Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS: In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS: The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS: Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT). METHODS: Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures. Machine-learning algorithms were employed to develop radiomics signature while the DL signature was developed using neural architecture search. The diagnostic efficiency was primarily quantified with the area under receiver operating characteristic curve (AUC). We interpreted the reasoning process of the radiomics signature and DL signature by radiomics voxel mapping and attention weight tracking. RESULTS: A total of 674 patients with pathologically-confirmed NSCLC were included from two institutions, with 143 of them having DM. The radiomics signature achieved AUCs of 0.885, 0.854, and 0.733 in the internal validation, prospective validation, and external validation while those for DL signature were 0.893, 0.786, and 0.780. The proposed signatures achieved a promising performance in predicting the DM of NSCLC and outperformed the approaches proposed in previous studies. Interpretability analysis revealed that both radiomics and DL signatures could detect the variations among voxels inside tumors, which helped in identifying the DM of NSCLC. CONCLUSIONS: Our study demonstrates the potential of LDCT-based radiomics and DL signatures for predicting DM in NSCLC. These signatures could help improve lung cancer screening regarding further diagnostic tests and treatment strategies.

Radiomics Nomogram with Added Nodal Features Improves Treatment Response Prediction in Locally Advanced Esophageal Squamous Cell Carcinoma: A Multicenter Study.

OBJECTIVE: We aimed to develop and validate a radiomics nomogram and determine the value of radiomic features from lymph nodes (LNs) for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: In this multicenter retrospective study, eligible participants who had undergone NCRT followed by radical esophagectomy were consecutively recruited. Three radiomics models (modelT, modelLN, and modelTLN) based on tumor and LN features, alone and combined, were developed in the training cohort. The radiomics nomogram was developed by incorporating the prediction value of the radiomics model and clinicoradiological risk factors using multivariate logistic regression, and was evaluated using the receiver operating characteristic curve, validated in two external validation cohorts. RESULTS: Between October 2011 and December 2018, 116 patients were included in the training cohort. Between June 2015 and October 2020, 51 and 27 patients from two independent hospitals were included in validation cohorts 1 and 2, respectively. The radiomics modelTLN performed better than the radiomics modelT for predicting pCR. The radiomics nomogram incorporating the predictive value of the radiomics modelTLN and heterogeneous after NCRT outperformed the clinicoradiological model, with an area under the curve (95% confidence interval) of 0.833 (0.765-0.894) versus 0.764 (0.686-0.833) [p = 0.088, DeLong test], 0.824 (0.718-0.909) versus 0.692 (0.554-0.809) [p = 0.012], and 0.902 (0.794-0.984) versus 0.696 (0.526-0.857) [p = 0.024] in all three cohorts. CONCLUSIONS: Radiomic features from LNs could provide additional value for predicting pCR in ESCC patients, and the radiomics nomogram provided an accurate prediction of pCR, which might aid treatment decision.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

OBJECTIVES: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS: To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS: The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT: Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.

Phytoremediation of cadmium from soil, air and water.

Potentially toxic elements (PTEs) pose a great threat to ecosystems and long-term exposure causes adverse effects to wildlife and humans. Cadmium induces a variety of diseases including cancer, kidney dysfunction, bone lesions, anemia and hypertension. Here we review the ability of plants to accumulate cadmium from soil, air and water under different environmental conditions, focusing on absorption mechanisms and factors affecting these. Cadmium possess various transport mechanisms and pathways roughly divided into symplast and apoplast pathway. Excessive cadmium concentrations in the environment affects soil properties, pH and microorganism composition and function and thereby plant uptake. At the same time, plants resist cadmium toxicity by antioxidant reaction. The differences in cadmium absorption capacity of plants need more exploration to determine whether it is beneficial for crop breeding or genetic modification. Identify whether plants have the potential to become hyperaccumulator and avoid excessive cadmium uptake by edible plants. The use of activators such as wood vinegar, GLDA (Glutamic acid diacetic acid), or the placement of earthworms and fungi can speed up phytoremediation of plants, thereby reducing uptake of crop varieties and reducing human exposure, thus accelerating food safety and the health of the planet.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

PURPOSE: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). METHODS: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. RESULTS: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. CONCLUSIONS: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Pyrolysis of Aesculus chinensis Bunge Leaves as for Extracted Bio-Oil Material.

