基于Oncomine数据库分析分泌性磷蛋白1在肝细胞癌中的表达及临床意义

目的分析分泌性磷蛋白1(SPP1)在肝细胞癌中的表达,探索其对肝癌患者预后的影响。方法基于Oncomine数据库,对肝细胞癌中的SPP1差异表达情况进行分析,探讨其意义;利用Kaplan Meier-Plotter在线分析工具,对患者生存情况进行分析;基于String数据库在线构建蛋白互作网络。结果在筛选到的4项研究中,肝癌组SPP1基因的表达分别为4.92±0.24,0.35±0.30,-0.92±0.29和5.12±0.50,与相应正常组表达量1.96±0.28,-1.23±0.06,-2.93±0.13和1.89±0.31相比较均显著增高,差异均有统计学意义(均P <0.001);SPP1高表达组患者诊断后总体中位生存期为37.8个月,较SPP1低表达组(84.4个月)明显降低,差异有统计学意义(P <0.001);在男性(41.0个月vs 84.7个月)、亚洲人群(7.2个月vs 56.2个月)、GradeⅢ级(5.7个月vs23.7个月)或未感染肝炎病毒患者(25.2个月vs 70.5个月)亚组中的总体生存期均显著下降(错误发现率<5%,P <0.001)。透明质酸受体、整合素β1、基质金属蛋白酶7、金属蛋白酶组织抑制因子1、白细胞介素6和纤维连接蛋白1等是显著的节点基因。结论 SPP1与肝细胞癌进展显著相关,高SPP1表达与HCC患者的不良存活密切相关。     

皮肤鳞状细胞癌的中医药治疗进展

皮肤鳞状细胞癌是具有发病率高、发展快、可转移、易误诊等特点的一种恶性肿瘤，对人类的健康造成威胁。随着中医的发展和现代医学实验研究技术的进步，中医药在皮肤鳞状细胞癌的现代研究方面也取得一定成果。目前临床上治疗鳞状细胞癌多采取手术及药物化疗等，因不良反应的存在，患者接受度低。中药具有多靶点、安全性高、不良反应少等优势，在治疗皮肤鳞状细胞癌中中医具有一定优势，本文将围绕中药单体、中药复方，以及针灸在治疗皮肤鳞状细胞癌的研究进展展开论述和思考。     

Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma

ObjectiveTo evaluate the performance of a deep learning (DL)-based radiomics strategy on contrast-enhanced computed tomography (CT) to predict microvascular invasion (MVI) status and clinical outcomes, recurrence-free survival (RFS) and overall survival (OS) in patients with early stage hepatocellular carcinoma (HCC) receiving surgical resection.MethodsAll 283 eligible patients were included retrospectively between January 2008 and December 2015, and assigned into the training cohort (n = 198) and the testing cohort (n = 85). We extracted radiomics features via handcrafted radiomics analysis manually and DL analysis of pretrained convolutional neural networks via transfer learning automatically. Support vector machine was adopted as the classifier. A clinical-radiological model for MVI status integrated significant clinical features and the radiological signature generated from the radiological model with the optimal area under the receiver operating characteristics curve (AUC) in the testing cohort. Otherwise, DL-based prognostic models were constructed in prediction of recurrence and mortality via Cox proportional hazard analysis.ResultsThe clinical-radiological model for MVI represented an AUC of 0.909, accuracy of 96.47%, sensitivity of 90.91%, specificity of 97.30%, positive predictive value of 83.33%, and negative predictive value of 98.63% in the testing cohort. The clinical-radiological models for identification of RFS and OS outperformed prediction performance of the clinical model or the DL signature alone. The DL-based integrated model for prognostication showed great predictive value with significant classification and discrimination abilities after validation.ConclusionsThe integrated DL-based radiomics models achieved accurate preoperative prediction of MVI status, and might facilitate predicting tumor recurrence and mortality in order to optimize clinical decisions for patients with early stage HCC.     

Time-varying association of second primary malignancy and long-term survival outcomes in patients with head and neck cancer

A high risk of developing second primary malignancy (SPM) has been reported among head and neck cancer patients. Here, we aimed to statistically quantify the impact of SPM development on the survival of head and neck cancer patients. Our study was conducted using the Surveillance, Epidemiology and End Results database to collect the data of 48 316 patients who received curative surgical resection for initial primary head and neck squamous cell carcinoma (IP-HNSCC) in 1975 to 2019. SPM diagnosis was treated as a time-varying covariate and multivariable Cox regression analysis was conducted to estimate the association between SPM development and survival, overall or by the subsite of IP-HNSCC. Of the included patients, 11 238 patients (23.3%) developed SPM during the follow-up period. A significant reduction in survival was observed among patients with SPM (hazard ratio [HR] for overall survival, 3.30; 95% confidence interval [CI]: 3.20-3.41). The impact of SPM development on reduced survival was more significant in patients with localized IP-HNSCC vs regional IP-HNSCC (HR_OS, 3.41; 95% CI: 3.24-3.6 vs HR_OS, 3.18; 95% CI: 3.05-3.31; P for interaction <.001). The survival impact of SPM development was more evident in younger patients than in older patients. SPM in lung and bronchus was associated with the most pronounced reduction in survival, overall and across all subsites of HNSCC. Our results indicated that SPM development led to a significant reduction in survival. A greater survival benefit may be achieved through intensive surveillance for SPM in lung and bronchus targeting younger patients and those with localized HNSCC.     

