Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.

Differentiating between benign and malignant sacral tumors is crucial for determining appropriate treatment options. This study aims to develop two benchmark fusion models and a deep learning radiomic nomogram (DLRN) capable of distinguishing between benign and malignant sacral tumors using multiple imaging modalities. We reviewed axial T2-weighted imaging (T2WI) and non-contrast computed tomography (NCCT) of 134 patients pathologically confirmed as sacral tumors. The two benchmark fusion models were developed using fusion deep learning (DL) features and fusion classical machine learning (CML) features from multiple imaging modalities, employing logistic regression, K-nearest neighbor classification, and extremely randomized trees. The two benchmark models exhibiting the most robust predictive performance were merged with clinical data to formulate the DLRN. Performance assessment involved computing the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV). The DL benchmark fusion model demonstrated superior performance compared to the CML fusion model. The DLRN, identified as the optimal model, exhibited the highest predictive performance, achieving an accuracy of 0.889 and an AUC of 0.961 in the test sets. Calibration curves were utilized to evaluate the predictive capability of the models, and decision curve analysis (DCA) was conducted to assess the clinical net benefit of the DLR model. The DLRN could serve as a practical predictive tool, capable of distinguishing between benign and malignant sacral tumors, offering valuable information for risk counseling, and aiding in clinical treatment decisions.

MiR-204-5p may regulate oxidative stress in myopia.

The mechanisms underlying myopia remain not fully understood. We proposed to examine the function and underlying mechanisms of miR-204-5p in myopia development. The miR-204-5p expression level was assessed in the vitreous humor (VH) of a cohort consisting of 11 patients with high myopia (HM) and 16 control patients undergoing vitrectomy. Then the functional implications of miR-204-5p in ARPE-19 cells were assessed. Thioredoxin-interacting protein (TXNIP) was found as a possible target of miR-204-5p through mRNA sequencing, and its interaction with miR-204-5p was confirmed employing luciferase assay and western blotting. Furthermore, the miR-204-5p function in regulating oxidative stress was examined by measuring reactive oxygen species (ROS) accumulation. The results indicated a significant reduction of miR-204-5p in the VH of HM patients. Overexpression of miR-204-5p suppressed cell proliferation, migration, invasion, and apoptosis in ARPE-19 cells. The direct targeting of miR-204-5p on TXNIP has been confirmed, and its downregulation mediated the miR-204-5p impacts on ARPE-19 cells. Moreover, miR-204-5p overexpression significantly reduced ROS accumulation by targeting TXNIP. Our findings revealed the possible contribution of the miR-204-5p/TXNIP axis in myopia development by regulating oxidative stress, which may provide new targets to combat this prevalent and debilitating condition.

Early postoperative interventions in the prevention and management of thyroidectomy scars.

Thyroidectomy scars, located on the exposed site, can cause distress in patients. Owing to the cosmetic importance of thyroidectomy scars, many studies have been conducted on its prevention and treatment. Scar formation factors mainly include inflammatory cell infiltration, angiogenesis, fibroblast proliferation, secretion of cytokines such as transforming growth factor (TGF)-ß1, and mechanical tension on the wound edges. Anti-scar methods including topical anti-scar agents, skin tension-bearing devices, and local injections of botulinum toxin, as well as lasers and phototherapies, that target these scar formation factors have been developed. However, current studies remain fragmented, and there is a lack of a comprehensive evaluation of the impacts of these anti-scar methods on treating thyroidectomy scars. Early intervention is a crucial but often neglected key to control hyperplastic thyroidectomy scars. Therefore, we review the currently adopted early postoperative strategies for thyroidectomy scar reduction, aiming to illustrate the mechanism of these anti-scar methods and provide flexible and comprehensive treatment selections for clinical physicians to deal with thyroidectomy scars.

MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas.

