[Breast cancer: News tools in imaging]

Artificial Intelligence at the Service of Medical Imaging in the Detection of Breast Tumors

Artificial intelligence is currently capable of imitating clinical reasoning in order to make a diagnosis, in particular that of breast cancer. This is possible, thanks to the exponential increase in medical images. Indeed, artificial intelligence systems are used to assist doctors and not replace them. Breast cancer is a cancerous tumor that can invade and destroy nearby tissue. Therefore, early and reliable detection of this disease is a great asset for the medical field. Some people use medical imaging techniques to diagnose this disease. Given the drawbacks of these techniques, diagnostic errors of doctors related to fatigue or inexperience, this work consists of showing how artificial intelligence methods, in particular artificial neural networks (ANN), deep learning (DL), support vector machines (SVM), expert systems, fuzzy logic can be applied on breast imaging, with the aim of improving the detection of this global scourge. Finally, the proposed system is composed of two (2) essential steps: the tumor detection phase and the diagnostic phase allowing the latter to decide whether the tumor is benign or malignant.

Impact of artificial intelligence in breast cancer screening with mammography

Objectives

To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.

Methods

A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or “continuous BI-RADS 100”.

Cohen’s kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.

Results

On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528–0.571) without AI and κ = 0.626, 95% CI (0.607–0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).

Conclusions

When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.

Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target, and pathological detection

BACKGROUND

This study aimed to identify the factors underlying the metastasis of breast cancer and sentinel lymph nodes and to screen and analyze the risk factors of sentinel lymph node metastasis to provide a reference and basis for clinical work.

METHODS

A total of 99 patients with breast cancer were enrolled in this study. These patients received treatment in our hospital between May 2017 and May 2020. The general information, characteristics of the color Doppler echocardiography, molybdenum, conventional pathology, and molecular pathology of the patients were collected. Factors influencing sentinel lymph node metastasis in breast cancer patients were retrospectively analyzed.

RESULTS

In this study, age, tumor diameter, BI-RADS category, pathology type, expression profiles of CK5/6, EGFR, and CK19, and TP53 and BRAC1/2 mutations were independent risk factors for sentinel lymph node metastasis in breast cancer (P < 0.05). The number and locations of tumors, quadrant of tumors, regularity of tumor margins, presence of blood flow signals, presence of posterior echo attenuation, presence of calcification, histological grade, molecular typing, and mutations of BRAF, ATM, and PALB2 were irrelevant factors (P > 0.05).

CONCLUSIONS

In conclusion, age, tumor diameter, BI-RADS category, invasive type, expression of CK5/6, EGFR, and CK19, and mutations in TP53 and BRAC1/2 were positively correlated with sentinel lymph node metastasis. These independent risk factors should be given more attention in clinical studies to strengthen the management and control of sentinel lymph node metastasis in high-risk breast cancer and support early chemotherapy or targeted therapy.