This study was supported by the National Natural Science Foundation of China (Grant No.32101188) and the General Project of Science and Technology Department of Sichuan Province (Grant No. 2021YFS0102), China.

This retrospective study was approved by the institutional review board of the West China Hospital, Sichuan University, Sichuan, China, and the requirement to obtain informed consent was waived.
1. Environment setup
<ol>
	<li>Graphic processing unit (GPU) software
	<ol>
		<li>To implement deep learning applications, first configure the GPU-related environment. Download and install GPU-appropriate software and drivers from the GPU&#39;s website. 
		&#8203;NOTE: See the Table of Materials for those used in this study.</li>
	</ol>
	</li>
	<li>Python3.8 installation
	<ol>
		<li>Open a terminal on the machine. Type the following: 
		Command line: sudo apt-get install python3.8 python-dev python-virtualenv</li>
	</ol>
	</li>
	<li>Pytorch1.7 installation
	<ol>
		<li>Follow the steps on the official website to download and install Miniconda.</li>
		<li>Create a conda environment and activate it. 
		Command line: conda create --name SwinFasterRCNN python=3.8 -y 
		Command line: conda activate SwinFasterRCNN</li>
		<li>Install Pytorch. 
		Command line: conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 </li>
	</ol>
	</li>
	<li>MMDetection installation
	<ol>
		<li>Clone from the official Github repository. 
		Command line: git clone https://github.com/open-mmlab/mmdetection.git</li>
		<li>Install MMDetection. 
		Command line: cd mmdetection 
		Command line: pip install -v -e .</li>
	</ol>
	</li>
</ol>
2. Data preparation
<ol>
	<li>Data collection
	<ol>
		<li>Collected the ultrasound images (here, 3,000 cases from a Grade-A tertiary hospital). Ensure that each case has diagnostic records, treatment plans, US reports, and the corresponding US images.</li>
		<li>Place all the US images in a folder named &#34;images.&#34; 
		NOTE: The data used in this study included 3,853 US images from 3,000 cases.</li>
	</ol>
	</li>
	<li>Data cleaning
	<ol>
		<li>Manually check the dataset for images of non-thyroid areas, such as lymph images.</li>
		<li>Manually check the dataset for images containing color Doppler flow.</li>
		<li>Delete the images selected in the previous two steps. 
		NOTE: After data cleaning, 3,000 images were left from 2,680 cases.</li>
	</ol>
	</li>
	<li>Data annotation
	<ol>
		<li>Have a senior doctor locate the nodule area in the US image and outline the nodule boundary. 
		NOTE: The annotation software and process can be found in Supplemental File 1.</li>
		<li>Have another senior doctor review and revise the annotation results.</li>
		<li>Place the annotated data in a separate folder called &#34;Annotations.&#34;</li>
	</ol>
	</li>
	<li>Data split
	<ol>
		<li>Run the python script, and set the path of the image in step 2.1.2 and the paths of the annotations in step 2.3.3. Randomly divide all the images and the corresponding labeled files into training and validation sets at a ratio of 8:2. Save the training set data in the &#34;Train&#34; folder and the validation set data in the &#34;Val&#34; folder. 
		NOTE: Python scripts are provided in Supplemental File 2.</li>
	</ol>
	</li>
	<li>Converting to the CoCo dataset format 
	NOTE: To use MMDetection, process the data into a CoCo dataset format, which includes a json file that holds the annotation information and an image folder containing the US images.
	<ol>
		<li>Run the python script, and input the annotations folder paths (step 2.3.3) to extract the nodule areas outlined by the doctor and convert them into masks. Save all the masks in the &#34;Masks&#34; folder. 
		NOTE: The Python scripts are provided in Supplemental File 3.</li>
		<li>Run the python script, and set the path of the masks folder in step 2.5.1 to make the data into a dataset in CoCo format and generate a json file with the US images. 
		NOTE: Python scripts are provided in Supplemental File 4.</li>
	</ol>
	</li>
</ol>
3. Swin Faster RCNN configuration
<ol>
	<li>Download the Swin Transformer model file (https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py), modify it, and place it in the &#8220;mmdetection/mmdet/models/backbones/&#8221; folder. Open the &#8220;swin_transformer.py&#8221; file in a vim text editor, and modify it as the Swin Transformer model file provided in&#160;Supplemental File 5. 
	Command line: vim swin_transformer.py</li>
	<li>Make a copy of the Faster R-CNN configuration file, change the backbone to Swin Transformer, and set up the FPN parameters. 
	Command line: cd mmdetection/configs/faster_rcnn 
	Command line: cp faster_rcnn_r50_fpn_1x_coco.py swin_faster_rcnn_swin.py 
	NOTE: The Swin Faster R-CNN configuration file (swin_faster_rcnn_swin.py) is provided in Supplemental File 6. The Swin Faster R-CNN network structure is shown in Figure 1.</li>
	<li>Set the dataset path to the CoCo format dataset path (step 2.5.2) in the configuration file. Open the &#34;coco_detection.py&#34; file in the vim text editor, and modify the following line: 
	data_root = &#34;dataset path(step 2.5.2)&#34; 
	Command line:vim mmdetection/configs/_base_/datasets/coco_detection.py</li>
</ol>
4. Training the Swin Faster R-CNN
<ol>
	<li>Edit mmdetection/configs/_base_/schedules/schedule_1x.py, and set the default training-related parameters, including the learning rate, optimizer, and epoch. Open the &#34;schedule_1x.py&#34; file in the vim text editor, and modify the following lines: 
	optimizer = dict(type=&#34;AdamW&#34;, lr=0.001, momentum=0.9, weight_decay=0.0001) 
	runner = dict(type=&#39;EpochBasedRunner&#39;, max_epochs=48) 
	Command line:vim mmdetection/configs/_base_/schedules/schedule_1x.py 
	NOTE: In this protocol for this paper, the learning rate was set to 0.001, AdamW optimizer was used, the maximum training epoch was set to 48, and the batch size was set to 16.</li>
	<li>Begin training by typing the following commands. Wait for the network to begin training for 48 epochs and for the resulting trained weights of the Swin Faster R-CNN network to be generated in the output folder. Save the model weights with the highest accuracy on the validation set. 
	Command line: cd mmdetection 
	Command line: python tools/train.py congfigs/faster_rcnn/swin_faster_rcnn_swin.py --work-dir ./work_dirs 
	NOTE: The model was trained on an &#34;NVIDIA GeForce RTX3090 24G&#34; GPU. The central processing unit used was the &#34;AMD Epyc 7742 64-core processor &#215; 128&#34;, and the operating system was Ubuntu 18.06. The overall training time was ~2 h.</li>
</ol>
5. Performing thyroid nodule detection on new images
<ol>
	<li>After training, select the model with the best performance on the validation set for thyroid nodule detection in the new images.
	<ol>
		<li>First, resize the image to 512 pixels x 512 pixels, and normalize it. These operations are performed automatically when the test script is run. 
		Command line: python tools/test.py congfigs/faster_rcnn/swin_faster_rcnn_swin.py --out ./output</li>
		<li>Wait for the script to automatically load the pretrained model parameters to the Swin Faster R-CNN, and feed the preprocessed image into the Swin Faster R-CNN for inference. Wait for the Swin Faster R-CNN to output the prediction box for each image.</li>
		<li>Finally, allow the script to automatically perform NMS postprocessing on each image to remove duplicate detection boxes. 
		NOTE: The detection results are output to the specified folder, which contains the images with the detection boxes and the bounding box coordinates in a packed file.</li>
	</ol>
	</li>
</ol>

