乳腺癌脑转移预测模型的研究

目的 探讨乳腺癌患者发生脑转移的危险因素和利用Logistic回归模型预测乳腺癌脑转移概率.方法 对165例乳腺癌患者的年龄、分期、雌激素受体(ER)、孕激素受体(PR)及人类表皮生长因子受体-2(Her-2)评分、月经状态、是否进行辅助化疗、远处第一转移灶、无转移生存期进行分析,建立乳腺癌脑转移预测模型.结果 确诊年龄＜ 35岁、腋淋巴结转移数≥4个、ER阴性、Her-2阳性是乳腺癌发生脑转移的独立危险因素(P＜0.05).获得的预测模型的预测能力为0.783±0.037,预测脑转移发生的灵敏度、特异度可分别达0.786和0.765.结论 临床可利用该模型预测乳腺癌患者脑转移的概率,且预测概率≥0.75.     

11例乳腺癌术后肝转移接受肝切除术患者的长期生存分析

【摘要】〓目的〓评估乳腺癌患者术后肝转移再次接受肝转移癌切除术的价值。方法〓回顾性分析2001年3月至2009年7月于台山市人民医院接受乳腺癌根治术患者1357人,其中术后出现肝脏转移者59人,分为两组:接受肝切除患者为11人(A组);出现肝转移但仅行保守治疗患者48例(B组);对两组患者的资料进行生存分析。结果〓11名乳腺癌复发患者接受再次肝切除的中位生存时间为23.4月,平均生存时间长于未接受肝切除组(14.5月),两组间有明显差异(P<0.05。结论〓本研究提示肝切除术能够延长肝脏复发性乳腺癌患者的生存期,特别是对单纯肝脏复发的患者,肝切除术是值得推荐的治疗方式。     

基于临床病理大数据的早发乳腺癌腋窝淋巴结转移风险预测模型构建

背景与目的：近年来,乳腺癌的发病人群趋于年轻化,并且更容易发生腋窝淋巴结（ALN）转移。本研究通过临床病理大数据平台分析年轻乳腺癌患者ALN转移的影响因素,并建立风险预测模型,为年轻乳腺癌患者的诊断和治疗提供参考依据。 方法：收集SEER数据库中2010—2015年间被诊断为乳腺癌并且接受了ALN手术的年轻患者的临床病理资料,采用单因素和多因素回归分析筛选ALN转移的影响因素,并以列线图的方式可视化。通过AUC/C指数量化列线图区分不同ALN状态患者的能力,采用bootstrap方法（1 000次重复,随机数种子设置为12）进行列线图预测性能内部验证。另外,收集2015—2017年在武汉大学中南医院初诊为乳腺癌的年轻患者资料,对模型行外部验证。 结果：共纳入SEER数据库中23 778例年轻乳腺癌患者,其中39.6%患者存在ALN转移。单因素Logistic回归分析显示,年龄、种族、肿瘤部位、病理学分级、肿瘤大小、胸壁或皮肤是否受侵以及ER、PR、HER-2状态与ALN转移有关（均P<0.001）;多因素Logistic回归分析显示：年龄、种族、婚姻状态、边侧、肿瘤部位、分级、肿瘤大小、胸壁或皮肤是否受侵以及ER与PR状态是ALN转移独立影响因素（均P<0.05）,据此建立风险预测模型。内部验证的校准曲线显示,利用该模型计算的预测值与真实值之间存在良好的一致性（AUC/C指数=0.716）。共纳入391例年轻乳腺癌患者作为外部验证数据集,其中49.9%患者初次手术发现有ALN转移。外部验证提示模型预测能力较好（AUC/C指数=0.798）。 结论：基于SEER数据库建立的年轻乳腺癌患者ALN转移的风险预测模型具有较好的预测能力,可为临床预测患者ALN转移风险提供参考。     

