Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives

doi:10.37015/AUDT.2023.230012

Abstract

Abstract:

Ultrasound is a commonly used imaging modality for breast cancer diagnosis and prognosis but suffers from false positives, false negatives and interobserver variability. Deep learning (DL), a subset of artificial intelligence, has the potential to improve the efficiency and accuracy of breast ultrasound. This article provides a comprehensive overview of DL applications for breast cancer diagnosis and treatment in ultrasound, including methodological descriptions of various DL models, and clinical applications on noise reduction, lesion localization, risk assessment, diagnosis, response evaluation and outcome prediction. Furthermore, the review highlights the importance of interpretability and small sample size learning of DL-based systems in clinical practice; specific recommendations for further expanding the clinical impact of DL-based systems are also provided.

Key words: Deep learning; Ultrasound; Breast cancer; Diagnosis; Treatment

Changyan Wang, BS, Haobo Chen, MS, Jieyi Liu, BS, Changchun Li, BS, Weiwei Jiao, BS, Qihui Guo, BS, Qi Zhang, PhD. Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives. Advanced Ultrasound in Diagnosis and Therapy, 2023, 7(2): 91-113.

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Table 2

Deep learning for breast tumor localization in ultrasound"

Ultrasound Modalities	References	Datasets	Tasks	DL Models	Results
B-mode	Liu et al., 2022 [81]	1603 cases with 17321 images	Detection	CNN	Sensitivity = 0.797 Specificity = 0.962 F1-score = 0.742
B-mode	Wang et al., 2022 [82]	Dataset 1: 163 images (109 benign and 54 malignant) Dataset 2: 780 images (437 benign, 210 malignant and 133 normal) Dataset 3: 562 images	Detection	An anchor-free network	Benign: Precision = 0.917 Recall = 0.980 Malignant: Precision= 0.888 Recall = 0.963
B-mode	Meng et al., 2023 [80]	3759 patients with 7040 images	Detection	Dual global attention neural network	Mean average precision = 0.836
ABUS	Wang et al., 2020 [84]	194 patients with 293 tumors and 70 patients without diseases	Detection	CNN	Sensitivity = 0.91 False positives per slice = 1.92
ABUS	Zhang et al., 2021 [76]	70 ABUS tumor volumes from 124 female patients	Detection	Bayesian YOLO based on Monte Carlo dropout	Sensitivity = 0.88 False positives per slice = 0.19
ABUS	Zhang et al. 2022 [83]	741 cases with 2538 volumes	Detection	YOLO	Detection rate = 0.78
ABUS	Malekmohammadi et al., 2023 [79]	60 volumes from 43 patients	Detection	Bi-ConvLSTM	Sensitivity = 82%
B-mode	Vakanski et al., 2020 [90]	510 images	Segmentation	U-Net with attention blocks	DSC = 0.91 JI = 0.84
B-mode	Yan et al., 2022 [85]	316 images (154 malignant and 162 benign)	Segmentation	Attention enhanced U-Net with hybrid dilated convolution	Mean IoU = 0.82 Accuracy = 0.96 Recall = 0.80
B-mode	Zhai et al., 2022 [88]	Dataset 1: 647 cases Dataset 2: 200 cases Dataset 3: 320 cases Dataset 4: 1805 cases	Segmentation	Asymmetric semi-supervised GAN	IoU = 0.68 Accuracy = 0.95 DSC = 0.81
B-mode	Punn et al., 2022 [91]	Dataset 1: 780 images (437 benign, 210 malignant and 133 normal) Dataset 2: 562 images	Segmentation	Residual cross spatial attention guided inception U-Net	DSC = 0.935 Mean IoU = 0.904
B-mode	Chen et al., 2022 [93]	780 images (437 benign, 210 malignant and 133 normal)	Segmentation	Bidirectional aware guidance network	Accuracy = 0.93 JI = 0.60 Precision = 0.76 Recall = 0.77 Specificity = 0.96 DSC = 0.70
B-mode	Iqbal et al., 2023 [86]	Dataset 1: 811 images Dataset 2: 42 images	Detection	Swin Transformer	F1 score = 0.771 JI = 0.597
B-mode	Yang et al. 2023 [87]	Dataset 1: 163 images Dataset 2: 780 images	Segmentation	CSwin-PNet	Dataset 1: IoU = 0.786; DSC = 0.872 Dataset 2: IoU = 0.751; DSC =0.837
B-mode	Chen et al., 2023 [92]	Dataset 1: 133 normal, 437 benign, and 210 malignant; Dataset 2: 110 benign and 53 malignant	Segmentation	Refinement residual convolutional network	JI = 0.72 Precision = 0.79 Recall = 0.83 Specificity = 0.98 DSC = 0.79
ABUS	Cao et al., 2020 [89]	107 patients with 170 volumes	Segmentation	Uncertainty aware temporal-ensembling	JI = 0.64; DSC= 0.74; Hausdorff distance = 3.81mm