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The authors declare no conflicts of interest.

This paper describes in detail how to perform the environment setup, data preparation, model configuration, and network training. In the environment setup phase, one needs to pay attention to ensure that the dependent libraries are compatible and matched. Data processing is a very important step; time and effort must be spent to ensure the accuracy of the annotations. When training the model, a &#34;ModuleNotFoundError&#34; may be encountered. In this case, it is necessary to use the &#34;pip install&#34; command to install the missing library. If the loss of the validation set does not decrease or oscillates greatly, one should check the annotation file and try to adjust the learning rate and batch size to make the loss converge.
Thyroid nodule detection is very important for the treatment of thyroid cancer. The CAD system can assist doctors in the detection of nodules, avoid differences in diagnosis results caused by subjective factors, and reduce the missed detection of nodules. Compared with existing CNN-based CAD systems, the network proposed in this paper introduces the Swin Transformer to extract ultrasound image features. By capturing long-distance dependencies, Swin Faster R-CNN can extract the nodule features from ultrasound images more efficiently. The experimental results show that Swin Faster R-CNN improves the sensitivity of nodule detection by ~3% compared to CNN backbone-based Faster R-CNN. The application of this technology can greatly reduce the burden on doctors, as it can detect thyroid nodules in early ultrasound examination and guide doctors to further treatment. However, due to the large number of parameters of the Swin Transformer, the inference time of Swin Faster R-CNN is ~100 ms per image (tested on NVIDIA TITAN 24G GPU and AMD Epyc 7742 CPU). It can be challenging to meet the requirements of real-time diagnosis with Swin Faster R-CNN. In the future, we will continue to collect cases to verify the effectiveness of this method and conduct further studies on dynamic ultrasound image analysis.

