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09-06-2024 | Breast Cancer | Research

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method

Authors: Yanfeng Li, Wengxing Long, Hongda Zhou, Tao Tan, Hui Xie

Published in: Breast Cancer Research and Treatment

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Abstract

Purpose

This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status.

Methods

Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models.

Result

Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit.

Conclusion

Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.
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Literature
20.
go back to reference Goldhirsch A et al (2011) Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22(8):1736–1747CrossRefPubMedPubMedCentral Goldhirsch A et al (2011) Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22(8):1736–1747CrossRefPubMedPubMedCentral
22.
go back to reference Ronneberger et al (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI, vol 18, pp 234–241 Ronneberger et al (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI, vol 18, pp 234–241
29.
go back to reference Jiang M, You C, Wang M, et al (2023) Controllable deep learning denoising model for ultrasound images using synthetic noisy image. In: Computer graphics international conference. Springer, Cham, pp 297–308 Jiang M, You C, Wang M, et al (2023) Controllable deep learning denoising model for ultrasound images using synthetic noisy image. In: Computer graphics international conference. Springer, Cham, pp 297–308
33.
go back to reference Cai J et al (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79CrossRef Cai J et al (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79CrossRef
35.
go back to reference Lin D, Tang X (2006) Conditional infomax learning: an integrated framework for feature extraction and fusion. In: Computer vision–ECCV 2006: 9th European conference on computer vision, vol 9, pp 68–82 Lin D, Tang X (2006) Conditional infomax learning: an integrated framework for feature extraction and fusion. In: Computer vision–ECCV 2006: 9th European conference on computer vision, vol 9, pp 68–82
36.
go back to reference Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRefPubMed Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRefPubMed
37.
go back to reference Kanyongo W, Ezugwu AE (2023) Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives. Inform Med Unlocked 38:101232CrossRef Kanyongo W, Ezugwu AE (2023) Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives. Inform Med Unlocked 38:101232CrossRef
38.
go back to reference Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28CrossRef Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28CrossRef
39.
go back to reference Yang L, Zhu D, Liu X, Cui P (2023) Robust feature selection method based on joint L2,1 norm minimization for sparse regression. Electronics 12:4450CrossRef Yang L, Zhu D, Liu X, Cui P (2023) Robust feature selection method based on joint L2,1 norm minimization for sparse regression. Electronics 12:4450CrossRef
Metadata
Title
Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method
Authors
Yanfeng Li
Wengxing Long
Hongda Zhou
Tao Tan
Hui Xie
Publication date
09-06-2024
Publisher
Springer US
Published in
Breast Cancer Research and Treatment
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-024-07375-x

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