Table 2

Table 3

Deep learning for breast cancer diagnosis in ultrasound"

Ultrasound Modalities	References	Datasets	Tasks	DL Models	Results
B-mode	Han et al., 2017 [13]	5151cases with 7408 images (4254 benign and 3154 malignant)	Classification of benign and malignant breast tumors	GoogLeNet	AUC = 0.9 Accuracy = 0.9 Sensitivity = 0.86 Specificity = 0.96
B-mode & color Doppler	Shen et al., 2021 [110]	5,442,907 B-mode and color Doppler images	Identify breast cancer	Deep neural network with attention module	AUC = 0.962
Color Doppler	Qi et al., 2019 [111]	8000 images from 2047 patients	Task1: Differentiate non-malignant and malignant Task2: Recognize solid nodules	CNN with multi-scale kernels and skip connections	Task1: Accuracy = 0.945 Sensitivity = 0.957 Specificity = 0.939 Task2: Accuracy = 0.901 Sensitivity = 0.935 Specificity = 0.832
B-mode	Cao et al., 2020 [112]	1,976 images (1890 contain one lesion, 80 contain two lesions, and 6 contain more than two lesions)	Classification of benign and malignant breast tumors	Noise filter network	Accuracy = 0.73 Precision = 0.69 Recall = 0.80 F1-score = 0.74
B-mode	Cao et al., 2021 [113]	935 images (473 benign and 462 malignant)	Classification of benign and malignant breast tumors	AlexNet	Accuracy = 75.8% Precision = 73.0% Recall = 80.1% F1-score = 0.764
B-mode	Huang et al., 2019 [114]	531 cases of Category 3, 443 cases of Category 4A, 376 cases of Category 4B, 565 cases of Category 4C, and 323 cases of Category 5	Evaluate breast tumors into five categories (“3”, “4A”, “4B”, “4C”, “5”)	CNN	Accuracy: 0.998 (“3”); 0.940 (“4A”); 0.734 (“4B”) 0.922 (“4C”); 0.876 (“5”)
B-mode & SWE	Li et al., 2021 [115]	599 images from 91 patients (64 benign and 27 malignant)	Classification of benign and malignant breast tumors	DenseNet [19]	AUC = 0.866
B-mode & CEUS	Yang et al., 2020 [116]	268 samples (146 malignant and 122 benign)	Identify breast cancer	Temporal sequence dual-branch network	Accuracy = 90.2%
B-mode & CEUS	Chen et al., 2021 [117]	221 breast lesions in 217 patients	Identify breast cancer	3D CNN	Accuracy = 86.3% Sensitivity = 97.2%
B-mode & CEUS	Gong et al., 2022 [118]	1070 tumors (507 malignant and 563 benign)	Classification of benign and malignant breast tumors	ResNet	AUC = 0.925 Accuracy = 0.897
B-mode & EUS	Yao et al., 2023 [47]	4580 cases (2226 malignant and 2354 benign)	Classification of benign and malignant breast tumors	GAN	AUC: 0.751 (real EUS) 0.767 (virtual EUS)
B-mode	Wang et al., 2018 [119]	650 US images	Segmentation and classification of benign and malignant breast tumors	Multi-feature guided CNN	Recall = 0.97 F-score = 0.968 Precision = 0.965
B-mode	Xie et al., 2018 [120]	1418 normal and 1182 cancerous samples	Segmentation and diagnosis of the breast cancer	ResNet and Mask R-CNN	Precision = 98.72% Recall = 98.05%
B-mode	Singh et al., 2019 [121]	150 malignant and 100 benign tumors	Segmentation and classification of benign and malignant breast tumors	cGAN	Accuracy = 85% Precision = 81% Recall = 92%
B-mode	Luo et al., 2022 [122]	Dataset 1: 292 images (160 benign and 132 malignant) Dataset 2: 1702 images (786 benign and 916 malignant)	Classification of benign and malignant breast tumors	DCNN	Accuracy = 90.78% Sensitivity = 91.18% Specificity = 90.44% F1-score = 91.46% AUC = 0.9549
B-mode	Xiao et al., 2018 [123]	2058 breast masses from 1422 patients (1370 benign and 688 malignant)	Discriminating benign cysts from malignant masses	Inception, ResNet, and Xception	Accuracy = 89.44% AUC = 93%
B-mode	Gheflati et al., 2022 [124]	Dataset 1: (133 normal, 437 malignant, and 210 benign) Dataset 2: (110 benign and 53 malignant)	Classification of benign and malignant breast tumors	ViT	Accuracy = 86.7% AUC = 95%
