局限性肾透明细胞癌预后相关模型的建立

目的建立并评估局限性肾透明细胞癌(cc RCC)患者术后预后相关预测模型。方法回顾性分析526例行肾根治术或肾部分切除术的cc RCC患者的临床资料。采用单因素及多因素Cox回归分析,并建立诺模图(Nomogram)。通过吻合线、决策曲线分析(DCA)和Harrell's一致性指数(CI)评估模型价值。结果单因素分析显示,年龄、临床症状、高血压病史、高脂血症、D-二聚体、白蛋白、贫血、术前肌酐、病理分级、肿瘤大小是无病生存期(DFS)的独立危险因素(P 0.05)。最终预测模型包括年龄、症状、贫血、D-二聚体和肿瘤大小。Nomogram的CI为0.78(95%CI为0.71～0.85)。术后3、5年的吻合线表明模型表现良好,DCA表明模型有临床获益。结论本研究构建的预测模型能够预测局限性cc RCC患者术后预后情况,可以为相关患者的术后随访及个性化疾病管理提供参考。     

CT影像组学预测肾透明细胞癌病理分级:Meta分析

目的 采用Meta分析观察CT影像组学预测肾透明细胞癌(ccRCC)病理分级的价值。方法 检索建库至2021年1月PubMed、Web of Science、EMbase及中国知网、中国生物医学文献服务系统和万方医学网CT影像组学预测ccRCC病理分级相关文献,并进行筛选、质量评价及资料提取;以Stata 16.0软件行Meta分析。结果 纳入16篇文献、2 489例患者共2 495个ccRCC病灶。CT影像组学预测ccRCC病理分级无明显阈值效益(r=0.12,P＜0.01)而具有较高异质性(I²≥50%),其合并敏感度0.85、合并特异度0.86,阳性似然比6.00、阴性似然比0.18、诊断比值比34.00,曲线下面积0.92。结论 CT影像组学预测ccRCC病理分级效能较佳。     

肾癌CT灌注成像和微血管密度及血管内皮生长因子相关性的研究

目的分析肾细胞癌(RCC)CT灌注参数强化峰值(PV)、血流量(BF)、血容积(BV)与肿瘤微血管密度(MVD)、血管内皮生长因子(VEGF)表达的相关性。方法对24例经手术病理证实的肾癌患者行多层螺旋CT灌注扫描,所得数据送入Advanced Workstation 4.2工作站,应用Perfusion 3软件分别测量肿瘤的灌注参数PV、BF、BV,用免疫组化过氧化物酶标记的链霉素卵白素法(SP法)检测RCC中MVD、VEGF的表达;研究VEGF阳性、阴性两组与灌注参数的关系及MVD与VEGF的关系。结果24例肾癌VEGF阳性率约54.2%(13/24),MVD平均值91.3±50.9。VEGF阳性肾癌与阴性肾癌的PV、BF、BV间有显著性差异,分别为74.32±32.49,53.38±29.43,P<0.05;938.58±89.63,907.29±112.59,P<0.05;89.73±9.37,62.14±11.23,P<0.05;VEGF阳性肾癌与阴性肾癌两组间的MVD值有显著性差异,分别为103.2±61.7,74.6±41.9,P<0.05;肾癌的VEGF与MVD间呈正相关(r=0.591,P<0.01);肾癌的CT灌注参数PV、BF、BV分别与MVD、VEGF表达存在正相关。结论肾癌CT灌注参数与MVD、VEGF表达存在正相关,灌注参数在一定程度上反映活体的肿瘤血管生成状况和恶性程度,有助于临床制定治疗方案,判断疗效及预后。     

Correlation between CT perfusion parameters and Fuhrman grade in pTlb renal cell carcinoma

Abdominal Radiology - To evaluate the correlation of CT perfusion parameters with the Fuhrman grade in pT1b (4–7&nbsp;cm) renal cell carcinoma (RCC). CT perfusion imaging and Fuhrman...     

