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Chen X, Li M, Su D. Machine learning models for differential diagnosing HER2-low breast cancer: A radiomics approach. Medicine (Baltimore) 2024; 103:e39343. [PMID: 39151526 DOI: 10.1097/md.0000000000039343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/19/2024] Open
Abstract
To develop machine learning models based on preoperative dynamic enhanced magnetic resonance imaging (DCE-MRI) radiomics and to explore their potential prognostic value in the differential diagnosis of human epidermal growth factor receptor 2 (HER2)-low from HER2-positive breast cancer (BC). A total of 233 patients with pathologically confirmed invasive breast cancer admitted to our hospital between January 2018 and December 2022 were included in this retrospective analysis. Of these, 103 cases were diagnosed as HER2-positive and 130 cases were HER2 low-expression BC. The Synthetic Minority Oversampling Technique is employed to address the class imbalance problem. Patients were randomly split into a training set (163 cases) and a validation set (70 cases) in a 7:3 ratio. Radiomics features from DCE-MRI second-phase imaging were extracted. Z-score normalization was used to standardize the radiomics features, and Pearson's correlation coefficient and recursive feature elimination were used to explore the significant features. Prediction models were constructed using 6 machine learning algorithms: logistic regression, random forest, support vector machine, AdaBoost, decision tree, and auto-encoder. Receiver operating characteristic curves were constructed, and predictive models were evaluated according to the area under the curve (AUC), accuracy, sensitivity, and specificity. In the training set, the AUC, accuracy, sensitivity, and specificity of all models were 1.000. However, in the validation set, the auto-encoder model's AUC, accuracy, sensitivity, and specificity were 0.994, 0.976, 0.972, and 0.978, respectively. The remaining models' AUC, accuracy, sensitivity, and specificity were 1.000. The DeLong test showed no statistically significant differences between the machine learning models in the training and validation sets (Z = 0, P = 1). Our study investigated the feasibility of using DCE-MRI-based radiomics features to predict HER2-low BC. Certain radiomics features showed associations with HER2-low BC and may have predictive value. Machine learning prediction models developed using these radiomics features could be beneficial for distinguishing between HER2-low and HER2-positive BC. These noninvasive preoperative models have the potential to assist in clinical decision-making for HER2-low breast cancer, thereby advancing personalized clinical precision.
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Affiliation(s)
- Xianfei Chen
- Department of Radiology, The First Affiliated Hospital, Hainan Medical University, Haikou, China
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Minghao Li
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Danke Su
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
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Wang H, Chen X, Yu R, Wei Z, Yao T, Gao C, Li Y, Wang Z, Yi D, Wu Y. E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation. Comput Biol Med 2022; 151:106206. [PMID: 36395592 DOI: 10.1016/j.compbiomed.2022.106206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/08/2022] [Accepted: 10/09/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. OBJECTIVE We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. METHODS Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. RESULTS We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. CONCLUSION Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.
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Affiliation(s)
- Haojia Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Rui Yu
- Tactical Health Service Department, NCO School of Army Medical University, Zhongshanxi Road 450, Qiaoxi District, Shijiazhuang, 050081, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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Zeng H, Huang H, Hou J, Cao J, Wang Y, Ma KK. Screen Content Video Quality Assessment Model Using Hybrid Spatiotemporal Features. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:6175-6187. [PMID: 36126028 DOI: 10.1109/tip.2022.3206621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In this paper, a full-reference video quality assessment (VQA) model is designed for the perceptual quality assessment of the screen content videos (SCVs), called the hybrid spatiotemporal feature-based model (HSFM). The SCVs are of hybrid structure including screen and natural scenes, which are perceived by the human visual system (HVS) with different visual effects. With this consideration, the three dimensional Laplacian of Gaussian (3D-LOG) filter and three dimensional Natural Scene Statistics (3D-NSS) are exploited to extract the screen and natural spatiotemporal features, based on the reference and distorted SCV sequences separately. The similarities of these extracted features are then computed independently, followed by generating the distorted screen and natural quality scores for screen and natural scenes. After that, an adaptive screen and natural quality fusion scheme through the local video activity is developed to combine them for arriving at the final VQA score of the distorted SCV under evaluation. The experimental results on the Screen Content Video Database (SCVD) and Compressed Screen Content Video Quality (CSCVQ) databases have shown that the proposed HSFM is more in line with the perceptual quality assessment of the SCVs perceived by the HVS, compared with a variety of classic and latest IQA/VQA models.
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