Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review

Clinical Application of Multimodal Sentinel Lymph Node Mapping Method in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy: An Interim Analysis

BACKGROUND

After neoadjuvant chemotherapy (NAC), the SLN identification rate is lower and has a higher false-negative rate than that at upfront surgery. This clinical trial aimed to confirm the effectiveness of sentinel lymph node (SLN) surgery by determining the lymph node identification rate using multimodal SLN marker methods in patients with advanced breast cancer undergoing NAC.

PATIENTS AND METHODS

This clinical study is a prospective single-center randomized controlled trial involving patients with breast cancer receiving NAC. Patients are randomized (1:1:1) into arm A that involves the use of radioisotope (RI) plus indocyanine green fluorescence (ICG-F); arm B, RI plus vital dye; and, arm C, ICG-F plus vital dye. A total of 348 patients are needed. An interim analysis was performed on 50% of the patients enrolled. The primary outcome of this trial was the SLN identification rate.

RESULTS

Among the 164 total patients (median age 51 years), T2 and N1 were the most common clinical stages. The identification rate of SLN was 95% in arm A, 92% in arm B, and 79% in arm C. To assess superior efficacy, the one-sided endpoint was set at α < 0.0056. Arms A and C showed a difference of 0.1597 in the detection rate (p = 0.0055).

CONCLUSIONS

The use of ICG-F plus vital dye for SLNB was the least effective. The results show that the choice of tracer should be radioisotope in combination with one of the other tracers to have the highest SLN identification rate when SLNB cannot be implemented conventionally due to the circumstances of each institution.

The feasibility and added value of indocyanine green tracing in guiding sentinel lymph node biopsy for breast cancer

OBJECTIVE

To compare the effectiveness of combined (indocyanine green [ICG]+ blue dye) tracing versus blue dye alone in guiding sentinel lymph node biopsy (SLNB) in breast cancer.

METHODS

A total of 112 female patients (mean ± SD age: 51.9 ± 11.9 years) with clinically node-negative (cN0) early-stage breast cancer were evaluated based on SLN tracing technique including methylene blue + ICG (n = 17), isosulfan blue + ICG (n = 19) and methylene blue alone (n = 76). Mapping patterns of each SLN, the number of total lymph nodes (TLNs) removed, including metastatic and hyperplastic lymph nodes, and the metastatic lymph node detection rate were analyzed for each tracing technique.

RESULTS

SLN detection rate was 100 % with complementary use of ICG. No significant difference was noted between methylene blue + ICG, isosulfan + ICG and methylene blue alone groups in terms of the mean ± SD number of TLNs removed (3.9 ± 2.5, 4.7 ± 3 and 3.7 ± 2.3, respectively) and metastatic lymph node detection rates (16.0 %, 16.25 % and 13.98 %, respectively). Complementary use of ICG revealed the N0 stage for 66.6 % of cases considered as Nx (cannot be detected) on blue dye alone. Also, 20.0 % of N0 and 11.1 % of N1 cases on blue dye were diagnosed with more advance nodal status (N1 and N2 respectively) after complementary use of ICG.

CONCLUSIONS

The combined tracing (ICG + blue dye) seems valuable not only in terms of the SLN detection rates and lymph node yield but also has an added value in providing more accurate nodal stating and thus a proper final tumor staging with considerable therapeutic implications.

A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer

OBJECTIVES

To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI.

MATERIALS AND METHODS

A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications.

RESULTS

The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model.

CONCLUSION

A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

A new fluorescenttargeting tracer contrasts dual tracers in sentinel lymph node biopsy of breast cancer

Purpose: To explore the clinical application value of indocyanine green (ICG)-rituximab in sentinel lymph node biopsy. Methods: This study included 156 patients with primary breast cancer: 50 patients were enrolled in dose-climbing test, and 106 patients were enrolled in verification test. This was to compare the consistency of ICG-rituximab and combined method in the detected lymph nodes. Results: According to the verification test, the imaging rate of ICG-rituximab was 97.3%. Compared with the combined method, the concordance rate of fluorescence method was 0.991 (28 + 78/107; p < 0.001). Conclusion: For ICG-rituximab as a fluorescent targeting tracer, the optimal imaging dose of ICG 93.75 μg/rituximab 375 μg can significantly reduce the imaging of secondary lymph nodes. Compared with the combined method, it has a higher concordance rate.

Use of Indocyanine Green Near-Infrared Imaging for Sentinel Lymph Node Biopsy in Early Oral Squamous Cell Carcinoma: A Pilot Study

PURPOSE

The current established technique for sentinel lymph node (SLN) biopsy is preoperative injection of 99mtechnetium-labeled nanosized colloids (99mTc) followed by single photon emission computed tomography and standard computed tomography (SPECT/CT) with subsequent intraoperative gamma probe-guided excision of the SLN. It is however time and resource consuming, causes radiation exposure and morbidity for the patient as the injection is done in the awake patient. Recently near-infrared imaging with indocyanine green (ICG) gained importance in SLN biopsy as a faster and more convenient technique. The objective of our study was to investigate the feasibility of SLN biopsy using ICG-imaging in early oral squamous cell carcinoma (OSCC).

METHODS

Single-centre pilot study of five patients with early-stage OSCC. For all patients, both techniques (99mTc and ICG) were performed. We injected 99mTc preoperatively in the awake patient, followed by SPECT/CT imaging. Intraoperatively ICG was injected around the primary tumor. Then the neck incision was performed according to the SPECT/CT images and SLN were detected by using a gamma probe and near-infrared fluorescence imaging of the ICG-marked lymph nodes intraoperatively. The excised lymph nodes were sent to histopathological examination according to the SLN dissection protocol.

RESULTS

In all five patients sentinel lymph nodes were identified. A total of 7 SLN were identified after injection of 99mTc, imaging with SPECT/CT and intraoperative use of a gamma probe. All these SLN were fluorescent and visible with the ICG technique. In two patients, we could identify additional lymph nodes using the ICG technique. Pathological analysis demonstrated occult metastasis in two of the cases.

CONCLUSIONS

Our study shows that ICG-guided SLN biopsy is a feasible technique, especially in combination with conventional radioisotope method and may help for intraoperative localization of SLN. Validation studies with bigger patient cohorts are needed to prove our results.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

BACKGROUND

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients.

METHODS

In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing.

RESULTS

For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05).

CONCLUSION

Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer

Objective

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.

Methods

A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).

Results

A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.

Conclusion

The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study

Background

Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities.

Methods

The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network.

Findings

The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05).

Interpretation

Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients.

Funding

National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics

OBJECTIVES

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer.

METHODS

A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves.

RESULTS

Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively.

CONCLUSIONS

The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients.