The incidence of thyroid cancer has increased rapidly since 1970, especially among middle-aged women1. Thyroid nodules may predict the emergence of thyroid cancer, and most thyroid nodules are asymptomatic2. The early detection of thyroid nodules is very helpful in curing thyroid cancer. Therefore, according to current practice guidelines, all patients with suspected nodular goiter on physical examination or with abnormal imaging findings should undergo further examination3,4.
Thyroid ultrasound (US) is a common method used to detect and characterize thyroid lesions5,6. US is a convenient, inexpensive, and radiation-free technology. However, the application of US is easily affected by the operator7,8. Features such as the shape, size, echogenicity, and texture of thyroid nodules are easily distinguishable on US images. Although certain US features-calcifications, echogenicity, and irregular borders-are often considered criteria for identifying thyroid nodules, the presence of interobserver variability is unavoidable8,9. The diagnosis results of radiologists with different levels of experience are different. Inexperienced radiologists are more likely to misdiagnose than experienced radiologists. Some characteristics of US such as reflections, shadows, and echoes can degrade the image quality. This degradation in image quality caused by the nature of US imaging makes it difficult for even experienced physicians to locate nodules accurately.
Computer-aided diagnosis (CAD) for thyroid nodules has developed rapidly in recent years and can effectively reduce errors caused by different physicians and help radiologists diagnose nodules quickly and accurately10,11. Various CNN-based CAD systems have been proposed for thyroid US nodule analysis, including segmentation12,13, detection14,15, and classification16,17. CNN is a multilayer, supervised learning model18, and the core modules of CNN are the convolution and pooling layers. The convolution layers are used for feature extraction, and the pooling layers are used for downsampling. The shadow convolutional layers can extract primary features such as the texture, edges, and contours, while deep convolutional layers learn high-level semantic features.
CNNs have had great success in computer vision19,20,21. However, CNNs fail to capture long-range contextual dependencies due to the limited valid receptive field of the convolutional layers. In the past, backbone architectures for image classification mostly used CNNs. With the advent of Vision Transformer (ViT)22,23, this trend has changed, and now many state-of-the-art models use transformers as backbones. Based on non-overlapping image patches, ViT uses a standard transformer encoder25 to globally model spatial relationships. The Swin Transformer24 further introduces shift windows to learn features. The shift windows not only bring greater efficiency but also greatly reduce the length of the sequence because self-attention is calculated in the window. At the same time, the interaction between two adjacent windows can be made through the operation of shifting (movement). The successful application of the Swin Transformer in computer vision has led to the investigation of transformer-based architectures for ultrasound image analysis26.
Recently, Li et al. proposed a deep learning approach28 for thyroid papillary cancer detection inspired by Faster R-CNN27. Faster R-CNN is a classic CNN-based object detection architecture. The original Faster R-CNN has four modules-the CNN backbone, the region proposal network (RPN), the ROI pooling layer, and the detection head. The CNN backbone uses a set of basic conv+bn+relu+pooling layers to extract feature maps from the input image. Then, the feature maps are fed into the RPN and the ROI pooling layer. The role of the RPN network is to generate region proposals. This module uses softmax to determine whether anchors are positive and generates accurate anchors by bounding box regression. The ROI pooling layer extracts the proposal feature maps by collecting the input feature maps and proposals and feeds the proposal feature maps into the subsequent detection head. The detection head uses the proposal feature maps to classify objects and obtain accurate positions of the detection boxes by bounding box regression.
This paper presents a new thyroid nodule detection network called Swin Faster R-CNN formed by replacing the CNN backbone in Faster R-CNN with the Swin Transformer, which results in the better extraction of features for nodule detection from ultrasound images. In addition, the feature pyramid network (FPN)29 is used to improve the detection performance of the model for nodules of different sizes by aggregating features of different scales.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>GPU RTX3090</td>
			<td>Nvidia</td>
			<td>1</td>
			<td>24G GPU</td>
		</tr>
		<tr>
			<td>mmdetection2.11.0</td>
			<td>SenseTime</td>
			<td>4</td>
			<td>https://github.com/open-mmlab/mmdetection.git</td>
		</tr>
		<tr>
			<td>python3.8</td>
			<td>&#8212;</td>
			<td>2</td>
			<td>https://www.python.org</td>
		</tr>
		<tr>
			<td>pytorch1.7.1</td>
			<td>Facebook</td>
			<td>3</td>
			<td>https://pytorch.org</td>
		</tr>
	</tbody>
</table>

a swin transformer-based model for thyroid nodule detection in ultrasound images

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid cancer. Convolutional neural networks (CNNs) have achieved good results in thyroid ultrasound image analysis tasks. However, due to the limited valid receptive field of convolutional layers, CNNs fail to capture long-range contextual dependencies, which are important for identifying thyroid nodules in ultrasound images. Transformer networks are effective in capturing long-range contextual information. Inspired by this, we propose a novel thyroid nodule detection method that combines the Swin Transformer backbone and Faster R-CNN. Specifically, an ultrasound image is first projected into a 1D sequence of embeddings, which are then fed into a hierarchical Swin Transformer. 
The Swin Transformer backbone extracts features at five different scales by utilizing shifted windows for the computation of self-attention. Subsequently, a feature pyramid network (FPN) is used to fuse the features from different scales. Finally, a detection head is used to predict bounding boxes and the corresponding confidence scores. Data collected from 2,680 patients were used to conduct the experiments, and the results showed that this method achieved the best mAP score of 44.8%, outperforming CNN-based baselines. In addition, we gained better sensitivity (90.5%) than the competitors. This indicates that context modeling in this model is effective for thyroid nodule detection.