B-mode & strain elastography	Misra et al., 2022 [125]	85 patients (42 benign and 43 malignant (80% for training and 20% for validation)	Classification of benign and malignant breast tumors	AlexNet and ResNet	Accuracy = 90%
ABUS	Xiang et al., 2021 [126]	396 patients with 444 tumors (226 malignant and 218 benign)	Classification of benign and malignant breast tumors	U-Net and residual-capsule neural network	Accuracy = 84.9% Sensitivity = 87.2% Specificity = 82.6% AUC = 0.9122
ABUS	Zhuang et al., 2021 [127]	214 ABUS sequences (86 malignant and 128 benign)	Classification of benign and malignant breast tumors	Shallowly dilated convolutional branch network and VGG	Accuracy = 0.9286 Recall = 0.8824 Precision = 0.9375 F1-score = 0.9091 AUC = 0.9721
ABUS	Malekmohammadi et al., 2023 [79]	60 volumes from 43 patients, including 42 malignant and 13 benign	Classification of benign and malignant breast tumors	Convolutional BiLSTM	Precision = 84% Recall = 84% Accuracy = 93% F1-score = 84% AUC = 97%
ABUS	Zhou et al., 2021 [128]	170 volumes from 107 patients	Tumor classification and segmentation	CNN	Accuracy = 0.741 Precision = 0.826 F1-score = 0.811
B-mode	Gu et al., 2023 [129]	396 patients (212 LNM, 184 non-LNM, 134 pCR, 262 non-pCR); 88 patients (255 LNM, 33 non-LNM, 134 pCR, 262 non-pCR)	Predict the axillary LNM after NAC	DLR nomogram	AUC = 0.863 Specificity = 0.818 negative predictive value = 0.872
B-mode	Guo et al., 2020 [130]	3049 images from 937 patients	Predict the risk of SLN and non-SLN metastasis	DLR	Sensitivity: 0.984 (SLNs) 0.984 (non-SLNs)
B-mode	Ozaki et al., 2022 [131]	628 images from 253 patients	Distinguish metastatic ALNs	CNN	AUC = 0.966, Sensitivity = 0.94 Specificity = 0.88
B-mode	Coronado-Gutierrez et al. 2019 [132]	118 images from105 patients	Diagnose ALN involvement	Fisher vector CNN	Accuracy = 0.864 Sensitivity = 0.849 Specificity = 0.877
B-mode	Sun et al., 2022 [133]	338 images from 169 patients	Predict axillary LNM	CNN	AUC = 0.72 Accuracy = 0.726 Sensitivity = 0.655 Specificity = 0.789
B-mode	Zhou et al., 2020 [134]	974 imaging from 756 patients and 81 imaging from 78 patients	Predict clinically negative axillary LNM	CNN (Inception)	AUC = 0.89 Sensitivity = 0.85 Specificity = 0.73
B-mode & SWE	Zheng et al., 2020 [135]	584 patients with 584 malignant breast lesions	Predict ALN status preoperatively in patients with early-stage breast cancer	DLR based on ResNet	AUC = 0.902 Sensitivity = 0.816
B-mode	Lee et al., 2020 [136]	153 malignant tumor images (59 axillary LNM and 94 non-metastasis)	Predict the axillary LNM status using breast cancer US	Mask R-CNN and CNN	Accuracy = 0.771 Sensitivity = 0.661 Specificity = 0.840 AUC = 0.759
B-mode	Sun et al., 2020 [97]	2395 images from 479 patients	Predict axillary LNM	DenseNet [19]	AUC = 0.95
B-mode	Jiang et al., 2021 [137]	4828 images from 1275 patients	Assessment of breast cancer molecular subtypes	DCNN	Accuracy ranging from 87.94% to 98.83% for each sub-category. Positive predictive value is 93.29% in discriminating luminal disease from non-luminal disease
B-mode	Boulenger et al., 2023 [138]	831 images from 145 patients	Identification of triple-negative breast cancer	VGG	AUC = 0.86 Accuracy = 85% Sensitivity = 86% Specificity = 86% F1-score = 0.74
B-mode	Zhang et al., 2021 [139]	2,822, 917 images	Predict molecular subtypes	DCNN	AUC: 0.864 (triple-negative subtype); 0.811 (HER2 (+) subtype); 0.837 (HR (+) subtype)
B-mode	Li et al., 2022 [140]	Dataset 1: 1,012 breast cancer patients with 2,284 images; Dataset 2: 117 breast cancer patients with 153 images	Classify molecular subtypes of breast cancer	DCNN	Classify luminal A from non-luminal A: AUC = 0.818 (Dataset 1); AUC = 0.686 (Dataset 2); Detect triple-negative breast cancer: AUC = 0.712 (Dataset 1)