Biomass rapid pyrolysis technology is easy to implement in continuous production and industrial application, and has become one of the leading technologies in the field of world renewable energy development. Agricultural and forestry waste is an important resource of renewable energy in China. In general, abandoned leaves in forest areas cause serious waste of resources. Its utilization may help to settle the problems of energy deficiency and environment pollution. In this study, Aesculus chinensis Bunge leaves (A. Bunge) are used as the research object to study the pyrolysis and extract. The results showed that there are a lot of bioactive components in A. Bunge leaves extract, including acetamide, 5-hydroxymethylfurfural, R-limonene, d-mannose, and dihydroxyacetone. The active components of A. Bunge leaves supply scientific evidence for the exploration and exploitation of this plant. The pyrolysis products of A. Bunge leaves are rich in organic acids, aldehydes, and ketones, which means that A. Bunge leaves can be used as a crude material for the manufacturing of bio-oil or bio-fuel. The pyrolysis products include batilol, pregnenolone, benzoic acid, butyrolactone, and propanoic acid, which can be used in biological medicine, chemical crude materials, and industrial raw material reagents. Therefore, A. Bunge leaves can be used as a good crude material for bio-oil or biofuel production. Combining A. Bunge leaves and fast pyrolysis methods can effectively solve the problem of forestry and agricultural residues in the future.

Nano Catalysis of Biofuels and Biochemicals from Cotinus coggygria Scop. Wood for Bio-Oil Raw Material.

Cotinus coggygria Scop. as a precious landscape shrub and a good afforestation species that is used in the pharmaceutical industry. In this paper, TG-FTIR, TG-DTG, and Py-GC/MS were used to study the biomaterials of Cotinus coggygria used as biofuels and biochemicals under the catalysis of nano-Mo/Fe2O3. The wood powder was extracted using a methanol/benzene solution, and the extract was analyzed by FTIR and GC-MS. The results showed that the pyrolysis products of Cotinus coggygria wood were rich in phenols, alcohols, and biofuels. The metal nano-Mo powder played a catalytic role in the interpretation of the gas in the species, where it accelerates gas products. Metal nano-Fe2O3 has a certain flame-retardant effect on the burning process of Cotinus coggygria wood, and the residual amount of pyrolysis is greater. The contents of the extract Formamide, 1-Hexanol, Levodopa, and 9,12-Octadecadienoic acid (Z,Z)- are not only widely used industrially but also play an important role in medicine. Cotinus coggygria is therefore an excellent biomaterial for biofuels and biochemicals.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Objective: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC). Methods: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort. Based on AB-MRI, a TL signature (TLS) and a radiomics signature (RS) were built with the least absolute shrinkage and selection operator (LASSO) logistic regression. Their diagnostic performances were validated and compared using areas under the receiver operating curve (AUCs), net reclassification improvement (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and stratification analysis. A convolutional filter visualization technique was used to map the response areas of LVI on the AB-MRI. Results: In the validation cohort, compared with RS, the TLS showed better capability in discriminating LVI-positive from LVI-negative lesions (AUC: 0.852 vs. 0.726, p < 0.001; IDI = 0.092, p < 0.001; NRI = 0.554, p < 0.001). The diagnostic performance of TLS was not affected by the menstrual state, molecular subtype, or contrast agent type (all p > 0.05). Moreover, DCA showed that the TLS added more net benefit than RS for clinical utility. Conclusions: An AB-MRI-based TLS was superior to RS for preoperative LVI prediction in patients with clinically node-negative IBC.

Incorporation of Nanocatalysts for the Production of Bio-Oil from Staphylea holocarpa Wood.

Biomass has been recognized as the most common source of renewable energy. In recent years, researchers have paved the way for a search for suitable biomass resources to replace traditional fossil fuel energy and provide high energy output. Although there are plenty of studies of biomass as good biomaterials, there is little detailed information about Staphylea holocarpa wood (S. holocarpa) as a potential bio-oil material. The purpose of this study is to explore the potential of S. holocarpa wood as a bio-oil. Nanocatalyst cobalt (II) oxide (Co3O4) and Nickel (II) oxide (NiO) were used to improve the production of bio-oil from S. holocarpa wood. The preparation of biofuels and the extraction of bioactive drugs were performed by the rapid gasification of nanocatalysts. The result indicated that the abundant chemical components detected in the S. holocarpa wood extract could be used in biomedicine, cosmetics, and biofuels, and have a broad industrial application prospect. In addition, nanocatalyst cobalt tetraoxide (Co3O4) could improve the catalytic cracking of S. holocarpa wood and generate more bioactive molecules at high temperature, which is conducive to the utilization and development of S. holocarpa wood as biomass. This is the first time that S. holocarpa wood was used in combination with nanocatalysts. In the future, nanocatalysts can be used to solve the problem of sustainable development of biological resources.

A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning.

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors.

Managing the hazardous waste cooking oil by conversion into bioenergy through the application of waste-derived green catalysts: A review.

