Automatic prediction of non-iodine-avid status in lung metastases for radioactive I131 treatment in differentiated thyroid cancer patients.

Objectives: The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI). Methods: Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive patients from other two hospitals. Stepwise validation was further performed according to the nodule's largest diameter. Results: The SE-Net deep learning network yielded area under the receiver operating characteristic curve (AUC) values of 0.879 (95% confidence interval: 0.852-0.906) and 0.713 (95% confidence interval: 0.613-0.813) for internal and external validation. With the LM diameter decreasing from ≥10mm to ≤4mm, the AUCs remained relatively stable, for smallest nodules (≤4mm), the model yielded an AUC of 0.783. Decision curve analysis showed that most patients benefited using deep learning to decide radioactive I131 treatment. Conclusion: This study presents a noninvasive, less radioactive and fully automatic approach that can facilitate suitable DTC patient selection for RAI therapy of LMs. Further prospective multicenter studies with larger study cohorts and related metabolic factors should address the possibility of comprehensive clinical transformation.

Natural Deep Eutectic Solvent-Assisted Extraction, Structural Characterization, and Immunomodulatory Activity of Polysaccharides from Paecilomyces hepiali.

Polysaccharides, which can be affected by different preparations, play a crucial role in the biological function of Paecilomyces hepiali (PHPS) as a health food. To explore high-valued polysaccharides and reduce the negative influence of human involvement, a green tailorable deep eutectic solvent (DES) was applied to optimize the extraction of polysaccharides (PHPS-D), followed by the evaluation of the structural properties and immunomodulation by comparison with the hot-water method (PHPS-W). The results indicated that the best system for PHPS-D was a type of carboxylic acid-based DES consisting of choline chloride and succinic acid in the molar ratio of 1:3, with a 30% water content. The optimal condition was as follows: liquid-solid ratio of 50 mL/g, extraction temperature of 85 °C, and extraction time of 1.7 h. The actual PHPS-D yield was 12.78 ± 0.17%, which was obviously higher than that of PHPS-W. The structural characteristics suggested that PHPS-D contained more uronic acid (22.34 ± 1.38%) and glucose (40.3 ± 0.5%), with a higher molecular weight (3.26 × 105 g/mol) and longer radius of gyration (78.2 ± 3.6 nm), as well as extended chain conformation, compared with PHPS-W, and these results were confirmed by AFM and SEM. Immunomodulatory assays suggested that PHPS-D showed better performance than PHPS-W regarding pinocytic activity and the secretion of NO and pro-inflammatory cytokines (IL-6, TNF-α and IL-1ß) by activating the corresponding mRNA expression in RAW264.7 cells. This study showed that carboxylic acid-based DES could be a promising tailorable green system for acidic polysaccharide preparation and the valorization of P. hepiali in functional foods.

Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer.

PURPOSE: The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS: We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS: 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS: The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.

Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer.

BACKGROUND: The treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC. METHOD: The retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set. RESULTS: Compared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram. CONCLUSIONS: The mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.

Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading.

PURPOSE: Pathological grade is important for the treatment selection and outcome prediction in bladder cancer (BCa). We aimed to construct a radiomics-clinical nomogram to preoperatively differentiate high-grade BCa from low-grade BCa. METHODS: A total of 185 BCa patients who received multiparametric MRI (mpMRI) before surgery between August 2014 and April 2020 were enrolled in our study. Radiomics features were extracted from the largest tumor located on dynamic contrast-enhancement and T2WI images. After feature selection, the synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (low-grade group). Radiomics signatures were constructed in the training set and assessed in the validation set. Univariable and multivariable logistic regression were applied to build a nomogram. RESULTS: The radiomics signature generated by the least absolute shrinkage and selection operator model achieved the optimal performance for BCa grading in both the SMOTE-balanced training [accuracy: 93.2%, area under the curve (AUC): 0.961] and validation sets (accuracy: 89.9%, AUC: 0.952). A radiomics-clinical nomogram incorporating the radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score had novel calibration and discrimination both in the training (AUC: 0.956) and validation sets (AUC: 0.958). Decision curve analysis presented the clinical utility of the nomogram for decision-making. CONCLUSIONS: The mpMRI-based radiomics signature had the potential to preoperatively predict the pathological grade of BCa. The proposed nomogram combining the radiomics signature with the VI-RADS score improved the diagnostic power, which may aid in clinical decision-making.

Correlation of vascular change and cognitive impairment in age-related macular degeneration patients.