Revolution CT全肾灌注成像动脉期数据对肾透明细胞癌灌注参数值的影响

目的 探讨Revolution CT全肾灌注成像去除动脉期数据对肾透明细胞癌灌注参数值的影响。方法 回顾性分析接受Revolution CT肾脏增强及灌注扫描、并经手术病理证实为肾透明细胞癌的患者10例。采用Revolution CT机轴扫模式进行灌注扫描,采用CT Perfusion 4D肾脏灌注软件包进灌注成像分析,对所有图像均进行2次后处理,第1次选择所有的灌注数据为A组;第2次去除第9~15期数据,共测量16期,为B组。选择右侧肾门水平腹主动脉为输入动脉,获得血流量（BF）图、血容量（BV）图、平均通过时间（MTT）图及表面通透性（PS）图。分别对比2组肿瘤与正常肾皮质间各灌注参数的差异,并比较A组和B组间肿瘤、正常肾皮质间各灌注参数值的差异。结果 A组和B组中肿瘤较正常肾皮质的BF值及PS值均减低（P均<0.05）,BV及MTT值差异均无统计学意义（P均>0.05）。2组病变侧各灌注参数值差异均无统计学意义（P均>0.05）,2组正常肾皮质的BF值差异有统计学意义（P=0.009）,余灌注参数值差异亦无统计学意义（P均>0.05）。结论 灌注扫描时间为600 s时,去除动脉期数据后测量的肾透明细胞癌各灌注参数值与包含动脉期数据测量的结果无差异,去除动脉期的数据可用于诊断肾透明细胞癌。     

18F-FDG PET/CT预测肾透明细胞癌的Fuhrman分级

目的 评价¹⁸F-FDG PET/CT对肾透明细胞癌Fuhrman分级的预测价值。方法 回顾性分析43例病理证实的肾透明细胞癌患者的¹⁸F-FDG PET/CT图像,分别应用目测法、最大标准摄取值(SUV_max)、肝脏和降主动脉的标准摄取比值(SUR_liver、SUR_aorta)对病灶的¹⁸F-FDG摄取进行评价。与病理结果对照,判定其对高级别(Fuhrman Ⅲ~Ⅳ级)病变的预测价值并进行各参数间的两两比较。结果 43例中的41例非囊性病变纳入后续分析。低、高级别组之间的SUV_max、SUR_liver及SUR_aorta差异均有统计学意义(t=-4.828、-5.630、-5.136,P均<0.001)。目测法、SUV_max、SUR_liver、SUR_aorta的曲线下面积分别为0.75、0.87、0.87、0.91,灵敏度分别为62.50%、87.50%、87.50%、87.50%,特异度分别为87.88%、84.85%、87.88%、87.88%。SUR_aorta与目测法的ROI曲线差异有统计学意义(P=0.018 8)。结论 ¹⁸F-FDG PET/CT对肾透明细胞癌的Furhman分级具有一定的预测价值,SUR_aorta是较好的指标。     

肾透明细胞癌的CT、MRI诊断

目的探讨肾透明细胞癌的CT、MRI表现.方法回顾性分析165例经手术病理证实的肾透明细胞癌的CT、MRI表现.结果 165例共有172处病灶.肿瘤的最大径为1～12 cm,平均4.6 cm.本组97.1%(167/172)肾透明细胞癌有共同的CT、MRI表现:平扫呈均匀或不均匀的等、稍低、稍高或混杂密度或信号;增强扫描皮髓期,肿瘤呈不均匀明显强化,强化最明显处强化程度与邻近肾皮质相似或更强.结论肾透明细胞癌多为富血供性,有特征性CT、MRI表现,可与其他亚型肾细胞癌鉴别诊断.     

CT值不均匀度诊断肾透明细胞癌

目的 探讨CT值不均匀度鉴别诊断肾透明细胞癌的价值。方法 回顾性分析经术后病理证实为肾细胞癌患者的CT图像,纳入169个肿瘤,分为透明细胞癌组(n=152)和乏血供肾细胞癌组(n=17)。测量CT值的标准差,同时测量皮髓质期和实质期肿瘤的CT值,及同层面正常肾脏皮质和腹主动脉的CT值并获得CT值比值参数: 皮髓质期肿瘤CT值/正常肾脏皮质CT值(TCOCM),皮髓质期肿瘤CT值/腹主动脉CT值(TAOCM),以及实质期肿瘤CT值/正常肾脏皮质CT值(TCON)、实质期肿瘤CT值/腹主动脉CT值(TAON)。对两组以上参数进行统计学分析。结果 皮髓质期透明细胞癌组的CT值标准差(29.60±9.57)高于乏血供肾细胞癌组(18.85±8.10;t=－4.46,P<0.001)。平扫和实质期两组肿瘤CT值标准差差异均无统计学意义(P均>0.05)。皮髓质期的CT值标准差与透明细胞癌的Fuhrman分级存在弱相关(r=－0.16,P=0.049)。透明细胞癌组的TCOCM、TAOCM、TCON、TAON均高于乏血供肾细胞癌(P均<0.05)。结论 肾透明细胞癌皮髓质期的CT值标准差高,且CT值标准差可能与Fuhrman分级相关。CT值标准差可作为反映透明细胞癌皮髓质期不均匀强化的定量指标。     

Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BackgroundThe tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.MethodsThe TME mRNAs were screened following the immune and stromal scores with the data from {"type":"entrez-geo","attrs":{"text":"GSE15641","term_id":"15641"}}GSE15641, {"type":"entrez-geo","attrs":{"text":"GSE29609","term_id":"29609"}}GSE29609, {"type":"entrez-geo","attrs":{"text":"GSE36895","term_id":"36895"}}GSE36895, {"type":"entrez-geo","attrs":{"text":"GSE46699","term_id":"46699"}}GSE46699, {"type":"entrez-geo","attrs":{"text":"GSE53757","term_id":"53757"}}GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.ResultsIn the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group.ConclusionThe TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients'' therapy.     

Angiogenesis of renal cell carcinoma: perfusion CT findings

Objective  

To observe the perfusion CT findings of renal cell carcinoma (RCC) and prospectively correlate perfusion CT parameters with tumor MVD and VEGF expression.     

肾透明细胞癌强化程度的CT评估价值

目的探讨16排螺旋CT双期增强扫描对不同大小肾透明细胞癌强化程度的价值。方法回顾性分析2l例经手术病理证实的肾透明细胞癌的CT强化程度．根据肿瘤直径大小把肾癌分成直径〈4em和〉4cm两组,分别测量各组肿瘤皮髓期的CT值和同层面肌肉组织的CT值,同时计算肿瘤的相对CT值,通过统计学分析两组肿瘤CT值的相关性。结果直径〈4cm组肿瘤的平均大小为（2．59±0．66）cm．平均CT值为（105．66±18．43）HU。平均相对CT值为（1．78＋0．24）;直径〉4cm组肿瘤的平均大小为（5．66±1．22）cm,平均CT值为（112．04±24．71）HU,平均相对CT值为（1．95±0．50）。两组肾透明细胞癌的CT值及相对CT值间差异无统计学意义（z、t分别：-0．14、-1．06,P均〉0．05）。结论双期增强CT能够准确反映肾透明细胞癌的血供特点。肾透明细胞癌的强化程度不能作为评估肿瘤生物学行为的独立因素。     

CT分次团注双期增强扫描在肾透明细胞癌中的应用

目的：探讨分次团注双期CT增强扫描在肾透明细胞癌中的临床应用价值。方法：选择我院B超或体检中怀疑肾脏占位患者120例,采用随机数字表法按照病例进行检查的先后顺序随机分成两组：实验组（60例）行肾脏分次团注双期CT增强扫描,对照组（60例）行常规三期增强扫描。术后对照病理结果,选择肾脏透明细胞癌患者共90例作为研究对象,实验组44例,对照组46例（120例患者中。肾脏透明细胞癌患者共90例,选择这90例患者为研究对象,其中来自实验组的44例,来自对照组46例）。对（最终纳入的90例肾脏透明细胞癌）患者CT扫描各期病变及健肾实质强化程度进行对比分析;计算两组患者所受有效辐射剂量。结果：①实验组肾透明细胞癌皮质期、实质一排泄期与对照组皮质期、排泄期平均CT值之间无统计学意义（P=0．064,P=0．105）;实验组肾透明细胞癌实质一排泄期与对照组实质期平均CT之间具有统计学意义（P=0．000）;②实验组健肾实质皮质期与对照组皮质期平均CT值之间无统计学意义（P=0．510）;实验组健肾实质一排泄期与对照组实质期及排泄期强化平均CT值之间具有统计学差异（P=0．000,P=0．001）。⑧实验组的平均有效剂量较对照组减低30％。结论：。肾脏分次团注双期增强扫描在定性诊断肾脏占位性病变的同时明显降低患者检查所受有效辐射剂量,具有重要临床应用价值。     