肝癌术后复发的分子机制及其预测研究进展

根治性切除是原发性肝癌的最重要治疗方式，但复发率高是影响病人术后生存的主要原因。根据复发时间，肝癌术后复发分为早期复发和晚期复发，前者主要与肿瘤侵袭转移有关，后者主要由于肝病背景基础上的多中心发生所致。早期预测术后复发风险有助于及时有效干预、改善预后。近十余年，临床上围绕肝癌术后复发进行了系统研究，对转移复发的分子机制有了新认识：肝癌转移潜能始于原发瘤早期；微环境炎性反应失衡促进癌症发生转移。同时，识别和鉴定出多种预测复发的分子标记物，创建并优化了肝癌转移分子预测模型。这些研究成果为临床更准确地把握肝癌复发转移规律，制订更加精准预防和治疗策略奠定基础。     

化学治疗是中晚期膀胱癌治疗的重要手段

膀胱癌是泌尿系统中最常见的恶性肿瘤。尽管在确诊时只有20%的患者属晚期,但大多数患者最终会发生复发或转移。目前对于浸润性膀胱癌的标准治疗原则以根治性膀胱切除术加区域淋巴结清扫为主,配合化疗与放疗。但术后2年内仍有约50%的患者出现局部复发和转移,一旦出现复发和转移,多数在2年内死亡。目前膀胱癌患者的长期生存率仍较低,化疗是延长这些晚期患者生存时间并改善其生活质量的有效治疗方法,可使多数患者的生存时间由3~6个月延长至1年左右,少数患者可获得长期生存。因此,化疗在中晚期膀胱癌中的地位越来越得到重视。作者将就化疗对中晚期膀胱癌的生存影响进行综述。     

乳腺癌术后复发转移相关因素研究进展

正乳腺癌是当前女性发病率最高的恶性肿瘤。随着早期诊断、早期治疗以及乳腺癌辅助治疗的不断完善,乳腺癌术后无病生存率和总体生存率均明显提高,术后局部复发和远处转移仍然是导致乳腺癌患者死亡的重要原因。乳腺癌的复发转移是指乳腺癌经过治疗后在原发灶附近或远隔器官出现病理性质完全相同肿瘤的现象。乳腺癌术后肿瘤复发和转移的问题严重影响临床治疗的效     

局部晚期乳腺癌新辅助化疗预后因素分析

目的 探讨术前接受长春瑞滨联合表柔比星(VE)方案治疗的局部晚期乳腺癌的预后影响因素.方法 回顾分析2001年9月至2006年5月术前接受3个周期VE方案化疗的119例局部晚期乳腺癌患者的临床病理资料.所有患者均经术前空心针活检证实为浸润性乳腺癌,新辅助化疗后接受手术治疗.术后根据新辅助化疗的临床疗效,再继续接受3个周期VE或标准的环磷酰胺+表柔比星+氟尿嘧啶(CEF)方案辅助化疗及局部区域放射治疗和相应的内分泌治疗.分析新辅助化疗前及术后临床病理资料与预后的关系.结果 新辅助化疗后临床完全缓解27例(22.7%),部分缓解78例(65.5%);肿瘤原发灶病理完全缓解(pCR)22例(18.5%).本组115例(96.6%)获得随访,随访时间9～76个月,中位时间63.4个月.无局部复发转移患者共72例(60.5%).5年无病生存率为58.7%,5年总生存率为71.3%.多因素分析显示,新辅助化疗前Ki-67(pre-Ki-67)高表达(P=0.012)、化疗后Ki-67(post-Ki-67)高表达(P=0.045)、化疗后病理未完全缓解(P=0.034)与无病生存时间的降低有关;pre-Ki-67高表达(P=0.017)、post-Ki-67高表达(P=0.001)、pre-ER阴性(P=0.002)、化疗后病理未完全缓解(P=0.034)与总生存时间的降低有关.结论 pre-Ki-67、post-Ki-67及pre-ER的表达水平和新辅助化疗后肿瘤原发灶病理状况是接受术前3个周期VE新辅助化疗局部晚期乳腺癌的独立预后因素.     

