Node Imaging Research Articles

Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet

Abstract This study aimed to determine whether EfficientNet-B0 was able to classify EGFR mutation subtypes with H&E stained whole slide images of lung and lymph node tissue. Background: Non-small cell lung cancer (NSCLC) accounts for the majority of all lung adenocarcinomas, with estimates that up to a third of such cases have a mutation in their epidermal growth factor receptor (EGFR). EGFR mutations can occur in various subtypes, such as Exon19 deletion, and L858R substitution, which are important for early therapy decisions. Here, we propose a deep learning approach for detecting and classifying EGFR mutation subtypes, which will greatly reduce the cost of determining mutation status, allowing for testing in a low resource setting. Methods: An EfficientNet-B0 model was trained with whole slide images of lung tissue or metastatic lymph nodes with known EGFR mutation subtype (wild type, exon19 deletion or L858R substitution). Regions of interest were tiled into 512x512 pixel images. The RGB .jpeg tiles are augmented by rotating 90°, 180°, 270°, and mirroring. The model was initialized with random parameters and trained with a batch size of 32, a learning rate of 0.0001 for 1 epoch before the validation loss increased for the next 5 epochs. Results: The model achieved a slide AUC of 0.8333, and a tile AUC of 0.8010. Slide AUC is the result of averaging all tiles within a slide and measuring performance based on correctly predicted slides (n=18). Tile AUC is the result of measuring performance based on correctly predicted tiles (n=102,000). Conclusion: Using EfficientNet-B0 architecture as the basis for our EGFR mutation classification system, we were able to create a top performing model and achieve a slide AUC of 0.833 and tile AUC of 0.801. Healthcare providers and researchers may utilize this AI model in clinical settings to allow for detection of EGFR mutation from routinely captured images and bypass expensive and time consuming sequencing methods. Table 1. Number of image tiles used and the number of slides they were extracted from. Train Validation Test Exon19 tiles 187,384 47,904 33,096 L858R tiles 166,288 19,512 26,136 Wild type tiles 225,944 27,696 42,768 Exon19 slides 47 6 6 L858R slides 46 6 6 WIld type slides 43 6 6 Citation Format: Daniel L. Franklin, Tara Pattilachan, Anthony Magliocco. Imaging based EGFR mutation subtype classification using EfficientNet [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5048.

CASTING: A Potent Supramolecular Strategy to Cytosolically Deliver STING Agonist for Cancer Immunotherapy and SARS-CoV-2 Vaccination

CASTING: A Potent Supramolecular Strategy to Cytosolically Deliver STING Agonist for Cancer Immunotherapy and SARS-CoV-2 Vaccination

Biocompatible Chemically Fueled Transient Polymer Nanoparticles for Temporally Programmable in Vivo Imaging

Biocompatible Chemically Fueled Transient Polymer Nanoparticles for Temporally Programmable in Vivo Imaging

In vivo non-invasive confocal fluorescence imaging beyond 1,700 nm using superconducting nanowire single-photon detectors.

Light scattering by biological tissues sets a limit to the penetration depth of high-resolution optical microscopy imaging of live mammals in vivo. An effective approach to reduce light scattering and increase imaging depth is to extend the excitation and emission wavelengths to the second near-infrared window (NIR-II) at >1,000 nm, also called the short-wavelength infrared window. Here we show biocompatible core-shell lead sulfide/cadmium sulfide quantum dots emitting at ~1,880 nm and superconducting nanowire single-photon detectors for single-photon detection up to 2,000 nm, enabling a one-photon excitation fluorescence imaging window in the 1,700-2,000 nm (NIR-IIc) range with 1,650 nm excitation-the longest one-photon excitation and emission for in vivo mouse imaging so far. Confocal fluorescence imaging in NIR-IIc reached an imaging depth of ~1,100 μm through an intact mouse head, and enabled non-invasive cellular-resolution imaging in the inguinal lymph nodes of mice without any surgery. We achieve in vivo molecular imaging of high endothelial venules with diameters as small as ~6.6 μm, as well as CD169 + macrophages and CD3 + T cells in the lymph nodes, opening the possibility of non-invasive intravital imaging of immune trafficking in lymph nodes at the single-cell/vessel-level longitudinally.