The thyroid US images were collected from two hospitals in China from September 2008 to February 2018. The eligibility criteria for including the US images in this study were conventional US examination before biopsy and surgical treatment, diagnosis with biopsy or postsurgical pathology, and age &#8805; 18 years. The exclusion criteria were images without thyroid tissues.
The 3,000 ultrasound images included 1,384 malignant and 1,616 benign nodules. The majority (90%) of the malignant nodules were papillary carcinoma, and 66% of the benign nodules were nodular goiter. Here, 25% of the nodules were smaller than 5 mm, 38% were between 5 mm and 10 mm, and 37% were larger than 10 mm.
All the US images were collected using Philips IU22 and DC-80, and their default thyroid examination mode was used. Both instruments were equipped with 5-13 MHz linear probes. For good exposure of the lower thyroid margins, all the patients were examined in the supine position with their backs extended. Both thyroid lobes and the isthmus were scanned in the longitudinal and transverse planes according to the American College of Radiology accreditation standards. All the examinations were carried out by two senior thyroid radiologists with &#8805;10 years of clinical experience. The thyroid diagnosis was based on the histopathological findings from fine needle aspiration biopsy or thyroid surgery.
In real life, as US images are corrupted by noise, it is important to conduct proper preprocessing of the US images, such as image denoising based on wavelet transform30, compressive sensing31, and histogram equalization32. In this work, we used histogram equalization to preprocess the US images, enhance image quality, and alleviate image quality degradation caused by noise.
In what follows, true positive, false positive, true negative, and false negative are referred to as TP, FP, TN, and FN, respectively. We used mAP, sensitivity, and specificity to evaluate the model&#39;s nodule detection performance. mAP is a common metric in object detection. Sensitivity and specificity were calculated using equation (1) and equation (2):
<img alt="Equation 1" src="/files/ftp_upload/64480/64480eq01v2.jpg" style="margin: auto;" /> (1)
<img alt="Equation 2" src="/files/ftp_upload/64480/64480eq02v2.jpg" style="margin: auto;" /> (2)
In this paper, TP is defined as the number of correctly detected nodules, which have an intersection over union (IoU) between the prediction box and the ground truth box of &#62;0.3 and a confidence score &#62;0.6. IoU is the intersection over union, which is computed by using equation (3):
<img alt="Equation 3" src="/files/ftp_upload/64480/64480eq03.jpg" style="margin: auto;" /> (3)
We compared several classic object detection networks, including SSD33, YOLO-v334, CNN backbone-based Faster R-CNN27, RetinaNet35, and DETR36. YOLO-v3 and SSD are single-stage detection networks, DETR is a transformer-based object-detection network, and Faster R-CNN and RetinaNet are two-stage detection networks. Table 1 shows that the performance of Swin Faster R-CNN is superior to the other methods, reaching 0.448&#160;mAP, which is 0.028 higher than CNN backbone&#39;s Faster R-CNN and 0.037 higher than YOLO-v3. By using Swin Faster R-CNN, 90.5% of thyroid nodules can be detected automatically, which is ~3% higher than CNN backbone-based Faster R-CNN (87.1%). As shown in Figure 2, using Swin Transformer as the backbone makes boundary positioning more accurate.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64480/64480fig01.jpg" /> 
Figure 1: Diagram of the Swin Faster R-CNN network architecture. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/64480fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64480/64480fig02.jpg" /> 
Figure 2: Detection results. The detection results for the same image are in a given row. The columns are the detection results, from left to right, for Swin Faster R-CNN, Faster R-CNN, YOLO-v3, SSD, RetinaNet, and DETR, respectively. The ground truths of the regions are marked with green rectangular boxes. The detection results are framed by the red rectangular boxes. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/64480fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Method</td>
			<td>Backbone</td>
			<td>mAP</td>
			<td>Sensitivity</td>
			<td>Specificity</td>
		</tr>
		<tr>
			<td>YOLO-v3</td>
			<td>DarkNet</td>
			<td>0.411</td>
			<td>0.869</td>
			<td>0.877</td>
		</tr>
		<tr>
			<td>SSD</td>
			<td>VGG16</td>
			<td>0.425</td>
			<td>0.841</td>
			<td>0.849</td>
		</tr>
		<tr>
			<td>RetinaNet</td>
			<td>ResNet50</td>
			<td>0.382</td>
			<td>0.845</td>
			<td>0.841</td>
		</tr>
		<tr>
			<td>Faster R-CNN</td>
			<td>ResNet50</td>
			<td>0.42</td>
			<td>0.871</td>
			<td>0.864</td>
		</tr>
		<tr>
			<td>DETR</td>
			<td>ResNet50</td>
			<td>0.416</td>
			<td>0.882</td>
			<td>0.86</td>
		</tr>
		<tr>
			<td>Swin Faster R-CNN without FPN</td>
			<td rowspan="2">Swin Transformer</td>
			<td>0.431</td>
			<td>0.897</td>
			<td>0.905</td>
		</tr>
		<tr>
			<td>Swin Faster R-CNN with FPN</td>
			<td>0.448</td>
			<td>0.905</td>
			<td>0.909</td>
		</tr>
	</tbody>
</table>
Table 1: Performance comparison with state-of-the-art object detection methods.
Supplemental File 1:&#160;Operating instructions for the data annotation and the software used. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/64480_Supp File 1_data-annotation.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Python script used to divide the dataset into the training set and validation set, as mentioned in step 2.4.1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/Supp File 2_train_val_split.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 3: Python script used to convert the annotations file into masks, as mentioned in step 2.5.1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/Supp File 3_marks2masks.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 4: Python script used to make the data into a dataset in CoCo format, as mentioned in step 2.5.2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/Supp File 4_masks2coco.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 5:&#160;The modified Swin Transformer model file mentioned in step 3.1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/Supp File 5_swin_transformer.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 6: The Swin Faster R-CNN configuration file mentioned in step 3.2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64480/Supp File 6_swin_faster_rcnn_swin.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images at JoVE.com

