Development of MRI-based radiomics predictive model for classifying endometrial lesions.

An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.

Application Values of 2D and 3D Radiomics Models Based on CT Plain Scan in Differentiating Benign from Malignant Ovarian Tumors.

BACKGROUND: Accurate identification of ovarian tumors as benign or malignant is highly crucial. Radiomics is a new branch of imaging that has emerged in recent years to replace the traditional naked eye qualitative diagnosis. OBJECTIVE: This study is aimed at exploring the difference in the application potential of two- (2D) and three-dimensional (3D) radiomics models based on CT plain scan in differentiating benign from malignant ovarian tumors. METHOD: A retrospective analysis was performed on 140 patients with ovarian tumors confirmed by surgery and pathology in our hospital from July 2017 to August 2020. These 140 patients were divided into benign group and malignant group according to the pathological results. The ITK-SNAP software was used to outline the regions-of-interest (ROI) of 2D or 3D tumors on the CT plain scan image of each patient; the texture features were extracted through analysis kit (AK), and the cases were randomly divided into training groups (n = 99) and validation group (n = 41) in a ratio of 7 : 3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform dimensionality reduction, followed by the construction of the radiomics nomogram model using the logistic regression method. The receiver operating characteristic (ROC) curve was drawn, and the calibration curve and decision curve analysis (DCA) were used to evaluate and verify the results of the radiomics nomogram and compare the differences between 2D and 3D diagnostic performance. RESULTS: There were 396 quantitative radiomics feature parameters extracted from 2D group and the 3D group, respectively. The area under the curve (AUC) of the radiomics nomogram of the 2D training group and the validation group were 0.96 and 0.97, respectively. The accuracy, specificity, and sensitivity of the training set were 92.9%, 88.9%, and 96.3%, respectively, and those of the validation set were 90.2%, 82.6%, and 100.0%, respectively. The AUCs of the radiomics nomogram of the 3D training group and validation group were 0.96% and 0.99%, respectively. The accuracy, sensitivity, and specificity of the training set were 92.9%, 96.3%, and 88.9%, respectively, and those of the validation set were 97.6%, 95.7%, and 100.0%, respectively. DeLong's test indicated that there was no statistical significance between the two sets (P > 0.05). CONCLUSIONS: For the differential diagnosis of benign and malignant ovarian tumors, the 2D and 3D radiomics nomogram models exhibited comparable diagnostic performance. Considering that the 2D model was cost-effective and time-efficient, it was more recommended to use 2D features in future research.

Recent advances in the tandem annulation of 1,3-enynes to functionalized pyridine and pyrrole derivatives.

Great progress has been made in the tandem annulation of enynes in the past few years. This review only presents the corresponding reactions of 1,3-enyne structural motifs to provide the functionalized pyridine and pyrrole derivatives. The functionalization reactions cover iodination, bromination, trifluoromethylation, azidation, carbonylation, arylation, alkylation, selenylation, sulfenylation, amidation, esterification, and hydroxylation. We also briefly introduce the applications of the products and the reaction mechanisms for the synthesis of corresponding N-heterocycles.

Establishment and characterization of an ovarian yolk sac tumor patient-derived xenograft model.

OBJECTIVE: The lack of appropriate preclinical models of ovarian yolk sac tumor (OYST) is currently hindering the pursuit of new methods of treatment and investigation of the pathogenesis of the disease. We developed and characterized an OYST patient-derived xenograft (PDX) model in this study. METHODS: Tumor fragments from a patient with an OYST were implanted subcutaneously into BALB/c Nude mice. Engrafted xenografts were compared with the original tumor according to histology, immunohistochemistry, humanized identified, and drug efficacy testing with in vivo treatment programs. RESULTS: There was a high degree of histologic and immunohistochemical (IHC) resemblance between the established PDX model and its corresponding human tumors. Bleomycin, etoposide, and cisplatin (JEB) chemotherapy regimens were effective in clinical patients and were effective in the OYST PDX model; therefore, the effect of PDX intervention was consistent with clinical outcomes of OYSTs. CONCLUSION: We have successfully established an OYST PDX model. This OYST model preserves the basic molecular features of the primary human tumor, thereby providing a valuable method to preclinically evaluate new treatments and explore disease pathogenesis.

A Rare Case of Primary Signet-Ring Cell Cervical Carcinoma: Early Stage with Independent Bilateral Ovarian Metastases.

