Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning studyResearch in context

Mingzhen Chen; Chunli Kong; Guihan Lin; Weiyue Chen; Xinyu Guo; Yaning Chen; Xue Cheng; Minjiang Chen; Changsheng Shi; Min Xu; Junhui Sun; Chenying Lu; Jiansong Ji

EClinicalMedicine (Sep 2023)

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning studyResearch in context

Mingzhen Chen,
Chunli Kong,
Guihan Lin,
Weiyue Chen,
Xinyu Guo,
Yaning Chen,
Xue Cheng,
Minjiang Chen,
Changsheng Shi,
Min Xu,
Junhui Sun,
Chenying Lu,
Jiansong Ji

Affiliations

Mingzhen Chen: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
Chunli Kong: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Guihan Lin: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Weiyue Chen: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Xinyu Guo: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
Yaning Chen: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
Xue Cheng: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Minjiang Chen: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Changsheng Shi: Department of Interventional Radiology, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang, China
Min Xu: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
Junhui Sun: Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Corresponding author. Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chenying Lu: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Corresponding author. Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, China.
Jiansong Ji: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China; Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China; Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China; Corresponding author. Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, China.

Journal volume & issue: Vol. 63
p. 102176

Abstract

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Summary: Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887–0.911), external test set 1 (AUC 0.885, 95% CI, 0.867–0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738–0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783–0.817), external test set 1 (AUC 0.763, 95% CI, 0.732–0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719–0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Published in EClinicalMedicine

ISSN: 2589-5370 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General)
Website: https://www.thelancet.com/journals/eclinm/home

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