胸段食管鳞癌术后复发模式及术后放疗靶区的研究北大核心

目的:研究胸段食管鳞癌术后复发模式,为术后放疗靶区勾画提供参考。方法:回顾分析我院2012年7月至2017年5月收治术后复发的81例胸段食管鳞癌患者的临床资料,参照AJCC第八版食管癌分期,将第1-8M站定义为上中纵隔淋巴结区,8Lo、9、15站定义为下纵隔淋巴结区,16-20站定义为上腹部淋巴结区。标记患者的复发部位,并分析局部复发、区域复发和远处转移的模式。结果:中位复发时间为12个月(2~103个月)。6例(7.4%)患者发生单纯局部复发,64例(79.0%)患者发生区域复发,11例(13.6%)患者发生远处转移。区域淋巴结复发中最高危的复发区域为上中纵隔淋巴引流区,此区域包含了82.8%的复发淋巴结,其次为上腹部淋巴结引流区(13.6%)。11例患者发生上腹部淋巴结复发,其中10(90.9%)例为胸下段,7例(63.6%)患者术后分期≥Ⅲ_(b)期。结论:胸段食管鳞癌术后复发模式以区域淋巴结复发为主,上中纵隔淋巴引流区为最高危复发区域,术后放疗靶区应重点包含。对于术后分期较晚的胸下段食管鳞癌,上腹部淋巴结引流区可能需要涵盖在放疗靶区内。吻合口、瘤床和下纵隔复发风险低,可不必常规涵盖在放疗靶区内。     

Impact of overweight and obesity on US renal cell carcinoma incidence trends (1995-2018)

In the United States, renal cell carcinoma (RCC) incidence and the prevalence of obesity, an established risk factor for RCC, have been increasing for several decades. RCC is more common among older individuals. We sought to quantify the contribution of excess adiposity to the rising incidence of RCC among individuals 60 years or older. National Institutes of Health-American Association of Retired Persons Diet and Health Study data (n = 453 859 participants, enrolled in 1995-1996, age at enrollment 50-71 years) were used to estimate multivariable-adjusted hazard ratios (HRs) for RCC across body mass index categories and HRs associated with smoking. Population attributable fractions (PAFs) were calculated using estimated HRs and annual overweight/obesity prevalence from the National Health Interview Survey (1985-2008). PAF estimates were combined with RCC incidence from Surveillance, Epidemiology and End Results-13 to calculate annual percent changes in RCC incidence attributable (and unrelated) to overweight/obesity. We found that between 1995 and 2018, among individuals aged 60 years and older, PAF for overweight/obesity increased from 18% to 29% for all RCCs. In comparison, the PAF for smoking declined from 12% to 9%. RCC incidence increased 1.8% per year (95% confidence interval [CI] 1.5%-2.1%) overall, while RCC incidence attributable to overweight/obesity increased 3.8% per year (95%CI 3.5%-4.2%) and RCC incidence unrelated to overweight/obesity increased 1.2% per year (95% CI 0.9%-1.4%). In conclusion, overweight/obesity appears to have contributed importantly to the rising incidence of RCC in the United States since the mid-1990s. Public health interventions focused on reducing overweight and obesity could help substantially in curbing this trend.     

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PurposeTo show that a deep learning (DL)–based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the “ground truth segmentation” than a conventional thresholding-based model.Materials and MethodsThis post hoc analysis included 36 patients with a diagnosis of hepatocellular carcinoma or other solid liver tumors who underwent conventional transarterial chemoembolization with an intraprocedural cone-beam CT. Semiautomatic segmentation of Lipiodol was obtained. Subsequently, a convolutional U-net model was used to output a binary mask that predicted Lipiodol deposition. A threshold value of signal intensity on cone-beam CT was used to obtain a Lipiodol mask for comparison. The dice similarity coefficient (DSC), mean squared error (MSE), center of mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist-segmented Lipiodol deposits) to obtain accuracy metrics for the 2 masks. These results were used to compare the model versus the threshold technique.ResultsFor all metrics, the U-net outperformed the threshold technique: DSC (0.65 ± 0.17 vs 0.45 ± 0.22, P < .001) and MSE (125.53 ± 107.36 vs 185.98 ± 93.82, P = .005). The difference between the CM predicted and the actual CM was 15.31 mm ± 14.63 versus 31.34 mm ± 30.24 (P < .001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22 ± 0.84 versus 2.58 ± 3.52 (P = .048) for the current model’s prediction and threshold technique, respectively.ConclusionsThis study showed that a DL framework could detect Lipiodol in cone-beam CT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intraprocedurally.