Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

Retracted: Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

[This retracts the article DOI: 10.1155/2022/8616535.].

Ultrasound Pretreatment Lymph Node Evaluation in Early-Stage Breast Cancer: Should We Biopsy High Suspicion Nodes?

BACKGROUND

With the growing incidence of breast cancer, efficient and correct staging is essential for further treatment decisions. Axillary ultrasound (US) remains the most common method for regional nodal involvement assessment. The aim of this study was to evaluate whether high-risk US features can accurately predict axillary lymph node metastasis.

METHODS

A total of 150 early-stage breast cancer patients (T1 or T2) were prospectively included in the study. Based on axillary US, patients were classified as normal, low-risk, or high-risk, with all patients in the low-risk and high-risk groups undergoing fine-needle aspiration (FNAB) and core-needle biopsies.

RESULTS

For the low-risk US group, a lower prediction rate of axillary nodal metastasis was achieved than for the group with high-risk features, recording a sensitivity of 66.6% vs. 89.2%, a specificity of 57.1% vs. 100%, a positive predictive value (PPV) of 26.6% vs. 100%, a negative predictive value (NPV) of 88% for both groups, and an accuracy of 58.9% vs. 94%, respectively. FNAB resulted in more false-negative results compared to core-needle biopsy in both low-risk and high-risk US groups.

CONCLUSIONS

Our findings suggest that high-risk US features can predict axillary lymph node metastasis with high accuracy.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

Application of artificial intelligence in predicting lymph node metastasis in breast cancer

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.

Progression of Selected Parameters of the Clinical Profile of Patients with Periodontitis Using Kohonen's Self-Organizing Maps

(1) Background: Periodontitis is an inflammatory condition that affects the tissues surrounding the tooth and causes clinical attachment loss, which is the loss of periodontal attachment (CAL). Periodontitis can advance in various ways, with some patients experiencing severe periodontitis in a short period of time while others may experience mild periodontitis for the rest of their lives. In this study, we have used an alternative methodology to conventional statistics, self-organizing maps (SOM), to group the clinical profiles of patients with periodontitis. (2) Methods: To predict the periodontitis progression and to choose the best treatment plan, we can use artificial intelligence, more precisely Kohonen's self-organizing maps (SOM). In this study, 110 patients, both genders, between the ages of 30 and 60, were included in this retrospective analysis. (3) Results: To discover the pattern of patients according to the periodontitis grade and stage, we grouped the neurons together to form three clusters: Group 1 was made up of neurons 12 and 16 that represented a percentage of slow progression of almost 75%; Group 2 was made up of neurons 3, 4, 6, 7, 11, and 14 in which the percentage of moderate progression was almost 65%; and Group 3 was made up of neurons 1, 2, 5, 8, 9, 10, 13, and 15 that represented a percentage of rapid progression of almost 60%. There were statistically significant differences in the approximate plaque index (API), and bleeding on probing (BoP) versus groups (p < 0.0001). The post-hoc tests showed that API, BoP, pocket depth (PD), and CAL values were significantly lower in Group 1 relative to Group 2 (p < 0.05) and Group 3 (p < 0.05). A detailed statistical analysis showed that the PD value was significantly lower in Group 1 relative to Group 2 (p = 0.0001). Furthermore, the PD was significantly higher in Group 3 relative to Group 2 (p = 0.0068). There was a statistically significant CAL difference between Group 1 relative to Group 2 (p = 0.0370). (4) Conclusions: Self-organizing maps, in contrast to conventional statistics, allow us to view the issue of periodontitis advancement by illuminating how the variables are organized in one or the other of the various suppositions.

Gold nanoparticles-based photothermal therapy for breast cancer

AuNPs-mediated photothermal therapy (PTT) is gaining popularity in both laboratory research and medical applications. It has proven clear advantages in breast cancer therapy over conventional thermal ablation because of its easily-tuned features of irradiation light with inside hyperthermia ability. Notwithstanding this significant progress, the therapeutic potential of AuNPs-mediated PTT in cancer treatments is still impeded by several challenges, including inherent non-specificity, low photothermal conversion effectiveness, and the limitation of excitation light tissue penetration. Given the rapid progress of AuNPs-mediated PTT, we present a comprehensive overview of significant breakthroughs in the recent advancements of AuNPs for PTT, focusing on breast cancer cells. With the improvement of chemical synthesis technology, AuNPs of various sizes and shapes with desired properties can be synthesized, allowing breast cancer targeting and treatment. In this study, we summarized the different sizes and features of four major types of AuNPs in this review: Au nanospheres, Au nanocages, Au nanoshells, and Au nanorods, and explored their benefits and drawbacks in PTT. We also discussed the diagnostic, bioconjugation, targeting, and cellular uptake of AuNPs, which could improve the performance of AuNP-based PTT. Besides that, potential challenges and future developments of AuNP-mediated PTT for clinical applications are discussed. AuNP-mediated PTT is expected to become a highly promising avenue in cancer treatment in the near future.