Abstract
Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.
Highlights
Breast cancer is a common malignant tumor that threatens women’s lives
It is very sensitive to intralesional calcification, and it is highly sensitive to the diagnosis of tumors with calcification, especially for nonmass cancers with calcification, and the detection rate is significantly higher than that of ultrasound [4]
(3) Two senior breast imaging diagnosticians analyzed the X-ray images, ultrasound images, and MRI images of all cases, and evaluated the composition of breast tissue according to the breast imaging report and data system standards proposed by the American College of Radiology. is topic aims to analyze the mammography, ultrasound, MRI, and 18F-FDG PET-CT manifestations of patients with axillary lymph node metastasis cancer as the first diagnosis, and to compare the effects of different imaging methods in the primary breast. e diagnosis efficiency of cancer foci provides a basis for the clinical development of diagnosis and treatment plans
Summary
Breast cancer is a common malignant tumor that threatens women’s lives. Breast-conserving surgery can be performed when detected early, and the prognosis is good, and the five-year survival rate is high. Diffusion-weighted imaging, magnetic resonance power, magnetic resonance perfusion imaging, etc., can increase the description of the internal biological characteristics of the tumor on the basis of morphology and provide help for the diagnosis of early breast cancer [5]. In order to increase the accuracy of breast cancer diagnosis and reduce the missed diagnosis rate, it is urgent to combine tumor internal functional imaging on the basis of morphology to assist diagnosis. Is paper proposes a method based on deep convolutional neural network that can automatically locate and identify the cancer metastasis area in the panoramic image of breast lymph nodes. Is topic aims to analyze the mammography, ultrasound, MRI, and 18F-FDG PET-CT manifestations of patients with axillary lymph node metastasis cancer as the first diagnosis (negative breast clinical palpation), and to compare the effects of different imaging methods in the primary breast. (3) Two senior breast imaging diagnosticians analyzed the X-ray images, ultrasound images, and MRI images of all cases, and evaluated the composition of breast tissue according to the breast imaging report and data system standards proposed by the American College of Radiology. is topic aims to analyze the mammography, ultrasound, MRI, and 18F-FDG PET-CT manifestations of patients with axillary lymph node metastasis cancer as the first diagnosis (negative breast clinical palpation), and to compare the effects of different imaging methods in the primary breast. e diagnosis efficiency of cancer foci provides a basis for the clinical development of diagnosis and treatment plans
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