Axillary lymph node imaging in mRNA, vector-based, and mix-and-match COVID-19 vaccine recipients: ultrasound features

ObjectivesTo assess ultrasound characteristics of ipsilateral axillary lymph nodes after two doses of four different COVID-19 vaccination protocols, to determine whether these parameters differed with age, and to describe how they changed on follow-up imaging.MethodsA total of 247 volunteer employees from our center who had received two doses of COVID-19 vaccination were recruited and followed prospectively. Axillary ultrasound of the ipsilateral vaccinated arm was performed the week after receiving the second dose to analyze lymph node features (number, long-axis, cortical thickness, morphology, and vascular imaging). Axillary lymphadenopathy resulting from four vaccination protocols—mRNA (BNT162b2, mRNA-1273), ChAdOx1-S, and mix-and-match—was compared. Analysis was conducted using the Kruskal-Wallis test and post hoc analysis with Bonferroni corrections. Nodal reactogenicity was evaluated for two age groups: young (< 45 years old) and middle-aged ( ≥ 45 years old). All parameters were compared between both groups using an unpaired-sample Student t test. A p value < 0.05 was considered statistically significant.ResultsSignificantly higher values for total number of visible nodes, cortical thickness, Bedi’s classification (p < 0.001), and vascularity (p < 0.05) were observed in mRNA vaccine recipients compared to full ChAdOx1-S protocol recipients. Moreover, mix-and-match protocol recipients showed greater nodal cortical thickness and higher Bedi’s classification than full ChAdOx1-S recipients (p < 0.001). Analyses between age groups revealed greater cortical thickness, Bedi’s classification, and color Doppler signal in younger patients (p < 0.05).ConclusionsNodal parameters of Bedi’s classification and cortical thickness were more often increased in mRNA and mix-and-match vaccine recipients when compared to ChAdOx1-S vaccine alone, especially in younger patients.Key Points• Hyperplastic lymphadenopathy was observed more frequently in mRNA and mix-and-match vaccine protocols compared to full vector-based vaccination.• Higher values for cortical thickness, Bedi’s classification, and color Doppler signal parameters were identified in younger patients.• Observed lymph node findings normalized in greater than 80% of patients by the third month following vaccination.

Creation of High-Dimensional Reduction Analysis-Compatible Histocytometry Files from Images of Densely-Packed Cells and/or Variable Stain Intensity.

The power of high-dimensional reduction techniques using multiparameter images has been demonstrated across a variety of different publications. Recently, we published an end-to-end low-cost GUI-based protocol for performing histocytometric spatial analysis on images derived from the most common microscope image formats. However, this protocol is limited by the normalized marker intensity outputs and the difficulty in processing images of highly aggregated and/or exceptionally heterogenous cell populations. Here we present the basic protocols required to construct an advanced histocytometric data file using only freeware. This data file is compatible with images containing cell nuclei clusters that are difficult to segment, and results in histocytometry files retaining the original marker intensity values of the microscopic images they were derived from. This is especially useful in cells that are phenotyped based on relative marker expression levels. Histocytometry data files produced by these protocols are compatible with high-dimensional reduction analysis using marker intensity data, such as tSNEs. This methodology is showcased using stitched microscopic images of murine lymph nodes, complex organs with highly aggregated heterogenous cell populations, that are typically difficult to segment. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Image preprocessing and generation of nuclei marker probability maps Basic Protocol 2: Cell segmentation using ilastik-derived probability maps Basic Protocol 3: Generation of histocytometric .fcs files.

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer.

Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5–3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.

Mobile Phone Flashlight‐Excited Red Afterglow Bioimaging

Organic room temperature phosphorescence (RTP) materials with ultralong lifetime possess the remarkable advantage in bioimaging for elimination of background noise by characteristic time scale. However, most of RTP luminogens need to be excited by the harmful ultraviolet (UV) lamp, and exhibit green or yellow emission with shallow tissue penetration, constraining the in vivo bioimaging for further application in clinical diagnosis and pathological study. In this text, the much safer excitation process by sunlight and mobile phone flashlight is realized by organic luminogens with various electronic pull-push systems. Moreover, the bright red RTP emission with lifetime up to 344ms is achieved by optimizing molecular geometry and electronic property. Especially, the mobile phone flashlight-excited red afterglow imaging of lymph nodes in living mice has been realized for the first time, affording a safe and conventional approach to achieve the afterglow imaging of living mice with deep issue penetration and high signal-to-noise ratios.

Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.

To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic performance of radiologists. We retrospectively gathered 300 images of normal and 328 images of axillary lymph nodes with breast cancer metastases for training. A DL model using the CNN architecture Xception was developed to analyze test data of 50 normal and 50 metastatic lymph nodes. A board-certified radiologist with 12years' experience. (Reader 1) and two residents with 3- and 1-year experience (Readers 2, 3), respectively, scored these test data with and without the assistance of the DL system for the possibility of metastasis. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our DL model had a sensitivity of 94%, a specificity of 88%, and an AUC of 0.966. The AUC of the DL model was not significantly different from that of Reader 1 (0.969; p = 0.881) and higher than that of Reader 2 (0.913; p = 0.101) and Reader 3 (0.810; p < 0.001). With the DL support, the AUCs of Readers 2 and 3 increased to 0.960 and 0.937, respectively, which were comparable to those of Reader 1 (p = 0.138 and 0.700, respectively). Our DL model demonstrated great diagnostic performance for differentiating benign from malignant axillary lymph nodes on breast ultrasound and for potentially providing effective diagnostic support to residents.

Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss.

BackgroundUltrasound imaging has been recognized as a powerful tool in clinical diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of ultrasound images. Various denoising algorithms cannot fully reduce speckle noise and retain image features well for ultrasound imaging. The application of deep learning in ultrasound image denoising has attracted more and more attention in recent years.MethodsIn the article, we propose a generative adversarial network with residual dense connectivity and weighted joint loss (GAN-RW) to avoid the limitations of traditional image denoising algorithms and surpass the most advanced performance of ultrasound image denoising. The denoising network is based on U-Net architecture which includes four encoder and four decoder modules. Each of the encoder and decoder modules is replaced with residual dense connectivity and BN to remove speckle noise. The discriminator network applies a series of convolutional layers to identify differences between the translated images and the desired modality. In the training processes, we introduce a joint loss function consisting of a weighted sum of the L1 loss function, binary cross-entropy with a logit loss function and perceptual loss function.ResultsWe split the experiments into two parts. First, experiments were performed on Berkeley segmentation (BSD68) datasets corrupted by a simulated speckle. Compared with the eight existing denoising algorithms, the GAN-RW achieved the most advanced despeckling performance in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effect. When the noise level was 15, the average value of the GAN-RW increased by approximately 3.58% and 1.23% for PSNR and SSIM, respectively. When the noise level was 25, the average value of the GAN-RW increased by approximately 3.08% and 1.84% for PSNR and SSIM, respectively. When the noise level was 50, the average value of the GAN-RW increased by approximately 1.32% and 1.98% for PSNR and SSIM, respectively. Secondly, experiments were performed on the ultrasound images of lymph nodes, the foetal head, and the brachial plexus. The proposed method shows higher subjective visual effect when verifying on the ultrasound images. In the end, through statistical analysis, the GAN-RW achieved the highest mean rank in the Friedman test.

Abstract P1-01-13: A prospective self-controlled study of indocyanine green instead of radioisotope for axillary sentinel lymph nodes in breast cancer

Abstract Objective: This self-controlled study aimed to investigate the feasibility of substituting indocyanine green (ICG) for radioisotope (RI) for combined imaging of axillary sentinel lymph node (SLN) in breast cancer. Study Design: Primary breast cancer, clinically axillary node-negative patients (n = 182) were prospectively enrolled from March 2015 to November 2020. ICG, methylene blue (MB), and RI were used to perform axillary SLNB. The main observation indices was the positivity of ICG + MB vs. RI + MB in axillary SLNB; the secondary observation indices were the axillary SLN detection rate, mean number of axillary SLN detected, mean number of metastatic axillary SLN detected and safety.Result: All 182 patients had axillary SLNs; a total of 925 axillary SLNs were detected. Pathological examination confirmed metastatic axillary SLN in 42 patients (total of 79 metastatic SLNs). Positivity, detection rate of SLNs, detection rate of metastatic SLNs, and the number of metastatic SLNs detected were comparable with RI+MB and ICG+MB (P&amp;gt;0.05). The mean number of axillary SLN detected was significantly higher with ICG+MB than with RI+MB (4.99±2.42 vs. 4.02±2.33, P&amp;lt;0.001). No tracer-related adverse events occurred.Conclusion: ICG appears to be a safe and effective axillary SLN tracer, and a feasible alternative to RI in combined imaging for axillary SLN of breast cancer. Citation Format: Yuting Jin, Long Yuan, Jun Jiang, Xiaowei Qi. A prospective self-controlled study of indocyanine green instead of radioisotope for axillary sentinel lymph nodes in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-01-13.