免疫检查点抑制剂在食管癌新辅助治疗中的研究进展

新辅助放化疗/化疗联合手术已成为局部进展期食管癌的标准治疗方案,尽管使患者生存获益,但仍有多数患者术后出现复发及远处转移。免疫检查点抑制剂通过激活T细胞发挥抗肿瘤作用,随着免疫治疗模式的不断演进,免疫治疗已成为晚期食管癌一线、二线治疗的重要策略之一。大量关于食管癌新辅助免疫治疗的研究正在进行中,有望为食管癌新辅助治疗注入新的活力。本文就目前关于食管癌新辅助免疫治疗的临床研究进展进行综述。     

国人早期乳腺癌远处转移预报模型的研究

目的：利用乳腺癌传统临床病理因素建立早期乳腺癌远处转移的预报模型，实现早期乳腺癌患者的“低危组”和“高危组”分类以指导临床个体化治疗。方法：通过多因素Logistic回归分析对早期乳腺癌危险因素进行筛选，在此基础上建立早期乳腺癌远处转移的预报模型，并观察其效果。结果：多因素Logistic回归分析从12个危险因素中筛选出3个建模因子: 腋淋巴结转移、肿块大小和C-erb-B2表达，由此建立Logistic回归模型，该模型回顾性与前瞻性预报的正确率分别为79.3％，69.2％。结论：可以通过预测模型将早期乳腺癌患者分为“低危组”和“高危组”两个亚型，以预测早期乳腺癌远处转移的危险性，该预测模型为乳腺癌患者术后个体化治疗提供了一定依据。     

多西他赛每周给药联合长春瑞滨方案治疗转移性乳腺癌的临床研究

乳腺癌是女性最常见的恶性肿瘤，远处转移是主要死亡原因之一。对术后转移的晚期乳腺癌患者，采用以化疗为主的综合治疗仍是最佳选择。近年来，随着多西他赛的问世，乳腺癌化疗疗效明显提高，已成为乳腺癌辅助化疗的一线用药。国外报道多西他赛联合长春瑞滨作为一线治疗用药的有效率为60％～89％。我科联合应用多西他赛加长春瑞滨方案治疗转移性乳腺癌患者26例，取得了较好的疗效，总结报道如下。     

Outcome beyond third-line chemotherapy for metastatic triple-negative breast cancer in the French ESME program

PurposeAmong metastatic breast cancer (MBC) patients, those with a triple-negative breast cancer phenotype (mTNBC) have the worst prognosis, but the benefit of chemotherapy beyond second line on outcome remains uncertain. The purpose of this study was to identify predictive factors of outcome after third- or fourth-line chemotherapy.MethodsThe ESME-MBC database is a French prospective real-life cohort with homogeneous data collection, including patients who initiated first-line treatment for MBC (2008–2016) in 18 cancer centers. After selection of mTNBC cases, we searched for independent predictive factors (Cox proportional-hazards regression models) for overall survival (OS) on third- and fourth-line chemotherapy (OS3, OS4). We built prognostic nomograms based on the main prognostic factors identified.ResultsOf the 22,266 MBC cases in the ESME cohort, 2903 were mTNBC, 1074 (37%) and 598 (20%) of which had received at least 3 or 4 lines of chemotherapy. PFS after first- and second-line chemotherapy (PFS1, PFS2) and number of metastatic sites ≥3 at baseline were identified by multivariate analysis as prognostic factors for both OS3 (HR = 0.76 95%CI[0.66–0.88], HR = 0.55 95%CI[0.46–0.65], HR = 1.36 95%CI[1.14–1.62], respectively), and OS4 (HR = 0.76 95%CI[0.63–0.91], HR = 0.56 95%CI[0.45–0.7], HR = 1.37 95%CI[1.07–1.74]), respectively. In addition, metastasis-free interval was identified as a prognostic factor for OS3 (p = 0.01), while PFS3 influenced OS4 (HR = 0.75 95%CI[0.57–0.98]). Nomograms predicting OS3 and OS4 achieved a C-index of 0.62 and 0.61, respectively.ConclusionThe duration of each previous PFS is a major prognostic factor for OS in mTNBC patients receiving third- or fourth-line chemotherapy. The clinical utility of nomograms including this information was not demonstrated.     

