Abstract
目的
探讨术前血小板参数对局部进展期肾癌预后的预测价值，为此类患者的危险分层提供参考。
方法
选择北京大学第三医院2015年1月—2017年12月局部进展期肾癌患者进行回顾性分析，依据随访过程中肿瘤是否复发或转移分成进展组和无进展组，比较两组术前血小板参数和临床资料的差异。通过受试者特征工作曲线(receiver operating characteristic curve, ROC)确定血小板参数的最佳临界值，Kaplan-Meier生存曲线分析不同血小板参数与疾病无进展生存时间(progression-free survival, PFS)的关系。通过Cox比例风险模型进行多因素分析确定PFS的独立危险因素。采用时间依赖ROC、净重新分类指数(net reclassification index, NRI)和综合判别改善指数(integrated discrimination improvement, IDI)评估纳入血小板参数后对SSIGN模型改良情况。
结果
共有215例患者入选本研究，其中192例(89.3%)患者获得随访，中位随访时间为36个月。64例(29.8%)患者随访过程中出现疾病进展，中位PFS为46个月。进展组患者的血小板数量(platelet count, PLT)相较无进展组高[(250.72±88.59)×109/L vs. (227.27±66.94)×109/L, P=0.042]，血小板分布宽度(platelet distribution width, PDW)相较无进展组低[(12.01±2.27)% vs. (13.31±2.74)%, P=0.001]。将285×109/L及12.65%作为PLT及PDW的最佳临界值，PLT≤285×109/L组患者中位PFS显著长于PLT＞285×109/L组(53个月vs. 41个月，P=0.033);PDW＞12.65%组患者中位PFS也显著长于PDW≤12.65%组(56个月vs. 41个月，P < 0.001)。多因素分析显示术前PDW(HR=0.735, P < 0.001)、细胞核分级Ⅲ~Ⅳ级(HR=2.425, P=0.001)、合并肉瘤样分化(HR=3.101, P=0.008)为PFS的独立危险因素。术前PDW联合SSIGN预后评分模型曲线下面积大于原有SSIGN模型[0.748 (95%CI：0.662~0.833) vs. 0.678 (95%CI: 0.583~0.773), P=0.193]，NRI为0.262(P=0.04)，IDI为0.085(P=0.01)，表明PDW纳入SSIGN模型后其预测能力提高。
结论
术前高PLT和低PDW与局部进展期肾癌不良预后相关，其中PDW是患者预后的独立危险因素，因此，术前PDW有助于对局部进展期肾癌进行危险分层。
Keywords: 肾癌, 局部进展期, 血小板, 预后
Abstract
Objective
To explore the prognostic value of preoperative platelet parameters in locally advanced renal cell carcinoma for the risk stratification of such patients.
Methods
Clinical data of patients with locally advanced renal cell carcinoma in the Third Hospital of Peking University from January 2015 to December 2017 were collected. The patients were divided into progression group and progression-free group according to follow-up data, and preoperative platelet parameters and clinical data between the two groups were compared. The optimal cut-off value of platelet parameters was determined by receiver operating characteristic curve (ROC) and analyzed by Kaplan-Meier survival curve. Cox proportional hazards model was used to analyze the independent risk factors of PFS. Time dependent ROC curve, net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the improvement of SSIGN model by incorporating platelet parameters.
Results
Of the 215 patients, 192 (89.3%) were followed up for a median of 36 months. Sixty-four patients (29.8%) had disease progression during the follow-up, and the median PFS was 46 months. In progression group, the platelet count (PLT) was higher [(250.72 ± 88.59)×109/L vs. (227.27 ± 66.94)×109/L, P=0.042] and the platelet distribution width (PDW) was lower [(12.01 ± 2.27)% vs. (13.31 ± 2.74)%, P = 0.001] than that of progression-free groups. 285×109 /L and 12.65% as the best cut-off values of PLT and PDW, the median PFS of PLT≤285×109 /L group was significantly longer than that of PLT>285×109 /L group (53 months vs. 41 months, P=0.033), and the median PFS of PDW>12.65% group was also significantly longer than that of PDW≤12.65% group (56 months vs. 41 months, P < 0.001). Multivariate analysis showed that preoperative PDW (HR=0.735, P < 0.001), nuclear grade Ⅲ to Ⅳ (HR=2.425, P=0.001) and sarcomatoid differentiation (HR=3.101, P=0.008) were independent risk factors for PFS. The area under the curve of PDW combined with SSIGN model was larger than that with the original SSIGN model [0.748 (95%CI: 0.662－0.833) vs. 0.678 (95%CI: 0.583－0.773), P=0.193], NRI was 0.262 (P=0.04), and IDI was 0.085 (P=0.01), indicating that the predictive ability of PDW combined with SSIGN model was improved.
