Search

Background: Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). Purpose: To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC. Study type: Retrospective. Population: A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC). Field Strength/Sequence: A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence. Assessment: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared. Statistical Tests: The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. Results: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831–0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779–0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526–0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). Conclusion: MRI radiomics models may be useful for discriminating DPHCC from non‐DPHCC before surgery. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Comprehensive Summary: We develop an efficient visible‐light photoreodox‐catalyzed hydrodecarboxylation and deuterodecarboxylation of fatty acids for alkanes and deuterium alkanes. The key to the efficient transformation should attribute to the co‐catalysis of the suitable methoxy substituted acridine photocatalyst (Mes‐1,3,6,8‐tetramethoxy‐Acr‐3",5"‐dimethoxy‐Ph) and the hydrogen atom transfer catalyst 4′,4′‐dimethyl diphenyldisulfane, possibly via facilitating of quenching the active alkyl radicals to overcome the competitive homocoupling products, olefins via the disproportionation, cracking products via the C—C bond β‐scission. [ABSTRACT FROM AUTHOR]

Purpose: Biomechanical properties can be used as biomarkers to diagnose tumors, monitor tumor development, and evaluate treatment efficacy. The purpose of this preliminary study is to characterize the biomechanical environment of two typical liver tumors, hemangiomas (HEMs) and hepatocellular carcinomas (HCCs), and to investigate the potential of using strain metrics as biomarkers for tumor diagnosis, based on a limited clinical dataset. Methods: Magnetic resonance (MR) tagging was used to quantify the motion and deformation of the two types of liver tumors. Displacements of the tumors arising from a heartbeat were measured over one cardiac cycle. Local biomechanical conditions of the tumors were characterized by estimating two principal strains (ε1 and ε2) and an octahedral shear strain (εsoct) of the tumor and its peripheral region. Biomechanical conditions of the tumors were compared with those of the arbitrarily selected regions from healthy volunteers. Results: We observed that the HCCs had significantly smaller strain values compared to their peripheral tissues. However, the HEMs did not have significantly different strains from those of the peripheral tissues, and were similar to healthy liver regions. The sensitivity of using ε1, ε2, and εsoct to diagnose HCC were all 1, while the sensitivity of using ε1, ε2, and εsoct to diagnose HEM were 0.67, 0.17, and 0.67, respectively. Conclusions: Lagrangian strain metrics provide insight into the biomechanical conditions of certain liver tumors in the human body and may provide another perspective for tumor characterization and diagnosis. [ABSTRACT FROM AUTHOR]

Purpose Ultrasound ( US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis ( CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. Methods To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means ( FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 [ABSTRACT FROM AUTHOR]