基于 DNA 超分子水凝胶的保护性 NK 细胞储库用于增强三阴性乳腺癌治疗
Protective NK Cell Reservoir Based on DNA Supramolecular Hydrogel for Enhanced Triple-Negative Breast Cancer Therapy.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
基于活检全切片图像的深度学习用于乳腺癌新辅助化疗前病理完全缓解预测：一项多中心研究
英文原题：Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.
查看英文原题
Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.
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研究概要
我们的研究表明，WSIs 结合深度学习有可能预测乳腺癌对 NAC 的 pCR。结合临床特征将提高预测性能。与 stromal TILs 相比，来自 DLPM 的 DLPs 可为 pCR 预测提供更多信息。
研究思路结论见上方概要
背景
预测接受新辅助化疗（NAC）患者的病理完全缓解（pCR）对于建立个体化治疗至关重要。肿瘤组织的全切片图像（WSIs）反映了肿瘤的组织病理学信息，这对于治疗反应有效性非常重要。在本研究中，我们旨在探究是否可以从WSIs中检测到pCR的预测信息。
方法
我们回顾性收集了四个队列共874例经活检确诊为乳腺癌的患者数据。在主要队列中，构建了一个深度学习病理模型（DLPM），利用活检WSIs预测pCR，随后在三个外部队列中进行了验证。DLPM能够为每位患者生成深度学习病理评分（DLPs）；选择间质TIL（肿瘤浸润淋巴细胞）（TILs）与DLPs进行比较。
结果
基于WSI特征的DLPM在队列中显示出良好的预测性能，最高AUC为0.72。另外，DLPM与临床特征的联合在所有队列中提供了更好的预测性能（AUC >0.70）。我们还评估了DLPM在三种不同乳腺亚型中的性能，其中对三阴性乳腺癌（TNBC）亚型的预测最佳（AUC：0.73）。此外，DLPM联合临床特征和间质TILs在原发队列（AUC：0.82）和验证队列1（AUC：0.80）中达到了最高AUC。
展开英文摘要原文
MATERIALS AND METHODS
We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.
RESULTS
The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).
CONCLUSION
Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.
论文信息
- 作者
- Li B、Li F、Liu Z、Xu F、Ye G、Li W、Zhang Y、Zhu T
- 第一作者单位
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.China
- 通讯作者单位
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, People's Republic of China, Beijing, 100191, China. Electronic address: jie.tian@ia.ac.cn.China
- 文献类型
- 多中心研究
- 期刊
- Breast (Edinburgh, Scotland)2022 Dec
- 原文标识
- PubMed 36308926 · DOI 10.1016/j.breast.2022.10.004
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