BACKGROUND: Primary signet-ring cell carcinoma of the uterine cervix (PSRCCC) is defined as a mucinous carcinoma. PSRCCC with independent bilateral ovarian metastases has not been previously reported in the literature. CASE PRESENTATION: Herein we describe a case of PSRCCC with ovarian involvement. The patient underwent a detailed complete physical examination, and surgery was then performed to resect all of the tumors. All tumors expressed human papillomavirus 18 no distant tumors were detected, and estrogen receptor and progesterone receptor testing were negative, suggesting that the cervix was the primary site. CONCLUSION: This is the first report of a case of PSRCCC metastasis to bilateral ovaries only. Conservative management of human papillomavirus-associated type endocervical adenocarcinomas with independent ovarian metastases should be considered.

A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography.

This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.

Synthesis of 2-Arylbenzothiazole and 2-Arylthiazole Derivatives via a Ru-Catalyzed meta-Selective C-H Nitration Reaction.

A Ru-catalyzed meta-selective C-H nitration of 2-arylbenzothiazoles and 2-arylthiazoles has been developed. A wide range of functional groups are tolerated, providing the meta-nitrated products in good to excellent yields using Cu(NO3)2·3H2O as the nitro source. The nitration could be carried out on a gram scale and used for the synthesis of promising therapeutic leads for human African trypanosomiasis.

Regioselective ortho-trifluoromethylthiolation of 2-arylbenzo[d]thiazole via tandem substrate-assisted C-H iodination and trifluoromethylthiolation.

A mild and efficient tandem benzo[d]thiazole directed C-H iodination and trifluoromethylthiolation for the synthesis of ortho-trifluoromethylthiolated 2-arylbenzo[d]thiazoles have been developed using AgSCF3 as a coupling partner. The reaction exhibits a diverse array of functional group tolerance giving the desired products in good to excellent yields. Regioselective trifluoromethylthiolation was observed at the less sterically hindered site when the phenyl ring of the substrate possesses a methyl group at its meta position.

N-Benzylcinnamide induces apoptosis in HPV16 and HPV18 cervical cancer cells via suppression of E6 and E7 protein expression.

Seventy percent of all cervical cancers are caused by human papillomavirus (HPV) infections. Natural products are being extensively explored for their potential ability to prevent and treat cervical cancers. N-benzylcinnamide (PT-3) is a natural product purified from Piper submultinerve. Whether or not PT-3 has an effect on cervical cancer cells is as yet unknown. Therefore, we set out to explore the mechanism of action behind PT-3 and how it affects cells that either contain or lack HPV DNA. Our results demonstrate that PT-3 slows the growth kinetics of CaSki (HPV-16 positive) and HeLa (HPV-18 positive) cells in a dose-dependent manner, but does not slows HPV-negative cells. Importantly, we also found that PT-3 induces apoptosis by suppressing expression of E6 and E7 viral oncogenes in HPV-infected cervical cancer CaSki and HeLa cells. Moreover, we found that suppression of E6 and E7 expression leads to modulations in p53 and protein retinoblastomas, which are not changed in HPV-negative cervical cancer C33A cells. These findings demonstrate that PT-3 can effectively promote apoptosis by downregulating expression of E6 and E7.

Palladium-catalyzed intermolecular oxidative cyclization of N-aryl enamines with isocyanides through double sp(2) C-H bonds cleavage: facile synthesis of 4-aminoquinoline derivatives.

An efficient method for the synthesis of 4-aminoquinolines via palladium-catalyzed intermolecular oxidative cyclization of N-aryl enamines and isocyanides through double sp(2) C-H bonds cleavage has been developed.

Synthesis of 2-trifluoromethylpyrazolo[5,1-a]isoquinolines via silver triflate-catalyzed or electrophile-mediated one-pot tandem reaction.

An efficient one-pot tandem cyclization/[3 + 2] cycloaddition reaction of N'-(2-alkynylbenzylidene)hydrazides with ethyl 4,4,4-trifluorobut-2-ynoate under silver triflate-catalyzed or electrophile-mediated conditions is described. Various trifluoromethylated pyrazolo[5,1-a]isoquinolines were afforded in moderate to excellent yield by this developed method.

AgOTf-catalyzed one-pot reactions of 2-alkynylbenzaldoximes with α,ß-unsaturated carbonyl compounds.

AgOTf-catalyzed one-pot reactions of 2-alkynylbenzaldoximes with various α,ß-unsaturated carbonyl compounds under mild conditions are described, which provides a facile and efficient pathway for the synthesis of 1-alkylated isoquinoline derivatives. The method tolerates a wide range of substrates and allows for the preparation of the products of interest in moderate to excellent yields.