This paper aims to compare the performance of the classical machine learning (CML) model and the deep learning (DL) model, and to assess the effectiveness of utilizing fusion radiomics from both CML and DL in distinguishing encephalitis from glioma in atypical cases. We analysed the axial FLAIR images of preoperative MRI in 116 patients pathologically confirmed as gliomas and clinically diagnosed with encephalitis. The 3 CML models (logistic regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP)), 3 DL models (DenseNet 121, ResNet 50 and ResNet 18) and a deep learning radiomic (DLR) model were established, respectively. The area under the receiver operating curve (AUC) and sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV) were calculated for the training and validation sets. In addition, a deep learning radiomic nomogram (DLRN) and a web calculator were designed as a tool to aid clinical decision-making. The best DL model (ResNet50) consistently outperformed the best CML model (LR). The DLR model had the best predictive performance, with AUC, sensitivity, specificity, accuracy, NPV and PPV of 0.879, 0.929, 0.800, 0.875, 0.867 and 0.889 in the validation sets, respectively. Calibration curve of DLR model shows good agreement between prediction and observation, and the decision curve analysis (DCA) indicated that the DLR model had higher overall net benefit than the other two models (ResNet50 and LR). Meanwhile, the DLRN and web calculator can provide dynamic assessments. Machine learning (ML) models have the potential to non-invasively differentiate between encephalitis and glioma in atypical cases. Furthermore, combining DL and CML techniques could enhance the performance of the ML models.

MiR-181a-5p may regulate cell proliferation and autophagy in myopia and the associated retinopathy.

The mechanism of myopia and the associated retinopathy remains unclear, and dysregulated microRNAs (miRNAs) are implicated in this disease. In this research, we purposed to find out the regulatory function that miRNAs play in myopia and the associated retinopathy. We first performed miRNA microarray analysis in a lens-induced myopia mouse model and found that miR-9-5p, miR-96-5p, miR-182-5p, miR-183-5p, and miR-181a-5p were elevated in the myopic retina. Then, we examined the functions and regulatory mechanisms of miR-181a-5p utilizing the human retinal pigment epithelium (RPE) cell line ARPE-19 by overexpressing miR-181a-5p. RNA sequencing (RNA-Seq) and qRT-PCR analysis were employed to identify differentially expressed genes after transfection. The qRT‒PCR outcomes, immunoblotting, and immunofluorescence indicated that the SGSH expression was significantly hindered through miR-181a-5p overexpression. MiR-181a-5p overexpression has the ability to elevate RPE cell proliferation and induce autophagy by targeting SGSH. We validated the negative influence of miR-181a-5p on the SGSH expression through luciferase reporter assays, which demonstrated its ability to target the 3' untranslated region of SGSH. The reversal of implications of miR-181a-5p overexpression was achieved through SGSH upregulation. We provided novel perspectives into the miR-181a-5p function in regulating myopia development and may serve as a target for therapy and molecular biomarker for myopia.

CircRNA expression profiles and regulatory networks in the vitreous humor of people with high myopia.

Myopia is a global health and economic issue. Circular RNAs (circRNAs) have been shown to play an important role in the pathogenesis of many ocular diseases. We first evaluated the circRNA profiles and possible roles in vitreous humor samples of individuals with high myopia by a competitive endogenous RNA (ceRNA) array. Vitreous humor samples were collected from 15 high myopic (5 for ceRNA array, and 10 for qPCR) and 15 control eyes (5 for ceRNA array, and 10 for qPCR) with idiopathic epiretinal membrane (ERM) and macular hole (MH). 486 circRNAs (339 upregulated and 147 downregulated) and 264 mRNAs (202 upregulated and 62 downregulated) were differentially expressed between the high myopia and control groups. The expression of hsa_circ_0033079 (hsa-circDicer1), hsa_circ_0029989 (hsa-circNbea), hsa_circ_0019072 (hsa-circPank1) and hsa_circ_0089716 (hsa-circEhmt1) were validated by qPCR. Pearson analysis and multivariate regression analysis showed positive and significant correlations for axial length with hsa-circNbea and hsa-circPank1. KEGG analysis showed that the target genes of circRNAs were enriched in the mTOR, insulin, cAMP, and VEGF signaling pathways. GO analysis indicated that circRNAs mainly targeted transcription, cytoplasm, and protein binding. CircRNA-associated ceRNA network analysis and PPI network analysis identified several critical genes for myopia. The expression of circNbea, circPank1, miR-145-5p, miR-204-5p, Nras, Itpr1 were validated by qPCR in the sclera of form-deprivation myopia (FDM) mice model. CircPank1/miR-145-5p/NRAS and circNbea/miR-204-5p/ITPR1 were identified and may be important in the progression of myopia. Our findings suggest that circRNAs may contribute to the pathogenesis of myopia and may serve as potential biomarkers.

