Redox Relay-Induced C-S Radical Cross-Coupling Strategy: Application in Nontraditional Site-Selective Thiocyanation of Quinoxalinones.

Oxidative cross-coupling is a powerful strategy to form C-heteroatom bonds. However, oxidative cross-coupling for constructing C-S bond is still a challenge due to sulfur overoxidation and poisoning transition-metal catalysts. Now, electrochemical redox relay using sulfur radicals formed in situ from inorganic sulfur source offers a solution to this problem. Herein, electrochemical redox relay-induced C-S radical cross-coupling of quinoxalinones and ammonium thiocyanate with bromine anion as mediator is presented. The electrochemical redox relay comprised initially the formation of sulfur radical via indirect electrochemical oxidation, simultaneous electrochemical reduction of the imine bond, electro-oxidation-triggered radical coupling involving dearomatization-rearomatization, and the reformation of the imine bond through anodic oxidation. Applying this strategy, various quinoxalinones bearing multifarious electron-deficient/-rich substituents at different positions were well compatible with moderate to excellent yields and good steric hindrance compatibility under constant current conditions in an undivided cell without transition-metal catalysts and additional redox reagents. Synthetic applications of this methodology were demonstrated through gram-scale preparation and follow-up transformation. Notably, such a unique strategy may offer new opportunities for the development of new quinoxalinone-core leads.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Brønsted Acid-Catalyzed Intramolecular Tandem Double Cyclization of γ-Hydroxy Acetylenic Ketones with Alkynes into Naphtho[1,2-b]furan-3-ones.

A metal-free and atom-economic route for the synthesis of naphtho[1,2-b]furan-3-ones has been realized via p-TsOH·H2O-catalyzed intramolecular tandem double cyclization of γ-hydroxy acetylenic ketones with alkynes in formic acid. The benzene-linked furanonyl-ynes are the key intermediates obtained by the scission/recombination of C-O double bonds. Further, the structural modifications of the representative product were implemented by reduction, demethylation, substitution, and [5 + 2]-cycloaddition.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVES: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. METHODS: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. RESULTS: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. CONCLUSION: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. CLINICAL RELEVANCE STATEMENT: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. KEY POINTS: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis.

OBJECTIVE: The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS: Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS: In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS: The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

A CT-Based Radiomics Nomogram Model for Differentiating Primary Malignant Melanoma of the Esophagus from Esophageal Squamous Cell Carcinoma.

Objective: The diagnosis of primary malignant melanoma of the esophagus (PMME) before treatment is essential for clinical decision-making. However, PMME may be misdiagnosed as esophageal squamous cell carcinoma (ESCC) sometimes. This research is aimed at devising a radiomics nomogram model of CT for distinguishing PMME from ESCC. Methods: In this retrospective analysis, 122 individuals with proven pathologically PMME (n = 28) and ESCC (n = 94) were registered from our hospital. PyRadiomics was applied to derive radiomics features from plain and enhanced CT images after resampling image into an isotropic resolution of 0.625 × 0.625 × 0.625 mm3. The diagnostic efficiency of the model was evaluated by an independent validation group. Results: For the purpose of differentiation between PMME and ESCC, a radiomics model was constructed using 5 radiomics features obtained from nonenhanced CT and 4 radiomics features derived from enhanced CT. A radiomics model including multiple radiomics features showed excellent discrimination efficiency with AUCs of 0.975 and 0.906 in the primary and validation cohorts, respectively. Then, a radiomics nomogram model was developed. The decision curve analysis has shown remarkable performance of this nomogram model for distinguishing PMME from ESCC. Conclusions: The proposed radiomics nomogram model based on CT could be used for distinguishing PMME from ESCC. Moreover, this model also contributed to helping clinicians determine an appropriate treatment strategy for esophageal neoplasms.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

BACKGROUND: The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. PATIENTS AND METHODS: Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers. RESULTS: A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. CONCLUSION: The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

Dual Proton/Silver-Catalyzed Serial (5 + 2)-Cycloaddition and Nazarov Cyclization of (E)-2-Arylidene-3-hydroxyindanones with Conjugated Eneynes: Synthesis of Indanone-Fused Benzo[cd]azulenes.

A one-pot step-economic tandem process involving (5 + 2)-cycloaddition and Nazarov cyclization reactions has been reported for the facile synthesis of indanone-fused benzo[cd]azulenes from (E)-2-arylidene-3-hydroxyindanones and conjugated eneynes. This highly regio- and stereoselective bisannulation reaction is enabled by dual silver and Brønsted acid catalysis and opens up a new avenue for the construction of important bicyclo[5.3.0]decane skeletons.

Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms.

PURPOSE: This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). METHODS: This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. RESULTS: The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. CONCLUSION: The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.

A systematic review and meta-analysis comparing tumor progression and complications between radiofrequency ablation and thyroidectomy for papillary thyroid carcinoma.

