specificity

accuracy and area under curve of the machine learning model based on CropNor-Subtracts-convnext-Ranger were higher than those of other machine learning models in predicting the efficacy of NAC in breast cancer. The CropNor-Subtracts-convnext-Ranger machine learning model was optimized using the validation set

with a cross entropy loss of 1.012 at the end of the optimization

sensitivity of 90.91%

specificity of 92.67% and accuracy of 91.97%. The sensitivity

specificity

accuracy

area under curve of CropNor-Subtract-convnext-Ranger machine learning model to predict the efficacy of breast cance

r NAC in the test set were 90.48%

92.81%

91.86%

0.924

and the consistency Kappa index with the pathological results was 0.832 (

0.001).

Conclusion

CropNor-Subtract-convnext-Ranger machine learning model shows good performance in predicting NAC response in breast cancer

and it has good clinical application value.

关键词

Keywords

references

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