Conclusion
Preoperative high PLT and low PDW are associated with adverse prognosis of locally advanced renal cell carcinoma, and PDW is an independent risk factor. Therefore, preoperative PDW could serve as biomarker for risk stratification of locally advanced renal cell carcinoma.
Keywords: Renal cell carcinoma, Locally advanced, Platelet, Prognosis
血小板由骨髓中成熟的多核巨细胞产生，主要参与机体的止血功能。近年研究显示，血小板可通过各种途径参与肿瘤发生、发展及远处转移[1]。肿瘤细胞可以激活血小板并释放相关细胞因子，促进肿瘤周围新生血管形成并协助肿瘤细胞发生上皮-间质转化[2]。活化后的血小板还能附着于循环肿瘤细胞表面，协助肿瘤细胞逃避免疫细胞识别杀伤，并介导肿瘤细胞远处种植等[3]。术前血小板参数[包括血小板数量(platelet count, PLT)、血小板平均体积(mean platelet volume, MPV)及血小板分布宽度(platelet distribution width, PDW)等]是临床上简便易得反映血小板活性的指标，相关研究表明，术前血小板参数在结直肠癌、肺癌、乳腺癌等肿瘤中具有预测预后的价值[4-6]。
肾癌目前缺乏成熟的血清肿瘤标志物，术前血小板参数是否与肾癌预后相关尚存有争议。对于转移性肾癌而言，PLT指标已纳入国际转移性肾细胞癌联合数据库(International Metastatic Renal Cell Carcinoma Database Consortium, IMDC)预后评分模型用于评估转移性肾癌预后[7]。在非转移性肾癌，虽有部分证据表明PLT、MPV与肿瘤预后相关[8-9]，但不同研究结果仍存在矛盾[10]，且非转移性肾癌预后模型如UISS、SSIGN等并未包含血小板参数[11-12]，因此术前血小板参数的预后价值仍需进一步探索，尤其对局部进展期肾癌而言，探索更多的预后标志物有助于患者危险分层，从而选择高危患者进行术后辅助治疗。本研究旨在探讨术前血小板参数对局部进展期肾癌患者预后的预测价值，并分析其对SSIGN预后模型改良情况，以更好地指导患者危险度分层。
1. 资料与方法
1.1. 病例资料
选择北京大学第三医院2015年1月—2017年12月行根治性手术治疗且组织病理学诊断明确为肾癌患者的病例资料进行回顾性分析，依据《欧洲泌尿外科协会(European Association of Urology, EAU)指南》及第8版TNM分期标准，将T3~4N0M0或T1~4N1M0期肾癌定义为局部进展期肾癌[13-14]。符合分期的肾癌患者纳入本研究。排除标准：(1)病历资料显示合并血液病、感染性疾病或自身免疫性疾病者; (2)双侧肾癌或复发性肾癌者; (3)长期服用抗血小板类药物者; (4)临床资料不完整者。本研究开始前已经北京大学第三医院伦理委员会审查批准(M2021003)。
1.2. 方法及定义