Novel immune‐related signature based on immune cells for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of the kidney and is characterized by poor prognosis. We sought to build an immune‐related prognostic signature and investigate its relationship with immunotherapy response in ccRCC.MethodsImmune‐related genes were identified by ssGSEA and WGCNA. The prognostic signature was conducted via univariate, least absolute shrinkage and selection operator, and multivariable Cox regression analyses. Kaplan‐Meier analysis, PCA, t‐SNE, and ROC were used to evaluate the risk model.ResultsA total of 119 immune‐related genes associated with prognosis were screened out. Six immune‐related genes (CSF1, CD5L, AIM2, TIMP3, IRF6, and HHLA2) were applied to construct a prognostic signature for KIRC. Kaplan–Meier analysis showed that patients in high‐risk group had a poorer survival outcome than in low‐risk group. The 1‐, 3‐ and 5‐year AUC of the prognostic signature was 0.754, 0.715, and 0.739, respectively. Univariate and multivariate Cox regression models demonstrated that the risk signature was an independent prognostic factor for KIRC survival. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group with more regulatory T‐cell infiltration showed a higher expression of immune negative regulation genes. The risk score had positively relationship with TIDE score and negatively with the response of immunotherapy. The IC50 values of axitinib, sunitinib, sorafenib, and temsirolimus were lower in the high‐risk group.ConclusionOur study defined a robust signature that may be promising for predicting clinical outcomes and immunotherapy and targeted therapy response in ccRCC patients.     

CT灌注成像在肾癌预后评估方面的应用价值

目的探讨CT灌注成像在评估肾癌预后和术式选择等方面的应用价值.方法 20例肾癌患者, 根据临床分期分成局限组和转移组;根据病理分级分成低度活性组和中高度活性组,及健康自愿者30人进行肾区CT平描,之后选择经肾门或肿瘤的最大层面进行动态增强扫描,应用感兴趣区域分析法,计算正常肾皮质和肾癌组织的灌注值.结果转移组肾癌灌注值显著高于局限组(t检验,P=0.015),中高度活性组肾癌灌注值亦显著高于低度活性组(t检验,P=0.014).结论 CT灌注成像在评估肾癌预后和术式选择等方面具有一定的临床应用价值.     

基于增强CT影像组学模型鉴别肾透明细胞癌与非透明细胞癌

目的 建立基于增强CT的影像组学模型,评估其鉴别肾透明细胞癌（ccRCC）与非透明细胞癌（non-ccRCC）的应用价值。方法 将147例ccRCC及32例non-ccRCC患者随机分为训练集125例和测试集54例。将所有患者的增强CT资料导入ITK-SNAP软件,手动勾画ROI,获得16个特征,分别建立基于特征的随机森林（RF）模型和逻辑回归（LR）模型,采用ROC曲线观察模型对ccRCC的诊断效能。结果 训练集RF模型诊断ccRCC的AUC为0.96（P<0.05）,特异度为1.00,敏感度0.83;LR模型诊断ccRCC的AUC为0.96（P<0.05）,特异度为1.00,敏感度为0.83。测试集RF模型诊断ccRCC的AUC为0.96（P<0.05）,特异度为1.00,敏感度为0.89;LR模型诊断ccRCC的AUC为0.88（P<0.05）,特异度为0.90,敏感度为0.77。结论 基于增强CT影像组学模型可用于鉴别ccRCC与non-ccRCC;RF模型诊断价值较LR模型更高。     