A Prospective Self-Controlled Study of Indocyanine Green, Radioisotope, and Methylene Blue for Combined Imaging of Axillary Sentinel Lymph Nodes in Breast Cancer.

PurposeThis self-controlled study aimed to clarify whether indocyanine green (ICG) could be an alternative tracer in the absence of radioisotope (RI) for combined imaging of axillary sentinel lymph node (SLN) in breast cancer.MethodsPrimary breast cancer, clinically axillary node-negative patients (n = 182) were prospectively enrolled from March 2015 to November 2020. ICG, methylene blue (MB), and RI were used to perform axillary sentinel lymph node biopsy (SLNB). The main observation index was the positivity of ICG + MB vs. RI + MB in axillary SLNB; the secondary observation indicators were the axillary SLN detection rate, mean number of axillary SLNs detected, mean number of metastatic axillary SLNs detected, and safety.ResultsAll 182 patients had axillary SLNs; a total of 925 axillary SLNs were detected. Pathological examination confirmed metastatic axillary SLN in 42 patients (total of 79 metastatic SLNs). Positivity, detection rate of SLNs, detection rate of metastatic SLNs, and the number of metastatic SLNs detected were comparable with RI+MB and ICG+MB (p > 0.05). The mean number of axillary SLNs detected was significantly higher with ICG+MB than with RI+MB (4.99 ± 2.42 vs. 4.02 ± 2.33, p < 0.001). No tracer-related adverse events occurred.ConclusionsICG appears to be a safe and effective axillary SLN tracer, and a feasible alternative to RI in combined imaging for axillary SLN of breast cancer.

Advanced diagnostic imaging of sentinel lymph node in early stage breast cancer.

Sentinel lymph node biopsy has been regarded as the standard procedure for early staging breast cancer. One of the key steps is to locate the sentinel lymph node (SLN). The recommended method is the joint use of blue dye and radioisotope. However, due to radionuclide radiation and high cost, it is urgent to develop more convenient and sensitive imaging methods to accurately locate SLN. This article discusses the advancement of accurately locating SLN by isotope tracer imaging, magnetic tracer method, computed tomographic lymphography, and trans‐lymphatic contrast‐enhanced ultrasound, as well as proposing new propose for clinical diagnosis.

The possibilities of ultrasound elastography in diagnostics of lymphomatous changes of superficial lymph nodes

Objective: to evaluate the possibilities of strain elastography (SE) in eSie Touch mode and shear wave elastography (SWE) with ARFI (Acoustic Radiation Force Impulse) technology in Virtual TouchTM Tissue Imaging (VTI) for diagnostics lymphomatous superficial lymph nodes (LN).Materials and methods. The prospective study included 168 patients with enlarged superficial LN. Based on a previous histological examination, patients were divided into three groups: group 1 (n = 108) – patients with lymphomas; group 2 (n = 30) – patients with metastatic superficial LN; group 3 (n = 30) – patients with reactive (inflammatory) changes in superficial LN. All patients underwent SE and SWE elastography of the enlarged LN using eSie Touch and VTI modes respectively.Results. The SE data: reactive LN was characterized by elastotypes 1 and 2 with a predominance of elastic structure in 90.0 % of cases. In patients with lymphomas, the elastotype 3 was more often determined (58.3 %). The stiffest LN with the elastotype 4 were metastatic LN, detected in 66.7 % of patients from this group. The results of VTI elastography showed that the stiffest structure is typical for metastatic LN (elastotypes 5–6 in 63.3 % of cases). Lymphomas had an intermediate degree of stiffness (elastotypes 3–4 in 81.5 % of cases). The lowest indicators of elasticity were found in patients with reactive LN (elastotypes 1–2 in 73.4 % of cases).Conclusion. The qualitative assessment of superficial LN stiffness, both using SE and VTI elastography, demonstrated statistically significant differences in the frequency of various types of superficial LN elastograms with lymphomatous, metastatic and reactive changes, which allows for more accurate differential diagnosis between these types of lymphadenopathy.