ADVANCES IN KNOWLEDGE

This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Current and Developing Lymphatic Imaging Approaches for Elucidation of Functional Mechanisms and Disease Progression

Study of the lymphatic system, compared to that of the other body systems, has been historically neglected. While scientists and clinicians have, in recent decades, gained a better appreciation of the functionality of the lymphatics as well as their role in associated diseases (and consequently investigated these topics further in their experimental work), there is still much left to be understood of the lymphatic system. In this review article, we discuss the role lymphatic imaging techniques have played in this recent series of advancements and how new imaging techniques can help bolster this wave of discovery. We specifically highlight the use of lymphatic imaging techniques in understanding the fundamental anatomy and physiology of the lymphatic system; investigating the development of lymphatic vasculature (using techniques such as intravital microscopy); diagnosing, staging, and treating lymphedema and cancer; and its role in other disease states.

Determining Accurate Dye Combinations for Sentinel Lymph Node Detection: A Systematic Review

Background

Lymphatic dyes are commonly used to map the drainage path from tumor to lymphatics, which are biopsied to determine if spread has occurred. A blue dye in combination with technetium-99 is considered the gold standard for mapping, although many other dyes and dye combinations are used. Not all of these substances have the same detection efficacy.

Methods

A systematic review of PubMed, SCOPUS, Web of Science, and Medline was performed. The predefined search terms were (indocyanine green OR isosulfan blue OR lymphazurin OR patent blue OR methylene blue OR fluorescein OR technetium-99) AND combination AND dye AND (sentinel lymph node biopsy OR lymphedema OR lymphatics OR lymph OR microsurgery OR cancer OR tumor OR melanoma OR carcinoma OR sarcoma).

Results

The initial search returned 4267 articles. From these studies, 37 were selected as candidates that met inclusion criteria. After a full-text review, 34 studies were selected for inclusion. Eighty-nine methods of sentinel lymph node (SLN) detection were trialed using 22 unique dyes, dye combinations, or other tracers. In total, 12,157 SLNs of 12,801 SLNs were identified. Dye accuracy ranged from 100% to 69.8% detection. Five dye combinations had 100% accuracy. Dye combinations were more accurate than single dyes.

Conclusions

Combining lymphatic dyes improves SLN detection results. Replacing technetium-99 with ICG may allow for increased access to SLN procedures with comparable results. The ideal SLN tracer is a low-cost molecule with a high affinity for lymphatic vessels due to size and chemical composition, visualization without specialized equipment, and no adverse effects.

Assessing the Sentimag system for guiding sentinel node biopsies in patients with breast cancer

INTRODUCTION

Sentinel lymph node biopsy for breast cancer is a method to localize and excise the first draining lymph node from an invasive cancer of the breast. The histopathologic evaluation of the sentinel lymph node is used for predicting recurrence and survival and thus, guiding oncologists for treatment-decision making to administer adjuvant therapies. The ability to identify the sentinel node depends on methods to map lymphatic drainage from the breast to the sentinel node and accurately discriminate that node from other non-sentinel lymph nodes of the axilla.

AREAS COVERED

This review covers the clinical demand for technologies to assist the surgeon in intraoperative lymphatic mapping to specifically identify the sentinel lymph node in patients with breast cancer. Performance characteristics are reviewed for superparamagnetic iron oxide tracers used in lymphatic mapping compared to other current available technologies for lymphatic mapping.

EXPERT OPINION

The Magtrace (superparamagnetic iron oxide tracer) Sentimag (handheld magnetic probe) system is an FDA-approved technology for intraoperative lymphatic mapping to facilitate sentinel lymph node biopsy in breast cancer with technologic performance characteristics that are equivalent to 99Technetium-sulfur colloid. Barriers to broader utilization primarily center around the need for nonmetallic devices to be used for the conduct of surgery, which would interfere with the paramagnetic method for tracer localization.

A Novel Injection Protocol Using Voluven®-Assisted Indocyanine Green with Improved Near-Infrared Fluorescence Guidance in Breast Cancer Sentinel Lymph Node Mapping-A Translational Study

BACKGROUND

Near-infrared (NIR) fluorescence-guided surgery with indocyanine green (ICG) has been demonstrated to provide high sensitivity in sentinel lymph node biopsy (SLNB) for breast cancer but has several limitations, such as unstable pharmacokinetics, limited fluorescence brightness, and undesired diffusion to neighboring tissues. This paper investigates the use of Voluven® as the solvent for ICG fluorescence-guided SLNB (ICG-SLNB).

METHODS

The photophysical properties of ICG in water and Voluven® were evaluated in laboratory experiments and in a mouse model. Nine patients with early breast cancer underwent subareolar injection of diluted ICG (0.25 mg/ml) for ICG-SLNB. Six of the nine patients received ICG dissolved in Voluven® (ICG:Voluven®), while three were administered ICG dissolved in water (ICG:water); a repetitive injection-observation protocol was followed for all patients. The mapping image quality was evaluated.

RESULTS

Laboratory experiments and in vivo mouse study showed improved fluorescence and better targeting using Voluven® as the solvent. ICG-SLNB with a repetitive injection-observation protocol was successfully performed in all nine patients. ICG:Voluven® administration had an overall better signal-to-background ratio (SBR) in sequential sentinel lymph nodes. The rates of transportation within the lymphatics were also improved using ICG:Voluven® compared with ICG:water.

CONCLUSIONS

From basic research to animal models to in-human trial, our study proposes a repetitive injection-observation technique with ICG:Voluven®, which is characterized by better transportation and more stable mapping quality for ICG-SLNB in breast cancer patients.

Optical Imaging in Human Lymph Node Specimens for Detecting Breast Cancer Metastases: A Review

Assessment of regional lymph node status in breast cancer is of important staging and prognostic value. Even though formal histological examination is the currently accepted standard of care, optical imaging techniques have shown promising results in disease diagnosis. In the present article, we review six spectroscopic techniques and focus on their use as alternative tools for breast cancer lymph node assessment. Elastic scattering spectroscopy (ESS) seems to offer a simple, cost-effective, and reproducible method for intraoperative diagnosis of breast cancer lymph node metastasis. Optical coherence tomography (OCT) provides high-resolution tissue scanning, along with a short data acquisition time. However, it is relatively costly and experimentally complex. Raman spectroscopy proves to be a highly accurate method for the identification of malignant axillary lymph nodes, and it has been further validated in the setting of head and neck cancers. Still, it remains time-consuming. Near-infrared fluorescence imaging (NIRF) and diffuse reflectance spectroscopy (DFS) are related to significant advantages, such as deep tissue penetration and efficiency. Fourier-transform infrared spectroscopy (FTIR) is a promising method but has significant drawbacks. Nonetheless, only anecdotal reports exist on their clinical use for cancerous lymph node detection. Our results indicate that optical imaging methods can create informative and rapid tools to effectively guide surgical decision-making.

