Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence

Examining the false-negative rate of a negative axillary node ultrasound-guided core needle biopsy in breast cancer patients undergoing upfront surgery

BACKGROUND

Axillary assessment in breast cancer is key to determining an upfront surgery or neoadjuvant chemotherapy (NAC) approach. We investigated the false-negative rate (FNR) of axillary-node ultrasound-guided core-needle biopsy (US-CNBx) and the surgical management of pN ​+ ​patients.

METHODS

This single-institution study from 2010 to 2020 included patients with benign findings on US-CNBx and upfront surgery. Statistical analyses were performed via t-tests and chi-squared tests.

RESULTS

95 axillae met inclusion, 23 were pN+, resulting in a US-CNBx FNR of 24.2 ​%. pN ​+ ​patients more frequently had cT2-T3 tumors vs pN0 patients (43.5 ​% vs 27.8 ​%, p ​= ​0.03). Of the 23 ​pN ​+ ​patients, 9 underwent breast-conserving surgery (BCS) and 14 underwent mastectomy. In those with BCS, 7 had 1-2 positive nodes, 2 had ≥3 nodes; 3 received an ALND. In those with mastectomies, 12 had 1-2 positive nodes, 2 had ≥3 positive nodes; 6 received an ALND.

CONCLUSION

In this cohort, US-CNBx had a FNR of 24.2 ​%. pN ​+ ​patients had a greater frequency of cT2-cT3 tumors, therefore clinicians should be cognizant of potential occult nodal disease despite negative CNBx when deciding management.

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography

ABSTRACT

The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results

Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assisted sonography directly on the device. However, this is not yet the case for lymph nodes.The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes. For this purpose, the two programs were used as a supplement to routine ultrasound examinations of superficial lymph nodes. The accuracy of detection by AI was then evaluated using a previously defined score. If available, a comparison was made with cross-sectional imaging.The programs that were used are able to adequately detect lymph nodes in the majority of cases (78.6%). Problems were caused in particular by a high proportion of echo-rich fat, blurred differentiation from the surrounding tissues and the occurrence of lymph node conglomerates. The available cross-sectional images did not contradict the classification of the lesion as a lymph node in any case.In the majority of cases, the tested programs are already able to detect and measure superficial lymph nodes. Further improvement can be expected through specific training of the software. Further developments and studies are required to assess risk of malignancy. · The inclusion of AI in imaging is increasingly becoming a scientific focus.. · The detection of lymph nodes is already possible using device-integrated AI software.. · Malignancy assessment of the detected lymph nodes is not yet possible.. · Rink M, Künzel J, Stroszczynski C et al. Smart scanning: automatic detection of superficially located lymph nodes using ultrasound - initial results. Fortschr Röntgenstr 2024; DOI 10.1055/a-2331-0951.

Evaluating Magnetic Seed Localization in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Receiving Neoadjuvant Systemic Therapy: A Comprehensive Review and Pooled Analysis

Background/Objectives: De-escalation of axillary surgery is made possible by advancements in both neoadjuvant systemic therapy (NST) and in localisation technology for breast lesions. Magseed®, developed in 2013 by Dr. Michael Douk of Cambridge, United Kingdom, is a wire-free localisation technology that facilitates the localisation and retrieval of lymph nodes for staging. Targeted axillary dissection (TAD), which entails marked lymph node biopsy (MLNB) and sentinel lymph node biopsy (SLNB), has emerged as the preferred method to assess residual disease in post-NST node-positive patients. This systematic review and pooled analysis evaluate the performance of Magseed® in TAD. Methods: The search was carried out in PubMed and Google Scholar. An assessment of localisation, retrieval rates, concordance between MLNB and SLNB, and pathological complete response (pCR) in clinically node-positive patients post NST was undertaken. Results: Nine studies spanning 494 patients and 497 procedures were identified, with a 100% successful deployment rate, a 94.2% (468/497) [95% confidence interval (CI), 93.7-94.7] localisation rate, a 98.8% (491/497) retrieval rate, and a 68.8% (247/359) [95% CI 65.6-72.0] concordance rate. pCR was observed in 47.9% (220/459) ) [95% CI 43.3-52.6] of cases. Subgroup analysis of studies reporting the pathological status of MLNB and SLNB separately revealed an FNR of 4.2% for MLNB and 17.6% for SLNB (p = 0.0013). Mean duration of implantation was 37 days (range: 0-188). Conclusions: These findings highlight magnetic seed localisation's efficacy in TAD for NST-treated node-positive patients, aiding in accurate axillary pCR identification and safe de-escalation of axillary surgery in excellent responders.

