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Abstract：
UNASSIGNED: Early prediction of treatment response to neoadjuvant chemotherapy (NACT) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a Siamese multi-task network (SMTN) for predicting pathological complete response (pCR) based on longitudinal ultrasound images at the early stage of NACT.
UNASSIGNED: In this multicentre, retrospective cohort study, a total of 393 patients with biopsy-proven HER2-positive breast cancer were retrospectively enrolled from three hospitals in china between December 16, 2013 and March 05, 2021, and allocated into a training cohort and two external validation cohorts. Patients receiving full cycles of NACT and with surgical pathological results available were eligible for inclusion. The key exclusion criteria were missing ultrasound images and/or clinicopathological characteristics. The proposed SMTN consists of two subnetworks that could be joined at multiple layers, which allowed for the integration of multi-scale features and extraction of dynamic information from longitudinal ultrasound images before and after the first /second cycles of NACT. We constructed the clinical model as a baseline using multivariable logistic regression analysis. Then the performance of SMTN was evaluated and compared with the clinical model.
UNASSIGNED: The training cohort, comprising 215 patients, were selected from Yunnan Cancer Hospital. The two independent external validation cohorts, comprising 95 and 83 patients, were selected from Guangdong Provincial People\'s Hospital, and Shanxi Cancer Hospital, respectively. The SMTN yielded an area under the receiver operating characteristic curve (AUC) values of 0.986 (95% CI: 0.977-0.995), 0.902 (95%CI: 0.856-0.948), and 0.957 (95%CI: 0.924-0.990) in the training cohort and two external validation cohorts, respectively, which were significantly higher than that those of the clinical model (AUC: 0.524-0.588, P all < 0.05). The AUCs values of the SMTN within the anti-HER2 therapy subgroups were 0.833-0.972 in the two external validation cohorts. Moreover, 272 of 279 (97.5%) non-pCR patients (159 of 160 (99.4%), 53 of 54 (98.1%), and 60 of 65 (92.3%) in the training and two external validation cohorts, respectively) were successfully identified by the SMTN, suggesting that they could benefit from regime adjustment at the early-stage of NACT.
UNASSIGNED: The SMTN was able to predict pCR in the early-stage of NACT for HER2-positive breast cancer patients, which could guide clinicians in adjusting treatment regimes.
UNASSIGNED: Key-Area Research and Development Program of Guangdong Province (No.2021B0101420006); National Natural Science Foundation of China (No.82071892, 82171920); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); the National Science Foundation for Young Scientists of China (No.82102019, 82001986); Project Funded by China Postdoctoral Science Foundation (No.2020M682643); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (202101AW070001); Scientific research fund project of Department of Education of Yunnan Province(2022J0249). Science and technology Projects in Guangzhou (202201020001;202201010513); High-level Hospital Construction Project (DFJH201805, DFJHBF202105).
UNASSIGNED: In this multicentre, retrospective cohort study, a total of 393 patients with biopsy-proven HER2-positive breast cancer were retrospectively enrolled from three hospitals in china between December 16, 2013 and March 05, 2021, and allocated into a training cohort and two external validation cohorts. Patients receiving full cycles of NACT and with surgical pathological results available were eligible for inclusion. The key exclusion criteria were missing ultrasound images and/or clinicopathological characteristics. The proposed SMTN consists of two subnetworks that could be joined at multiple layers, which allowed for the integration of multi-scale features and extraction of dynamic information from longitudinal ultrasound images before and after the first /second cycles of NACT. We constructed the clinical model as a baseline using multivariable logistic regression analysis. Then the performance of SMTN was evaluated and compared with the clinical model.
UNASSIGNED: The training cohort, comprising 215 patients, were selected from Yunnan Cancer Hospital. The two independent external validation cohorts, comprising 95 and 83 patients, were selected from Guangdong Provincial People\'s Hospital, and Shanxi Cancer Hospital, respectively. The SMTN yielded an area under the receiver operating characteristic curve (AUC) values of 0.986 (95% CI: 0.977-0.995), 0.902 (95%CI: 0.856-0.948), and 0.957 (95%CI: 0.924-0.990) in the training cohort and two external validation cohorts, respectively, which were significantly higher than that those of the clinical model (AUC: 0.524-0.588, P all < 0.05). The AUCs values of the SMTN within the anti-HER2 therapy subgroups were 0.833-0.972 in the two external validation cohorts. Moreover, 272 of 279 (97.5%) non-pCR patients (159 of 160 (99.4%), 53 of 54 (98.1%), and 60 of 65 (92.3%) in the training and two external validation cohorts, respectively) were successfully identified by the SMTN, suggesting that they could benefit from regime adjustment at the early-stage of NACT.
UNASSIGNED: The SMTN was able to predict pCR in the early-stage of NACT for HER2-positive breast cancer patients, which could guide clinicians in adjusting treatment regimes.