查阅北京大学第三医院电子病历系统，收集患者的临床及组织病理学资料，包括年龄, 性别, 体重指数(body mass index, BMI)，既往合并症情况，手术资料(手术入路、手术时间及术中出血量)，肿瘤位置及大小，组织病理学类型，细胞核分级，是否合并坏死、肉瘤样分化、横纹肌样分化，是否肾周侵犯，是否合并静脉瘤栓等。收集所有患者术前1周内血小板参数结果，包括PLT、MPV及PDW等。依据SSIGN预后评分模型，对本组患者进行预后评分，分值分成0~3分组、4~6分组及7~11分组[11]。细胞核分级标准主要依据2016年WHO/ISUP(International Society of Urological Pathology)标准进行，Ⅰ~Ⅱ级为高分化，Ⅲ~Ⅳ级为低分化[15]。
收集患者随访资料，患者随访通常采用门诊或电话随访方式进行。推荐患者术后1年内每3个月复诊1次，第2年每6个月复诊1次，随后每年复诊1次。每次复诊常规行实验室和影像学(胸部X线片，泌尿系统超声、CT或MRI)检查。对未复诊的患者进行电话随访并记录生存情况。疾病进展定义为随访过程中影像学检查高度怀疑或是经组织病理学确诊的肿瘤局部复发或远处转移。结局指标采用疾病无进展生存时间(progression-free survival, PFS)，即从手术当日起至疾病出现进展的时间。
1.3. 统计学分析
采用SPSS26.0软件进行统计分析，连续变量以用均数±标准差或中位数(四分位数)表示，组间比较采用两独立样本t检验或Mann-Whitney U检验。分类变量采用例数(比例)表示，组间比较采用Pearson χ2检验或Fisher确切概率法。采用受试者工作特征曲线(receiver operating characteristic curve, ROC)确定血小板参数对于疾病进展的最佳临界值。通过Kaplan-Meier生存曲线分析不同血小板参数与本组患者PFS的关系。采用Cox比例风险回归模型对危险因素进行多因素分析。通过时间依赖ROC曲线及曲线下面积(area under curve, AUC)、净重新分类指数(net reclassification index, NRI)和综合判别改善指数(integrated discrimination improvement, IDI)来评估血小板参数对疾病预后模型的改良情况，P < 0.05认为差异具有统计学意义。
2. 结果
2.1. 本组患者预后情况及单因素分析
本组共215例患者，男性151例(70.2%)，女性64例(29.8%)，年龄15~86岁，平均年龄(59.33±11.42)岁。血小板参数: 平均PLT为(235.65±76.63)×109/L，平均MPV为(9.98±1.20) fL，平均PDW为(12.87±2.72)%。共计192例(89.3%)患者获得随访，中位随访时间为36个月(四分位数：27~40个月)，其中64例(29.8%)患者随访过程中出现疾病进展，中位PFS时间为46个月(四分位数：38~57个月)。
依据疾病是否进展，将本组患者分为进展组和无进展组(表 1)，进展组相较于无进展组在临床症状(P < 0.001)，手术时间(P < 0.001)，术中出血量(P=0.027)，细胞核分级(P < 0.001)，是否合并坏死(P < 0.001)、肉瘤样分化(P=0.044)、脉管侵犯(P=0.011)、静脉瘤栓(P < 0.001)、淋巴结侵犯(P=0.018)及SSIGN预后评分(P=0.013)等临床资料上差异具有统计学意义。在术前血小板参数上，进展组相较于无进展组PLT较高[(250.72±88.59)×109/L vs. (227.27±66.94)×109/L, P=0.042]且PDW较低[(12.01±2.27)% vs. (13.31±2.74)%, P=0.001]，而在MPV上差异无统计学意义。
表 1.