Development and validation of a nomogram for predicting survival of advanced breast cancer patients in China

BackgroundThere is a lack of prognostic models predicting the overall survival (OS) of advanced breast cancer (ABC) patients in China.MethodsData from the China National Cancer Center database that recorded 4039 patients diagnosed with breast cancer between 1987 and 2019 were extracted and a total of 2263 ABC participants were enrolled in this study, which were further randomized 3:1 and divided into training (n = 1706) and validation (n = 557) groups. The nomogram was built based on independent predictors identified by univariate and multivariate cox regression analyses. The discriminatory and predictive capacities of the nomogram were assessed by Harrell’s concordance index (C-index) and calibration plots.ResultsUnivariate and multivariate analyses found that age, Eastern Cooperative Oncology Group (ECOG) score, T-stage, N-stage, tumor subtype, the presence of distant lymph node (DLN)/liver/brain metastasis, local therapy, efficacy of first-line therapy and metastatic-free interval (MFI) were significantly related to OS (all P < 0.05). These variables were incorporated into a nomogram to predict the 2-year and 3-year OS of ABC patients. The C-indexes of the nomogram were 0.700 (95% confidence interval [CI]: 0.683–0.717) for the training set and 0.686 (95% CI: 0.652–0.719) for the validation set. The calibration curves revealed satisfactory consistency between actual survival and nomogram prediction in both the internal and external validations. The nomogram was capable of stratifying patients into different risk cohorts.ConclusionsWe constructed and validated a nomogram that might serve as an efficient tool to provide prognostic prediction for ABC patients and guide the physicians to make personalized treatment decisions.     

Impact of body mass index on overall survival in patients with metastatic breast cancer

BackgroundHigh Body mass index (BMI) is a risk factor for breast cancer among postmenopausal women and an adverse prognostic factor in early-stage. Little is known about its impact on clinical outcomes in patients with metastatic breast cancer (MBC).MethodsThe National ESME-MBC observational cohort includes all consecutive patients newly diagnosed with MBC between Jan 2008 and Dec 2016 in the 18 French comprehensive cancer centers.ResultsOf 22 463 patients in ESME-MBC, 12 999 women had BMI data available at MBC diagnosis. Median BMI was 24.9 kg/m² (range 12.1–66.5); 20% of women were obese and 5% underweight. Obesity was associated with more de novo MBC, while underweight patients had more aggressive cancer features. Median overall survival (OS) of the BMI cohort was 47.4 months (95% CI [46.2–48.5]) (median follow-up: 48.6 months). Underweight was independently associated with a worse OS (median OS 33 months; HR 1.14, 95%CI, 1.02–1.27) and first line progression-free survival (HR, 1.11; 95%CI, 1.01; 1.22), while overweight or obesity had no effect.ConclusionOverweight and obesity are not associated with poorer outcomes in women with metastatic disease, while underweight appears as an independent adverse prognostic factor.     

Validation of the CPS+EG and Neo-Bioscore staging systems after preoperative systemic therapy for breast cancer in a single center in China

BackgroundPrognostic assessment after preoperative systemic therapy (PST) plays a vital role in determining treatment in breast cancer patients. Many researchers have sought to develop a system to quantitate residual tumor and its correlation with prognosis after PST. This retrospective study validated the CPS + EG staging system and Neo-Bioscore in a single center in China.MethodsData from patients with non-metastatic primary breast cancer who were treated with PST and surgery from Jan. 2008 to Dec. 2014 at the Breast Disease Center of Peking University First Hospital, China, were reviewed. DFS, DSS and OS were calculated using the K-M curve and AUC. Multivariate analysis was used for a Cox proportional hazards model. All calculations were performed with SAS 9.4.ResultsA total of 403 patients were enrolled in this study. The median follow-up period was 45 (range 11–107) months. The five-year DFS, DSS and OS rates were 86.4%, 91.2% and 90.5%, respectively. The CS, PS, CPS + EG staging system and Neo-Bioscore stratified patients according to DFS, DSS, and OS after PST, with all P values < 0.0001. The CPS + EG staging system and Neo-Bioscore stratified prognosis after PST better than CS. HER2-positive patients without trastuzumab treatment had obviously worse DFS and OS than other subgroups with different HER2 statuses that scored a 3 in the Neo-Bioscore system.ConclusionsThe CPS + EG staging system and Neo-Bioscore can improve prognostic prediction in non-pCR breast cancer patients after PST and, provided unfavorable prognostic factors such as insufficient treatment are incorporated, will have broader clinical applicability.     