UNASSIGNED: Key-Area Research and Development Program of Guangdong Province (No.2021B0101420006); National Natural Science Foundation of China (No.82071892, 82171920); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); the National Science Foundation for Young Scientists of China (No.82102019, 82001986); Project Funded by China Postdoctoral Science Foundation (No.2020M682643); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (202101AW070001); Scientific research fund project of Department of Education of Yunnan Province(2022J0249). Science and technology Projects in Guangzhou (202201020001;202201010513); High-level Hospital Construction Project (DFJH201805, DFJHBF202105).
摘要：
■早期预测人表皮生长因子受体2（HER2）阳性乳腺癌患者对新辅助化疗（NACT）的治疗反应，有助于及时调整治疗方案。我们旨在开发和验证暹罗多任务网络（SMTN），用于在NACT早期基于纵向超声图像预测病理完全反应（pCR）。
■在这个多中心，回顾性队列研究，本研究于2013年12月16日至2021年3月5日期间,回顾性纳入了中国三家医院经活检证实为HER2阳性乳腺癌的393例患者,并将其分为一个培训队列和两个外部验证队列.接受完整周期NACT且有手术病理结果的患者符合入选条件。关键排除标准是缺少超声图像和/或临床病理特征。拟议的SMTN由两个子网络组成，这些子网络可以在多个层连接，这允许在NACT的第一个/第二个周期之前和之后，从纵向超声图像中集成多尺度特征并提取动态信息。我们使用多变量逻辑回归分析构建了临床模型作为基线。然后评估SMTN的性能并与临床模型进行比较。
■培训队列，包括215名患者，选取云南省肿瘤医院。两个独立的外部验证队列，包括95和83名患者，选自广东省人民医院,山西省肿瘤医院，分别。SMTN产生0.986（95％CI：0.977-0.995）的受试者工作特征曲线下面积（AUC）值，0.902（95CI：0.856-0.948），和0.957（95CI：0.924-0.990）在训练队列和两个外部验证队列中，分别,显著高于临床模型(AUC:0.524-0.588,P均<0.05)。在两个外部验证队列中，抗HER2治疗亚组中SMTN的AUC值为0.833-0.972。此外，279例非pCR患者中的272例（97.5％）（160例中的159例（99.4％），54人中的53人（98.1%），在培训和两个外部验证队列中，65人中有60人(92.3%)，分别)被SMTN成功识别，这表明他们可以从NACT早期阶段的制度调整中受益。
■SMTN能够预测HER2阳性乳腺癌患者NACT早期的pCR，这可以指导临床医生调整治疗方案。
■广东省重点地区研究发展计划(No.2021B0101420006);国家自然科学基金(No.82071892,82171920);广东省人工智能医学图像分析与应用重点实验室(No.2022B1212010011);国家科学基金青年科学基金(No.82102019,82001986);云南省科学基金20AW16843;云南省科学基金项目202020广州市科技项目（202201020001；202201010513）；高级医院建设项目（DFJH201805，DFJHBF202105）。
■在这个多中心，回顾性队列研究，本研究于2013年12月16日至2021年3月5日期间,回顾性纳入了中国三家医院经活检证实为HER2阳性乳腺癌的393例患者,并将其分为一个培训队列和两个外部验证队列.接受完整周期NACT且有手术病理结果的患者符合入选条件。关键排除标准是缺少超声图像和/或临床病理特征。拟议的SMTN由两个子网络组成，这些子网络可以在多个层连接，这允许在NACT的第一个/第二个周期之前和之后，从纵向超声图像中集成多尺度特征并提取动态信息。我们使用多变量逻辑回归分析构建了临床模型作为基线。然后评估SMTN的性能并与临床模型进行比较。
■培训队列，包括215名患者，选取云南省肿瘤医院。两个独立的外部验证队列，包括95和83名患者，选自广东省人民医院,山西省肿瘤医院，分别。SMTN产生0.986（95％CI：0.977-0.995）的受试者工作特征曲线下面积（AUC）值，0.902（95CI：0.856-0.948），和0.957（95CI：0.924-0.990）在训练队列和两个外部验证队列中，分别,显著高于临床模型(AUC:0.524-0.588,P均<0.05)。在两个外部验证队列中，抗HER2治疗亚组中SMTN的AUC值为0.833-0.972。此外，279例非pCR患者中的272例（97.5％）（160例中的159例（99.4％），54人中的53人（98.1%），在培训和两个外部验证队列中，65人中有60人(92.3%)，分别)被SMTN成功识别，这表明他们可以从NACT早期阶段的制度调整中受益。
■SMTN能够预测HER2阳性乳腺癌患者NACT早期的pCR，这可以指导临床医生调整治疗方案。
■广东省重点地区研究发展计划(No.2021B0101420006);国家自然科学基金(No.82071892,82171920);广东省人工智能医学图像分析与应用重点实验室(No.2022B1212010011);国家科学基金青年科学基金(No.82102019,82001986);云南省科学基金20AW16843;云南省科学基金项目202020广州市科技项目（202201020001；202201010513）；高级医院建设项目（DFJH201805，DFJHBF202105）。