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics.

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

Visualization and analysis of mapping knowledge domains for optic neuritis: a bibliometric research from 2013 to 2022.

PURPOSE: To explore the global research trends, hotspots and frontiers of optic neuritis (ON) over the past decade through qualitative and quantitative analysis of bibliometrics. METHODS: Publications on ON from 2013 to 2022 were retrieved from Web of Science Core Collection (WoSCC). VOSviewer and CiteSpace were mainly used to facilitate bibliometric analysis and visualization. RESULTS: A total of 3027 papers were retrieved from peer-reviewed publications and the annual research output increased over time. Neurosciences neurology was the most published area. The USA was the most productive and influential country, and in the focus of international cooperation. University College London was the most productive organization and Charite Medical University of Berlin had the largest number of cooperating partners. Paul F contributed the largest number of publications and Wingerchuk DM ranked first among the co-cited authors. Multiple Sclerosis and Related Disorders was the most prolific journal publishing ON research. The most co-cited references mainly focused on the diagnostic criteria for neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS). The keywords formed the following four clusters: the pathophysiology of MS-ON; the autoantibody markers and diagnostic criteria of NMOSD-ON and myelin oligodendrocyte glycoprotein associated disorder-ON (MOGAD-ON); the epidemiology and clinical characteristics of ON; and the treatment of ON. CONCLUSION: This bibliometrics analysis showed a systematic view of the evolutionary process, research hotspots, and future directions of ON research. It can provide insights for ON research and valuable information for neuro-ophthalmologic specialists to evaluate research policies and promote international cooperation.

CT-guided microcoil localization of pulmonary nodules before VATS: clinical experience in 1059 patients.

OBJECTIVE: To retrospectively evaluate the efficacy and safety of CT-guided microcoil localization of pulmonary nodules before video-assisted thoracoscopic surgery (VATS). METHODS: A total of 1059 consecutive patients with 1331 pulmonary nodules treated between July 2018 and April 2021 were included in this study. Of the 1331 nodules, 1318 were localized using the tailed method and 13 were localized using the non-tailed method. The localization technical success rate and complications of the microcoil localization procedure were assessed. Univariate and multivariate logistic regression analyses were used to determine potential risk factors for technical failure, pneumothorax, and pulmonary hemorrhage. RESULTS: The technical success rate of the localization procedure was 98.4% (1310/1331 nodules). Nodule location in the lower lobes (p = 0.015) and need for a longer needle path (p < 0.001) were independent predictors of technical failure. All localization procedure-related complications were minor (grade 1 or 2) adverse events, with the exception of one grade 3 complication. The most common complications were pneumothorax (302/1331 nodules [22.7%]) and pulmonary hemorrhage (328/1331 nodules [24.6%]). Male sex (p = 0.001), nodule location in the middle (p = 0.003) and lower lobes (p = 0.025), need for a longer needle path (p < 0.001), use of transfissural puncture (p = 0.042), and simultaneous multiple localizations (p < 0.001) were independent risk factors for pneumothorax. Female sex (p = 0.015), younger age (p = 0.023), nodules location in the upper lobes (p = 0.011), and longer needle path (p < 0.001) were independent risk factors for pulmonary hemorrhage. CONCLUSIONS: CT-guided microcoil localization of pulmonary nodules before VATS using either the tailed or non-tailed method is effective and safe. CLINICAL RELEVANCE STATEMENT: CT-guided microcoil localization of pulmonary nodules before VATS resection is effective and safe when using either the tailed or non-tailed method. Nodules requiring transfissural puncture and multiple nodules requiring simultaneous localizations can also be successfully localized with this method. KEY POINTS: • Pre-VATS CT-guided microcoil localization of pulmonary nodules by tailed or non-tailed method was effective and safe. • When the feasible puncture path was beyond the scope of wedge resection, localization could be performed using the non-tailed method. • Although transfissural puncture and simultaneous multiple localization were independent risk factors for pneumothorax, they remained clinically feasible.

Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining.

BACKGROUND: There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. OBJECTIVE: To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. METHODS: The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. RESULTS: In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. CONCLUSION: This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.

Prediction of macrotrabecular-massive hepatocellular carcinoma by using MR-based models and their prognostic implications.

PURPOSE: To evaluate the efficacy of MRI-based radiomics and clinical models in predicting MTM-HCC. Additionally, to investigate the ability of the radiomics model designed for MTM-HCC identification in predicting disease-free survival (DFS) in patients with HCC. METHODS: A total of 336 patients who underwent oncological resection for HCC between June 2007 and March 2021 were included. 127 patients in Cohort1 were used for MTM-HCC identification, and 209 patients in Cohort2 for prognostic analyses. Radiomics analysis was performed using volumes of interest of HCC delineated on pre-operative MRI images. Radiomics and clinical models were developed using Random Forest algorithm in Cohort1 and a radiomics probability (RP) of MTM-HCC was obtained from the radiomics model. Based on the RP, patients in Cohort2 were divided into a RAD-MTM-HCC (RAD-M) group and a RAD-non-MTM-HCC (RAD-nM) group. Univariate and multivariate Cox regression analyses were employed to identify the independent predictors for DFS of patients in Cohort2. Kaplan-Meier curves were used to compare the DFS between different groups pf patients based on the predictors. RESULTS: The radiomics model for identifying MTM-HCC showed AUCs of 0.916 (95% CI: 0.858-0.960) and 0.833 (95% CI: 0.675-0.935), and the clinical model showed AUCs of 0.760 (95% CI: 0.669-0.836) and 0.704 (95% CI: 0.532-0.843) in the respective training and validation sets. Furthermore, the radiomics biomarker RP, portal or hepatic vein tumor thrombus, irregular rim-like arterial phase hyperenhancement (IRE) and AFP were independent predictors of DFS in patients with HCC. The DFS of RAD-nM group was significantly higher than that of the RAD-M group (p < .001). CONCLUSION: MR-based clinical and radiomic models have the potential to accurately diagnose MTM-HCC. Moreover, the radiomics signature designed to identify MTM-HCC also can be used to predict prognosis in patients with HCC, realizing the diagnostic and prognostic aims at the same time.

Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning radiomics (DLR) model to accurately predict the response to NAC in patients diagnosed with OS using preoperative MR images. METHODS: We reviewed axial T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted (T1CE) of 106 patients pathologically confirmed as OS. First, the Auto3DSeg framework was utilized for automated OS segmentation. Second, using three feature extraction methods, nine risk classification models were constructed based on three classifiers. The area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, negative predictive value and positive predictive value were calculated for performance evaluation. Additionally, we developed a deep learning radiomics nomogram with clinical indicators. RESULTS: The model for OS automatic segmentation achieved a Dice coefficient of 0.868 across datasets. To predict the response to NAC, the DLR model achieved the highest prediction performance with an accuracy of 93.8% and an AUC of 0.961 in the test sets. We used calibration curves to assess the predictive ability of the models and performed decision curve analysis to evaluate the clinical net benefit of the DLR model. CONCLUSION: The DLR model can serve as a pragmatic prediction tool, capable of identifying patients with poor response to NAC, providing information for risk counseling, and assisting in making clinical treatment decisions. Poor responders are better advised to undergo immunotherapy and receive the best supportive care.

Comparison of amiodarone and esmolol for prevention of reperfusion ventricular fibrillation in individuals undergoing heart valve or aortic surgery: a study protocol for a randomized controlled clinical trial.

