Types Of Nodules Research Articles

A Novel Integrated Prognosis & Diagnosis System for Lung Cancer Disease Detection using Soft Computing Techniques

Nowadays, lung cancer is one of the ranking first causes of mortality worldwide among men and women. Although there are a lot of treatment options like surgery, radiotherapy, and chemotherapy, five-year survival rate for patients is quite low. However, survival rate may go up to 54% in case lung cancer is identified in an early stage. Therefore, early detection of lung cancer is vital to decrease lung cancer mortality. Medical Experts are continuously trying to find the best solution for the early prediction and diagnosis of Lung Cancer Disease; in this Research work, an attempt has been made to design and develop a novel integrated soft computing predictive system to handle various types of patients’ clinical data to diagnose the lung cancer disease. Here data mining techniques are used to handle the numeric and textual data, image processing techniques are used to handle CT scan images, neural networks are used to train the lung cancer patient images, and fuzzy inference mechanism is used to predict the lung cancer stages. This integrated approach results in detection of lung cancer disease with Prognosis and suggesting diagnosis by the expert system for lung cancer disease. Even in cases of small-sized nodules (3–10 mm), the proposed system is able to determine the nodule type with 96% accuracy.

Raman-based cytopathology: an approach to improve diagnostic accuracy in medullary thyroid carcinoma.

Medullary thyroid carcinoma (MTC) is a rare form of thyroid malignancy that can be diagnostically challenging on fine needle aspiration (FNA) cytology. Ancillary tests such as elevated serum or immunohistochemical positive calcitonin have been helpful, yet they can occasionally provide false positive results. In search for an alternative method to improve diagnostic accuracy (DA), we applied hyperspectral Raman spectroscopy to characterize the biochemical composition of single cells from MTC and compared their spectral information to cells from other types of thyroid nodules. Hyperspectral Raman images of 117 MTC single cells from digested tissue were obtained with a line-scan hyperspectral Raman microscope and compared to 127 benign and 121 classic variant of papillary thyroid carcinoma (CVPTC) cells. When principal component analysis and linear discriminant analysis were used to classify the spectral data, MTC cells were differentiated from benign and CVPTC cells with 97% and 99% DA, respectively. In addition, MTC cells exhibited a prominent Raman peak at 1003 cm-1, whose intensity is 84% and 226% greater on average than that observed in benign and CVPTC cells, respectively. When specifically utilizing only this peak as a spectral marker, MTC cells were separated from benign and CVPTC cells with 87% and 95% DA, respectively. As this peak is linked to phenylalanine, which is known to be associated with calcitonin release in thyroid parafollicular cells, the increased intensity further suggests that this Raman peak could potentially be a new diagnostic marker for MTC. Furthermore, preliminary data from MTC cells (n=21) isolated from a simulated FNA procedure provided similar Raman signatures when compared to single cells from digestion. These results suggest that "Raman-based cytopathology" can be used as an adjunct technique to improve the diagnostic accuracy of FNA cytopathology at a single cell level.

Low-Dose Aspirin in High-Risk Individuals With Screen-Detected Subsolid Lung Nodules: A Randomized Phase II Trial.

Lung cancer screening by helical low-dose computed tomography detects nonsolid nodules that may be lung adenocarcinoma precursors. Aspirin’s anti-inflammatory properties make it an attractive target for prevention of multiple cancers, including lung cancer. Therefore, we conducted a phase IIb trial (NCT02169271) to study the efficacy of low-dose aspirin to reduce the size of subsolid lung nodules (SSNs). A total of 98 current or former smokers (67.3% current) undergoing annual low-dose computed tomography screening with persistent SSNs were randomly assigned to receive aspirin 100 mg/day or placebo for 1 year. There was no difference in change in the sum of the longest diameters of target nodules in the placebo and aspirin arm after 12 months of treatment (-0.12 mm [SD = 1.55 mm] and +0.30 mm [SD= 2.54 mm], respectively; 2-sided P = .33 primary endpoint). There were no changes observed in subgroup analyses by individual characteristics or nodule type. One year of low-dose aspirin did not show any effect on lung SSNs. SSNs regression may not be the proper target for aspirin, and/or longer duration may be needed to see SSNs modifications.