Waste cooking oil (WCO) is a hazardous waste generated at staggering values globally. WCO disposal into various ecosystems, including soil and water, could result in severe environmental consequences. On the other hand, mismanagement of this hazardous waste could also be translated into the loss of resources given its energy content. Hence, finding cost-effective and eco-friendly alternative pathways for simultaneous management and valorization of WCO, such as conversion into biodiesel, has been widely sought. Due to its low toxicity, high biodegradability, renewability, and the possibility of direct use in diesel engines, biodiesel is a promising alternative to mineral diesel. However, the conventional homogeneous or heterogeneous catalysts used in the biodiesel production process, i.e., transesterification, are generally toxic and derived from non-renewable resources. Therefore, to boost the sustainability features of the process, the development of catalysts derived from renewable waste-oriented resources is of significant importance. In light of the above, the present work aims to review and critically discuss the hazardous WCO application for bioenergy production. Moreover, various waste-oriented catalysts used to valorize this waste are presented and discussed.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). MATERIALS AND METHODS: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). RESULTS: In total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824-0.936), 0.915 (95% CI: 0.846-0.959), and 0.914 (95% CI: 0.848-0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. CONCLUSION: The DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

Pyrolysis of Aesculus chinensis Bunge Seed with Fe2O3/NiO as nanocatalysts for the production of bio-oil material.

The rapid thermal cracking technology of biomass can convert biomass into bio-oil and is beneficial for industrial applications. Agricultural and forestry wastes are important parts of China's energy, and their high-grade utilization is useful to solve the problem of energy shortages and environmental pollution. To the best of our knowledge, the impact of nanocatalysts on converting biowastes for bio-oil has not been studied. Consequently, we examined the production of bio-oil by pyrolysis of Aesculus chinensis Bunge Seed (ACBS) using nanocatalysts (Fe2O3 and NiO catalysts) for the first time. The pyrolysis products of ACBS include 1-hydroxy-2-propanone (3.97%), acetic acid (5.42%), and furfural (0.66%). These chemical components can be recovered for use as chemical feedstock in the form of bio-oil, thus indicating the potential of ACBS as a feedstock to be converted by pyrolysis to produce value-added bio-oil. The Fe2O3 and NiO catalysts enhanced the pyrolysis process, which accelerated the precipitation of gaseous products. The pyrolysis rates of the samples gradually increased at DTGmax, effectively promoting the catalytic cracking of ACBS, which is beneficial to the development and utilization of ACBS to produce high valorization products. Combining ACBS and nanocatalysts can change the development direction of high valorization agricultural and forestry wastes in the future.

Added benefits of early detection of other diseases on low-dose CT screening.

Low-dose CT screening for lung cancer provides images of the entire chest and upper abdomen. While the focus of screening is on finding early lung cancer, radiology leadership has embraced the fact that the information contained in the images presents a new challenge to the radiology profession. Other findings in the chest and upper abdomen were not the reason for obtaining the screening CT scan, nor symptom-prompted, but still need to be reported. Reporting these findings and making recommendations for further workup requires careful consideration to avoid unnecessary workup or interventions while still maximizing the benefit that early identification of these other diseases provided. Other potential findings, such as cardiovascular disease and chronic pulmonary obstructive diseases actually cause more deaths than lung cancer. Existing recommendations for workup of abnormal CT findings are based on symptom-prompted indications for imaging. These recommendations may be different when the abnormalities are identified in asymptomatic people undergoing CT screening for lung cancer. I-ELCAP, a large prospectively collected multi-institutional and multi-national database of screenings, was used to analyze CT findings identified in screening for lung cancer. These analyses and recommendations were made by radiologists in collaboration with clinicians in different medical specialties.

Mitigation of indoor air pollution: A review of recent advances in adsorption materials and catalytic oxidation.

Indoor air pollution with toxic volatile organic compounds (VOCs) and fine particulate matter (PM2.5) is a threat to human health, causing cancer, leukemia, fetal malformation, and abortion. Therefore, the development of technologies to mitigate indoor air pollution is important to avoid adverse effects. Adsorption and photocatalytic oxidation are the current approaches for the removal of VOCs and PM2.5 with high efficiency. In this review we focus on the recent development of indoor air pollution mitigation materials based on adsorption and photocatalytic decomposition. First, we review on the primary indoor air pollutants including formaldehyde, benzene compounds, PM2.5, flame retardants, and plasticizer: Next, the recent advances in the use of adsorption materials including traditional biochar and MOF (metal-organic frameworks) as the new emerging porous materials for VOCs absorption is reviewed. We review the mechanism for mitigation of VOCs using biochar (noncarbonized organic matter partition and adsorption) and MOF together with parameters that affect indoor air pollution removal efficiency based on current mitigation approaches including the mitigation of VOCs using photocatalytic oxidation. Finally, we bring forward perspectives and directions for the development of indoor air mitigation technologies.

