Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography

Ozaki, Jo; Fujioka, Tomoyuki; Yamaga, Emi; Hayashi, Atsushi; Kujiraoka, Yu; Imokawa, Tomoki; Takahashi, Kanae; Okawa, Sayuri; Yashima, Yuka; Mori, M.; Kubota, Kazunori; Oda, Goshi; Nakagawa, Tsuyoshi; Tateishi, Ukihide

doi:10.1007/s11604-022-01261-6

Cited by 26 publications

(22 citation statements)

References 23 publications

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“…The ACR-BIRADS category is based on the morphological features visible on US images. This approach has high sensitivity but relatively low specificity, thereby leading to unnecessary biopsy and excessive diagnosis ( 19 , 20 ). The SWE examination has been reported to be able to increase specificity and sensitivity for predicting breast cancer ( 8 – 10 ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, deep convolutional neural network (CNN)-based approaches have been considered as an effective approach for the feature extraction and classification of US images in breast cancer diagnosis ( 15 – 18 ). However, most of the CNN models used in the diagnosis of breast cancer have been based on the US or SWE images of intratumoral tissue rather than peritumoral tissue ( 15 – 20 ). The peritumoral stiffness of breast lesions is an accurate predictor of breast cancer ( 9 – 12 ).…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

Xie

Liu²,

Pei³

et al. 2023

Front. Oncol.

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ObjectiveOur aim was to develop dual-modal CNN models based on combining conventional ultrasound (US) images and shear-wave elastography (SWE) of peritumoral region to improve prediction of breast cancer.MethodWe retrospectively collected US images and SWE data of 1271 ACR- BIRADS 4 breast lesions from 1116 female patients (mean age ± standard deviation, 45.40 ± 9.65 years). The lesions were divided into three subgroups based on the maximum diameter (MD): ≤15 mm; >15 mm and ≤25 mm; >25 mm. We recorded lesion stiffness (SWV1) and 5-point average stiffness of the peritumoral tissue (SWV5). The CNN models were built based on the segmentation of different widths of peritumoral tissue (0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm) and internal SWE image of the lesions. All single-parameter CNN models, dual-modal CNN models, and quantitative SWE parameters in the training cohort (971 lesions) and the validation cohort (300 lesions) were assessed by receiver operating characteristic (ROC) curve.ResultsThe US + 1.0 mm SWE model achieved the highest area under the ROC curve (AUC) in the subgroup of lesions with MD ≤15 mm in both the training (0.94) and the validation cohorts (0.91). In the subgroups with MD between15 and 25 mm and above 25 mm, the US + 2.0 mm SWE model achieved the highest AUCs in both the training cohort (0.96 and 0.95, respectively) and the validation cohort (0.93 and 0.91, respectively).ConclusionThe dual-modal CNN models based on the combination of US and peritumoral region SWE images allow accurate prediction of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

Xie

Liu²,

Pei³

et al. 2023

Front. Oncol.

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“…An interesting study by Ozaki et al evaluated the performances of a DL-model using a CNN Xception architecture [ 35 ]. The model scored an AUC of 0.966, which was comparable with to the results scored by a radiologist with 12 years of experience (0.969; p = 0.881) [ 35 ].…”

Section: Studies Using Radiomics For Breast Cancer Lymph Node Predictionmentioning

confidence: 99%

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Vrdoljak

Krešo

Kumrić

et al. 2023

Cancers

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Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

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“…Several studies have sought to determine the features of structures in ultrasound images in order to facilitate a diagnosis (5)(6)(7). Studies have sought to determine the characteristics of ultrasound waves propagated by a structure using the power-law shot noise (PLSN) model (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

Improving the sensitivity of liver tumor classification in ultrasound images <i>via</i> a power-law shot noise model

Karako

Mihara

Hasegawa

et al. 2023

BST

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Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography

Cited by 26 publications

References 23 publications

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Improving the sensitivity of liver tumor classification in ultrasound images <i>via</i> a power-law shot noise model

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