Background: Papillary thyroid cancer (PTC) is the most frequent thyroid cancers worldwide. The efficacy and acceptability of radiofrequency ablation (RFA) in the treatment of PTC have been intensively studied. The aim of this study is to focus on extra detailed that may influent for PTC or papillary thyroid microcarcinoma (PTMC). Materials and methods: We identified a total of 1,987 records of a primary literature searched in PubMed, Embase, Cochrane Library, and Google Scholar by key words, from 2000 to 2022. The outcome of studies included complication, costs, and local tumor progression. After scrutiny screening and full-text assessment, six studies were included in the systematic review. Heterogeneity was estimated using I2, and the quality of evidence was assessed for each outcome using the GRADE guidelines. Results: Our review enrolled 1,708 patients reported in six articles in the final analysis. There were 397 men and 1,311 women in the analysis. Two of these studies involved PTC and four focused on PTMC. There were 859 patients in the RFA group and 849 patients in the thyroidectomy group. By contrast, the tumor progression of RFA group was as same as that surgical groups [odds ratio, 1.31; 95% CI, 0.52-3.29; heterogeneity (I2 statistic), 0%, p = 0.85]. The risk of complication rates was significantly lower in the RFA group than that in the surgical group [odds ratio, 0.18; 95% CI, 0.09-0.35; heterogeneity (I2 statistic), 40%, p = 0.14]. Conclusions: RFA is a safe procedure with a certain outcome for PTC. RFA can achieve a good efficacy and has a lower risk of major complications.

Surgical treatment of liver inflammatory pseudotumor-like follicular dendritic cell sarcoma: A case report.

BACKGROUND: Inflammatory pseudotumor-like follicular dendritic cell sarcoma (IPT-like FDCS) is rare with a low malignant potential. Hepatic IPT-like FDCS has similar clinical features to hepatocellular carcinoma (HCC), making it extremely difficult to distinguish between them in clinical practice. We describe the case of a young female patient diagnosed with HCC before surgery, which was pathologically diagnosed as IPT-like FDCS after the left half of the liver was resected. During 6 mo of follow-up, the patient recovered well with no signs of recurrence or metastasis. CASE SUMMARY: A 23-year-old female patient with a 2-year history of hepatitis B presented to the Affiliated Hospital of Guizhou Medical University. She was asymptomatic at presentation, and the findings from routine laboratory examinations were normal except for slightly elevated alpha-fetoprotein levels. However, ultrasonography revealed a 3-cm diameter mass in the left hepatic lobe, and abdominal contrast-enhanced computed tomography revealed that the tumor had asymmetrical enhancement during the arterial phase, which declined during the portal venous phase, and had a pseudo-capsule appearance. Based on the findings from clinical assessments and imaging, the patient was diagnosed with HCC, for which she was hospitalized and had undergone laparoscopic left hepatectomy. However, the tumor specimens submitted for pathological analyses revealed IPT-like FDCS. After surgical removal of the tumor, the patient recovered. In addition, the patient continued to recover well during 6 mo of follow-up. CONCLUSION: Hepatic IPT-like FDCS is difficult to distinguish from HCC. Hepatectomy may provide beneficial outcomes in non-metastatic hepatic IPT-like FDCS.

Qualitative and quantitative parameters on hepatobiliary phase of gadoxetic acid-enhanced MR imaging for predicting pathological response to preoperative systemic therapy in colorectal liver metastases.

PURPOSE: To explore the value of parameters of the on hepatobiliary phase (HBP) of pre-treatment gadoxetic acid-enhanced magnetic resonance imaging (MRI) for predicting pathological response to systemic therapy in colorectal liver metastases (CRLMs), compared with response evaluation criteria in solid tumors, version 1.1 (RECIST 1.1). METHODS: A total of 96 patients with CRLMs who underwent gadoxetic acid-enhanced MRI prior to treatment and then liver resection from January 2017 to December 2021 were enrolled. The pathological response was assessed by the percentage of residual tumors (RTs), and CRLMs were classified into two groups according to the pathological response grade (PRG): (1) strong response (including PRG2 and PRG3, RTs ≤ 10%), and weak response (PRG1, RTs > 10%). Two radiologists evaluated the enhancement pattern and degree of CRLMs on the HBP. The diameter, mean and standard deviation (SD) value of signal intensity (SI) of CRLMs on pre-contrast and HBP images were recorded. Relative tumor enhancement (RTE) and the SD ratio (SDR) were calculated. These parameters were analyzed in terms of pathological response on a lesion-by-lesion basis. RESULTS: Totally, 263 CRLMs were classified into: the strong response group (PRG2, n = 57; PRG3, n = 7) and the weak response group (PRG1, n = 199). RTE and SDR values were significantly higher in the strong response group than in the weak response group (P < 0.001). RTE values (P < 0.001) and SDR values (P = 0.031) were independent factors for predicting strong response. The area under curve (AUC) of RTE and SDR values were 0.725 and 0.652, respectively. The combination of these parameters was 0.750, which performed better than RECIST 1.1 (0.750 vs 0.531; P < 0.001). CONCLUSIONS: RTE and SDR values on HBP are potential features in predicting pathological response to systemic therapy in CRLMs.

Electro-oxidation induced O-S cross-coupling of quinoxalinones with sodium sulfinates for synthesizing 2-sulfonyloxylated quinoxalines.

