Photocatalytic C-H Disulfuration for the Preparation of Indolizine-3-disulfides.

A photocatalytic C-H disulfuration of indolizines was developed, giving an approach to a wide variety of indolizine-3-disulfides with good yields. Trisulfide dioxides were explored as a high-efficient disulfuration reagent. This disulfuration reaction could be scaled up to grams. Mechanistic studies support a photoinduced pathway involving the generation of indolizine cationic radicals. A bulky alkyl substituent on terminal sulfur of trisulfide dioxide A was necessary for selective formation of disulfide over monosulfide.

Four-Component Cyclization of Naphthol/Thionaphthol/Naphthylamine, Formaldehyde, and DBU in Water.

A practical and environmentally benign cascade multicomponent condensation of naphthol/thionaphthol/naphthylamine, formaldehyde, and DBU in water without any catalysts has been achieved. A wide variety of dihydrooxazine, dihydrothiazine, and tetrahydrobenzoquinazoline derivatives N-substituted with a tether bearing a caprolactam unit were afforded in moderate to good yields. The advantages of being cost-effective, metal-free, and easily handled and the use of water as medium made this protocol conform with the principle of green synthesis.

The Predictive Value of Magnetic Resonance Imaging-based Texture Analysis in Evaluating Histopathological Grades of Breast Phyllodes Tumor.

PURPOSE: Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs. METHODS: Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy. RESULTS: Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences. CONCLUSION: MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.

Additive value of texture analysis based on breast MRI for distinguishing between benign and malignant non-mass enhancement in premenopausal women.

BACKGROUND: Non-mass enhancement (NME) is a diagnostic dilemma and highly reliant on the experience of the radiologists. Texture analysis (TA) could serve as an objective method to quantify lesion characteristics. However, it remains unclear what role TA plays in a predictive model based on routine MRI characteristics. The purpose of this study was to explore the value of TA in distinguishing between benign and malignant NME in premenopausal women. METHODS: Women in whom NME was histologically proven (n = 147) were enrolled (benign: 58; malignant: 89) was retrospective. Then, 102 and 45 patients were classified as the training and validation groups, respectively. Scanning sequences included Fat-suppressed T2-weighted and fat-suppressed contrast-enhanced T1-weighted which were acquired on a 1.5T MRI system. Clinical and routine MR characteristics (CRMC) were evaluated by two radiologists according to the Breast Imaging and Reporting and Data system (2013). Texture features were extracted from all post-contrast sequences in the training group. The combination model was built and then assessed in the validation group. Pearson's chi-square test and Mann-Whitney U test were used to compare categorical variables and continuous variables, respectively. Logistic regression analysis and receiver operating characteristic curve were employed to assess the diagnostic performance of CRMC, TA, and their combination model in NME diagnosis. RESULTS: The combination model showed superior diagnostic performance in differentiating between benign and malignant NME compared to that of CRMC or TA alone (AUC, 0.887 vs 0.832 vs 0.74). Moreover, compared to CRMC, the model showed high specificity (72.5% vs 80%). The results obtained in the validation group confirmed the model was promising. CONCLUSIONS: With the combined use of TA and CRMC could afford an improved diagnostic performance in differentiating between benign and malignant NME.

Radiation-induced occult insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The nomogram based on quantitative apparent diffusion coefficients for discrimination.

BACKGROUND: Radiation-induced insufficiency fractures (IF) is frequently occult without fracture line, which may be mistaken as metastasis. Quantitative apparent diffusion coefficient (ADC) shows potential value for characterization of benign and malignant bone marrow diseases. The purpose of this study was to develop a nomogram based on multi-parametric ADCs in the differntiation of occult IF from bone metastasis after radiotherapy (RT) for cervical cancer. METHODS: This study included forty-seven patients with cervical cancer that showed emerging new bone lesions in RT field during the follow-up. Multi-parametric quantitative ADC values were measured for each lesion by manually setting region of interests (ROIs) on ADC maps, and the ROIs were copied to adjacent normal muscle and bone marrow. Six parameters were calculated, including ADCmean, ADCmin, ADCmax, ADCstd, ADCmean ratio (lesion/normal bone) and ADCmean ratio (lesion/muscle). For univariate analysis, receiver operating characteristic curve (ROC) analysis was performed to assess the performance. For combined diagnosis, a nomogram model was developed by using a multivariate logistic regression analysis. RESULTS: A total of 75 bone lesions were identified, including 48 occult IFs and 27 bone metastases. There were significant differences in the six ADC parameters between occult IFs and bone metastases (p < 0.05), the ADC ratio (lesion/ muscle) showed an optimal diagnostic efficacy, with an area under ROC (AUC) of 0.887, the sensitivity of 95.8%, the specificity of 81.5%, respectively. Regarding combined diagnosis, ADCstd and ADCmean ratio (lesion/muscle) were identified as independent factors and were selected to generate a nomogram model. The nomogram model showed a better performance, yielded an AUC of 0.92, the sensitivity of 91.7%, the specificity of 96.3%, positive predictive value (PPV) of 97.8% and negative predictive value (NPV) of 86.7%, respectively. CONCLUSIONS: Multi-parametric ADC values demonstrate potential value for differentiating occult IFs from bone metastasis, a nomogram based on the combination of ADCstd and ADCmean ratio (lesion/muscle) may provide an improved classification performance.