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules.

PURPOSE: To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS: The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS: Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.

Comparison of the clinical and MRI features of patients with hepatic hemangioma, epithelioid hemangioendothelioma, or angiosarcoma.

BACKGROUND: Comparisons of hepatic epithelioid hemangioendothelioma (HEHE), hepatic hemangioma, and hepatic angiosarcoma (HAS) have rarely been reported. The purpose of our study was to analyze the clinical and magnetic resonance imaging (MRI) findings of these conditions. METHODS: A total of 57 patients (25 with hemangioma, 13 with HEHE, and 19 with HAS) provided hepatic vascular endothelial cell data between June 2006 and May 2017. RESULTS: The proportions of cases with circumscribed margins were 88% (22/25), 84.6% (11/13), and 31.6% (6/19) for hemangioma, HEHE, and HAS, respectively (P < 0.001). HAS lesions were less likely to have circumscribed margins. The proportions of lesions with hemorrhaging were 4% (1/25), 30.8% (4/13), and 36.8% (7/19) for hemangioma, HEHE, and HAS, respectively (P = 0.014). HEHE and HAS cases were more likely to show heterogeneous signals on T1-weighted (T1WI) MRI. HEHE and HAS cases were more likely to show heterogeneous signals on T2-weighted (T2WI) MRI. Centripetal enhancement was the most common pattern in vascular tumors, with proportions of 100, 46.2% (6/13), and 68.4% (13/19) for hemangioma, HEHE, and HAS, respectively. The difference in enhancement pattern between HEHE and HAS was not significant, but rim enhancement was more common for HEHE (46.2%, 6/13). CONCLUSIONS: Our study revealed clinical and imaging differences between HEHE and HAS. The platelet count (PLT) and coagulation function of the HAS group decreased, whereas the alpha-fetoprotein (AFP) level increased. The 5-year survival rate for HAS was significantly lower than that of HEHE. A higher malignancy degree indicated a more blurred lesion margin, easier occurrence of hemorrhaging, and more heterogeneous T1WI and T2WI signals.

LINC00173.v1 promotes angiogenesis and progression of lung squamous cell carcinoma by sponging miR-511-5p to regulate VEGFA expression.

BACKGROUND: Anti-angiogenic therapy represents a promising strategy for non-small-cell lung cancer (NSCLC) but its application in lung squamous cell carcinoma (SQC) is limited due to the high-risk adverse effects. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) mediate in tumor progression by participating in the regulation of VEGF in NSCLC, which might guide the development of new antiangiogenic strategies. METHODS: Differential lncRNA expression in SQC was analyzed in AE-meta and TCGA datasets, and further confirmed in lung cancer tissues and adjacent normal tissues with RT-qPCR and in-situ hybridization. Statistical analysis was performed to evaluate the clinical correlation between LINC00173.v1 expression and survival characteristics. A tube formation assay, chick embryo chorioallantoic membrane assay and animal experiments were conducted to detect the effect of LINC00173.v1 on the proliferation and migration of vascular endothelial cells and tumorigenesis of SQC in vivo. Bioinformatics analysis, RNA immunoprecipitation and luciferase reporter assays were performed to elucidate the downstream target of LINC00173.v1. The therapeutic efficacy of antisense oligonucleotide (ASO) against LINC00173.v1 was further investigated in vivo. Chromatin immunoprecipitation and high throughput data processing and visualization were performed to identify the cause of LINC00173.v1 overexpression in SQC. RESULTS: LINC00173.v1 was specifically upregulated in SQC tissues, which predicted poorer overall and progression-free survival in SQC patients. Overexpression of LINC00173.v1 promoted, while silencing LINC00173.v1 inhibited the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells in vitro and in vivo. Our results further revealed that LINC00173.v1 promoted the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells by upregulating VEGFA expression by sponging miR-511-5p. Importantly, inhibition of LINC00173.v1 via the ASO strategy reduced the tumor growth of SQC cells, and enhanced the therapeutic sensitivity of SQC cells to cisplatin in vivo. Moreover, our results showed that squamous cell carcinoma-specific factor ΔNp63α contributed to LINC00173.v1 overexpression in SQC. CONCLUSION: Our findings clarify the underlying mechanism by which LINC00173.v1 promotes the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC, demonstrating that LINC00173.v1-targeted drug in combination with cisplatin may serve as a rational regimen against SQC.