BACKGROUND: Amiodarone and esmolol can help to prevent and treat post-cardiac surgery reperfusion ventricular fibrillation. However, the relative efficacies of these two drugs remain unknown. The aim of the current trial is to compare the performances of amiodarone and esmolol for preventing reperfusion ventricular fibrillation following open heart surgery. METHODS/DESIGN: This is a single-center, prospective, double-blind, controlled clinical trial. A total of 260 patients undergoing heart valve or aortic surgery will be assigned randomly to treatment with prophylactic esmolol (intervention group) or amiodarone (control group). The main outcome is the incidence of reperfusion ventricular fibrillation following aortic opening during extracorporeal circulation. The secondary outcomes are the rate of automatic cardiac resuscitation, energy and frequency of electrical defibrillation, number of electrical defibrillations, and pacemaker use in the two groups of patients. Information on the patients' general condition and the durations of anesthesia, extracorporeal circulation, aortic occlusion, and operation time will be recorded. We will also compare the heart rate, mean arterial pressure, and central venous pressure between the two groups of patients at induction of anesthesia (T1), start of surgery (T2), start of extracorporeal circulation (T3), aortic block (T4), aortic opening (T5), after opening for 10 (T6), 20 (T7), and 30 min (T8), at cessation of extracorporeal circulation (T9), and at the end of surgery (T10) and compare blood gas analysis results at T1, T5, T9, and T10. DISCUSSION: This study will determine if prophylactic esmolol is more effective than amiodarone for reducing the incidence of reperfusion ventricular fibrillation in patients undergoing heart valve or aortic surgery. TRIAL REGISTRATION: China Clinical Trials Registry ChiCTR1900026429. Registered on 2019.10.9.

Genotypic diversity and antifungal susceptibility of Cryptococcus neoformans species complex from China, including the diploid VNIII isolates from HIV-infected patients in Chongqing region.

Although previous studies on the genotypic diversity and antifungal susceptibility of the Cryptococcus neoformans species complex (CNSC) isolates from China revealed ST5 genotype isolates being dominant, the information about the CNSC isolates from Chinese HIV-infected patients is limited. In this study, 171 CNSC isolates from HIV-infected patients in the Chongqing region of Southwest China were genotyped using the International Society for Human and Animal Mycology-multilocus sequence typing consensus scheme, and their antifungal drug susceptibilities were determined following CLSI M27-A3 guidelines. Among 171 isolates, six sequence types (STs) were identified, including the dominant ST5 isolates, the newly reported ST15, and four diploid VNIII isolates (ST632/ST636). Moreover, a total of 1019 CNSC isolates with STs and HIV-status information were collected and analyzed from Mainland China in the present study. A minimum spanning analysis grouped these 1019 isolates into three main subgroups, which were dominated by the ST5 clonal complex (CC5), followed by the ST31 clonal complex (CC31) and ST93 clonal complex (CC93). The trend of resistance or decreasing susceptibility of clinical CNSC isolates to azole agents within HIV-infected patients from the Chongqing region is increasing, especially resistance to fluconazole.

In this paper, novel ST15 and four diploid VNIII isolates (ST632/ST636) were found in 171 CNSC isolates in Southwest China, including evidence for resistance to fluconazole. Moreover, we clustered the 1019 clinical CNSC isolates reported so far from Mainland China into three major subgroups.

Comparing acute normovolumic hemodilution with autologous platelet-rich plasma for blood preservation during aortic surgery : study protocol for a randomized controlled clinical trial.

BACKGROUND: Both acute normovolumic hemodilution (ANH) and autologous platelet-rich plasma (aPRP) have been demonstrated blood-protective effects in cardiac aortic surgery; however, the efficacies of the two methods have not been compared. This study aims to compare the effects of aPRP and ANH prior to aortic surgery on postoperative bleed and other outcomes. METHODS AND ANALYSIS: This is a prospective, single-center, double-blind controlled clinical trial including 160 patients randomized 1:1 to receive aPRP (test group) or autologous whole blood (ANH, control group). The primary objective is to compare the drainage volumes in the two groups at 24, 48, and 72 h postoperatively. Secondary outcomes include input of allogeneic blood and blood products and durations of aortic block, extracorporeal circulation, deep hypothermic arrest of circulation, tracheal extubation, hospital stay, requirement for secondary surgical hemostasis, and application of intra-aortic balloon pump or extracorporeal membrane oxygenation in the two groups. In addition, heart rate, systolic blood pressure, diastolic blood pressure, central venous pressure, and thromboelastography recorded before blood reservation (T1), after blood reservation (T2), before blood transfusion (T3), and after the blood is returned (T4) to the transfusion will be compared between the two groups of patients. DISCUSSION: This study will demonstrate if the use of aPRP could reduce the risk of bleeding after aortic surgery compared with ANH. The results are expected to have practical clinical applications in terms of more effective blood protection and shorter hospital stay. TRIAL REGISTRATION: This study was registered with the Chinese Clinical Trial Registry ( http://www.chictr.org.cn/ ) with the ID ChiCTR 1900023351.Registered on May 23, 2019. TRIAL STATUS: Recruiting start date: July 1, 2019; expected recruiting end date: July 1, 2024 Version number and date: Version 2 of 05-04-2019.

