Thyroid Embolization for Nonsurgical Treatment of Nodular Goiter: A Single-Center Experience in 56 Consecutive Patients

PurposeTo investigate the safety and efficacy of thyroid artery embolization (TAE) in the treatment of nodular goiter (NG).MethodsDuring a 5.5-year period, 56 consecutive patients with a NG underwent TAE. In Group A, there were 20 patients with a solitary/dominant 5–11-cm nodule, and in Group B, there were 36 patients with numerous nodules. Of the 56 patients, 47 (84%) had a retrosternal goiter and 25 had hyperthyroidism. In all patients, clinical and radiological evaluations were made at baseline and 6 months after TAE, and these parameters were statistically compared.ResultsIn 56 patients, 145 of the 146 thyroid arteries were successfully embolized. The 30-day mortality rate was 1.8%. Minor and major complications occurred in 25 and 2 patients, respectively. Six months after the TAE, the mean nodule volume was reduced from 80.2 mL to 25.0 mL, the mean thyroid volume was reduced from 147.0 mL to 62.6 mL, and the mean intrathoracic extension was reduced from 31.7 mm to 15.9 mm (P < .001). Of the 22 patients with non–Graves hyperthyroidism, 19 (86%) became euthyroid. The mean thyroid-related patient-reported outcome scores improved from 155.4 to 70.4 (P < .001). Of the 51 patients, 50 (98%) declared that they would recommend TAE to other patients with NG.ConclusionsTAE is safe and effective for the treatment of NG, with a significant volume reduction of the nodule(s) and thyroid gland.     

A Pilot First-in-Human Study of Embrace,a Polyethylene Glycol-Based Liquid Embolic Agent,in the Embolization of Malignant and Benign Hypervascular Tumors

PurposeTo investigate the safety and efficacy of an aqueous polyethylene glycol-based liquid embolic agent, Embrace Hydrogel Embolic System (HES), in the treatment of benign and malignant hypervascular tumors.Materials and MethodsA prospective, single-arm, multicenter study included 8 patients, 5 males and 3 females, with a median age of 58.5 years (30–85 years), who underwent embolization in 8 tumors between October 2019 and May 2020. Technical success was defined as successful delivery of HES to the index vessel, with disappearance of >90% of the targeted vascular enhancement or, for portal vein embolization, occlusion of the portal branches to the liver segments for future resection. The volume of HES administered, ease of use (5 point Likert scale), administration time, and adverse events (AEs) were recorded. Evaluation was performed at 7, 30, and 90 days via clinical assessment and blood testing, and follow-up imaging was performed at 30 days.ResultsEight patients were enrolled, and 10 embolizations were performed in 8 lesions. Tumors included hepatocellular carcinoma (n = 4), renal angiomyolipoma (n = 3), and intrahepatic cholangiocarcinoma (n = 1). Technical success was 100%, and the average ease of use was 3.3 ± 1.0 SD. The HES delivery time was 1–28 minutes (median, 16.5 minutes), and the HES volume injected was 0.4–4.0 mL (median, 1.3 mL). All patients reached 30-day follow-up with imaging, and 6 patients reached 90-day follow-up. There were 3 serious AEs in 2 patients that were unrelated to the embolic agent.ConclusionHES resulted in a 100% embolization technical success rate. The product ease of use was acceptable, and no target vessel recanalization was noted on follow-up imaging at 30 days.     

不同年龄阿尔茨海默病患者脑核团ADC值与年龄的相关性

目的 探讨不同年龄阿尔茨海默病（AD）患者ADC值与年龄的相关性。方法 选取30例AD患者（AD组）和年龄与之相匹配的30名志愿者（对照组），按年龄段各分为6个亚组[55~59岁（n=3）、60~64岁（n=4）、65~70岁（n=9）、71~74岁（n=5）、75~80岁（n=6）、>80岁（n=3）]，测量双侧海马、红核、尾状核、杏仁体、壳核ADC值，并进行配对t检验、独立样本t检验、单因素方差分析及Pearson相关分析。结果 AD组红核左、右侧ADC值有统计学差异（P=0.022）。AD组不同年龄亚组右侧海马、双侧尾状核、右侧壳核ADC值差异有统计学意义（P均<0.05）；2组不同年龄亚组双侧海马、壳核、尾状核ADC值差异有统计学意义（P均<0.05）。AD组右侧海马（r=0.615，P<0.001）、右侧壳核（r=0.653，P=0.001）及双侧尾状核（左侧：r=0.397，P=0.030；右侧：r=0.429，P=0.020）ADC值与年龄呈正相关。结论 AD患者右侧海马和壳核、双侧尾状核ADC值随年龄增加而增大。ADC值可为临床预测和早期诊断AD脑内右侧海马和壳核、双侧尾状核神经退行性病变提供参考。     

Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.     

Optimal combination of antiangiogenic therapy for hepatocellular carcinoma

The success of sorafenib in prolonging survival of patients with hepatocellular carcinoma (HCC) makes therapeutic inhibition of angiogenesis a component of treatment for HCC. To enhance therapeutic efficacy, overcome drug resistance and reduce toxicity, combination of antiangiogenic agents with chemotherapy, radiotherapy or other targeted agents were evaluated. Nevertheless, the use of antiangiogenic therapy remains suboptimal regarding dosage, schedule and duration of therapy. The issue is further complicated by combination antiangiogenesis to other cytotoxic or biologic agents. There is no way to determine which patients are most likely respond to a given form of antiangiogenic therapy. Activation of alternative pathways associated with disease progression in patients undergoing antiangiogenic therapy has also been recognized. There is increasing importance in identifying, validating and standardizing potential response biomarkers for antiangiogenesis therapy for HCC patients. In this review, biomarkers for antiangiogenesis therapy including systemic, circulating, tissue and imaging ones are summarized. The strength and deficit of circulating and imaging biomarkers were further demonstrated by a series of studies in HCC patients receiving radiotherapy with or without thalidomide.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.