首诊Ⅳ期乳腺癌生存预测模型建立并验证：一项基于机器算法的研究

目的 建立首诊Ⅳ期乳腺癌的生存预测模型,筛选适合行原发灶手术切除的首诊Ⅳ期乳腺癌中适合行的患者。方法 收集美国国立癌症研究所监测、流行病学和结果（SEER）数据库中1973~2015年间确诊为首诊Ⅳ期乳腺癌患者病例。采用Kaplan Meier法进行生存分析,采用log-rank检验分析比较生存率的差别。利用LASSO回归分析筛选出与患者预后相关的临床病理性特征,进一步利用多因素Cox回归分析建立风险评分（risk score）方程及预测模型,使用受试者工作特征曲线（ROC）曲线下面积（AUC）来评价模型的灵敏度和特异度。结果 本研究共纳入7379例首诊Ⅳ期乳腺癌患者,其中手术患者2703例（36.6%）,非手术患者4676例（63.4%）。LASSO回归分析显示年龄、病理类型、肿瘤临床分期、ER状态、PR状态、HER-2状态、骨转移状态、肝转移状态、肺转移状态、淋巴结转移状态是首诊Ⅳ期乳腺癌患者独立预后影响因素。进一步建立首诊Ⅳ期乳腺癌的风险评分和nomogram预后模型,在预测1年和3年总生存中表现出良好的准确性（训练组AUC：1年总生存：0.75,3年总生存：0.73;验证组AUC：1年总生存：0.72,3年总生存：0.75）,在训练组及验证组的一致性指数分别为0.700（95%CI：0.69-0.71）、0.695（95%CI：0.67-0.71）。在风险评分中取最佳cutoff值,将患者分为低、中、高危风险评分组,进一步分析发现低危及中危风险评分组患者能从手术获益（低危风险评分组：训练组：HR=0.49,95%CI：0.42-0.57,P<0.001;验证组：HR=0.43,95%CI：0.34-0.55,P<0.001; 中危风险评分组：训练组：HR=0.75,95%CI：0.65-0.86,P<0.001;验证组：HR=0.72,95%CI：0.57-0.90,P=0.003）,但高危风险评分组患者则无法从手术获益（训练组：HR=0.65,95%CI：0.41-1.02,P=0.066;验证组：HR=0.83,95%CI：0.41-1.69,P=0.610）。结论 本研究基于机器算法建立首诊Ⅳ期乳腺癌的风险评估模型,能有效区分首诊Ⅳ期乳腺癌低危风险、中危风险和高危风险患者,且不推荐高风险（评分>360）患者进行手术治疗。     

胆囊鳞癌及腺鳞癌临床病理特征分析及预后预测模型构建

目的 分析胆囊鳞癌和腺鳞癌的预后影响因素并构建预后预测模型。方法 回顾性分析2012年1月至2021年12月东方肝胆外科医院行外科手术切除的114例胆囊鳞癌及腺鳞癌患者的临床资料。通过单因素和多因素Cox回归分析确定胆囊鳞癌和腺鳞癌的预后影响因素,并构建列线图（Nomogram）预测模型。应用C-指数、ROC曲线以及校准曲线对模型进行评估。结果 单因素分析结果提示,T分期、TNM分期、切缘性质、血管侵犯、联合肝切除、淋巴结清扫是胆囊鳞癌和腺鳞癌患者术后生存的影响因素。多因素Cox回归分析提示,只有T分期、联合肝切除是鳞癌和腺鳞癌患者术后生存的重要影响因素,此时赤池信息测量准则（AIC）值最小（720.66）,并据此建立胆囊鳞癌和腺鳞癌Nomogram预测模型。该模型C-指数为0.614(95%Ci 0.585～0.643)。1年、2年、3年ROC曲线下面积分别为0.605、0.598、0.592。校准曲线图可见实际观测值与预测值具有较好的一致性。结论 T分期、联合肝切除是胆囊鳞癌和腺鳞癌患者术后生存的重要影响因素,据此建立的Nomogram具有一定的区分度和准确度,有一定的临床参考价值...     