Ultrasound radiomics based on axillary lymph nodes images for predicting lymph node metastasis in breast cancer

Objectives

To determine whether ultrasound radiomics can be used to distinguish axillary lymph nodes (ALN) metastases in breast cancer based on ALN imaging.

Methods

A total of 147 breast cancer patients with 41 non-metastatic lymph nodes and 109 metastatic lymph nodes were divided into a training set (105 ALN) and a validation set (45 ALN). Radiomics features were extracted from ultrasound images and a radiomics signature (RS) was built. The Intraclass correlation coefficients (ICCs), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) methods were used to select the ALN status-related features. All images were assessed by two radiologists with at least 10 years of experience in ALN ultrasound examination. The performance levels of the model and radiologists in the training and validation subgroups were then evaluated and compared.

Result

Radiomics signature accurately predicted the ALN status, achieved an area under the receiver operator characteristic curve of 0.929 (95%CI, 0.881-0.978) and area under curve(AUC) of 0.919 (95%CI, 95%CI, 0.841-0.997) in training and validation cohorts respectively. The radiomics model performed better than two experts' prediction of ALN status in both cohorts (P<0.05). Besides, prediction in subgroups based on baseline clinicopathological information also achieved good discrimination performance, with an AUC of 0.937, 0.918, 0.885, 0.930, and 0.913 in HR+/HER2-, HER2+, triple-negative, tumor sized ≤ 3cm and tumor sized>3 cm, respectively.

Conclusion

The radiomics model demonstrated a good ability to predict ALN status in patients with breast cancer, which might provide essential information for decision-making.

Evaluation of the efficacy of using indocyanine green associated with fluorescence in sentinel lymph node biopsy

INTRODUCTION

Sentinel lymph node biopsy is the technique recommended for the axillary staging of patients with breast cancer in the initial stages without clinical axillary involvement. Three techniques are widely used globally to detect sentinel lymph nodes: patent blue, the radiopharmaceutical technetium 99 with gamma probe, and the combination of these two.

OBJECTIVES

To evaluate the sentinel lymph node detection rate with an innovative technique: indocyanine green (ICG) associated with fluorescence in breast cancer patients, and compare it with patent blue and a combination of patent blue and indocyanine green.

METHODS

99 patients were sequentially (not randomly) allocated into 3 arms with 33 patients submitted to sentinel lymph node techniques. One arm underwent patent blue dying, the other indocyanine green, and the third received a combination of both. The detection rates between arms were compared.

RESULTS

The detection rate in identifying the sentinel lymph node was 78.8% with patent blue, 93.9% with indocyanine green, and 100% with the combination. Indocyanine green identified two sentinel nodes in 48.5% of patients; the other groups more commonly had only one node identified. The mean time to sentinel lymph node identification was 20.6 ± 10.7 SD (standard deviation) minutes among patients submitted to the patent blue dye, 8.6 ± 6.6 minutes in the indocyanine green arm, and 10 ± 8.9 minutes in the combined group (P<0.001; Student's test). The mean surgery time was 69.4 ± 16.9; 55.1 ± 13.9; and 69.4 ± 19.3 minutes respectively (P<0.001; Student's test).

CONCLUSIONS

The sentinel lymph node detection rate by fluorescence using indocyanine green was 93.9%, considered adequate. The rates using patent blue, indocyanine green, and patent blue plus indocyanine green (combined) were significantly different, and the indocyanine green alone is also acceptable, since it has a good performance in sentinel lymph node identification and it can avoid tattooing, with a 100% sentinel lymph node detection rate when combined with patent blue.

Top 100 cited classical articles in sentinel lymph nodes biopsy for breast cancer

Background

The sentinel lymph node biopsy (SLNB) takes on a critical significance in breast cancer surgery since it is the gold standard for assessing axillary lymph node (ALN) metastasis and determining whether to perform axillary lymph node dissection (ALND). A bibliometric analysis is beneficial to visualize characteristics and hotspots in the field of sentinel lymph nodes (SLNs), and it is conducive to summarizing the important themes in the field to provide more insights into SLNs and facilitate the management of SLNs.

Materials and methods

Search terms relating to SLNs were aggregated and searched in the Web of Science core collection database to identify the top 100 most cited articles. Bibliometric tools were employed to identify and analyze publications for annual article volume, authors, countries, institutions, keywords, as well as hotspot topics.

Results

The period was from 1998 to 2018. The total number of citations ranged from 160 to 1925. LANCET ONCOLOGY and JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION were the top two journals in which the above articles were published. Giuliano, AE was the author with the highest number of articles in this field with 15. EUROPEAN INST ONCOL is the institution with the highest number of publications, with 35 articles. Hotspots include the following 4 topics, false-negative SLNs after neoadjuvant chemotherapy; prediction of metastatic SLNs; quality of life and postoperative complications; and lymphography of SLNs.

Conclusion

This study applies bibliometric tools to analyze the most influential literature, the top 100 cited articles in the field of SLNB, to provide researchers and physicians with research priorities and hotspots.

A review of contrast-enhanced ultrasound using SonoVue® and Sonazoid™ in non-hepatic organs

Contrast-enhanced ultrasound (CEUS) is a dependable modality for the diagnosis of various clinical conditions. A judicious selection of ultrasound contrast agent (UCA) is imperative for optimizing imaging and improving diagnosis. Approved UCAs for imaging the majority of organs include SonoVue, a pure blood agent, and Sonazoid, which exhibits an additional Kupffer phase. Despite the fact that the two UCAs are increasingly being employed, there is a lack of comparative reviews between the two agents in different organs diseases. This review represents the first attempt to compare the two UCAs in non-hepatic organs, primarily including breast, thyroid, pancreas, and spleen diseases. Through comparative analysis, this review provides a comprehensive and objective evaluation of the performance characteristics of SonoVue and Sonazoid, with the aim of offering valuable guidance for the clinical application of CEUS. Overall, further clinical evidences are required to compare and contrast the dissimilarities between the two UCAs in non-hepatic organs, enabling clinicians to make an appropriate selection based on actual clinical applications.

Novel Dual Tracer Indocyanine Green and Radioisotope Versus Gold Standard Sentinel Lymph Node Biopsy in Breast Cancer: The GREENORBLUE Trial

BACKGROUND

The methods for sentinel lymph node (SLN) biopsy in breast cancer have been variable in type and number of tracers. Some units have abandoned the use of blue dye (BD) due to adverse reactions. Fluorescence-guided biopsy with indocyanine green (ICG) is a relatively novel technique. This study compared the clinical efficacy and costs between novel dual tracer ICG and radioisotope (ICG-RI) with "gold standard" BD and radioisotope (BD-RI).