Pelvic insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The added value of DWI for characterization.

OBJECTIVES: We sought to determine the added value of diffusion-weighted magnetic resonance imaging (DWI) in the differentiation of pelvic insufficiency fracture (PIF) from bone metastasis after radiotherapy in cervical cancer patients. METHODS: In the present study, 42 cervical cancer patients after radiotherapy with 61 bone lesions (n = 40, PIFs; n = 21, bone metastasis) were included. Conventional MRI and DWI were performed in all patients. For qualitative imaging diagnosis, two sets of images were reviewed independently by three observers, including a conventional MRI set (unenhanced T1-weighted, T2-weighted, and enhanced T1-weighted images) and a DWI set (conventional MRIs, DW images, and ADC maps). The mean ADC value of each lesson was measured on ADC maps. The diagnostic performance was assessed by using the area under the receiver operating characteristic curve (Az), and sensitivity and specificity were determined. RESULTS: For all observers, the Az value and sensitivity of the DWI set showed improvement compared with the conventional MRI set. The observer who had the least experience (3 years) demonstrated significant improvement in diagnostic performance with the addition of DWI; Az value increased from 0.804 to 0.915 (p = 0.042) and sensitivity increased from 75.0 to 92.5% (p = 0.035). The mean ADCs of the PIFs were significantly higher than the bone metastases (p < 0.001); ADC values > 0.97 × 10-3 mm2/s yielded an Az of 0.887, a sensitivity of 92.5%, and a specificity of 76.2%. CONCLUSIONS: The addition of DWI to conventional MRI improved the differentiation of PIF from bone metastasis after RT in patients with cervical cancer. KEY POINTS: • DWI showed additive value to conventional MRI in the differentiation of PIF from bone metastasis after RT. • For qualitative diagnosis, the addition of DWI can improve diagnostic performance compared with conventional MRI alone and can particularly improve the sensitivity. • Quantitative ADC assessment showed potential value for identifying PIF from bone metastasis.

The Utility of Texture Analysis Based on Breast Magnetic Resonance Imaging in Differentiating Phyllodes Tumors From Fibroadenomas.

Background: The preoperative diagnosis of phyllodes tumors (PTs) of the breast is critical to appropriate surgical treatment. However, reliable differentiation between PT and fibroadenoma (FA) remains difficult in daily clinical practice. The purpose of this study was to investigate the utility of breast MRI texture analysis for differentiating PTs from FAs. Materials and Methods: Forty-two PTs and 42 FAs were enrolled in this retrospective study. Clinical and conventional MRI features (CCMF) and MRI texture analysis were used to distinguish between PT and FA. Texture features were extracted from the axial short TI inversion recovery T2-weighted (T2W-STIR), T1-weighted pre-contrast, and two contrast-enhanced series (first contrast and third contrast). The Mann-Whitney U test was used to select statistically significant features of texture analysis and CCMF. Using a linear discriminant analysis, the most discriminative features were determined from statistically significant features. The K-nearest neighbor classifier and ROC curve were applied to evaluate the diagnostic performance. Results: With a higher classification accuracy (89.3%) and an AUC of 0.89, the texture features on T2W-STIR outperformed the texture features on other MRI sequences and CCMF. The AUC of the combination of CCMF with texture features on T2W-STIR was significantly higher than that of CCMF or texture features on T2W-STIR alone (p < 0.05). Based on the result of the classification accuracy (95.2%) and AUC (0.95), the diagnostic performance of the combination strategy performed better than texture features on T2W-STIR or CCMF separately. Conclusions: Texture features on T2W-STIR showed better diagnostic performance compared to CCMF for the distinction between PTs and FAs. After further validation of multi-institutional large datasets, MRI-based texture features may become a potential biomarker and be a useful medical decision tool in clinical trials having patients with breast fibroepithelial neoplasms.