Age-related macular degeneration (AMD) is one of the leading causes of blindness among the elderly. However, the correlation between vascular change and cognitive impairment in AMD disease is still unknown. In our study, we investigate the blood flow change among different layers of the retina in the AMD eye and normal fellow eye of AMD patients and its influence with patients' cognition. Our study applies optical coherence tomography angiography (OCTA) to assess the blood flow of the retina in AMD patients and the healthy controls (HCs). Magnetic resonance imaging (MRI) and Montreal Cognitive Assessment (MoCA) were performed to evaluate the cognitive change of the individuals. The results showed that deep capillary plexus density, superficial capillary plexus density, retina thickness and retinal nerve fiber layer thickness deduction existed in both eyes of the AMD patient compared with the HCs. The reduced vessel density in the choroidal layer only existed in the AMD eye of the patients while the fellow eye of patients and HCs did not change much. Furthermore, the AMD patient got a lower MoCA score compared to the HCs. Our results illustrate that the fellow eye of the AMD patient underwent vessel density change, which may lead to the early stage of AMD. The lower score of the MoCA test in AMD patients refers to the cognitive impairment. These findings show the significance of taking actions to prevent the progress of AMD in the fellow eye, as well as paying more attention to the development of cognitive impairment of these patients.

Evaluation of Vesical Imaging-Reporting and Data System (VI-RADS) scoring system in predicting muscle invasion of bladder cancer.

BACKGROUND: To investigate the accuracy of using the Vesical Imaging-Reporting and Data System (VI-RADS) scoring system in prediction preoperative muscle invasion of bladder cancer. METHODS: The study retrospectively reviewed consecutive patients with bladder cancer who received multiparametric magnetic resonance imaging (MRI) between January 2017 and June 2019. Clinical and pathological parameters were collected. Bladder tumors were re-evaluated with 5-point VI-RADS scoring system by two experienced radiologists independently. The VI-RADS score was compared with postoperative pathology for each tumor for determining muscle invasion. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each VI-RADS cutoff. RESULTS: A total of 126 patients were included in analysis, with 82 patients received transurethral resection of bladder tumor (TURBt) while 44 underwent radical cystectomy. Fifty patients were muscle-invasive bladder cancer and 76 were non-muscle invasive tumor confirmed pathologically. VI-RADS score was only predictive factor to muscle invasion in multivariate analysis. Setting VI-RADS score greater than or equal to 4 reached the best sensitivity and specificity of 94.00% and 92.11%, with PPV and NPV value of 88.68% and 95.89%. CONCLUSIONS: VI-RADS score system is a promising and effective modality in determining detrusor muscle invasion of bladder cancer preoperatively.

Cerebral Amyloid Angiopathy (CAA)-Related Inflammation: Comparison of Inflammatory CAA and Amyloid-ß-Related Angiitis.

Cerebral amyloid angiopathy-related inflammation (CAA-ri) is a relatively rare syndrome of reversible encephalopathy and could be divided into two subtypes of inflammatory CAA (ICAA) and amyloid-ß-related angiitis (ABRA) according to histopathology. We present a case of pathologically proved ABRA with partial seizures and headache, and a focal lesion in the right temporal lobes on magnetic resonance imaging. Summarized from previous 139 ABRA and ICAA cases, ABRA is preferred when the lesion is enhanced on MRI and requires combination drug therapy, while ICAA is highly suspected with ApoE genotype of ɛ4/ɛ4. More clinical markers for diagnosis of CAA-ri warrant further researches.

Whole-organ CT perfusion of the pancreas: impact of iterative reconstruction on image quality, perfusion parameters and radiation dose in 256-slice CT-preliminary findings.

BACKGROUND: This study was performed to assess whether iterative reconstruction can reduce radiation dose while maintaining acceptable image quality, and to investigate whether perfusion parameters vary from conventional filtered back projection (FBP) at the low-tube-voltage (80-kVp) during whole-pancreas perfusion examination using a 256-slice CT. METHODS: 76 patients with known or suspected pancreatic mass underwent whole-pancreas perfusion by a 256-slice CT. High- and low-tube-voltage CT images were acquired. 120-kVp image data (protocol A) and 80-kVp image data (protocol B) were reconstructed with conventional FBP, and 80-kVp image data were reconstructed with iDose(4) (protocol C) iterative reconstruction. The image noise; contrast-to-noise ratio (CNR) relative to muscle for the pancreas, liver, and aorta; and radiation dose of each protocol were assessed quantitatively. Overall image quality was assessed qualitatively. Among 76 patients, 23 were eventually proven to have a normal pancreas. Perfusion parameters of normal pancreas in each protocol including blood volume, blood flow, and permeability-surface area product were measured. RESULTS: In the quantitative study, protocol C reduced image noise by 36.8% compared to protocol B (P<0.001). Protocol C yielded significantly higher CNR relative to muscle for the aorta, pancreas and liver compared to protocol B (P<0.001), and offered no significant difference compared to protocol A. In the qualitative study, protocols C and A gained similar scores and protocol B gained the lowest score for overall image quality (P<0.001). Mean effective doses were 23.37 mSv for protocol A and 10.81 mSv for protocols B and C. There were no significant differences in the normal pancreas perfusion values among three different protocols. CONCLUSION: Low-tube-voltage and iDose(4) iterative reconstruction can dramatically decrease the radiation dose with acceptable image quality during whole-pancreas CT perfusion and have no significant impact on the perfusion parameters of normal pancreas compared to the conventional FBP reconstruction using a 256-slice CT scanner.