The diagnostic value of ultrasonography in detection of different types of thyroid nodules

BackgroundThis article discusses the importance of high frequency ultrasonography in detection of different types of thyroid nodules considering only the histopathological examination of the surgical specimens as the final diagnosis. We studied 50 patients referred to ENT clinic with a thyroid nodule. Ultrasonography and ultrasound-guided fine-needle aspiration biopsy were done to all the patients. Thyroid surgery was done according to FNAB results.ResultFrom 50 thyroid specimens, the US could predict the malignancy in 18 specimens. By histopathology, only 16 specimens were malignant, and 34 were benign thyroid disease. The sensitivity, specificity, and accuracy of US were 100%, 94.12%, and 96% respectively. The most suspicious ultrasongraphic feature was microcalcification followed by taller than wider (T ˃ W).ConclusionHigh frequency ultrasound is a very important tool to predict the malignant possibility during thyroid nodule evaluation.

A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma.

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based logistic regression model to predict STAS in lung adenocarcinoma. This retrospective study was approved by the institutional review board of two centers and included 578 patients (462 from center I and 116 from center II) with pathologically confirmed lung adenocarcinoma. STAS was identified from 90 center I patients (19.5%) and 28 center II patients (24.1%) from. The maximum diameter, nodule area, and area of solid components in part-solid nodules were measured. Twenty-one semantic characteristics were assessed. Univariate analysis was used to select CT characteristics, which were associated with STAS in the patient cohort of center I. Multivariable logistic regression was used to develop a CT characteristics-based model on those variables with statistical significance. The model was validated in the validation cohort and then tested in the external test cohort (patients from center II). The diagnostic performance of the model was measured by area under the curve (AUC) of receiver operating characteristic (ROC). At univariate analysis, age and 11 CT characteristics, including the maximum diameter of the tumor, the maximum area of the tumor, the area and ratio of the solid component, nodule type, pleural thickening, pleural retraction, mediastinal lymph node enlargement, vascular cluster sign, and lobulation, specula were found to be significantly associated with STAS. The optimal logistic regression model included age, maximum diameter and ratio of solid component with odds ratio (OR) value of 0.967 (95% CI: 0.944-0.988), 1.027 (95% CI: 1.008-1.046) and 5.14 (95% CI: 2.180-13.321), respectively. This model achieved an AUC of 0.801 (95% CI: 0.709-0.892) and 0.692 (95% CI: 0.518-0.866) in the validation cohort and the external test cohort, respectively. The difference was not statistically significant (P=0.280). CT-based logistic regression machine learning model could preoperatively predict STAS in lung adenocarcinoma with excellent diagnosis performance, which could be supplementary to routine CT interpretation.

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?

The purpose of this study was to determine how well radiologists could visually detect a change in lung nodule size on the basis of visual image perception alone. Under IRB approval, 109 standard chest CT image series were anonymized and exported from PACS. Nine hundred forty virtual lung nodule pairs (six baseline diameters, six relative volume differences, two nodule types-solid and ground glass-and 14 repeats) were digitally inserted into the chest CT image series (same location, different sizes between the pair). These digitally altered CT image pairs were shown to nine radiologists who were tasked to visually determine which image contained the larger nodule using a two-alternative forced-choice perception experimental design. These data were statistically analyzed using a generalized linear mixed effects model to determine how accurately the radiologists were able to correctly identify the larger nodule. Nominal baseline nodule diameter, relative volume difference, and nodule type were found to be statistically significant factors (p < 0.001) in influencing the radiologists' accuracy. For solid (ground-glass) nodules, the baseline diameter needed to be at least 6.3 mm (13.2 mm) to be able to visually detect a 25% change in volume with 95 ± 1.4% accuracy. Accuracy was lowest for the nodules with the smallest baseline diameters and smallest relative volume differences. Additionally, accuracy was lower for ground-glass nodules compared to solid nodules. Factors that impacted visual size assessment were baseline nodule diameter, relative volume difference, and solid versus non-solid nodule type, with larger and more solid lesions offering a more precise assessment of change. • For solid nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 6.3-mm baseline diameter. • For ground-glass nodules, radiologists could visually detect a 25% change in volume with 95% accuracy for nodules having greater than 13.2-mm baseline diameter. • Accuracy in detecting a change in nodule size began to stabilize around 90-100% for nodules with larger baseline diameters (> 8 mm for solid nodules, > 12 mm for ground-glass nodules) and larger relative volume differences (>15% for solid nodules, > 25% for ground-glass nodules).

Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study.

The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT). An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated. The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05). The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.

How frequently does hepatocellular carcinoma develop in at-risk patients with a negative liver MRI examination with intravenous Gadobenate dimeglumine?

To determine the rate of development of clinically significant liver nodules (LR-4, LR-5, LR-M) after a negative MRI in an HCC screening population. This retrospective study included patients at risk of developing HCC requiring imaging surveillance who had undergone multiphase Gadobenate dimeglumine-enhanced MRI that was negative and had follow up LI-RADS compliant multiphase CTs or MRIs for at least 12months or positive follow-up within 12months. Follow-up examinations were classified as negative (no nodules or only LR-1 nodules) or positive (nodule other than LR-1). Time-to-first positive examination, types of nodules, and cumulative incidence of nodule development were recorded. 204 patients (mean age 58.9 ± 10.2years, 128 women), including 172 with cirrhosis, were included. Based CT/MRI follow-up (median 35months, range 12-80months), the overall cumulative incidence of developing a nodule was 10.5%. Cumulative incidence of nodule development was: 0.5% at 6-9months and 2.1% at 12 ± 3months, including one LR-4 nodule, one LR-M nodule, and two LR-3 nodules. The cumulative incidence of clinically significant nodule development was 1.1% at 9-15months. 70% (143/204) of patients also underwent at least one US follow-up, and no patient developed a positive US examination following index negative MRI. Clinically significant liver nodules develop in 1.1% of at-risk patients in the first year following negative MRI. While ongoing surveillance is necessary for at-risk patients, our study suggests than longer surveillance intervals after a negative MRI may be reasonable and that further research is needed to explore this possibility.

Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey.

Lung cancer is the most common cancer in the world. Computed tomography (CT) is the standard medical imaging modality for early lung nodule detection and diagnosis that improves patient's survival rate. Recently, deep learning algorithms, especially convolutional neural networks (CNNs), have become a preferred methodology for developing computer-aided detection and diagnosis (CAD) schemes of lung CT images. Several CNN-based research projects have been initiated to design robust and efficient CAD schemes for the detection and classification of lung nodules. This paper reviews the recent works in this area and gives an insight into technical progress. First, a brief overview of CNN models and their basic structures is presented in this investigation. Then, we provide an analytic comparison of the existing approaches to discover recent trend and upcoming challenges. We also introduce an objective description of both handcrafted and deep learning features, as well as the types of nodules, the medical imaging modalities, the widely used databases, and related works in the last three years. The articles presented in this work were selected from various databases. About 57% of reviewed articles published in the last year. Our analysis reveals that several methods achieved promising performance with high sensitivity rates ranging from 66% to 100% under the false-positive rates ranging from 1 to 15 per CT scan. It can be noted that CNN models have contributed to the accurate detection and early diagnosis of lung nodules. From the critical discussion and an outline for prospective directions, this survey provide researchers valuable information to master the deep learning concepts and to deepen their knowledge of the trend and latest techniques in developing CAD schemes of lung CT images.

Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network

The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called joint-training annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis.

Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks

Thyroid cancer is the most common endocrine cancer and its incidence has continuously increased worldwide. In this paper, we focus on the challenging problem of nodule detection from ultrasound scans. In current clinical practice, this task is performed manually, which is tedious, subjective and highly depends on the clinical experience of radiologists. We propose a novel deep neural network architecture with carefully designed loss function regularization, and network hyperparameters to perform nodule detection without complex post-processing refinement steps. The local training and validation datasets consist of 2461 and 820 ultrasound frames acquired from 60 and 20 patients with a high degree of variability, respectively. The core of the proposed method is a deep learning framework based on multi-task model Mask R-CNN. We have developed a loss function with regularization that prioritizes detection over segmentation. Validation was conducted for 821 ultrasound frames from 20 patients. The proposed model can detect various types of thyroid nodules. The experimental results indicate that our proposed method is effective in thyroid nodule detection. Comparisons with the results by Faster R-CNN and conventional Mask R-CNN demonstrate that the proposed model outperforms the prior state-of-the-art detection methods.

Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization.