A Comprehensive Grading System for a Magnetic Sentinel Lymph Node Biopsy Procedure in Head and Neck Cancer Patients.

Simple SummaryWith 30% of clinically negative early-stage oral cancer patients harboring occult metastasis, an accurate staging of metastatic lymph nodes (LN) is of utmost importance for treatment planning. A magnetic sentinel lymph node biopsy (SLNB) procedure is offered as an alternative to conventional SLNB in oral oncology, however, a grading system is missing. A proper grading system is preferred to connect the different components of the magnetic SLNB: preoperative imaging, intraoperative detection, and histopathological examination of sentinel lymph nodes (SLNs). This study aims to provide a first grading system based on the distribution of a magnetic tracer, by means of preoperative magnetic resonance imaging (MRI), intraoperative estimation of iron content, and histopathological assessment of resected nodes. Pre- and post-operative MRI and harvested SLNs of eight tongue cancer patients with successful magnetic SLNB procedure were used for analyses.A magnetic sentinel lymph node biopsy ((SLN)B) procedure has recently been shown feasible in oral cancer patients. However, a grading system is absent for proper identification and classification, and thus for clinical reporting. Based on data from eight complete magnetic SLNB procedures, we propose a provisional grading system. This grading system includes: (1) a qualitative five-point grading scale for MRI evaluation to describe iron uptake by LNs; (2) an ex vivo count of resected SLN with a magnetic probe to quantify iron amount; and (3) a qualitative five-point grading scale for histopathologic examination of excised magnetic SLNs. Most SLNs with iron uptake were identified and detected in level II. In this level, most variance in grading was seen for MRI and histopathology; MRI and medullar sinus were especially highly graded, and cortical sinus was mainly low graded. On average 82 ± 58 µg iron accumulated in harvested SLNs, and there were no significant differences in injected tracer dose (22.4 mg or 11.2 mg iron). In conclusion, a first step was taken in defining a comprehensive grading system to gain more insight into the lymphatic draining system during a magnetic SLNB procedure.

Fabrication of semiconducting polymer-blend dots with strong near-infrared fluorescence and afterglow luminescence for bioimaging

Fluorescence imaging at longer wavelength and autofluorescence-free imaging were two main strategies to improve the optical imaging performance of traditional near-infrared (NIR) fluorescence imaging, and the design of corresponding imaging agents were heavily explored recently. Herein, polymer-blend dots (PBdots) with strong fluorescence and afterglow luminescence in both NIR-I (700–900 nm) and NIR-II (900–1700 nm) windows were successfully fabricated by encapsulating two functional semiconducting polymers into one nanoparticle. With efficient intra-particle interactions, the PBdots exhibited an afterglow lifetime of 181 s after 730 nm NIR laser activation, which can be activated in vivo. The obtained PBdots with similar emission intensities in NIR-I and NIR-II regions enable fair comparison between different optical imaging modalities, which provides a clear guidance for the choice of suitable imaging modality for different purpose. As the first imaging agents which integrate NIR-II fluorescence imaging and NIR-I afterglow imaging, the PBdots also provide both high resolution and large signal-to-background ratio for blood vessel, lymph node and tumor imaging in living mice.

Diagnosis of lymph node metastasis in head and neck squamous cell carcinoma using deep learning.

BackgroundTo build an automatic pathological diagnosis model to assess the lymph node metastasis status of head and neck squamous cell carcinoma (HNSCC) based on deep learning algorithms.Study DesignA retrospective study.MethodsA diagnostic model integrating two‐step deep learning networks was trained to analyze the metastasis status in 85 images of HNSCC lymph nodes. The diagnostic model was tested in a test set of 21 images with metastasis and 29 images without metastasis. All images were scanned from HNSCC lymph node sections stained with hematoxylin–eosin (HE).ResultsIn the test set, the overall accuracy, sensitivity, and specificity of the diagnostic model reached 86%, 100%, and 75.9%, respectively.ConclusionsOur two‐step diagnostic model can be used to automatically assess the status of HNSCC lymph node metastasis with high sensitivity.Level of evidenceNA.

Ipsilateral Lymphadenopathy After COVID-19 Vaccination in Patients With Newly Diagnosed Breast Cancer.

