1
|
Liu H, Zou L, Xu N, Shen H, Zhang Y, Wan P, Wen B, Zhang X, He Y, Gui L, Kong W. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer. NPJ Breast Cancer 2024; 10:22. [PMID: 38472210 PMCID: PMC10933422 DOI: 10.1038/s41523-024-00628-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.
Collapse
Affiliation(s)
- Han Liu
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Liwen Zou
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Nan Xu
- Department of Ultrasound, Jinling Hospital, Medical School of Nanjing University/General Hospital of Eastern Theater Command, Nanjing, 210002, China
| | - Haiyun Shen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Yu Zhang
- Department of Mathematics, Nanjing University, Nanjing, 210008, China
| | - Peng Wan
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China
| | - Baojie Wen
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Xiaojing Zhang
- Department of Ultrasound, Taizhou Hospital Affiliated to Nanjing University of Chinese Medicine, Taizhou, 225300, China
| | - Yuhong He
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Luying Gui
- School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing, 210094, China.
| | - Wentao Kong
- Department of Ultrasound, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
| |
Collapse
|
2
|
A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8860011. [PMID: 33425311 PMCID: PMC7772044 DOI: 10.1155/2020/8860011] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 12/07/2020] [Accepted: 12/13/2020] [Indexed: 01/01/2023]
Abstract
Prompt diagnosis of benign and malignant breast masses is essential for early breast cancer screening. Convolutional neural networks (CNNs) can be used to assist in the classification of benign and malignant breast masses. A persistent problem in current mammography mass classification via CNN is the lack of local-invariant features, which cannot effectively respond to geometric image transformations or changes caused by imaging angles. In this study, a novel model that trains both texton representation and deep CNN representation for mass classification tasks is proposed. Rotation-invariant features provided by the maximum response filter bank are incorporated with the CNN-based classification. The fusion after implementing the reduction approach is used to address the deficiencies of CNN in extracting mass features. This model is tested on public datasets, CBIS-DDSM, and a combined dataset, namely, mini-MIAS and INbreast. The fusion after implementing the reduction approach on the CBIS-DDSM dataset outperforms that of the other models in terms of area under the receiver operating curve (0.97), accuracy (94.30%), and specificity (97.19%). Therefore, our proposed method can be integrated with computer-aided diagnosis systems to achieve precise screening of breast masses.
Collapse
|
3
|
Youk JH, Kwak JY, Lee E, Son EJ, Kim JA. Grayscale Ultrasound Radiomic Features and Shear-Wave Elastography Radiomic Features in Benign and Malignant Breast Masses. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2020; 41:390-396. [PMID: 31703239 DOI: 10.1055/a-0917-6825] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
PURPOSE To identify and compare diagnostic performance of radiomic features between grayscale ultrasound (US) and shear-wave elastography (SWE) in breast masses. MATERIALS AND METHODS We retrospectively collected 328 pathologically confirmed breast masses in 296 women who underwent grayscale US and SWE before biopsy or surgery. A representative SWE image of the mass displayed with a grayscale image in split-screen mode was selected. An ROI was delineated around the mass boundary on the grayscale image and copied and pasted to the SWE image by a dedicated breast radiologist for lesion segmentation. A total of 730 candidate radiomic features including first-order statistics and textural and wavelet features were extracted from each image. LASSO regression was used for data dimension reduction and feature selection. Univariate and multivariate logistic regression was performed to identify independent radiomic features, differentiating between benign and malignant masses with calculation of the AUC. RESULTS Of 328 breast masses, 205 (62.5 %) were benign and 123 (37.5 %) were malignant. Following radiomic feature selection, 22 features from grayscale and 6 features from SWE remained. On univariate analysis, all 6 SWE radiomic features (P < 0.0001) and 21 of 22 grayscale radiomic features (P < 0.03) were significantly different between benign and malignant masses. After multivariate analysis, three grayscale radiomic features and two SWE radiomic features were independently associated with malignant breast masses. The AUC was 0.929 for grayscale US and 0.992 for SWE (P < 0.001). CONCLUSION US radiomic features may have the potential to improve diagnostic performance for breast masses, but further investigation of independent and larger datasets is needed.
Collapse
Affiliation(s)
- Ji Hyun Youk
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of
| | - Jin Young Kwak
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of
| | - Eunjung Lee
- Computational Science and Engineering, Yonsei University, Seoul, Korea, Republic of
| | - Eun Ju Son
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of
| | - Jeong-Ah Kim
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of
| |
Collapse
|
4
|
Martinez-Vargas JD, Duque-Muñoz L, Vargas-Bonilla F, Lopez JD, Castellanos-Dominguez G. Enhanced Data Covariance Estimation Using Weighted Combination of Multiple Gaussian Kernels for Improved M/EEG Source Localization. Int J Neural Syst 2019; 29:1950001. [PMID: 30859856 DOI: 10.1142/s0129065719500011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback-Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity.
Collapse
Affiliation(s)
- J D Martinez-Vargas
- 1Instituto Tecnológico Metropolitano, Medellín, Colombia.,4Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
| | - L Duque-Muñoz
- 2SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA. AE & CC, Instituto Tecnológico Metropolitano, Medellín, Colombia
| | - F Vargas-Bonilla
- 3SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Medellín, Colombia
| | - J D Lopez
- 3SISTEMIC, Engineering Faculty, Universidad de Antioquia UDEA, Medellín, Colombia
| | - G Castellanos-Dominguez
- 4Signal Processing and Recognition Group, Universidad Nacional de Colombia, Manizales, Colombia
| |
Collapse
|