基于SEER数据库男性乳腺癌手术切除病人预后分析及列线图建立

目的 分析男性乳腺浸润性导管癌手术切除病人的独立预后因素及构建预后列线图，同时验证该模型的准确性。方法 从美国国立癌症研究监测、流行病学和最终结果（SEER）数据库中下载2010—2018年间诊断为男性乳腺浸润性导管癌且经过手术切除的1662例病人的临床病理特征及治疗信息。随机数字分组法将病人按照3∶1分为训练队列（1246例）和验证队列（416例）。 通过单因素及多因素 COX分析筛选出独立预后因素并构建预测1、3、5年的总生存率（OS）的列线图。一致性指数（c-指数）和校准曲线确定列线图预测的准确性和判别能力。结果 年龄、肿瘤直径、临床TNM、病理学分级、婚姻状态5个指标均是OS的独立预后因素（P均<0.05）。基于独立预后因素构建了1、3、5年OS的列线图。训练队列中列线图的c-指数为0.730（95%CI 0.694-0.766），高于美国癌症联合委员会（AJCC）临床TNM分期系统 0.628（95%CI 0.588-0.668）；验证队列列线图的c-指数为0.737（95%CI 0.680-0.794），高于AJCC 临床TNM分期系统 0.584（95%CI 0.516-0.652）。校准曲线表明列线图预测生存率与实际生存率具有良好的一致性。结论 基于年龄、肿瘤直径、临床TNM、病理学分级、婚姻状态的独立预后因素构建的列线图能较准确地显示男性乳腺癌手术切除病人预后，有利于进行临床个体化预后评估。     

基于SEER数据库的胃癌肝转移预后因素分析与预后模型构建

背景与目的：胃癌其因具有恶性程度高、易早期转移等特点而导致患者往往具有较差的临床预后,其中胃癌肝转移（GCLM）更是导致患者死亡的主要因素,然而,目前对于GCLM的预后评价手段仍然存在着一定的不足。因此,本研究利用SEER数据库分析GCLM患者的临床病理特征和预后风险因素,从而建立具有良好预测能力的评估模型,以提升对患者个体化预后的评估能力。 方法：从SEER数据库中提取2010—2015年确诊的GCLM患者的临床资料。根据纳入和排除标准,严格筛选后纳入研究病例共2 554例,按7:3比例随机分配为建模集（1 790例）和验证集（764例）,比较建模集与验证集中患者的临床基线特征差异,用Cox等比例回归模型与Fine-Gray竞争风险模型分别筛选出GCLM患者总体生存期（OS）与癌症特异性生存期（CSS）的独立危险因素。基于建模集Cox或Fine-Gray风险模型的多元回归分析及AIC因素优化的结果,构建预测GCLM患者OS或CSS的列线图模型。最后,采用一致性指数、ROC曲线和校正曲线评估模型预测的可靠性。 结果： 建模集与验证集患者的基线特征无明显差异。分析结果显示,患者年龄、化疗、肿瘤分级、原发灶切除和原发灶数目是影响GCLM患者OS预后的独立危险因素,而化疗、肿瘤分级、原发灶切除和原发灶数目是影响GCLM患者CSS预后的独立危险因素（均P<0.05）。基于上述指标分别构建列线图模型并进行评价,预测OS与CSS列线图模型的一致性指数均明显高于AJCC-TNM分期系统（建模集：0.706 vs. 0.560、0.670 vs. 0.554;验证集：0.769 vs. 0.534、0.744 vs. 0.518）,并且ROC曲线分析亦展示出预测模型具有较高的准确度。最后,校正曲线分析显示,构建的列线图模型预测患者OS或CSS的生存率与实际观察值均具有良好的一致性。 结论： 基于SEER数据库分析构建的列线图模型在预测GCLM患者OS和CSS方面有较高的准确性,将有助于临床医师对GCLM患者制定个体化的治疗策略。     