Pulmonary nodules are a potential manifestation of lung cancer. In computer-aided diagnosis (CAD) of lung cancer, it is of great significance to extract the complete boundary of the pulmonary nodules in the computed tomography (CT) scans accurately. It can provide doctors with important information such as tumor size and density, which assist doctors in subsequent diagnosis and treatment. In addition to this, in the molecular subtype and radiomics of lung cancer, segmentation of lung nodules also plays a pivotal role. Existing methods are difficult to use only one model to simultaneously treat the boundaries of multiple types of lung nodules in CT images. In order to solve the problem, this paper proposed a three-dimensional (3D)-UNET network model optimized by a 3D conditional random field (3D-CRF) to segment pulmonary nodules. On the basis of 3D-UNET, the 3D-CRF is used to optimize the sample output of the training set, so as to update the network weights in training process, reduce the model training time, and reduce the loss rate of the model. We selected 936 sets of pulmonary nodule data for the lung image database consortium and image database resource initiative (LIDC-IDRI)1 database to train and test the model. What's more, we used clinical data from partner hospitals for additional validation. The results show that our method is accurate and effective. Particularly, it shows more significance for the optimization of the segmentation of adhesive pulmonary nodules (the juxta-pleural and juxta-vascular nodules) and ground glass pulmonary nodules (GGNs).

Diagnostic significance of DNA methylation of PTEN and DAPK in thyroid tumors.

DNA Methylation of the tumour suppressor gene leading to gene silencing plays an important role in thyroid tumour development. The purpose was to determine the DNA methylation status of phosphatase and tensin homolog (PTEN) and death-associated protein kinase (DAPK) in patients with thyroid nodules and to explore whether they can be used as molecular diagnostic tools to differentiate benign and malignant thyroid nodules. Thyroid tissue and blood samples were obtained from normal healthy individuals (controls) and patients suffering from clinically diagnosed thyroid nodular disease [papillary thyroid carcinoma (PTC), adenoma and nodular goitre]. DNA methylation level, mRNA expression and protein expression of PTEN and DAPK in the thyroid tissues and peripheral blood were detected using methylation-specific PCR, semi-quantitative reverse transcription PCR and Western blot, respectively. Diagnostic sensitivity, specific and accuracy of detection were evaluated between blood and thyroid tissue. There was a significant increase in the level of DNA methylation of PTEN and DAPK in PTC (P<.05) compared with controls and other types of thyroid nodules. Levels of the mRNA of both PTEN and DAPK were lower in PTC in both peripheral blood and tissue samples compared with controls, while there was concomitant decrease of both PTEN and DAPK protein expression in PTC tissues (P<.05). There was no significant difference in diagnostic specificity, sensitivity and accuracy between blood sample and thyroid tissues. Hypermethylated status of both PTEN and DAPK in peripheral blood and tissue samples can be useful biomarkers for clinical diagnosis and, distinguishing of benign and malignant thyroid nodules.

Combination of generative adversarial network and convolutional neural network for automatic subcentimeter pulmonary adenocarcinoma classification.

The efficient and accurate diagnosis of pulmonary adenocarcinoma before surgery is of considerable significance to clinicians. Although computed tomography (CT) examinations are widely used in practice, it is still challenging and time-consuming for radiologists to distinguish between different types of subcentimeter pulmonary nodules. Although there have been many deep learning algorithms proposed, their performance largely depends on vast amounts of data, which is difficult to collect in the medical imaging area. Therefore, we propose an automatic classification system for subcentimeter pulmonary adenocarcinoma, combining a convolutional neural network (CNN) and a generative adversarial network (GAN) to optimize clinical decision-making and to provide small dataset algorithm design ideas. A total of 206 nodules with postoperative pathological labels were analyzed. Among them were 30 adenocarcinomas in situ (AISs), 119 minimally invasive adenocarcinomas (MIAs), and 57 invasive adenocarcinomas (IACs). Our system consisted of two parts, a GAN-based image synthesis, and a CNN classification. First, several popular existing GAN techniques were employed to augment the datasets, and comprehensive experiments were conducted to evaluate the quality of the GAN synthesis. Additionally, our classification system processes were based on two-dimensional (2D) nodule-centered CT patches without the need of manual labeling information. For GAN-based image synthesis, the visual Turing test showed that even radiologists could not tell the GAN-synthesized from the raw images (accuracy: primary radiologist 56%, senior radiologist 65%). For CNN classification, our progressive growing wGAN improved the performance of CNN most effectively (area under the curve =0.83). The experiments indicated that the proposed GAN augmentation method improved the classification accuracy by 23.5% (from 37.0% to 60.5%) and 7.3% (from 53.2% to 60.5%) in comparison with training methods using raw and common augmented images respectively. The performance of this combined GAN and CNN method (accuracy: 60.5%±2.6%) was comparable to the state-of-the-art methods, and our CNN was also more lightweight. The experiments revealed that GAN synthesis techniques could effectively alleviate the problem of insufficient data in medical imaging. The proposed GAN plus CNN framework can be generalized for use in building other computer-aided detection (CADx) algorithms and thus assist in diagnosis.