METHODS

Single-surgeon study of 150 prospective patients with early breast cancer undergoing SLN biopsy (2021-2022) using ICG-RI compared with a retrospective cohort of 150 consecutive previous patients using BD-RI. Number of SLNs identified, rate of failed mapping, identification of metastatic SLNs, and adverse reactions were compared between techniques. Cost-minimisation analysis performed by using Medicare item numbers and micro-costing analysis.

RESULTS

Total number of SLNs identified with ICG-RI and BD-RI was 351 and 315, respectively. Mean number of SLNs identified with ICG-RI and BD-RI was 2.3 (standard deviation [SD] 1.4) and 2.1 (SD 1.1), respectively (p = 0.156). There were no cases of failed mapping with either dual technique. Metastatic SLNs were identified in 38 (25.3%) ICG-RI patients compared with 30 (20%) BD-RI patients (p = 0.641). There were no adverse reactions to ICG, whereas four cases of skin tattooing and anaphylaxis were associated with BD (p = 0.131). ICG-RI cost an additional AU$197.38 per case in addition to the initial cost for the imaging system.

CLINICAL TRIAL REGISTRATION

 ACTRN12621001033831.

CONCLUSIONS

Novel tracer combination, ICG-RI, provided an effective and safe alternative to "gold standard" dual tracer. The caveat was the significantly greater costs associated with ICG.

Blue Wheals and Blue Angioedema Induced by Blue Dyes: A Systematic Review

BACKGROUND

Blue wheals and blue angioedema, the adverse reactions to blue dye injections with or without anaphylaxis, are poorly defined.

OBJECTIVE

The objective is to review the characteristics (ie, sex and age at onset, interval between blue dye injection and symptom onset, clinical manifestations, duration of blue wheals or angioedema), natural courses, and treatments of blue dye adverse reactions.

METHODS

A review of the articles published through July 2021 was performed per the Preferred Reporting Items for Systematic Reviews and Meta-Analysis recommendations.

RESULTS

Across 523 patients (175 studies) with any adverse reactions to blue dye injections, wheals, angioedema, or both occurred in 193 patients (36.9%). Of these 193 patients, 68 patients (35.2%) developed blue wheals or angioedema, 118 (61.1%) had ordinary wheals or angioedema (nonbluish), and 7 had both (3.6%). We reviewed 169 patients with available data (99 with ordinary lesions and 70 with blue lesions). Patent blue violet had the highest rate of inducing blue wheals or angioedema (odds ratio 4.9). Almost half of the patients with blue wheals or angioedema developed systemic symptoms; and of those with systemic symptoms, all except 1 progressed to anaphylaxis. On-demand treatments with antihistamines, corticosteroids, and epinephrine were commonly used and effective.

CONCLUSIONS

Using blue dyes can lead to blue wheals or angioedema and systemic reactions. In patients with a history of a severe allergic reaction to a blue dye, repeat administration of a blue dye should be used only after carefully weighing all the risks and benefits.

The Role of Sentinel Lymph Node Biopsy in Breast Cancer Patients Who Become Clinically Node-Negative Following Neo-Adjuvant Chemotherapy: A Literature Review

BACKGROUND

In clinically node-positive (cN+) breast cancer (BC) patients who become clinically node-negative (cN0) following neoadjuvant chemotherapy (NACT), sentinel lymph node biopsy (SLNB) after lymphatic mapping with lymphoscintigraphy is not widely accepted; therefore, it has become a topic of international debate.

OBJECTIVE

Our literature review aims to evaluate the current use of this surgical practice in a clinical setting and focuses on several studies published in the last six years which have contributed to the assessment of the feasibility and accuracy of this practice, highlighting its importance and oncological safety. We have considered the advantages and disadvantages of this technique compared to other suggested methods and strategies. We also evaluated the role of local irradiation therapy after SLNB and state-of-the-art SLN mapping in patients subjected to NACT.

METHODS

A comprehensive search of PubMed and Cochrane was conducted. All studies published in English from 2018 to August 2023 were evaluated.

RESULTS

Breast units are moving towards a de-escalation of axillary surgery, even in the NACT setting. The effects of these procedures on local irradiation are not very clear. Several studies have evaluated the oncological outcome of SLNB procedures. However, none of the alternative techniques proposed to lower the false negative rate (FNR) of SLNB are significant in terms of prognosis.

CONCLUSIONS

Based on these results, we can state that lymphatic mapping with SLNB in cN+ BC patients who become clinically node-negative (ycN0) following NACT is a safe procedure, with a good prognosis and low axillary failure rates.

New techniques versus standard mapping for sentinel lymph node biopsy in breast cancer: a systematic review and meta-analysis

New tracers for sentinel lymph node biopsy (SLNB), as indocyanine green (ICG), superparamagnetic iron oxide (SPIO) and micro bubbles, have been recently introduced in clinical practice showing promising but variable results. We reviewed the available evidence comparing these new techniques with the standard tracers to evaluate their safety. To identify all available studies, a systematic search was performed in all electronic databases. Data regarding sample size, mean number of SLN harvested for patient, number of metastatic SLN and SLN identification rate of all studies were extracted. No significant differences were found in terms of SLNs identification rates between SPIO, RI and BD but with a higher identification rate with the use of ICG. No significant differences were also found for the number of metastatic lymph nodes identified between SPIO, RI and BD and the mean number of SLNs identified between SPIO and ICG versus conventional tracers. A statistically significant differences in favor of ICG was reported for the comparison between ICG and conventional tracers for the number of metastatic lymph nodes identified. Our meta-analysis demonstrates that the use of both ICG and SPIO for the pre-operative mapping of sentinel lymph nodes in breast cancer treatment is adequately effective.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study

Background

For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images.

Methods

In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740.

Findings

For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model.

Interpretation

The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively.

Funding

National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Evaluation of Dual Dye Technique for Sentinel Lymph Node Biopsy in Breast Cancer: Two-Arm Open-Label Parallel Design Non-Inferiority Randomized Controlled Trial

INTRODUCTION

Radioisotope and blue dye are standard agents for performing sentinel lymph node (SLN) biopsy in breast cancer. The paucity of nuclear medicine facility poses logistic challenge. This study evaluated performance of radioisotope & methylene blue (MB) with indocyanine green (ICG) and MB for SLNB.