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Here, a new model for thyroid nodule detection in ultrasound images is proposed, which uses Swin Transformer as the backbone to perform long-range context modeling. Experiments prove that it performs well in terms of sensitivity and accuracy.

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid ...

a-swin-transformer-based-model-for-thyroid-nodule-detection

Nanyang Technological University

Research

JoVE Journal

Medicine

1.7K Views.  West China Hospital of Sichuan University. Here, a new model for thyroid nodule detection in ultrasound images is proposed, which uses Swin Transformer as the backbone to perform long-range context modeling. Experiments prove that it performs well in terms of sensitivity and accuracy.

Video: A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound

Angiogenesis is an essential feature of cancer growth and metastasis formation. In bone metastasis, angiogenic factors are pivotal for tumor cell proliferation in the bone marrow cavity as well as for interaction of tumor and bone cells resulting in local bone destruction. Our aim was to develop a model of experimental bone metastasis that allows in vivo assessment of angiogenesis in skeletal lesions using non-invasive imaging techniques.
For this purpose, we injected 105 MDA-MB-231 human breast cancer cells into the superficial epigastric artery, which precludes the growth of metastases in body areas other than the respective hind leg1. Following 25-30 days after tumor cell inoculation, site-specific bone metastases develop, restricted to the distal femur, proximal tibia and proximal fibula1. Morphological and functional aspects of angiogenesis can be investigated longitudinally in bone metastases using magnetic resonance imaging (MRI), volumetric computed tomography (VCT) and ultrasound (US).
MRI displays morphologic information on the soft tissue part of bone metastases that is initially confined to the bone marrow cavity and subsequently exceeds cortical bone while progressing. Using dynamic contrast-enhanced MRI (DCE-MRI) functional data including regional blood volume, perfusion and vessel permeability can be obtained and quantified2-4. Bone destruction is captured in high resolution using morphological VCT imaging. Complementary to MRI findings, osteolytic lesions can be located adjacent to sites of intramedullary tumor growth. After contrast agent application, VCT angiography reveals the macrovessel architecture in bone metastases in high resolution, and DCE-VCT enables insight in the microcirculation of these lesions5,6. US is applicable to assess morphological and functional features from skeletal lesions due to local osteolysis of cortical bone. Using B-mode and Doppler techniques, structure and perfusion of the soft tissue metastases can be evaluated, respectively. DCE-US allows for real-time imaging of vascularization in bone metastases after injection of microbubbles7.
In conclusion, in a model of site-specific breast cancer bone metastases multi-modal imaging techniques including MRI, VCT and US offer complementary information on morphology and functional parameters of angiogenesis in these skeletal lesions.

In the pathogenesis of bone metastasis, angiogenesis is a crucial process and therefore represents a target for imaging and therapy. Here, we present a rat model of site-specific breast cancer bone metastasis and describe strategies to non-invasively image angiogenesis in vivo using magnetic resonance imaging, volumetric computed tomography and ultrasound.

Angiogenesis is an essential feature of cancer growth and metastasis formation. In bone metastasis, angiogenic factors are pivotal for tumor cell ...

Cerenkov Luminescence Imaging (CLI) for Cancer Therapy Monitoring

In molecular imaging, positron emission tomography (PET) and optical imaging (OI) are two of the most important and thus most widely used modalities1-3. PET is characterized by its excellent sensitivity and quantification ability while OI is notable for non-radiation, relative low cost, short scanning time, high throughput, and wide availability to basic researchers. However, both modalities have their shortcomings as well. PET suffers from poor spatial resolution and high cost, while OI is mostly limited to preclinical applications because of its limited tissue penetration along with prominent scattering optical signals through the thickness of living tissues.
 Recently a bridge between PET and OI has emerged with the discovery of Cerenkov Luminescence Imaging (CLI)4-6. CLI is a new imaging modality that harnesses Cerenkov Radiation (CR) to image radionuclides with OI instruments. Russian Nobel laureate Alekseyevich Cerenkov and his colleagues originally discovered CR in 1934. It is a form of electromagnetic radiation emitted when a charged particle travels at a superluminal speed in a dielectric medium7,8. The charged particle, whether positron or electron, perturbs the electromagnetic field of the medium by displacing the electrons in its atoms. After passing of the disruption photons are emitted as the displaced electrons return to the ground state. For instance, one 18F decay was estimated to produce an average of 3 photons in water5. 
 Since its emergence, CLI has been investigated for its use in a variety of preclinical applications including in vivo tumor imaging, reporter gene imaging, radiotracer development, multimodality imaging, among others4,5,9,10,11. The most important reason why CLI has enjoyed much success so far is that this new technology takes advantage of the low cost and wide availability of OI to image radionuclides, which used to be imaged only by more expensive and less available nuclear imaging modalities such as PET.
 Here, we present the method of using CLI to monitor cancer drug therapy. Our group has recently investigated this new application and validated its feasibility by a proof-of-concept study12. We demonstrated that CLI and PET exhibited excellent correlations across different tumor xenografts and imaging probes. This is consistent with the overarching principle of CR that CLI essentially visualizes the same radionuclides as PET. We selected Bevacizumab (Avastin; Genentech/Roche) as our therapeutic agent because it is a well-known angiogenesis inhibitor13,14. Maturation of this technology in the near future can be envisioned to have a significant impact on preclinical drug development, screening, as well as therapy monitoring of patients receiving treatments.