患者临床组织病理学资料及与疾病进展相关单因素分析
Clinicopathological data and univariate analysis of disease progression
| Characteristics | Progression free (n=128, 66.7%) | Progression (n=64, 33.3%) | P |
| CCRCC，clear cell renal cell carcinoma; BMI, body mass index; PLT, platelet count; MPV, mean platelet volume; PDW, platelet distribution width; * selected into multivariate analysis. | |||
| Age/years, x±s | 59.8±12.7 | 58.6±8.6 | 0.490 |
| Gender, n(%) | |||
| Male | 87 (68) | 46 (71.9) | 0.622 |
| Female | 41 (32) | 18 (28.1) | |
| Symptom at presentation, n(%) | 35 (27.3) | 38 (59.4) | < 0.001* |
| BMI/(kg/m2)，x±s | 25.4±3.5 | 24.7±3.3 | 0.135 |
| Comorbidities, n(%) | |||
| Hypertension | 54 (42.2) | 29 (45.3) | 0.758 |
| Diabetes mellitus | 26 (20.3) | 6 (9.4) | 0.065 |
| Surgical approach, n(%) | |||
| Open | 50 (39.1) | 29 (45.3) | 0.439 |
| Laparoscopic | 78 (60.9) | 35 (54.7) | |
| Surgical time/min, x±s | 193.60±94.03 | 276.23±130.14 | < 0.001 |
| Interoperative blood loss/mL, x±s | 359.53±951.83 | 711.72±1 063.19 | 0.027 |
| Tumor side, n(%) | 1.000 | ||
| Left | 69 (53.9) | 34 (53.1) | |
| Right | 59 (46.1) | 30 (46.9) | |
| Tumor size/cm, x±s | 6.6±3.7 | 7.1±2.8 | 0.345 |
| Histologic subtype, n(%) | 0.649 | ||
| CCRCC | 113 (88.3) | 55 (85.9) | |
| Non-CCRCC | 15 (11.7) | 9 (14.1) | |
| Nuclear grade, n(%) | < 0.001* | ||
| Ⅰ to Ⅱ | 86 (67.2) | 27 (42.2) | |
| Ⅲ to Ⅳ | 32 (25) | 36 (56.3) | |
| Unknow | 10 (7.8) | 1 (1.6) | |
| Necrosis, n(%) | 27 (21.1) | 33 (51.6) | < 0.001* |
| Sarcomatoid differentiation, n(%) | 4 (3.1) | 7 (10.9) | 0.044* |
| Rheumatoid differentiation, n(%) | 5 (3.9) | 5 (7.8) | 0.305 |
| Lymphovascular invasion, n(%) | 14 (10.9) | 17 (26.6) | 0.011* |
| Renal sinus invasion, n(%) | 95 (74.2) | 47 (73.4) | 1.000 |
| Perirenal fat invasion, n(%) | 30 (23.4) | 23 (35.9) | 0.087 |
| Urinary collecting system invasion, n(%) | 3 (2.3) | 5 (7.8) | 0.120 |
| Venous tumor thrombus, n(%) | 37 (28.9) | 38 (59.4) | < 0.001* |
| Lymph node invasion, n(%) | 2 (1.6) | 6 (9.4) | 0.018* |
| Adrenal invasion, n(%) | 4 (3.1) | 6 (9.4) | 0.086 |
| SSIGN score, n(%) | 0.013 | ||
| 0－3 | 46 (35.9) | 12 (18.8) | |
| 4－6 | 73 (57) | 41 (64.1) | |
| 7－11 | 9 (7) | 11 (17.2) | |
| Platelet indices | |||
| PLT/(×109/L)，x±s | 227.27±66.94 | 250.72±88.59 | 0.042* |
| MPV/fL, x±s | 10.12±1.26 | 9.96±0.97 | 0.394 |
| PDW/%, x±s | 13.31±2.74 | 12.01±2.27 | 0.001* |
依据ROC曲线分析结果，最终将285×109/L和12.65%作为PLT和PDW的最佳临界值。PLT≤285×109/L组患者中位PFS时间显著长于PLT＞285×109/L组(53个月vs. 41个月，P=0.033，图 1)，并且PDW＞12.65%组患者中位PFS时间也显著长于PDW≤12.65%组(56个月vs. 41个月，P < 0.001)。
图 1.
Kaplan-Meier法绘制患者在术前不同水平PLT(A)和PDW(B)的肾癌无进展生存曲线
Kaplan-Meier for progression-free survival curve of renal cell carcinoma patients with different level of PLT (A) and PDW (B)
2.2. 疾病进展相关多因素分析
多因素回归模型变量纳入标准为：(1)单因素分析差异具有统计学意义(P < 0.05);(2)具有临床预后价值。将是否伴有临床症状，细胞核分级，是否合并坏死、肉瘤样分化、脉管侵犯、静脉瘤栓、淋巴结转移，以及术前PLT(连续变量)、PDW(连续变量)等变量纳入Cox比例风险模型，采用逐步向前最大似然比法进行分析。疾病进展的独立危险因素包括术前PDW(HR=0.735, P < 0.001)、细胞核分级Ⅲ~Ⅳ级(HR=2.425, P=0.001)和合并肉瘤样分化(HR=3.101, P=0.008，表 2)。
表 2.