PATIENTS AND METHODS

This randomized controlled trial was conducted from December 2019 to July 2022 comparing SLN identification proportions of radioisotope-blue dye [Group A] with dual dye (MB + ICG; Group B]. Secondary objective included time required and cost effectiveness of performing SLNB. Sample size of 70 (35 in each arm) was calculated. Upfront operable node negative early breast cancer was included in the study. Clinico-demographic data, number & type of SLN, time taken were noted. Cost analysis was done including the equipment, manpower & consumables. Chi-square/Fisher exact test was used to compare proportion between two groups. p value of less than 0.05 was considered to represent statistical significance.

RESULTS

Seventy patients randomized to either group were similar in clinico-demographic and tumor characteristics. SLN identification rate (IR) was 91.43% in group A and 100% in group B. Overall IR of MB, radioisotope and ICG were 91.43%, 91.43% and 100%, respectively. Mean number of SLNs identified were 3 in group A and 4 in group B. Median time required for SLNB was 12 min and 14 min in either group, respectively. Cost of performing SLNB was higher in Group B.

CONCLUSION

SLNB using dual dye is non-inferior to radioisotope-blue dye in upfront operable early breast cancer. Trial registration number Clinical Trial registry India CTRI/2020/02/023503.

Can inflammatory indices predict sentinel lymph node status in patients with early-stage breast cancer?

Breast cancer research has focused on the early detection and treatment of breast cancer. Axillary lymph node status is essential for primary breast cancer staging, recurrence, and survival. The current quest for precision medicine is to identify predictive markers that offer the advantage of individualized treatment options. This study aimed to investigate the value of inflammatory indices in predicting positive sentinel nodes in breast cancer. We studied 602 patients with early-stage breast cancer who underwent sentinel lymph node biopsies (SLNB) at the Bülent Ecevit University General Surgery Clinic. We obtained data, including the clinical and demographic characteristics of the patients, such as age, histological type, and sentinel lymph nodes. Neutrophil, lymphocyte, platelet, and monocyte counts were obtained from preoperative complete blood count test data from the patient registry. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory index (SII), and sentinel lymph node biopsy were analyzed. Sentinel LAP was negative in 391 (65%) patients and positive in 211 (35%). In the receiver operating characteristic curve analysis, no significant difference was found between SLNB positivity and negativity in terms of NLR, PLR, LMR, or SII. In contrast to previous research, NLR, PLR, LMR, or SII did not affect SLNB positivity prediction in our study.

Effective treatment of metastatic sentinel lymph nodes by dual-targeting melittin nanoparticles

Sentinel lymph node (SLN) metastasis is an important promoter of distant metastasis in breast cancer. Therefore, the timely diagnosis and precise treatment are crucial for patient staging and prognosis. However, the simultaneous diagnosis of metastasis and the implementation of imaging-guided SLN therapy is challenging. Here, we report a melittin-loaded and hyaluronic acid (HA)-conjugated high-density lipoprotein (HDL) mimic phospholipid scaffold nanoparticle (MLT-HA-HPPS), which dually-target to both breast cancer and its SLN and efficiently inhibit SLN metastasis in the LN metastasis model. The melittin peptide was successfully loaded onto HA-HPPS via electrostatic interactions, and MLT-HA-HPPS possesses effective cytotoxicity for breast cancer 4T1 cells. Moreover, the effective delivery of MLT-HA-HPPS from the primary tumor into SLN is monitored by NIR fluorescence imaging, which greatly benefits the prognosis and treatment of metastatic SLNs. After paracancerous administration, MLT-HA-HPPS can efficiently inhibit primary tumor growth with an inhibition rate of 81.3% and 76.5% relative to the PBS-treated control group and HA-HPPS group, respectively. More importantly, MLT-HA-HPPS can effectively inhibit the growth of the metastatic SLNs with an approximately 78.0%, 79.1%, and 64.2% decrease in SLNs weight than those in PBS, HA-HPPS, and melittin-treated mice, respectively. Taken together, the MLT-HA-HPPS may provide an encouraging theranostic of SLN drug delivery strategy to inhibit primary tumor progression and prevent SLN metastasis of breast cancer.

Sentinel Lymph Node Detection in Breast Cancer: An Innovative Technique

(1) Background: Sentinel lymph node biopsy is important in the search for metastases, especially in patients with malignant breast disease. Our study proposed new techniques to prevent complications such as possible postoperative seroma formation, pain or hypoesthesia of the axillary cord and medial arm surface, as well as motor deficits, to avoid disabling outcomes and presents initial data from our experience with the sentinel lymph node biopsy technique. (2) Methods: We mainly used two radioactive tracer detection techniques and a new technique using a radiotracer called Sentimag-magtrace. The positive lymph node was located and removed to perform histologic analysis. In our study, we evaluate 100 patients who underwent breast cancer surgery. (3) Results: We calculated the identification rates of the different methods of sentinel lymph node detection and found that it was 88.9% using radioactive tracers vs. 89.5% using the magnetic tracer technology (Sentimag). (4) Conclusions: Thus, this technique avoids radiation exposure for both patients and health care providers, and can reduce costs and time.

Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.

Development and Validation of a Nomogram for Predicting Axillary Lymph Node Metastasis in Breast Cancer

BACKGROUND

Axillary lymph node (ALN) status is a key prognosis indicator for breast cancer patients. To develop an effective tool for predicting axillary lymph node metastasis in breast cancer, a nomogram was established based on mRNA expression data and clinicopathological characteristics.

MATERIALS AND METHODS

A 1062 breast cancer patients with mRNA data and clinical information were obtained from The Cancer Genome Atlas (TCGA). We first analyzed the differentially expression genes (DEGs) between ALN positive and ALN negative patients. Then, logistic regression, least absolute shrinkage and selection operator (Lasso) regression, and backward stepwise regression were performed to select candidate mRNA biomarkers. The mRNA signature was constructed by the mRNA biomarkers and corresponding Lasso coefficients. The key clinical factors were obtained by Wilcoxon-Mann-Whitney U test or Pearson's χ² test. Finally, the nomogram for predicting axillary lymph node metastasis was developed and evaluated by concordance index (C-index), calibration curve, decision curve analysis (DCA), and receptor operating characteristic (ROC) curve. Furthermore, the nomogram was externally validated using Gene Expression Omnibus (GEO) dataset.

RESULTS

The nomogram for predicting ALN metastasis yielded a C-index of 0.728 (95% CI: 0.698-0.758) and an AUC of 0.728 (95% CI: 0.697-0.758) in the TCGA cohort. In the independent validation cohort, the C-index and AUC of the nomogram were up to 0.825 (95% CI: 0.695-0.955) and 0.810 (95% CI: 0.666-0.953), respectively.

CONCLUSION

This nomogram could predict the risk of axillary lymph node metastasis in breast cancer and may provide a reference for clinicians to design individualized axillary lymph node management strategies.

Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.