The functionalization of quinoxalinones is synthetically and biologically appealing, however, C2 functionalized quinoxalinones is not reported via environmentally friendly approach. Herein, we disclosed C2-O sulfonylation of quinoxalinones via our developed electrochemical oxidative O-S coupling strategy for synthesizing 2-sulfonyloxylated quinoxalines. Applying this protocol, quinoxalin-ones and sodium sulfinates as the starting materials, a wide range of 2-sulfonyloxyl quinoxaline derivatives were obtained in moderate to good yields with good functional-group tolerance under mild conditions without additional oxidants. The utility of this methodology and the sulfonyloxyl handles was demonstrated trough gram-scale preparation and the synthesis of 2-substituted quinoxaline-based bioactive molecules, respectively.

Comparison Between Percutaneous Kyphoplasty and Percutaneous Vertebroplasty in Terms of Efficacy in Osteoporotic Vertebral Compression Fractures: A Meta-analysis.

Background and Aim: Osteoporotic vertebral compression fractures (OVCFs) are acknowledged to be common fractures, especially in the elderly population. Minimally invasive percutaneous methods of treatment for these fractures such as kyphoplasty (KP) and vertebroplasty (VP) have been valid and effective tools for decreasing clinical problems, which are associated with more beneficial effects compared with traditional methods such as open surgery or conservative treatment. Hence, we conducted the current meta-analysis in order to gather updated evidence for the systematic assessment of clinical and radiographic outcomes of KP compared with VP. Methods: We searched articles published based on the electronic databases, including PubMed, EMBASE, and Cochrane Library. Publications of studies comparing KP with VP in the treatment of OVCFs were collected. After rigorous and thorough review of study quality, we extracted the data on the basis of eligible trials, which analyzed the summary hazard ratios (HRs) of the end points of interest. Results: Our inclusion criteria involved a total of 6 studies. In total, data from 644 patients, 330 who received VP and 284 who received KP, were included in the review. There was no significant difference in either group in terms of visual analog scale (VAS) scores (MD = 0.17; 95% CI, -0.39 to 0.73; P = .56), risk of cement leakage (odds ratio [OR] = 1.31; 95% CI, 0.62 to 2.74; P = .47) or Oswestry Disability Index (ODI) scores (MD = 0.51; 95% CI, -1.87 to 2.88; P = .68). Nevertheless, the injected cement volume (MD = -0.52; 95% CI, -0.88 to -0.15; P = .005) in the VP group was linked to a markedly lower statistically significant trend compared with the KP group. Conclusion: This meta-analysis evaluated acceptable efficacy levels across the involved trials. VP injected cement volume had several advantages in this meta-analysis. Yet, no significant differences were observed in terms of VAS scores, ODI scores, or cement leakage when KP was compared to VP therapy. Given the combined results of our study, the optimal treatment for patients with OVCFs should be determined by further high-quality multicenter randomized controlled trials with longer follow-up and larger sample sizes.

Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics.

BACKGROUND: The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs. AIM: To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs. METHODS: A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group (n = 69) and evaluated in a test group (n = 34). RESULTS: The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training group vs 0.642 (95%CI: 0.379-0.870), 0.881 (95%CI: 0.772-0.990), and 0.894 (95%CI: 0.773-1.000) in the test group, respectively. The AUC of the nomogram model was significantly larger than that of the radiological model in both the training group (Z = 2.795, P = 0.0052) and test group (Z = 2.785, P = 0.0054). The decision curve of analysis showed that the nomogram model produced increased benefit across the entire risk threshold range. CONCLUSION: Radiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

Radiomics analysis based on multiple parameters MR imaging in the spine: Predicting treatment response of osteolytic bone metastases to chemotherapy in breast cancer patients.

PURPOSE: To assess the value of radiomics, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and stretched-exponential (SE) MR imaging in prediction of therapeutic response in patients with spinal metastases before chemotherapy. METHODS: Thirty-six patients with 190 osteolytic metastatic lesions from breast cancer were prospectively enrolled and underwent MR imaging before and after 6 months' treatment on a 1.5 T MRI. According to MDA criteria, 68 lesions were categorized as progressive disease (PD) and 122 lesions were categorized as stable or improvement (non-PD). The regions of interest (ROIs) were manually drawn on DWI, T1WI, T2WI and FS-T2WI by two radiologists with ITK-SNAP. The ADCall (multiple b-values method), IVIM parameters (D, D* and f) and SE parameters (DDC and α) were generated. The radiomics features were extracted from the ROIs. RESULTS: The mean values of ADC, DDC, and D before treatment were significantly higher in non-PD group than those in PD group (P = 0.001). The radiomics based on ADCall had the highest AUC value (0.852), followed by that of the T2WI (0.829) and FS-T2WI (0.798). The radiomics model using ADCall and FS-T2WI showed excellent efficiency in predicting treatment response with AUCs of 0.905 and 0.873 in training and validation cohorts. The radiomics model had better performance than that of ADCall, D, and DDC for predicting treatment response of bone metastases. CONCLUSION: Radiomics model based on ADCall and FS-T2WI could predict the treatment response and contribute to assisting clinicians in accurately choosing appropriate management.