Abstract
This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patients with breast tumor were included in this study, all of whom underwent routine ultrasound examinations. Subsequently, TUI was used to assist in guiding the positioning, and the light scattering tomography system was used to further measure the lesions. A deep learning model was established to process the imaging results, and the pathological test results were undertaken as the gold standard for the efficiency of different imaging methods to diagnose the breast tumors. The results showed that, among 120 patients with breast tumor, 56 were benign lesions and 64 were malignant lesions. The average total amount of hemoglobin (HBT) of malignant lesions was significantly higher than that of benign lesions (P < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of TUI in the diagnosis of breast cancer were 90.4%, 75.6%, 81.4%, 84.7%, and 80.6%, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of ultrasound in the diagnosis of breast cancer were 81.7%, 64.9%, 70.5%, 75.9%, and 80.6%, respectively. In addition, for suspected breast malignant lesions, the combined application of ultrasound and tomography can increase the diagnostic specificity to 82.1% and the accuracy to 83.8%. Based on the above results, it was concluded that TUI combined with ultrasound had a significant effect on benign and malignant diagnosis of breast cancer and can significantly improve the specificity and accuracy of diagnosis. It also reflected that deep learning technology had a good auxiliary role in the examination of diseases and was worth the promotion of clinical application.
Highlights
Breast cancer has become the most common malignant tumor in women in the world, and data show that the prevalence of breast cancer has shown an increasing trend in recent years [1, 2]. e early diagnosis and identification of breast cancer and the realization of the diagnosis of benign and malignant breast lesions play very important roles in improving the treatment effect and prognosis of patients
Ultrasound-guided positioning light scattering tomography can measure the difference in light absorption of breast lesions and surrounding normal tissues through two wavelengths in the Computational Intelligence and Neuroscience near-infrared band and detect the relevant indicators of the diseased tissues because the level of hemoglobin concentration can quantitatively map the amount of neovascularization in the tumor and achieve the purpose of distinguishing breast lesions from benign and malignant tumors [7]
For the bi-RadS4 ultrasound classification, ultrasound was combined with tomographic ultrasound imaging (TUI)
Summary
Breast cancer has become the most common malignant tumor in women in the world, and data show that the prevalence of breast cancer has shown an increasing trend in recent years [1, 2]. e early diagnosis and identification of breast cancer and the realization of the diagnosis of benign and malignant breast lesions play very important roles in improving the treatment effect and prognosis of patients. Light scattering tomography can detect biological tissue structure, state, and molecular function information through near-infrared light [3] It uses the different absorption coefficients of light from different tissues of the human body and reflects the optical characteristics of the internal organs of the human body through the diffuse scattering of the light by the tissues of multiple wavelengths and projection directions [4]. Ultrasound-guided positioning light scattering tomography can measure the difference in light absorption of breast lesions and surrounding normal tissues through two wavelengths in the Computational Intelligence and Neuroscience near-infrared band and detect the relevant indicators of the diseased tissues because the level of hemoglobin concentration can quantitatively map the amount of neovascularization in the tumor and achieve the purpose of distinguishing breast lesions from benign and malignant tumors [7]. In the era of big data, the use of current medical data, combined with artificial intelligence learning methods, can give full play to the powerful thrust of technology on medical progress [8]
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