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References	Datasets	Tasks	DL Models	Results
Latif et al., 2020 [63]	100 benign and 150 malignant	Despeckling and classification of benign and malignant breast tumors	CNN	Denoising enhanced the classifier’s accuracy from 84% to 88%
Priyanka et al., 2020 [67]	500 images (kidney and breast)	Despeckling	Residual learning network	Naturalness image quality evaluator = 4.19
Feng et al., 2020 [68]	676 breast, 157 liver and 100 spinal US images	Despeckling	VGGNet	Naturalness image quality evaluator = 8.65
Hee et al., 2022 [69]	7 patients	Despeckling	Wavelet-based GAN	Equivalent number of looks = 190.87; Contrast-to-Noise Ratio = 31.68

Ultrasound Modalities	References	Datasets	Tasks	DL Models	Results
B-mode	Gu et al., 2021 [155]	168 women with 168 lesions	Classification of non-responding and responding patients	DLR	DLR-2: AUC = 0.812; DLR-4: AUC = 0.937; Specificity = 0.905;
B-mode	Wu et al., 2022 [156]	801 patients	Tumor segmentation and classification of pathological complete response	U-Net	AUC = 0.927
B-mode	Taleghamar et al., 2022 [157]	181 patients	Classification of non-responding and responding patients	ResNet	Accuracy = 0.88 AUC = 0.86
B-mode & SWE	Zhang et al., 2020 [135]	584 women with 584 malignant breast lesions	Classification of ALN status	ResNet	Disease-free axilla vs. axillary metastasis: AUC = 0.902 low vs. heavy metastatic burden of axillary disease: AUC = 0.905
B-mode	Zhou et al., 2020 [134]	Dataset 1: 756 patients with 974 images; Dataset 2: 78 patients with 81 images	Classification of ALN status	Inception-ResNet	AUC = 0.89 Sensitivity = 0.85 Specificity = 0.73