Cerenkov Luminescence Imaging CLI for Cancer Therapy Monitoring

Use of Cerenkov Luminescence Imaging (CLI) for monitoring preclinical cancer treatment is described here. This method takes advantage of Cerenkov Radiation (CR) and optical imaging (OI) to visualize radiolabeled probes and thus provides an alternative to PET in preclinical therapeutic monitoring and drug screening.

In  molecular imaging, positron emission tomography (PET) and optical imaging (OI)  are two of the most important and thus most widely used modalities1-3. ...

An Orthotopic Mouse Model of Anaplastic Thyroid Carcinoma

Several types of animal models of human thyroid carcinomas have been established, including subcutaneous xenograft and orthotopic implantation of cancer cells into immunodeficient mice. Subcutaneous xenograft models have been valuable for preclinical screening and evaluation of new therapeutic treatments. There are a number of advantages to using a subcutaneous model; 1) rapid, 2) reproducible, and 3) tumor establishment, growth, and response to therapeutic agents may be monitored by visual inspection. However, substantial evidence has shed light on the short-comings of subcutaneous xenograft models1-3. For instance, medicinal treatments demonstrating curative properties in subcutaneous xenograft models often have no notable impact on the human disease. The microenvironment of the site of xenographic transplantation or injection lies at the heart of this dissimilarity.
Orthotopic tumor xenograft models provide a more biologically relevant context in which to study the disease. The advantages of implanting diseased cells or tissue into their anatomical origin equivalent within a host animal includes a suitable site for tumor-host interactions, development of disease-related metastases and the ability to examine site-specific influence on investigational therapeutic remedies. Therefore, orthotopic xenograft models harbor far more clinical value because they closely reproduce human disease. For these reasons, a number of groups have taken advantage of an orthotopic thyroid cancer model as a research tool4-7.
Here, we describe an approach that establishes an orthotopic model for the study of anaplastic thyroid carcinoma (ATC), which is highly invasive, resists treatment, and is virtually fatal in all diagnosed patients. Cultured ATC cells are prepared as a dissociated cellular suspension in a solution containing a basement membrane matrix. A small volume is slowly injected into the right thyroid gland. Overall appearance and health of the mice are monitored to ensure minimal post-operative complications and to gauge pathological penetrance of the cancer. Mice are sacrificed at 4 weeks, and tissue is collected for histological analysis. Animals may be taken at later time-points to examine more advance progression of the disease. Production of this orthotopic mouse model establishes a platform that accomplishes two objectives: 1) further our understanding of ATC pathology, and 2) screen current and future therapeutic agents for efficacy in combating ATC.

Generation of an orthotopic mouse model of anaplastic thyroid carcinoma is described here. This technique employs surgical placement of human anaplastic thyroid cancer cells into the thyroid of immunodeficient mice, thus creating a more clinically relevant setting to study disease progression as well as screen innovative therapeutic interventions.

Several types of animal models of human thyroid carcinomas have been established, including subcutaneous xenograft and orthotopic implantation of cancer ...

Substernal Thyroid Biopsy Using Endobronchial Ultrasound-guided Transbronchial Needle Aspiration

Substernal thyroid goiter (STG) represents about 5.8% of all mediastinal lesions1. There is a wide variation in the published incidence rates due to the lack of a standardized definition for STG. Biopsy is often required to differentiate benign from malignant lesions. Unlike cervical thyroid, the overlying sternum precludes ultrasound-guided percutaneous fine needle aspiration of STG. Consequently, surgical mediastinoscopy is performed in the majority of cases, causing significant procedure related morbidity and cost to healthcare. Endobronchial Ultrasound-guided Transbronchial Needle Aspiration (EBUS-TBNA) is a frequently used procedure for diagnosis and staging of non-small cell lung cancer (NSCLC). Minimally invasive needle biopsy for lesions adjacent to the airways can be performed under real-time ultrasound guidance using EBUS. Its safety and efficacy is well established with over 90% sensitivity and specificity. The ability to perform EBUS as an outpatient procedure with same-day discharges offers distinct morbidity and financial advantages over surgery. As physicians performing EBUS gained procedural expertise, they have attempted to diversify its role in the diagnosis of non-lymph node thoracic pathologies. We propose here a role for EBUS-TBNA in the diagnosis of substernal thyroid lesions, along with a step-by-step protocol for the procedure.

