Background/Objectives: Patients with breast cancer who do not achieve a complete response to neoadjuvant chemotherapy (NAC) may benefit from intensified adjuvant systemic therapy. However, such treatment escalation is typically delayed until after tumour resection, which occurs several months into the treatment course. Quantitative ultrasound (QUS) can detect early microstructural changes in tumours and may enable timely identification of non-responders during NAC, allowing for earlier treatment intensification. In our previous prospective observational study, 100 breast cancer patients underwent QUS imaging before and four times during NAC. Machine learning algorithms based on QUS texture features acquired in the first week of treatment were developed and achieved 78% accuracy in predicting treatment response. In the current study, we aimed to validate these algorithms in an independent prospective cohort to assess reproducibility and confirm their clinical utility. Methods: We included breast cancer patients eligible for NAC per standard of care, with tumours larger than 1.5 cm. QUS imaging was acquired at baseline and during the first week of treatment. Tumour response was defined as a ≥30% reduction in target lesion size on the resection specimen compared to baseline imaging. Results: A total of 51 patients treated between 2018 and 2021 were included (median age 49 years; median tumour size 3.6 cm). Most were estrogen receptor–positive (65%) or HER2-positive (33%), and the majority received dose-dense AC-T (n = 34, 67%) or FEC-D (n = 15, 29%) chemotherapy, with or without trastuzumab. The support vector machine algorithm achieved an area under the curve of 0.71, with 86% accuracy, 91% specificity, 50% sensitivity, 93% negative predictive value, and 43% positive predictive value for predicting treatment response. Misclassifications were primarily associated with poorly defined tumours and difficulties in accurately identifying the region of interest. Conclusions: Our findings validate QUS-based machine learning models for early prediction of chemotherapy response and support their potential as non-invasive tools for treatment personalization and clinical trial development focused on early treatment intensification.
**背景/目的：** 对于新辅助化疗未能达到完全缓解的乳腺癌患者，强化辅助全身治疗可能带来获益。然而，此类治疗升级通常延迟至肿瘤切除术后进行，这发生在治疗开始数月之后。定量超声能够检测肿瘤早期的微结构变化，或可在新辅助化疗期间及时识别无应答者，从而实现更早的治疗强化。在我们先前的前瞻性观察性研究中，100名乳腺癌患者在新辅助化疗前及化疗期间接受了四次定量超声成像。基于治疗第一周获取的定量超声纹理特征开发的机器学习算法，在预测治疗反应方面达到了78%的准确率。本研究旨在一个独立的前瞻性队列中验证这些算法，以评估其可重复性并确认其临床效用。 **方法：** 我们纳入了符合标准治疗条件、适合接受新辅助化疗且肿瘤大于1.5厘米的乳腺癌患者。在基线期和治疗第一周进行定量超声成像。肿瘤反应定义为与基线影像相比，切除标本中靶病灶大小减少≥30%。 **结果：** 共纳入2018年至2021年间治疗的51名患者（中位年龄49岁；中位肿瘤大小3.6厘米）。大多数为雌激素受体阳性（65%）或HER2阳性（33%），多数患者接受了剂量密集型AC-T方案（n=34，67%）或FEC-D方案（n=15，29%）化疗，联合或不联合曲妥珠单抗。支持向量机算法预测治疗反应的曲线下面积为0.71，准确率为86%，特异性为91%，敏感性为50%，阴性预测值为93%，阳性预测值为43%。错误分类主要与肿瘤边界不清以及难以准确识别感兴趣区域有关。 **结论：** 我们的研究结果验证了基于定量超声的机器学习模型可用于早期预测化疗反应，并支持其作为一种无创工具用于个体化治疗以及专注于早期治疗强化的临床试验开发的潜力。
肿瘤(癌症)患者之家