肾癌术后发生进展Cox比例风险模型多因素分析
Multivariate analysis results of Cox proportional hazards model for postoperative progression of renal cell carcinoma
| Items | B | SE | Wald | P | Exp(B) (95%CI) |
| Ref, reference; PDW, platelet distribution width; * as continuous variable. | |||||
| Nuclear grade | |||||
| Ⅰ to Ⅱ | Ref | － | － | － | － |
| Ⅲ to Ⅳ | 0.886 | 0.275 | 10.343 | 0.001 | 2.425 (1.413－4.161) |
| Unknow | －0.984 | 1.021 | 0.929 | 0.335 | 0.374 (0.051－2.764) |
| Sarcomatoid differentiation | 1.132 | 0.428 | 6.999 | 0.008 | 3.101 (1.341－7.170) |
| PDW* | －0.308 | 0.067 | 20.884 | < 0.001 | 0.735 (0.644－0.839) |
2.3. PDW指标对SSIGN预后评分模型的改良程度
为评估PDW指标对SSIGN预后评分模型改良程度，采用时间依赖的ROC曲线，NRI及IDI进行分析。鉴于本组患者中位随访时间为36个月，故选择术后36个月作为疾病是否进展的时间评估截点。术前PDW联合SSIGN预后评分模型曲线下面积较原有SSIGN模型增加[AUC=0.748 (95%CI: 0.662~0.833) vs. AUC=0.678 (95%CI: 0.583~0.773), P=0.193，图 2]，NRI为0.262(P=0.04)，IDI为0.085(P=0.01)，表明PDW纳入SSIGN模型后其预测能力有所提高。
图 2.
时间依赖ROC曲线评估术前PDW联合SSIGN预后评分模型对原有模型改良情况
The time-dependent ROC curve was used to evaluate the improvement of the SSIGN model by incorporating PDW compared to original model
3. 讨论
肾癌的发病率约占成人恶性肿瘤的3%~5%，是第三常见的泌尿系统肿瘤，且发病率逐年递增[16]。目前指南推荐，局限性肾癌的治疗主要方式为根治性手术切除，而转移性肾癌的治疗则需要采取联合手术、靶向治疗或免疫治疗为主的综合治疗策略[14]。对于局部进展期肾癌的治疗方式与局限性肾癌一致，术后是否进行辅助治疗仍存有争议。ASSURE试验研究舒尼替尼、索拉菲尼及安慰剂在高危局限性肾癌术后辅助治疗的价值，发现组间总体生存率(overall survival, OS)和疾病无进展生存率(disease free survival, DFS)的差异均无统计学意义[17]。另一项研究探讨培唑帕尼对高危局限性肾癌术后辅助治疗的价值，也发现并无生存获益[18]。然而，S-TRAC试验却发现高危局限性肾癌术后使用舒尼替尼相较安慰剂组，中位DFS显著延长(6.2年vs. 4.0年，P=0.04)[19]。不同临床试验结果不一致的原因在于研究人群的异质性，可能在S-TRAC研究中所入组的患者更加高危，进而能够在辅助治疗中获益[19]。因此，对于局部进展期肾癌进行进一步危险分层具有重要临床意义。目前非转移性肾癌的预后模型包括UISS、SSIGN、Leibovich及Karakiewicz等，包含的预测指标包括临床症状、体力状况评分(eastern cooperative oncology group perfor-mance status, ECOG-PS)、TNM分期、Fuhrman分级和是否合并坏死等[11-12, 20-21]。然而，一项研究前瞻性入组1 647例高危局限性肾癌队列并对现有预测模型进行外部验证，发现所有模型预测能力均出现大幅下降[22]。因此，现有预后预测模型对局部进展期肾癌的评估仍然具有局限性，并不能协助医生精准地筛选出高危人群。鉴于此，局部进展期肾癌仍需探索更多预后标志物以提供更加精准的危险分层。
血小板参数是术前常规检查的项目，是一种简便易得、可及性高的指标。目前已有研究发现，术前PLT、MPV及PDW可以实现对不同肿瘤预后的预测价值[4-6]。本课题组前期对肾癌合并瘤栓患者进行预后分析发现，PLT升高是肾癌合并瘤栓患者术后出现疾病进展的独立危险因素[23]。本研究进一步分析了215例局部进展期肾癌患者，单因素分析时发现术前PLT和PDW两者均与患者PFS相关; 多因素回归分析表明PDW是局部进展期肾癌预后的独立危险因素。目前尚未见文献报道PDW在肾癌预后中的预测价值。在其他肿瘤的研究中，Liu等[5]对750例非小细胞肺癌患者预后研究发现，术前低PDW的患者中位OS及DFS显著缩短。Yue等[24]对273例肝细胞癌患者预后分析发现，PDW是肝细胞癌OS的独立危险因素。此外，在宫颈癌、食管癌及子宫内膜癌等肿瘤的研究中也得出类似的结果[25-27]。本研究为进一步探讨PDW的预后价值，将PDW纳入SSIGN预后评分模型中，发现其对原有模型预测能力具有提高作用，提示PDW可作为生物标志物为局部进展期肾癌提供更加精准的预后分层。
PDW与肿瘤进展转移相关的具体机制目前尚不明确，既往研究表明，肿瘤细胞能够刺激血小板从而变成活化状态，激活的血小板可以通过各种途径促进肿瘤细胞发生转移[1]。PDW是衡量血小板异质性的一种指标，可一定程度反映血小板活化状态而不受血小板数量的影响[28]。因此，术前PDW也能在一定程度上反映肿瘤预后。然而，关于PDW具体值的变化在不同肿瘤研究结果也存在矛盾情况。PDW在肺癌、肝癌及宫颈癌等肿瘤中的研究结果与本研究结果一致，发现术前低PDW为肿瘤不良预后的独立危险因素; 但是在乳腺癌、鼻咽癌及神经母细胞瘤等肿瘤中却发现，术前高PDW为肿瘤不良预后的预测因素[29-31]。PDW对肿瘤预后预测价值似乎取决于具体肿瘤类型，其内在作用机制尚不清楚，有待进一步研究阐明。
本研究为单中心回顾性研究，存在一定局限性, 如回顾性研究会缺失一些重要数据(如患者具体用药情况等)，这些因素可能会对血小板产生潜在影响从而发生混杂偏倚。此外，不同中心所使用的采血管、血液检测仪器也存在差异，因而存在一定的信息偏倚。因此本研究所得出的结果仍然需要在前瞻性多中心队列中进行验证。
综上所述，术前血小板参数对于局部进展期肾癌的预后具有一定预测价值。术前高PLT及低PDW与局部进展期肾癌不良预后相关，其中PDW为患者预后相关的独立危险因素。因此，术前PDW有助于对局部进展期肾癌进行危险分层。