Substernal thyroid lesions are common, and need to be differentiated from malignancy. Obtaining percutaneous fine needle biopsy is not possible due to its retrosternal location. This article proposes a protocol for biopsy of substernal thyroid lesions using Endobronchial Ultrasound-guided Transbronchial Needle Aspiration (EBUS-TBNA).

Substernal thyroid goiter (STG) represents about 5.8% of all mediastinal lesions1. There is a wide variation in the published incidence rates due to the ...

Intra-Operative Neural Monitoring of Thyroid Surgery in a Porcine Model

Intraoperative injury to the recurrent laryngeal nerve (RLN) can cause vocal cord paralysis, which interferes with speech and can potentially interfere with breathing. In recent years, intraoperative neural monitoring (IONM) has been widely adapted as an adjunct technique to localize the RLN, detect RLN injury, and predict vocal cord function during the operations. Many studies have also used animal models to investigate new applications of IONM technology and to develop reliable strategies for preventing intraoperative RLN injury. The aim of this article is to introduce a standard protocol for using a porcine model in IONM research. The article demonstrates the procedures for inducing general anesthesia, performing tracheal intubation, and experimental design to investigate the electrophysiological characteristics of RLN injuries. Applications of this protocol can improve overall efficacy in implementing the 3R principle (replacement, reduction and refinement) in porcine IONM studies.

This study aims to develop a standard protocol of intra-operative neural monitoring of thyroid surgery in a porcine model. Here, we present a protocol to demonstrate general anesthesia, to compare different types of electrodes, and to investigate the electrophysiological characteristics of the normal and injured recurrent laryngeal nerves.

Intraoperative injury to the recurrent laryngeal nerve (RLN) can cause vocal cord paralysis, which interferes with speech and can potentially interfere ...

Isometric Contractility Measurement of the Mouse Mesenteric Artery Using Wire Myography

The wire myograph technique is used to assess the contractility of vascular smooth muscles in response to depolarization, GPCR agonists/inhibitors and drugs. It is widely used in many studies on the physiological functions of vascular smooth muscle, the pathogenesis of vascular diseases such as hypertension, and the development of smooth muscle relaxant drugs. The mouse is a widely used model animal with a large pool of disease models and genetically modified strains. We introduced this method to measure the isometric contraction of mouse mesenteric artery in detail. A 1.4-mm segment of mouse mesenteric resistance artery was isolated and mounted on a myograph chamber by passing two steel wires through its lumen. After equilibration and normalization steps, the vessel segment was potentiated by a high-K+ solution twice prior to the contraction assay. As an example of the application of this method in drug development, we measured the relaxant effect of a novel natural substance, neoliensinine, isolated from a Chinese herb, embryos of the lotus seed (Nelumbo nucifera Gaertn.) on mouse mesenteric arteries. The vessel segments mounted on the myograph chamber were stimulated with a high-K+ solution. When the force tension reached a stable sustained phase, cumulative doses of neoliensinine were added to the chamber. We found that neoliensinine had a dose-dependent relaxant effect on smooth muscle contraction, thus suggesting that it bears potential activity against hypertension. In addition, as the vessel segment can survive at least 4 hours after mounting and maintain contractility induced by the high-K+ solution for many times, we suggest that the wire myograph system may be used for the time-consuming process of drug screening.

The wire myograph technique is used to investigate vascular smooth muscle functions and screen new drugs. We report a detailed protocol for measuring the isometric contractility of the mouse mesenteric artery and for screening new relaxants of vascular smooth muscle.

The wire myograph technique is used to assess the contractility of vascular smooth muscles in response to depolarization, GPCR agonists/inhibitors and ...

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or excision. Many of these nodules are identified and excised via minimally invasive thoracic surgery; however, subcentimeter and subsolid nodules are frequently difficult to identify intra-operatively. This can be mitigated by the use of electromagnetic transthoracic needle localization. This protocol delineates the step-by-step process of electromagnetic localization from the pre-operative period to the postoperative period and is an adaptation of the electromagnetically guided percutaneous biopsy previously described by Arias et al. Pre-operative steps include obtaining a same day CT followed by the generation of a three-dimensional virtual map of the lung. From this map, the target lesion(s) and an entry site are chosen. In the operating room, the virtual reconstruction of the lung is then calibrated with the patient and the electromagnetic navigation platform. The patient is then sedated, intubated, and placed in the lateral decubitus position. Using a sterile technique and visualization from multiple views, the needle is inserted into the chest wall at the prechosen skin entry site and driven down to the target lesion. Dye is then injected into the lesion and, then, continuously during needle withdrawal, creating a tract for visualization intra-operatively. This method has many potential benefits when compared to the CT-guided localization, including a decreased radiation exposure and decreased time between the dye injection and the surgery. Dye diffusion from the pathway occurs over time, thereby limiting intra-operative nodule identification. By decreasing the time to surgery, there is a decrease in wait time for the patient, and less time for dye diffusion to occur, resulting in an improvement in nodule localization. When compared to electromagnetic bronchoscopy, airway architecture is no longer a limitation as the target nodule is accessed via a transparenchymal approach. Details of this procedure are described in a step-by-step fashion.

