An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study

Creators

Yu Liu, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
Min Liu, Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center
Zhen Li, Department of 3rd Breast Surgery, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University
Weitao Ye, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
Xiaotang Yang, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University
Ying Wang, Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University
Lei Wu, Guangdong Cardiovascular Institute
Changhong Liang, Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
Pinxiong Li, Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
Yuxiang Wang, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University
Dong Chen, Department of Medical Ultrasound, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University
Zhenhui Li, Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University
Zaiyi Liu, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences
Yanfen Cui, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University
Yu Xie, Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University

Description

Abstract Background The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR. Methods A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first–second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed. Results The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891–0.963] and 0.914 (95% CI 0.853–0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665–0.804) and 0.610 (95% CI 0.504–0.716)], and radiologist interpretation [0.632 (95% CI 0.570–0.693) and 0.724 (95% CI 0.644–0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793–0.955)–0.897 (0.851–0.943) in the external test cohorts]. Conclusions We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.

Publication Date

1-1-2022

Publisher

figshare Academic Research System

DOI

10.6084/m9.figshare.c.6317123.v1

Document Type

Data Set

Identifier

10.6084/m9.figshare.c.6317123.v1

Embargo Date

1-1-2022

Share

COinS