分子影像学杂志2025，Vol.48Issue(11)：1325-1332,8.DOI:10.12122/j.issn.1674-4500.2025.11.01
深度学习超声影像组学列线图预测浸润性乳腺癌Ki-67表达
Deep learning ultrasound radiomics nomogram for predicting Ki-67 expression in invasive breast cancer
摘要
Abstract
Objective To explore the value of a deep learning-based ultrasound radiomics nomogram in predicting Ki-67 expression levels in invasive breast cancer.Methods A retrospective single-center study was conducted,collecting complete preoperative clinical data and ultrasound images from 465 patients with pathologically confirmed invasive breast cancer at the First Affiliated Hospital of Bengbu Medical University from January to December 2024.Image acquisition was performed using Mindray Resona 7 and Samsung HS60 color Doppler ultrasound systems.Based on immunohistochemical results,patients were divided into high and low Ki-67 expression groups and randomly assigned to training(n=326)and validation(n=139)cohorts at a 7:3 ratio.ITK-SNAP software was used to segment tumors from the largest 2D ultrasound cross-sectional images,with interobserver consistency of ROI delineation assessed by ICC.Pyradiomics was employed to extract radiomics features from tumor tissues,and four deep learning networks were pretrained to construct clinical,ultrasound radiomics,fusion,and combined nomogram models.Diagnostic performance and clinical utility were evaluated using ROC curves,calibration curves,and decision curve analysis.Results Nineteen optimal ultrasound radiomics features and the DenseNet121 deep learning model showed the best performance(P<0.05).In the training cohort,the AUCs for the clinical model,ultrasound radiomics model,deep learning model,fusion model,and nomogram were 0.79(95%CI:0.74-0.84),0.85(95%CI:0.81-0.90),0.87(95%CI:0.83-0.91),0.94(95%CI:0.91-0.97),and 0.95(95%CI:0.93-0.98),respectively.In the validation cohort,the corresponding AUCs were 0.76(95%CI:0.68-0.84),0.78(95%CI:0.70-0.85),0.81(95%CI:0.74-0.88),0.91(95%CI:0.86-0.96),and 0.93(95%CI:0.89-0.98).Conclusion The deep learning-based ultrasound radiomics nomogram can effectively predict Ki-67 expression in invasive breast cancer.关键词
深度学习/组学/乳腺癌/Ki-67/列线图Key words
deep learning/radiomics/breast cancer/Ki-67/nomogram引用本文复制引用
路丽丽,李林,杜欢,张盼盼,朱银花,贾晓涵,李阳..深度学习超声影像组学列线图预测浸润性乳腺癌Ki-67表达[J].分子影像学杂志,2025,48(11):1325-1332,8.基金项目
安徽省卫生健康科研项目(AHWJ2023A20372) （AHWJ2023A20372）
蚌埠医学院自然科学重点科技项目(2021byzd066) （2021byzd066）
蚌埠市科技创新指导类项目(20230129) （20230129）