Identification of Metastatic Lymph Nodes Using Indocyanine Green Fluorescence Imaging

Indocyanine green (ICG) has been used to detect several types of tumors; however, its ability to detect metastatic lymph nodes (LNs) remains unclear. Our goal was to determine the feasibility of ICG in detecting metastatic LNs. We established a mouse model and evaluated the potential of ICG. The feasibility of detecting metastatic LNs was also evaluated in patients with lung or esophageal cancer, detected with computed tomography (CT) or positron-emission tomography (PET)/CT, and scheduled to undergo surgical resection. Tumors and metastatic LNs were successfully detected in the mice. In the clinical study, the efficacy of ICG was evaluated in 15 tumors and fifty-four LNs with suspected metastasis or anatomically key regional LNs. All 15 tumors were successfully detected. Among the fifty-four LNs, eleven were pathologically confirmed to have metastasis; all eleven were detected in ICG fluorescence imaging, with five in CT and seven in PET/CT. Furthermore, thirty-four LNs with no signals were pathologically confirmed as nonmetastatic. Intravenous injection of ICG may be a useful tool to detect metastatic LNs and tumors. However, ICG is not a targeting agent, and its relatively low fluorescence makes it difficult to use to detect tumors in vivo. Therefore, further studies are needed to develop contrast agents and devices that produce increased fluorescence signals.

A radiomics model development via the associations with genomics features in predicting axillary lymph node metastasis of breast cancer: a study based on a public database and single-centre verification

AIM

To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients with breast cancer.

MATERIALS AND METHODS

Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics-genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis.

RESULTS

After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve [AUC] = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases.

CONCLUSION

The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation

PURPOSE

Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions. While breast biopsy is referred to as the "gold standard" in assessing both the activity and degree of breast cancer, it is an invasive and time-consuming approach.

METHOD

The current study's primary objective was to develop a novel deep-learning architecture based on the InceptionV3 network to classify ultrasound breast lesions. The main promotions of the proposed architecture were converting the InceptionV3 modules to residual inception ones, increasing their number, and altering the hyperparameters. In addition, we used a combination of five datasets (three public datasets and two prepared from different imaging centers) for training and evaluating the model.

RESULTS

The dataset was split into the train (80%) and test (20%) groups. The model achieved 0.83, 0.77, 0.8, 0.81, 0.81, 0.18, and 0.77 for the precision, recall, F1 score, accuracy, AUC, Root Mean Squared Error, and Cronbach's α in the test group, respectively.

CONCLUSIONS

This study illustrates that the improved InceptionV3 can robustly classify breast tumors, potentially reducing the need for biopsy in many cases.

Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study

BACKGROUND

Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice.

PURPOSE

To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer.

MATERIAL AND METHODS

A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed.

RESULTS

The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004).

CONCLUSION

IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.

Indocyanine Green and Methylene Blue Dye Guided Sentinel Lymph Node Biopsy in Early Breast Cancer: A Single-Center Retrospective Survival Study in 1574 Patients

BACKGROUND

Currently, the standard tracing method is to use blue dyes and radioisotope as the tracer for sentinel lymph node biopsy (SLNB). However, there are variations in the choice of tracer in different countries and regions. Some new tracers are also gradually applied in clinical practice, but there is still a lack of long-term follow-up data to confirm their clinical application value.

PATIENTS AND METHODS

Clinicopathological and postoperative treatment follow-up data were collected from patients with early-stage cTis-2N0M0 breast cancer who underwent SLNB using a dual-tracer method of ICG combined with MB. Statistical indicators including the identification rate, the number of sentinel lymph nodes (SLNs), regional lymph node recurrence, disease-free survival (DFS) and overall survival (OS) were analyzed.

RESULTS

Among the 1574 patients, SLNs were successfully detected during surgery in 1569 patients, with a detection rate of 99.7%; the median number of SLNs removed was 3. A total of 1531 patients were included in the survival analysis, with a median follow-up of 4.7 (0.5-7.9) years. In total, patients with positive SLNs had a 5-year DFS and OS of 90.6% and 94.7%, respectively. The 5-year DFS and OS of patients with negative SLNs were 95.6% and 97.3%, respectively. The postoperative regional lymph node recurrence rate was 0.7% in patients with negative SLNs.

CONCLUSION

Indocyanine green combined with methylene blue dual-tracer method is safe and effective in sentinel lymph node biopsy in patients with early breast cancer.

Artificial Intelligence for Breast US

US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.

Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer

BACKGROUND

In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy.

OBJECTIVE

This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status.

METHODS

A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models.

RESULTS

For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively.

CONCLUSIONS

Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

Evaluation of New Tracers in the Identification of Sentinel Lymph Node in Patients with Early Breast Cancer

Purpose

Sentinel node mapping is the standard of care for evaluation of axilla for women with early node negative breast cancer. Validation of a new tracer for sentinel node biopsy requires full axillary lymph node dissection to establish its performance indicators. This exposes about 70% of women to unnecessary axillary dissection with its attendant morbidity.

Aims and Objective

To investigate the predictive value of identification of sentinel lymph nodes by a tracer for knowing its sensitivity and false-negative rates (FNR).

Methods

A linear regression on data from a network meta-analysis was carried out, and the correlation between identification and sensitivity and its predictive value were ascertained.

Results

A strong linear relationship was observed between identification and sensitivity of sentinel node biopsy (correlation coefficient r = 0.97). The sensitivity and false negativity can be predicted by the identification rate. An identification rate of 93% corresponds to sensitivity = 90.51% and a FNR = 9.49%. The current literature on newer tracers has been succinctly reviewed.

Conclusion

The linear regression demonstrated a very high predictive value of identification rate for ascertaining the sensitivity and FNRs of sentinel node biopsy. A new tracer for sentinel node biopsy can be introduced in clinical practice, if it achieves an identification rate of 93% or more.

