A series of aminoalkyl-substituted polyfluorene copolymers with benzothiadiazole (BTDZ) of different content were synthesized by Suzuki coupling reaction, and their quaternized ammonium polyelectrolyte derivatives were obtained through a postpolymerization treatment on the terminal amino groups. Copolymers are soluble in environmentally friendlier solvents, such as alcohols. It was found that the efficient energy transfer occurs by exciton trapping on the narrow band gap BTDZ site under UV illumination. Only 1% of BTDZ content is needed to completely quench a fluorene emission for both the neutral and the quaternized copolymers in the neat film. Absolute PL efficiencies of copolymer films were greatly enhanced as a result of the suppression of excimer formation. Light-emitting devices fabricated from these copolymers show high external quantum efficiencies over 3% and 1% for the neutral precursor and the quaternized copolymers, respectively, with high work function metals such as Al as a cathode. To the best of our knowledge, this is the first report on an electroluminescent polymer which bears the high EL efficiency, the electron-injection ability from high work function metals, and the solubility in environment-friendly solvents at the same time. These features make them a promising candidate for the next generation of light-emitting copolymers in PLED flat panel display application.

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Objective The aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predicting non-response to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC). Methods We retrospectively enrolled 425 patients with LARC [allocated in a 3:1 ratio to a primary ( n = 318) or validation ( n = 107) cohort] who received neoadjuvant therapy before surgery. All patients underwent T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI scans before receiving neoadjuvant therapy. We extracted 2424 radiomic features from the pretherapeutic, multiparametric MR images of each patient. The Wilcoxon rank-sum test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression were successively performed for feature selection, whereupon a multiparametric MRI-based radiomic model was established by means of multivariate logistic regression analysis. This feature selection and multivariate logistic regression analysis was also performed on all single-modality MRI data to establish four single-modality radiomic models. The performance of the five radiomic models was evaluated by receiver operating characteristic (ROC) curve analysis in both cohorts. Results The multiparametric, MRI-based radiomic model based on 16 features showed good predictive performance in both the primary ( p < 0.01) and validation ( p < 0.05) cohorts, and performed better than all single-modality models. The area under the ROC curve of this multiparametric MRI-based radiomic model achieved a score of 0.822 (95% CI 0.752–0.891). Conclusions We demonstrated that pretherapeutic, multiparametric MRI radiomic features have potential in predicting non-response to neoadjuvant therapy in patients with LARC. Electronic supplementary material The online version of this article (10.1245/s10434-019-07300-3) contains supplementary material, which is available to authorized users.

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Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this multicenter retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-), and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV-, and deep-radiomics signature were built, respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: Five, seven, and nine features were selected for building handcrafted-, CV-, and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis (DCA) further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Wu et al. Radiomics Approach Predicting ESCC LNM Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.

Bowel function deteriorates frequently after low anterior resection for rectal cancer. Severe bowel dysfunction is significantly associated with preoperative long-course radiotherapy and a lower-third tumor, and the thickening of rectal wall after radiation is a strong predictor. Treatment decisions and patient consent should be implemented with raising awareness of bowel symptom burdens. See Video Abstract at http://links.lww.com/DCR/A317.

Objective:The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC.Background:Adequate detection and staging of PC from CRC remain difficult.Methods:The primary tumors in synchronous PC were delineated on preoperative contrast-enhanced computed tomography (CT) images. The features of adjacent peritoneum were extracted to build a ResNet3D + SVM classifier. The performance of ResNet3D + SVM classifier was evaluated in the test set and was compared to routine CT which was evaluated by radiologists.Results:The training set consisted of 19,814 images from 54 patients with PC and 76 patients without PC. The test set consisted of 7837 images from 40 test patients. The ResNet-3D spent only 34 seconds to analyze the test images. To increase the accuracy of PC detection, we have built a SVM classifier by integrating ResNet-3D features with twelve PC-specific features (P < 0.05). The ResNet3D + SVM classifier showed accuracy of 94.11% with AUC of 0.922 (0.912–0.944), sensitivity of 93.75%, specificity of 94.44%, positive predictive value (PPV) of 93.75%, and negative predictive value (NPV) of 94.44% in the test set. The performance was superior to routine contrast-enhanced CT (AUC: 0.791).Conclusions:The ResNet3D + SVM classifier based on deep learning algorithm using ResNet-3D framework has shown great potential in prediction of synchronous PC in CRC.