Department of Ultrasound, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.
CAS 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.
Eur Radiol. 2022 Mar;32(3):2099-2109. doi: 10.1007/s00330-021-08293-y. Epub 2021 Oct 15.
Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage.
In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration.
In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC.
The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients.
• We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
乳腺癌（BC）是全球女性中最常见的癌症，新辅助化疗（NAC）被认为是大多数 BC 患者的标准治疗方法。然而，患者对 NAC 的反应率存在差异，这导致了对 NAC 反应不佳的患者的治疗延迟，并影响了他们的预后。本研究旨在探讨深度学习放射组学（DLR）在早期预测 NAC 反应中的可行性。
本前瞻性研究共纳入 2016 年 3 月至 2020 年 12 月期间经临床病理证实的 168 例 BC 患者。所有患者均完成 NAC 治疗，并在三个时间点（NAC 前、第二疗程后和第四疗程后）进行超声检查（US）。我们开发了两个用于预测 NAC 第二和第四疗程后反应的 DLR 模型，即 DLR-2 和 DLR-4。此外，提出了一种新的深度学习放射组学管道（DLRP），用于逐步预测 NAC 给药不同时间点的反应。
在验证队列中，DLR-2 的 AUC 为 0.812（95%CI：0.770-0.851），NPV 为 83.3%（95%CI：76.5-89.6）。DLR-4 的 AUC 为 0.937（95%CI：0.913-0.955），特异性为 90.5%（95%CI：86.3-94.2）。此外，DLRP 成功识别出 21 例非反应患者中的 19 例，表明他们可以在 NAC 的早期阶段从治疗策略调整中受益。
所提出的 DLRP 策略有望有效预测 BC 患者 NAC 的早期反应。
我们提出了两种新的深度学习放射组学（DLR）模型，用于基于不同 NAC 时间点的 US 图像预测 BC 患者对新辅助化疗（NAC）的反应。
结合两种 DLR 模型，提出了一种深度学习放射组学管道（DLRP），用于逐步预测 NAC 反应。
DLRP 可为 BC 患者和医生提供一种有效且可行的工具，以在早期预测对 NAC 的反应，并确定进一步的个性化治疗方案。