Triple mapping for axillary staging after neoadjuvant therapy: Axillary reverse mapping with indocyanine green and dual agent sentinel lymph node biopsy

Axillary staging is 1 of the major issues of current breast cancer management after neoadjuvant systemic therapy (NST). Sentinel lymph node biopsy (SLNB) is an option for clinically node negative patients. Axillary reverse mapping (ARM) was introduced to identify and preserve the lymphatic drainage from the arm. The aim of the presented study is to employ triple mapping (radiocolloid, blue dye and indocyanine green [ICG]) to assess the crossover rate and metastatic involvement of ARM nodes after NST. Clinically node positive patients before NST who were converted to N0 and scheduled for targeted axillary dissection were included. sentinel lymph node (SLN) mapping was performed via dual agent mapping. ICG was used for ARM procedure. Blue, hot and fluorescent nodes and lymphatics were visualized in the axilla using infrared camera system and dual opto-nuclear probe (Euoroprobe3). Fifty-two patients underwent targeted axillary dissection and ARM procedures 12 out of whom had axillary node dissection. 45 of the 52 patients had at least 1 hot or blue SLN identified intraoperatively. Of these, 61.5% cases had hot SLNs, 42.3% had hot and blue, 15.4% had hot/blue/fluorescent, 7.7% had blue/fluorescent, 6 11.5% had hot/fluorescent and 7 13.5% had only clipped nodes. The overall identification rate of ARM-nodes by means of ICG technique was 86.5%. Overall crossover of ARM nodes with SLNs was determined in 36.5%. The ICG intensity was found to be higher in both hot and blue SLNS (8 out of 18 ICG positive cases, 44.4%). In 3 of 52 patients (5.7%) metastatic SLNs were hot or blue but fluorescent which predicts metastatic involvement of the ARM-nodes. More than 1-third of the patients revealed a crossover between arm and breast draining nodes. The higher observed rate of overlap might partially explain why more patients develop clinically significant lymphedema after NST even after sentinel lymph node biopsy alone. The triple mapping provides valuable data regarding the competency of lymphatic drainage and would have the potential to serve selecting patients for lymphovenous by-pass procedures at the index procedure. NST reduces the metastatic involvement of the ARM nodes. However, conservative axillary staging with sparing ARM nodes after NST necessitates further studies with larger sample size and longer follow-up.

Evolution and refinement of magnetically guided sentinel lymph node detection in breast cancer: meta-analysis

BACKGROUND

Superparamagnetic iron oxide nanoparticles (SPIO) have been used as a tracer for sentinel lymph node (SLN) localization in breast cancer, demonstrating comparable performance to the combination of radioisotope (RI) and blue dye (BD).

METHODS

A systematic literature search and meta-analysis with subgroup and meta-regression analysis were undertaken to update the available evidence, assess technique evolution, and define knowledge gaps. Recommendations were made using the GRADE approach.

RESULTS

In 20 comparative studies, the detection rate was 97.5 per cent for SPIO and 96.5 per cent for RI ± BD (risk ratio 1.006, 95 per cent c.i. 0.992 to 1.019; P = 0.376, high-certainty evidence). Neoadjuvant therapy, injection site, injection volume or nodal metastasis burden did not affect the detection rate, but injection over 24 h before surgery increased the detection rate on meta-regression. Concordance was 99.0 per cent and reverse concordance 97.1 per cent (rate difference 0.003, 95 per cent c.i. -0.009 to 0.015; P = 0.656, high-certainty evidence). Use of SPIO led to retrieval of slightly more SLNs (pooled mean 1.96 versus 1.89) with a higher nodal detection rate (94.1 versus 83.5 per cent; RR 1.098, 1.058 to 1.140; P < 0.001; low-certainty evidence). In meta-regression, injection over 24 h before surgery increased the SPIO nodal yield over that of RI ± BD. The skin-staining rate was 30.8 per cent (very low-certainty evidence), and possibly prevented with use of smaller doses and peritumoral injection.

CONCLUSION

The performance of SPIO is comparable to that of RI ± BD. Preoperative injection increases the detection rate and nodal yield, without affecting concordance. Whether skin staining and MRI artefacts are reduced by lower dose and peritumoral injection needs to be investigated.

A Comparison of Skin Staining after Sentinel Lymph Node Biopsy in Women Undergoing Breast Cancer Surgery Using Blue Dye and Superparamagnetic Iron Oxide Nanoparticle (SPIO) Tracers

Superparamagnetic iron oxide nanoparticles (SPIO) are a tracer for sentinel lymph node (SLN) detection. In a preplanned secondary analysis of a prospective clinical trial (SentiDose) we reported on skin staining after SPIO and blue dye (BD) injections. For SPIO, either a 1.5 mL retroareolar injection on the day of surgery or a 1.0 mL peritumoral/retroareolar injection 1-7 days before surgery was given. A 1.0 mL sub-/intradermal periareolar injection of BD was also administered to all these women. Staining was then assessed at 6, 12 and 24 months after surgery. A total of 270 women received SPIO and were operated on with breast-conserving surgery. Of these, 204 women also received BD. A total of 58 (21.5%) women had an SPIO stain 6 months postoperatively with a median size of 6.8 cm² (p = 0.56), while 51 (25.0%) had a BD stain with a median size of 8.5 cm² (p = 0.93). The incidence and size of SPIO and BD staining decreased over time reciprocally. At 24 months, the incidence and median size of SPIO was 23 (8.6%) and 4 cm², respectively. For BD, the incidence was 14 (6.3%, p = 0.13), and the median size was 3.5 cm² (p = 0.18). There was, therefore, no statistically significant difference in the incidence or size of skin staining between SPIO and BD over time.

Three-dimensional visual technique based on CT lymphography data combined with methylene blue in endoscopic sentinel lymph node biopsy for breast cancer

BACKGROUND

The combined application of blue dye and radioisotopes is currently the primary mapping technique used for sentinel lymph node biopsy (SLNB) in breast cancer patients. However, radiocolloid techniques have not been widely adopted, especially in developing countries, given the strict restrictions on radioactive materials. Consequently, we carried out a retrospective study to evaluate the feasibility and accuracy of three-dimensional visualization technique (3DVT) based on computed tomography-lymphography (CT-LG) in endoscopic sentinel lymph node biopsy (ESLNB) for breast cancer.

METHODS

From September 2018 to June 2020, 389 patients who underwent surgical treatment of breast cancer in our department were included in this study. The CT-LG data of these patients were reconstructed into digital 3D models and imported into Smart Vision Works V1.0 to locate the sentinel lymph node (SLN) and for visual simulation surgery. ESLNB and endoscopic axillary lymph node dissection were carried out based on this new technique; the accuracy and clinical value of 3DVT in ESLNB were analyzed.

RESULTS

The reconstructed 3D models clearly displayed all the structures of breast and axilla, which favors the intraoperative detection of SLNs. The identification rate of biopsied SLNs was 100% (389/389). The accuracy, sensitivity, and false-negative rate were 93.83% (365/389), 93.43% (128/137), and 6.57% (9/137), respectively. Upper limb lymphedema occurred in one patient 3 months after surgery during the 12-month follow-up period.

CONCLUSIONS

Our 3DVT based on CT-LG data combined with methylene blue in ESLNB ensures a high identification rate of SLNs with low false-negative rates. It, therefore, has the potential to serve as a new method for SLN biopsy in breast cancer cases.

“Light green up”: Indocyanine Green Fluorescence Imaging–guided Robotic Bilateral Inguinal Lymphadenectomy by the Hypogastric Subcutaneous Approach for Penile Cancer

Background

Inguinal lymphadenectomy is of great significance in the management of penile cancer, which aims to mitigate the progression of lymph node metastasis. It is important to improve the efficiency of lymph node dissection and reduce surgical complications.