A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI.

BACKGROUND: Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE: To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE: Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION: 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE: 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT: AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS: The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION: An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Radiomics analysis based on CT for the prediction of pulmonary metastases in ewing sarcoma.

OBJECTIVES: This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) within 2 years after diagnosis. MATERIALS AND METHODS: A total of 143 patients with a histopathological diagnosis of ES were enrolled in this study (114 in the training cohort and 29 in the validation cohort). The regions of interest (ROIs) were handcrafted along the boundary of each tumor on the CT and CT-enhanced (CTE) images, and radiomic features were extracted. Six different models were built, including three radiomics models (CT, CTE and ComB models) and three clinical-radiomics models (CT_clinical, CTE_clinical and ComB_clinical models). The area under the receiver operating characteristic curve (AUC), and accuracy were calculated to evaluate the different models, and DeLong test was used to compare the AUCs of the models. RESULTS: Among the clinical risk factors, the therapeutic method had significant differences between the MT and non-MT groups (P＜0.01). The six models performed well in predicting pulmonary metastases in patients with ES, and the ComB model (AUC: 0.866/0.852 in training/validation cohort) achieved the highest AUC among the six models. However, no statistically significant difference was observed between the AUC of the models. CONCLUSIONS: In patients with ES, clinical-radiomics model created using radiomics signature and clinical features provided favorable ability and accuracy for pulmonary metastases prediction.

A Strain Transfer Model for Detection of Pitting Corrosion and Loading Force of Steel Rebar with Distributed Fiber Optic Sensor.

Steel rebar corrosion is one of the predominant factors influencing the durability of marine and offshore reinforced concrete structures, resulting in economic loss and the potential threat to human safety. Distributed fiber optic sensors (DFOSs) have gradually become an effective method for structural health monitoring over the past two decades. In this work, a strain transfer model is developed between a steel rebar and a DFOS, considering pitting-corrosion-induced strain variation in the steel rebar. The Gaussian function is first adopted to describe the strain distribution near the corrosion pit of the steel rebar and then is substituted into the governing equation of the strain transfer model, and the strain distribution in the DFOS is analytically obtained. Tensile tests are also conducted on steel rebars with artificially simulated corrosion pits, which are used to validate the developed model. The results show that the Gaussian function can be used to describe the strain variation near a corrosion pit with a depth less than 50% of the steel rebar diameter, and the strain distribution in the DFOS analytically determined based on the developed strain transfer model agrees well with the tensile test results. The corrosion pit depth and loading force in the steel rebars estimated based on the proposed model agree well with the actual values, and therefore, the developed strain transfer model is effective in detecting pitting corrosion and loading force in steel rebars.

[Sources, Disposal Technologies, and Environmental Impact of Photopolymerization-based 3D Printing Plastic Waste].

In the 3D printing industry, photopolymerization-based 3D printing is considered to have the characteristics of high printing accuracy and mature technology. Therefore, it is of wide concern in industrial application and academic research. With the rapid development of photopolymerization-based technology, photopolymerization-based plastic waste will inevitably be produced in the process of product manufacturing and use. This kind of plastic waste is a new type of organic solid waste with an incalculable growth rate, and its impact on the environment is difficult to predict. Based on available research results, the latest research progress of sources, disposal technologies, and environmental impact of photopolymerization-based plastic waste were summarized and analyzed. The results revealed that the photopolymerization-based plastic waste was covalently crosslinked with thermosetting plastic. It had relatively higher activation energy and photo-sensitive chromogenic groups. There were some potential hazards to the environment and biosome caused by the raw material, printing process, and waste disposal process of photopolymerization-based plastic. Therefore, prospects and suggestions were proposed for the possibility of future disposal of photopolymerization-based plastic waste, in order to provide a reference for developing the photopolymerization-based 3D printing industry.