DIAGNOSIS OF LUNG NODULES FROM 2D COMPUTER TOMOGRAPHY SCANS

Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78[Formula: see text]mm to 22.48[Formula: see text]mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] and one distance of separation ([Formula: see text] pixel). In the classification step, two classifiers are proposed to classify two types of nodules based on their locations: as juxtapleural or solitary nodules. The two classifiers are a deep learning convolutional neural network (CNN) and the K-nearest neighbor (KNN) algorithm. Random oversampling and 10-fold cross-validation are used to improve the results. In our CAD system, the highest accuracy and sensitivity rates achieved by the CNN were 96% and 95%, respectively, for solitary nodule detection. The highest accuracy and sensitivity rates achieved by the KNN model were 93.8% and 96.7%, respectively, and K was set to 1 to detect juxtapleural nodules.

MON-504 VEGFA and VEGFR2 Expression in Different Histological Types of Thyroid Nodules: Could Immunohistochemistry Have a Clinical Utility?

Vascular endothelial growth factors (VEGFs) are a family of proteins involved in several elements that play an important role in the development of blood vessels. Besides acting in angiogenesis, VEGFA has important roles in chemotaxis, for macrophages and granulocytes, and vasodilation. VEGFA binds to VEGFR2, that acts on the MAPK and PI3K pathways, fundamental pathways for thyroid carcinogenesis. In order to assess the expression of VEGFA and VEGFR2, in different thyroid nodules, we used a Tissue MicroArray including 91 benign (74 females, 16 males, 49.84±12.65years old) and 125 malignant thyroid nodules (97 females, 28 males, 46.57±14.87 years old). Clinical and pathology data were obtained from 47 goiters; 43 follicular adenomas (FA) and a total of 104 papillary thyroid carcinomas (PTC), including 35 classic papillary thyroid carcinomas (CPTC), 30 follicular variant of PTC (FVPTC), 25 oxifilic variant of PTC (OVPTC), 14 tall cell papillary thyroid carcinomas (TCPTC); and 21 follicular thyroid carcinomas (FTC). All patients were managed according to a standard protocol based on current guidelines and followed-up for 116.9±70.8 months. VEGFA protein expression did not differentiate benign from malignant thyroid nodules. However, VEGFA was more frequently expressed in the less differentiated thyroid tissues. In fact, 95.8% of the FTC had positive expression. On the contrary, the intensity of this protein expression was progressively lower according to the process of cellular dedifferentiation (Goiter: 21.4%; FA: 16.3%; PTC: 8.7% and FTC: 0.0%; x2 = 0.031). There was no difference in VEGFR2 expression between malignant and benign nodules (x2= 0.108), but this protein showed more intense expression in tissues that also presented Hürthle cells (x2 <0.0001). We were not able to find any correlation, neither of VEGFA nor with VEGFR2 expression, and any other feature of aggressiveness, including invasion, metastasis, lymph node metastasis, and distant metastasis. We conclude that VEGFA and VEGFR2 expression may help identify less differentiated tumors and the analysis of a larger cohort may prove the clinical utility of these markers.

A Homeotic Mutation Changes Legume Nodule Ontogeny into Actinorhizal-Type Ontogeny.