Presented here is a protocol for lung nodule localization using dye marking via electromagnetically navigated transthoracic needle access. The technique described here can be accomplished in the peri-operative period to optimize nodule localization and to successful resection when performing minimally invasive thoracic surgery.

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or ...

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica

Pleural effusion is a prevalent clinical finding of many pulmonary diseases. Having a useful animal pleural effusion model is very important to study these pulmonary diseases. Previous pleural effusion models paid more attention to biological factors rather than nanoparticles in the environment. Here, we introduce a model to make pleural effusion in rats by intratracheal instillation of polyacrylate/nanosilica, and a method of nanoparticle isolation in the pleural effusion. By intratracheal instillation of polyacrylate/nanosilica with concentrations of 3.125, 6.25 and 12.5 mg/kg&#8729;mL, the pleural effusion in rats presented on day 3, peaked at days 7-10 in 6.25 and 12.5 mg/kg&#8729;mL groups, then slowly decreased and disappeared on day 14. When the concentration of polyacrylate/nanosilica increased, the pleural effusion is produced more and faster. This pleural fluid was detected by ultrasound examination or CT chest scanning and confirmed by dissection of rats. Silica nanoparticles were observed in the rats' pleural effusion by transmission electron microscope. These results showed that the exposure to polyacrylate/nanosilica leads to the induction of pleural effusion, which was consistent with our previous report in humans. Additionally, this model is beneficial for the further study of nanotoxicology and the pleural effusion diseases.

Here, we present a protocol to construct a pleural effusion model in rats by intratracheal instillation of polyacrylate/nanosilica.

Pleural effusion is a prevalent clinical finding of many pulmonary diseases. Having a useful animal pleural effusion model is very important to study ...

A Personalized 3D-Printed Model for Preoperative Evaluation in Thyroid Surgery

The anatomic structure of the surgical area of thyroid cancer is complex. It is very important to comprehensively and carefully evaluate the tumor location and its relation with the capsule, trachea, esophagus, nerves, and blood vessels before operation. This paper introduces an innovative 3D-printed model establishment method based on computerized tomography (CT) DICOM images. We established a personalized 3D-printed model of the cervical thyroid surgery field for each patient who needed thyroid surgery to help clinicians evaluate the key points and difficulties of the surgery and select the operation methods of key parts as a basis. The results showed that this model is conducive to preoperative discussion and the formulation of operation strategies. In particular, as a result of the clear display of the recurrent laryngeal nerve and parathyroid gland locations in the thyroid operation field, injury to them can be avoided during surgery, the difficulty of thyroid surgery reduced, and the incidence of postoperative hypoparathyroidism and complications related to recurrent laryngeal nerve injury reduced too. Moreover, this 3D-printed model is intuitive and aids communication for the signing of informed consent by patients before surgery.

Here, a new method of establishing a personalized 3D-printed model for preoperative evaluation of thyroid surgery is proposed. It is conducive to preoperative discussion, reducing the difficulty of thyroid surgery.

The anatomic structure of the surgical area of thyroid cancer is complex. It is very important to comprehensively and carefully evaluate the tumor ...

Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways

Airway management remains a crucial part of perioperative care. The conventional approach to assessing potentially difficult airways emphasizes the LEMON method, which looks for and evaluates the Mallampati classification, signs of obstruction, and neck mobility. Clinical findings help predict a higher likelihood of difficult tracheal intubation, but no clinical result reliably excludes difficult intubation. Ultrasound as an adjunct to clinical examination can provide the clinician with a dynamic anatomical airway assessment, which is impossible with clinical examination alone. In the hands of anesthesiologists, ultrasound is becoming more popular in the perioperative period. This method is particularly applicable for identifying proper endotracheal tube positioning in specific patient populations, such as those who are morbidly obese and patients with head and neck cancer or trauma. The focus is on identifying the normal anatomy, correctly positioning the endotracheal tube, and refining the parameters that predict difficult intubation. Several ultrasound measurements are clinical indicators of difficult direct laryngoscopy in the literature. A meta-analysis revealed that the distance from the skin to the epiglottis (DSE) is most associated with a difficult laryngoscopy. An ultrasound of the airway could be applied in routine practice as an adjunct to the clinical examination. A full stomach, rapid sequence intubation, gross visual anatomical abnormalities, and restricted neck flexibility prevent using ultrasound to assess the airway. The airway evaluation is performed with a linear array transducer of 12-4 MHz, with the patient in the supine position, with no pillow, and with the head and neck in a neutral position. The central axis of the neck is where the ultrasound parameters are measured. These image acquisitions guide the standard ultrasound examination of the airway.

A point-of-care ultrasound (POCUS) is a simple, non-invasive, and portable tool that enables dynamic airway assessment. Several studies have attempted to determine the role of ultrasound parameters as an adjunct to clinical examination in predicting difficult laryngoscopies.

Airway management remains a crucial part of perioperative care. The conventional approach to assessing potentially difficult airways emphasizes the LEMON ...