This study aimed to evaluate the imaging and pathological findings in axillary lymph nodes in patients with breast cancer who received concurrent ipsilateral coronavirus disease 2019 (COVID-19) vaccination. Of the 19 women with breast cancer who received concurrent COVID-19 vaccination shot in the arm ipsilateral to breast cancer, axillary lymphadenopathy was observed in 84.2% (16 of 19) of patients on ultrasound (US) and 71.4% (10 of 14) of patients on magnetic resonance imaging (MRI), and 21.0% (4 of 19) of patients were diagnosed with metastasis. Abnormal US and MRI findings of cortical thickening, effacement of the fatty hilum, round shape, and asymmetry in the number or size relative to the contralateral side were noted in more than half of the non-metastatic and metastatic lymph nodes; however, statistical significance was not noted. Axillary lymphadenopathy is commonly observed in patients with breast cancer who receive concurrent ipsilateral COVID-19 vaccination without specific differential imaging features. Thus, understanding the limitations of axillary imaging and cautious interpretation is necessary to avoid overestimation or underestimation of the axillary disease burden.

Evaluation of perfluoropropane (C3F8)-filled chitosan polyacrylic acid nanobubbles for ultrasound imaging of sentinel lymph nodes and tumors.

Accurate sentinel lymph node (SLN) identification is an important prerequisite for sentinel lymph node biopsy (SLNB). However, existing SLN mapping techniques, mainly imaging-guided methods, are severely restricted by the high cost of the instruments, harmful radiation or unsatisfactory imaging depths. Herein, we prepared a new ultrasound contrast agent by filling perfluoropropane (C3F8) into chitosan polyacrylic acid nanobubbles for precise SLN identification. The obtained ultrasound contrast agent, coined C3F8-CS-PAA nanobubbles, presents a nanometer size with a diameter of approximately 120 nm. The C3F8-CS-PAA nanobubbles of desirable size are able to enter lymphatic vessels and accumulate in the sentinel lymph node to enhance ultrasound imaging. As a result, the injection of C3F8-CS-PAA nanobubbles can remarkably enhance the ultrasound imaging lymph system, providing image guidance for sentinel lymph node biopsy. Furthermore, it was shown that such C3F8-CS-PAA nanobubbles can effectively permeate into the tumor region via the tumor-enhanced permeability and retention (EPR) effect to enhance tumor ultrasound imaging for monitoring tumorigenesis. This work highlights a novel nanoscale ultrasound contrast agent for the lymphatic system and tumor imaging, with great promise for subsequent studies and clinical applications.

Drug Release from Gelsolin-Targeted Phase-Transition Nanoparticles Triggered by Low-Intensity Focused Ultrasound.

PurposeCurrent strategies for tumour-induced sentinel lymph node detection and metastasis therapy have limitations. It is essential to identify and provide warnings earlier for tumour metastasis to carry out effective clinical interventions. In addition, traditional cancer chemotherapy encounters drastic limitations due to the nonspecific delivery of antitumour drugs and severe side effects. We aimed to exploit the potential of gelsolin (GSN) monoclonal antibody as a targeting agent and perfluorohexane (PFH) as a phase-transition agent to maximize the cytotoxic effect of poly(lactic-co-glycolic acid) (PLGA) nanoparticle-based drug controllable release systems for Hca-F cells.MethodsWe co-encapsulated PFH and doxorubicin (DOX) into PLGA nanoparticles (NPs) and further conjugated GSN monoclonal antibody onto the surface of NPs to form GSN-targeted phase transition polymer NPs (GSN-PLGA-PFH-DOX) for both imaging and therapy of tumours and metastatic lymph nodes. To promote and trigger drug release on demand, low-intensity focused ultrasound (LIFU) was applied to achieve a controllable release of the encapsulated drug.ResultsGSN-PLGA-PFH-DOX NPs exhibited characteristics such as a narrow size distribution and smooth surface. GSN-PLGA-PFH-DOX NPs could also specifically bind to Hca-F cells and increase the ultrasound contrast agent (UCA) image contrast intensity. GSN-PLGA-PFH-DOX NPs enable GSN-mediated targeting and biotherapeutic effects as well as LIFU-responsive drug release, resulting in synergistic cytotoxic effects in GSN-overexpressing cells in vitro.ConclusionOur work might provide a strategy for the imaging and chemotherapy of primary tumours and their metastases.