Funding Statement
国家自然科学基金(81972381)
Supported by the National Natural Science Foundation of China (81972381)
References
- 1.Gay LJ, Felding-Habermann B. Contribution of platelets to tumour metastasis. Nat Rev Cancer. 2011;11(2):123–134. doi: 10.1038/nrc3004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Labelle M, Begum S, Hynes RO. Direct signaling between platelets and cancer cells induces an epithelial-mesenchymal-like transition and promotes metastasis. Cancer Cell. 2011;20(5):576–590. doi: 10.1016/j.ccr.2011.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Nieswandt B, Hafner M, Echtenacher B, et al. Lysis of tumor cells by natural killer cells in mice is impeded by platelets. Cancer Res. 1999;59(6):1295–1300. [PubMed] [Google Scholar]
- 4.Zhu X, Cao Y, Lu P, et al. Evaluation of platelet indices as diagnostic biomarkers for colorectal cancer. Sci Rep. 2018;8(1):11814. doi: 10.1038/s41598-018-29293-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Liu C, Zhang H, Qi Q, et al. The preoperative platelet distribution width: A predictive factor of the prognosis in patients with non-small cell lung cancer. Thorac Cancer. 2020;11(4):918–927. doi: 10.1111/1759-7714.13340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Liu S, Fang J, Jiao D, et al. Elevated platelet count predicts poor prognosis in breast cancer patients with supraclavicular lymph node metastasis. Cancer Manag Res. 2020;12(6):6069–6075. doi: 10.2147/CMAR.S257727. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Heng DY, Xie W, Regan MM, et al. External validation and comparison with other models of the international metastatic renal-cell carcinoma database consortium prognostic model: a population-based study. Lancet Oncol. 2013;14(2):141–148. doi: 10.1016/S1470-2045(12)70559-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Choi JY, Ko YH, Song PH. Clinical significance of preoperative thrombocytosis in patients who underwent radical nephrectomy for nonmetastatic renal cell carcinoma. Investig Clin Urol. 2016;57(5):324–329. doi: 10.4111/icu.2016.57.5.324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Seles M, Posch F, Pichler GP, et al. Blood platelet volume represents a novel prognostic factor in patients with nonmetastatic renal cell carcinoma and improves the predictive ability of established prognostic scores. J Urol. 2017;198(6):1247–1252. doi: 10.1016/j.juro.2017.07.036. [DOI] [PubMed] [Google Scholar]
- 10.Karakiewicz PI, Trinh QD, Lam JS, et al. Platelet count and preoperative haemoglobin do not significantly increase the performance of established predictors of renal cell carcinoma-specific mortality. Eur Urol. 2007;52(5):1428–1436. doi: 10.1016/j.eururo.2007.03.036. [DOI] [PubMed] [Google Scholar]
- 11.Frank I, Blute ML, Cheville JC, et al. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol. 2002;168(6):2395–2400. doi: 10.1016/S0022-5347(05)64153-5. [DOI] [PubMed] [Google Scholar]