Objective

To detail a novel technique for robotic bilateral inguinal lymphadenectomy through the hypogastric subcutaneous approach by indocyanine green (ICG) fluorescence imaging, which promotes the identification and dissection of inguinal lymph nodes with considerable safety.

Design, setting, and participants

Ten eligible penile cancer patients who underwent ICG fluorescence imaging–guided robotic bilateral inguinal lymphadenectomy were prospectively enrolled (ICG group). Sixteen patients who underwent the surgery without ICG were retrospectively set as the control (non-ICG) group. Follow-up records for at least 12 mo were required.

Surgical procedure

Inguinal lymphadenectomy was performed by the hypogastric subcutaneous approach. The ICG solution was subcutaneously injected into the prepuce at the beginning of surgery, and ICG fluorescence imaging–guided robotic-assisted bilateral inguinal lymphadenectomy was conducted.

Measurements

Clinical outcomes were collected. The primary study outcome measurement was the number of dissected inguinal lymph nodes.

Results and limitations

The numbers of inguinal overall, superficial, and deep lymph nodes retrieved were all higher in the ICG than in the non-ICG group (p < 0.05). No patients had severe perioperative complications. No difference was found in the overall complication rate and 12-mo survival between two groups (p > 0.05).

Conclusions

ICG fluorescence imaging–guided robotic inguinal lymphadenectomy via the hypogastric subcutaneous approach is feasible and safe for patients with penile cancer, which is beneficial for dissecting more inguinal lymph nodes with few surgical complications.

Patient summary

We developed a promising indocyanine green fluorescence imaging–guided technique to perform robotic bilateral inguinal lymphadenectomy on patients with penile cancer, which conduces to remove more inguinal lymph nodes with limited complications.

The Application of Magnetic Nanoparticles for Sentinel Lymph Node Detection in Clinically Node-Negative Breast Cancer Patients: A Systemic Review and Meta-Analysis

Superparamagnetic iron oxide (SPIO), an alternative mapping agent, can be used to identify sentinel lymph nodes in patients with clinically node-negative breast cancer. However, its performance in comparison with the standard method, using a radioisotope (technetium-99 m, Tc) alone or in combination with blue dye, remains controversial. Hence, a systematic review and meta-analysis were conducted to evaluate the diagnostic accuracy of SPIO and its clinical impact in the management of breast cancer. The PubMed, Embase, and Cochrane databases were comprehensively searched from inception to 1 May 2022. Cohort studies regarding the comparison of SPIO with standard methods for sentinel lymph node identification were included. A total of 19 prospective cohort studies, which collectively included 2298 clinically node-negative breast cancer patients undergoing sentinel lymph node identification through both the standard method and SPIO, were identified. The detection rate for sentinel lymph nodes (RR, 1.06; 95% CI, 1.05−1.08; p < 0.001) was considerably higher in the SPIO cohorts than in the standard method cohorts, although this difference was not significant in detected patients, patients with positive sentinel lymph nodes, or positive sentinel lymph nodes. Compared with the standard method, the SPIO method could be considered as an alternative standard of care for sentinel lymph node detection in patients with clinically node-negative breast cancer.

Sentinel lymph node detection in thyroid carcinoma using 68Ga-tilmanocept PET/CT: a proof-of-concept study protocol

Sentinel lymph node biopsy (SLNB) is a diagnostic staging procedure. The procedure aims to identify the first draining lymph node(s), which are most likely to contain metastases. SLNB is applied in various cancers, but not currently in thyroid carcinoma. However, treatment strategies are changing, making SLNB clinically relevant. SLNB may lead to more accurate staging, prevent unnecessary treatment and help achieve earlier curation. 68Ga-tilmanocept PET/computed tomography (CT) can better localize sentinel lymph nodes (SLNs) near the primary tumor than planar scintigraphy and single-photon emission computed tomography (SPECT)/CT. This paper describes the rationale and design of a study investigating SLNB using 68Ga-tilmanocept PET/CT and indocyanine-green-99mTc-nanocolloid in ten differentiated and medullary thyroid carcinoma patients. Localization and number of SLNs, pathology result, optimal scan protocol, surgical time and surgeon's experience are examined. Clinical Trial Registration: 2021-002470-42 (EudraCT).

Carbohydrate-based drugs launched during 2000-2021

Carbohydrates are fundamental molecules involved in nearly all aspects of lives, such as being involved in formating the genetic and energy materials, supporting the structure of organisms, constituting invasion and host defense systems, and forming antibiotics secondary metabolites. The naturally occurring carbohydrates and their derivatives have been extensively studied as therapeutic agents for the treatment of various diseases. During 2000 to 2021, totally 54 carbohydrate-based drugs which contain carbohydrate moities as the major structural units have been approved as drugs or diagnostic agents. Here we provide a comprehensive review on the chemical structures, activities, and clinical trial results of these carbohydrate-based drugs, which are categorized by their indications into antiviral drugs, antibacterial/antiparasitic drugs, anticancer drugs, antidiabetics drugs, cardiovascular drugs, nervous system drugs, and other agents.

Short-term and long-term outcomes of indocyanine green for sentinel lymph node biopsy in early-stage breast cancer

Background

Indocyanine green (ICG) is becoming a frequently used sentinel lymph node (SLN) tracer of breast cancer in China. However, there is still a lack of data on its safety. We reported the clinical outcome of ICG as a tracer of SLN over a median 67-month follow-up period to evaluate its feasibility in clinically node-negative patients with breast cancer.

Methods

A total of 194 consecutive patients underwent sentinel lymph node biopsy (SLNB) with ICG, radioisotopes (RI) and methylene blue (MB), or with ICG and MB. The SLN mapping data by each tracer was recorded, and safety outcomes were analyzed through follow-up.

Results

With the triad mapping (N = 44), the identification rate of SLN by ICG was 95.5%, slightly higher than that of MB (86.4%) and comparable with RI (95.5%) and combined methods (95.5%, 100%) (p = 0.068). Analysis of all candidates (N = 194) demonstrated that the identification rate of SLN by ICG or by ICG and MB was 99%, significantly higher than that by MB (92.8%) (p < 0.0001). No tracer-related allergic reaction and permanent skin staining of ICG were observed. Local disease progression was reported in 2 of the 194 patients at the ipsilateral axilla. After remedial axillary lymph node dissection, no disease progression was detected at follow-up.

Conclusions

ICG as an SLN tracer is more accurate than MB and comparable to the combined methods and has good clinical safety. ICG can be considered a useful supplement or suitable alternative to traditional tracers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02719-7.