Association of Ki-67 Change Pattern After Core Needle Biopsy and Prognosis in HR+/HER2− Early Breast Cancer Patients

Li, Shuai; Chen, Xiaosong; Shen, Kunwei

doi:10.3389/fsurg.2022.905575

Cited by 2 publications

(1 citation statement)

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“…High Ki67 expression implies active cell proliferation; the Ki67 index can reflect the proliferation capacity of tumor cells [ 6 , 7 ]. High Ki67 expression in PCa is a poor prognostic factor and is correlated with overall survival, disease-free survival, and distant metastasis [ 8 , 9 ]. The existing methods for detecting Ki67 expression levels are invasive, complex, barely reproducible, and susceptible to subjective influence [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

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Background To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). Materials The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. Results Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. Conclusion The multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

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