Some plants fix atmospheric nitrogen by hosting symbiotic diazotrophic rhizobia or Frankia bacteria in root organs known as nodules. Such nodule symbiosis occurs in 10 plant lineages in four taxonomic orders: Fabales, Fagales, Cucurbitales, and Rosales, which are collectively known as the nitrogen-fixing clade. Nodules are divided into two types based on differences in ontogeny and histology: legume-type and actinorhizal-type nodules. The evolutionary relationship between these nodule types has been a long-standing enigma for molecular and evolutionary biologists. Recent phylogenomic studies on nodulating and nonnodulating species in the nitrogen-fixing clade indicated that the nodulation trait has a shared evolutionary origin in all 10 lineages. However, this hypothesis faces a conundrum in that legume-type and actinorhizal-type nodules have been regarded as fundamentally different. Here, we analyzed the actinorhizal-type nodules formed by Parasponia andersonii (Rosales) and Alnus glutinosa (Fagales) and found that their ontogeny is more similar to that of legume-type nodules (Fabales) than generally assumed. We also show that in Medicago truncatula, a homeotic mutation in the co-transcriptional regulator gene NODULE ROOT1 (MtNOOT1) converts legume-type nodules into actinorhizal-type nodules. These experimental findings suggest that the two nodule types have a shared evolutionary origin.

Predictive model for the diagnosis of benign/malignant small pulmonary nodules.

There is some doubt that all nodules <8 mm are really mainly benign and that simple follow-up is adequate in all cases. The purpose of this study is to create a predictive model for the diagnosis of benign and malignant small pulmonary nodules.This was a retrospective case–control study of patients who had undergone pulmonary nodule resection at the Zhejiang University Jinhua Hospital. Patients with pulmonary nodules of ≤10 mm in size on chest high-resolution computed tomography were included. Patients’ demographic characteristics, clinical features, and high-resolution computed tomography findings were collected. Logistic regression and receiver-operating characteristic analysis were used to create a predictive model for malignancy.A total of 216 patients were included: 160 with malignant and 56 with benign nodules. Nodule density (odds ratio [OR] = 0.996, 95% confidence interval [CI]: 0.993–0.998, P = .001), vascular penetration sign (OR = 3.49, 95% CI: 1.39–8.76, P = .008), nodule type (OR = 4.27, 95% CI: 1.48–12.29, P = .007), and incisure surrounding nodules (OR = 0.18, 95% CI: 0.04–0.84, P = .03) were independently associated with malignant nodules. These factors were used to create a mathematical model that had an area under the receiver-operating characteristic curve of 0.744. Using a cut-off of 0.762 resulted in 63.1% sensitivity and 75.0% specificity.This study proposes a pulmonary nodule prediction model that can estimate benign/malignant lung nodules with good sensitivity and specificity. Mixed ground-glass nodules, vascular penetration sign, density of lung nodules, and the absence of incisure signs are independently associated with malignant lung nodules.

Partially cystic thyroid cancer on conventional and elastographic ultrasound: a retrospective study and a machine learning-assisted system.

BackgroundThyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning–assisted system based on ultrasound (US) and elastography.MethodsPatients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated.ResultsA total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model.ConclusionsUS features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.

Differential diagnosis of thyroid nodules through a combination of multiple ultrasonography techniques: A decision-tree model.

The present study aimed to establish a decision tree (DT) model by combining the parameters of conventional gray-scale ultrasonography (US), elastosonography (ES), color Doppler US (CDUS) and contrast-enhanced US (CEUS) for the differential diagnosis of thyroid nodules. A single-center, retrospective study of 321 thyroid nodules was conducted. For 222 nodules, parameters of conventional gray-scale US, CDUS, ES and CEUS were evaluated using univariate logistic regression. Factors for with P<0.10 were further assessed using multivariate logistic regression. Significant factors (P<0.05) were used to establish a DT. The diagnostic accuracy of this DT was then evaluated by its application to the other 99 nodules. After univariate logistic analysis, factors including gender, number of nodules and diffuse disease were excluded, due to P>0.10. The results of multivariate logistic analysis determined that the following factors were required for the DT: Extent of blood flow determined by CDUS (P=0.002), area ratio determined by ES (P=0.033), peak phase patterns determined by CEUS (P<0.001) and micro-calcification determined by conventional gray-scale US (P=0.015). When compared to the pathological or cytological results of 99 nodules, the resulting DT had a sensitivity of 98.6%, specificity of 80.1%, positive predictive value of 93.5% and negative predictive value of 95.5%. These results suggested that a DT combining conventional gray-scale US, ES, CDUS and CEUS may be helpful for differentiating between types of thyroid nodules.