Application and prospects of AI-based radiomics in ultrasound diagnosis

Artificial intelligence (AI)-based radiomics has attracted considerable research attention in the field of medical imaging, including ultrasound diagnosis. Ultrasound imaging has unique advantages such as high temporal resolution, low cost, and no radiation exposure. This renders it a preferred imaging modality for several clinical scenarios. This review includes a detailed introduction to imaging modalities, including Brightness-mode ultrasound, color Doppler flow imaging, ultrasound elastography, contrast-enhanced ultrasound, and multi-modal fusion analysis. It provides an overview of the current status and prospects of AI-based radiomics in ultrasound diagnosis, highlighting the application of AI-based radiomics to static ultrasound images, dynamic ultrasound videos, and multi-modal ultrasound fusion analysis.

Future Directions in the Assessment of Axillary Lymph Nodes in Patients with Breast Cancer

Background and Objectives: Breast cancer (BC) is a leading cause of morbidity and mortality worldwide, and accurate assessment of axillary lymph nodes (ALNs) is crucial for patient management and outcomes. We aim to summarize the current state of ALN assessment techniques in BC and provide insights into future directions. Materials and Methods: This review discusses various imaging techniques used for ALN evaluation, including ultrasound, computed tomography, magnetic resonance imaging, and positron emission tomography. It highlights advancements in these techniques and their potential to improve diagnostic accuracy. The review also examines landmark clinical trials that have influenced axillary management, such as the Z0011 trial and the IBCSG 23-01 trial. The role of artificial intelligence (AI), specifically deep learning algorithms, in improving ALN assessment is examined. Results: The review outlines the key findings of these trials, which demonstrated the feasibility of avoiding axillary lymph node dissection (ALND) in certain patient populations with low sentinel lymph node (SLN) burden. It also discusses ongoing trials, including the SOUND trial, which investigates the use of axillary ultrasound to identify patients who can safely avoid sentinel lymph node biopsy (SLNB). Furthermore, the potential of emerging techniques and the integration of AI in enhancing ALN assessment accuracy are presented. Conclusions: The review concludes that advancements in ALN assessment techniques have the potential to improve patient outcomes by reducing surgical complications while maintaining accurate disease staging. However, challenges such as standardization of imaging protocols and interpretation criteria need to be addressed. Future research should focus on large-scale clinical trials to validate emerging techniques and establish their efficacy and cost-effectiveness. Over-all, this review provides valuable insights into the current status and future directions of ALN assessment in BC, highlighting opportunities for improving patient care.

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

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.

Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease

OBJECTIVES

Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD.

METHODS

One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated.

RESULTS

A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%.

CONCLUSIONS

An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.

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.

Evaluating the Risk of Inguinal Lymph Node Metastases before Surgery Using the Morphonode Predictive Model: A Prospective Diagnostic Study in Vulvar Cancer Patients

Ultrasound examination is an accurate method in the preoperative evaluation of the inguinofemoral lymph nodes when performed by experienced operators. The purpose of the study was to build a robust, multi-modular model based on machine learning to discriminate between metastatic and non-metastatic inguinal lymph nodes in patients with vulvar cancer. One hundred and twenty-seven women were selected at our center from March 2017 to April 2020, and 237 inguinal regions were analyzed (75 were metastatic and 162 were non-metastatic at histology). Ultrasound was performed before surgery by experienced examiners. Ultrasound features were defined according to previous studies and collected prospectively. Fourteen informative features were used to train and test the machine to obtain a diagnostic model (Morphonode Predictive Model). The following data classifiers were integrated: (I) random forest classifiers (RCF), (II) regression binomial model (RBM), (III) decisional tree (DT), and (IV) similarity profiling (SP). RFC predicted metastatic/non-metastatic lymph nodes with an accuracy of 93.3% and a negative predictive value of 97.1%. DT identified four specific signatures correlated with the risk of metastases and the point risk of each signature was 100%, 81%, 16% and 4%, respectively. The Morphonode Predictive Model could be easily integrated into the clinical routine for preoperative stratification of vulvar cancer patients.

Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis-A Rapid Review

BACKGROUND

Ultrasound (US) is a fundamental diagnostic tool in breast imaging. However, US remains an operator-dependent examination. Research into and the application of artificial intelligence (AI) in breast US are increasing. The aim of this rapid review was to assess the current development of US-based artificial intelligence in the field of breast cancer.

METHODS

Two investigators with experience in medical research performed literature searching and data extraction on PubMed. The studies included in this rapid review evaluated the role of artificial intelligence concerning BC diagnosis, prognosis, molecular subtypes of breast cancer, axillary lymph node status, and the response to neoadjuvant chemotherapy. The mean values of sensitivity, specificity, and AUC were calculated for the main study categories with a meta-analytical approach.

RESULTS

A total of 58 main studies, all published after 2017, were included. Only 9/58 studies were prospective (15.5%); 13/58 studies (22.4%) used an ML approach. The vast majority (77.6%) used DL systems. Most studies were conducted for the diagnosis or classification of BC (55.1%). At present, all the included studies showed that AI has excellent performance in breast cancer diagnosis, prognosis, and treatment strategy.

CONCLUSIONS

US-based AI has great potential and research value in the field of breast cancer diagnosis, treatment, and prognosis. More prospective and multicenter studies are needed to assess the potential impact of AI in breast ultrasound.