肾透明细胞癌Fuhrman核分级与CT征象的关系

目的 探讨肾透明细胞癌(CCRCC)的CT征象与病理Fuhrman核分级之间的关系.方法 回顾性分析55例经病理证实的肾CCRCC的CT表现,包括肿块皮髓期最大CT值、衰减值(△P₁、△P₂、△P₃)、最大直径、肿瘤假包膜及强化环,并与病理核分级进行统计学分析.结果 低级别组(Fuhrman核分级Ⅰ~Ⅱ级)病灶皮髓期最大CT值显著高于高级别组(Fuhrman核分级Ⅲ~Ⅳ级,t=3.214,P=0.002);低级别组CCRCC的衰减值△P₂(皮髓期与排泄期)明显高于高级别组(t=3.363,P=0.001),而高、低级别组间△P₁(皮髓期与实质期)、△P₃(实质期与排泄期)的差异均无统计学意义(P均 >0.05);Ⅰ级与Ⅱ级病灶的皮髓期最大CT值、△P₁、△P₂、△P₃差异均无统计学意义(P均 >0.05);高、低级别组的假包膜及强化环构成比差异均有统计学意义χ²=4.935,P=0.026;χ²=6.727,P=0.009);各核分级病灶间假包膜情况的差异均无统计学意义χ²=0.132,P=0.716);高级别组肿瘤最大直径明显高于低级别组(t=－2.363,P=0.022);假包膜、强化环不完整/无的病灶最大直径明显高于完整者(P均 <0.05).结论 肾CCRCC核分级越低,皮髓期强化越明显,衰退也越快;CCRCC核分级越高,肿瘤直径越大,假包膜、强化环的完整性也越差.     

Prognostic biomarkers in renal cell carcinoma

A hallmark of renal cell carcinoma is its variable prognosis. Surgical resection of primary renal cell carcinoma can be curative when the disease is localized. However, approximately 20% of patients with early stages of localized renal cell carcinomas subsequently develop metastasis after the primary tumor is removed. The median survival for patients with metastatic disease is approximately 13 months. Therefore, there is a great need for biomarkers to predict metastasis and prognosis. Many prognostic biomarkers were studied in the past decade. In recent years, several promising biomarkers, including CAIX, B7-H1 and IMP3, have also been identified by large retrospective studies. Further validation of these biomarkers is essential to transfer the research data into clinical practice. Eventually, an outcome prediction model with biomarkers, staging system and other risk factors will identify high-risk patients with likelihood of progression and formulate different follow-up protocols or systematic treatments for these patients.     

基于增强CT的放射组学模型用于鉴别肾透明细胞癌与非透明细胞癌

【】目的：建立放射组学在肾癌增强CT图像的应用模型,并评估模型鉴别肾透明细胞癌与非透明细胞癌的准确性。方法：回顾性分析100例经病理证实的增强CT肾癌病例,其中肾透明细胞癌81例,非透明细胞癌19例;使用ITK-SNAP图像分割软件对增强CT图像的所有病灶动脉期进行感兴趣区(ROI)勾画并融合成为感兴趣区容积(VOI);本次研究应用A.K软件(Artificial Intelligence Kit,GE医疗,美国)一共获得了396个组学特征,对样本的两个分组分别按照7：3的比例随机分为训练集和测试集,使用单因素方差分析+秩和检验、一般线性模型、前10% mutual information 进行特征降维,最终筛选出了8个特征,建立了两个相关特征的模型,所有模型均在训练集和测试集分别进行ROC曲线分析。结果：基于增强CT病灶动脉期筛选的8个特征建立了两个组学模型,分别是随机森林(Random Forest, RF)模型和逻辑回归(Logistic)模型。RF在训练集的AUC为0.988,敏感性为0.93,特异性为,1,精确度为1;在测试集AUC为0.972,敏感性为0.875,特异性为1,精确度为1。逻辑回归模型在训练集AUC为0.865,敏感性为0.667,特异性为0.923,精确度为0.974;在测试集AUC为0.868,敏感性为0.75,特异性为1,精确度为1。结论：与Logistic模型相比, RF模型在鉴别肾透明细胞癌与非透明细胞癌具有更高的准确度。     

卵巢透明细胞癌的CT表现

目的 探讨CT诊断卵巢透明细胞癌的临床价值.方法 回顾分析经手术病理证实的6例卵巢透明细胞癌的CT表现及肿瘤的大小、边缘、囊性成分的密度、囊内结构、实性成分的特征.结果 本组肿瘤均为单侧,大小为7.2～11.3 cm,平均8.83 cm,边缘光滑,均以囊性为主,囊性成分CT值为15～28 HU,平均21.50 HU,其中囊液CT值≥20 HU者4例;5例为单房囊性肿块.肿瘤的实性成分均显著强化.术前CT均未诊断为卵巢透明细胞癌.结论 卵巢透明细胞癌的CT表现虽具有一定特征,但缺乏特异性,确诊有赖于病理学检查.