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Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study.
Huang, Yao; Cao, Ying; Hu, Xiaofei; Lan, Xiaosong; Chen, Huifang; Tang, Sun; Li, Lan; Cheng, Yue; Gong, Xueqin; Wang, Wei; Jiang, Fujie; Yin, Ting; Wang, Xiaoxia; Zhang, Jiuquan.
Afiliação
  • Huang Y; School of Medicine, Chongqing University, Chongqing, China.
  • Cao Y; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Hu X; School of Medicine, Chongqing University, Chongqing, China.
  • Lan X; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Chen H; Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
  • Tang S; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Li L; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Cheng Y; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Gong X; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Wang W; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Jiang F; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Yin T; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Wang X; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China.
  • Zhang J; MR Collaborations, Siemens Healthineers Ltd., Chengdu, China.
J Magn Reson Imaging ; 2023 Dec 18.
Article em En | MEDLINE | ID: mdl-38109316
ABSTRACT

BACKGROUND:

Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection.

PURPOSE:

To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection. STUDY TYPE Multicenter, longitudinal. POPULATION Center A 162 female patients (50.63 ± 8.41 years) divided 73 into training and internal validation cohorts. Center B 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort. FIELD STRENGTH/SEQUENCE Center A single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B single vendor 1.5 T with volume interpolated breath-hold examination sequence. ASSESSMENT Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features. STATISTICAL TESTS Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant.

RESULTS:

In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709). DATA

CONCLUSION:

The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer. EVIDENCE LEVEL 1 TECHNICAL EFFICACY Stage 4.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article