胰腺腺鳞癌临床病理特征分析及预后模型建立

目的 探讨胰腺腺鳞癌（ASCP）病人临床病理特征对术后预后的预测价值。方法 回顾性分析2012年10月至2019年9月海军军医大学附属长海医院肝胆胰腺外科行根治性切除术的126例ASCP病人的临床病理资料和随访资料。分析不同Ki-67指数病人的预后差异，采用最小P值法确定Ki-67指数的截断值（40%）。对临床病理特征进行单因素和多因素分析以确定预后影响因素，并依此建立列线图 （Nomogram）预后模型，并使用决策曲线分析法（DCA）进行评估。结果 高Ki-67指数组脉管癌栓阳性病人的比例高于低Ki-67指数组（P=0.005）。单因素分析显示，肿瘤分化程度、肿瘤直径、T分期、N分期、脉管癌栓和Ki-67指数与ASCP病人术后生存相关；多因素分析显示，肿瘤分化程度、肿瘤直径、N分期与Ki-67指数均为ASCP病人术后独立预后预测因素，据此建立Nomogram预后预测模型，内部验证的C-index为0.714（95%CI 0.663-0.765），高于TNM分期预后模型（C-index=0.581，95%CI 0.523-0.639），在预测总生存时间方面表现出良好的准确性。DCA 同样显示 Nomogram 预后模型较 TNM分期预后模型具有更高的临床获益。结论 低分化、肿瘤直径>4 cm、N分期（N1+N2）、Ki-67指数＞40%的ASCP病人术后预后不良，据此建立的Nomogram预后模型较TNM分期预后模型具有更高的准确性及临床获益。     

Impact of modified Glasgow prognostic score on predicting prognosis and modification of risk model for patients with metastatic renal cell carcinoma treated with first line tyrosine kinase inhibitor

Introduction and ObjectivesIntermediate risk group of the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria is thought to consist of patients with different prognoses. This study investigated the impact of a pretreated modified Glasgow prognostic score (mGPS), which is defined on the basis of the pretreated serum albumin and C-reactive protein level, on predicting the prognosis of patients with metastatic renal cell carcinoma (mRCC) and its usefulness for the re-stratification of patients into a more improved risk model.Materials and MethodsOne hundred ninety-six mRCC patients treated with first-line tyrosine kinase inhibitor (TKI) were retrospectively investigated. All patients were classified into either a high-mGPS or a low-mGPS group on the basis of mGPS score upon starting systemic therapy, the overall survival (OS) and cancer specific survival (CSS) rates in each group were compared. We use decision curve analysis and calculate C-index based on OS and CSS to compare IMDC+mGPS model and IMDC model.ResultsThe categories of favorable, intermediate, and poor risk groups in the IMDC model were assessed in 32, 113, and 51 cases, respectively. The low- and high-mGPS groups consisted of 149 and 47 cases. The median OS in the high- and low-mGPS groups were 38.4 months and 5.6 months, and their median CSSs were 41.0 months and 5.6 months, respectively (P < 0.0001). Multivariate analysis showed that a high mGPS, multiple metastatic organs, and hypercalcemia were independent predictive factors for a worse OS (P = 0.0260). Next, we divided the intermediate risk group into two subgroups using the mGPS score. The OS and CSS for the high-mGPS subgroup were significantly worse than those for the low-mGPS one (P = 0.0024, median OS: 21.0 months and 33.7 months, P = 0.0007, median CSS: 21.0 months and 39.8 months), and there was no significant difference in OS between the high-mGPS subgroup in the intermediate risk group and poor risk group (P = 0.2250). The value of C-index based on OS at IMDC and IMDC+mGPS model were 0.6771 and 0.6967, and those based on CSS were 0.6850 and 0.7080, respectively. In decision curve analysis to evaluate the clinical net benefit using the IMDC+mGPS model compared to the IMDC model, there was no significant difference between the two groups.ConclusionmGPS is useful for establishing a more improved prognostic model that is able to stratify mRCC patients treated with first-line TKI.