- 12.Zisman A, Pantuck AJ, Dorey F, et al. Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol. 2001;19(6):1649–1657. doi: 10.1200/JCO.2001.19.6.1649. [DOI] [PubMed] [Google Scholar]
- 13.Amin MB, Edge SB, Greene FL, et al. AJCC cancer staging manual. Chicago: Springer; 2017. pp. 739–747. [Google Scholar]
- 14.Ljungberg B, Bensalah K, Canfield S, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol. 2015;67(5):913–924. doi: 10.1016/j.eururo.2015.01.005. [DOI] [PubMed] [Google Scholar]
- 15.Moch H, Cubilla AL, Humphrey PA, et al. The 2016 WHO classification of tumours of the urinary system and male genital organs-part A: renal, penile, and testicular tumours. Eur Urol. 2016;70(1):93–105. doi: 10.1016/j.eururo.2016.02.029. [DOI] [PubMed] [Google Scholar]
- 16.Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. doi: 10.3322/caac.21654. [DOI] [PubMed] [Google Scholar]
- 17.Haas NB, Manola J, Dutcher JP, et al. Adjuvant treatment for high-risk clear cell renal cancer: updated results of a high-risk subset of the ASSURE randomized trial. JAMA Oncol. 2017;3(9):1249–1252. doi: 10.1001/jamaoncol.2017.0076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Motzer RJ, Haas NB, Donskov F, et al. Randomized phase Ⅲ trial of adjuvant pazopanib versus placebo after nephrectomy in patients with localized or locally advanced renal cell carcinoma. J Clin Oncol. 2017;35(35):3916–3923. doi: 10.1200/JCO.2017.73.5324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Motzer RJ, Ravaud A, Patard JJ, et al. Adjuvant sunitinib for high-risk renal cell carcinoma after nephrectomy: subgroup analyses and updated overall survival results. Eur Urol. 2018;73(1):62–68. doi: 10.1016/j.eururo.2017.09.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Leibovich BC, Blute ML, Cheville JC, et al. Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer. 2003;97(7):1663–1671. doi: 10.1002/cncr.11234. [DOI] [PubMed] [Google Scholar]
- 21.Karakiewicz PI, Briganti A, Chun FK, et al. Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol. 2007;25(11):1316–1322. doi: 10.1200/JCO.2006.06.1218. [DOI] [PubMed] [Google Scholar]
- 22.Correa AF, Jegede O, Haas NB, et al. Predicting renal cancer recurrence: defining limitations of existing prognostic models with prospective trial-based validation. J Clin Oncol. 2019;37(23):2062–2071. doi: 10.1200/JCO.19.00107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Xiao R, Xu C, He W, et al. Preoperative anaemia and thrombocytosis predict adverse prognosis in non-metastatic renal cell carcinoma with tumour thrombus. BMC Urol. 2021;21(1):31. doi: 10.1186/s12894-021-00796-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Yue CX, Liu YX, Yun ZY, et al. Decreased platelet distribution width predicts a worse prognosis in patients undergoing surgical resection for hepatocellular carcinoma. Cancer Biomark. 2019;26(3):361–366. doi: 10.3233/CBM-190474. [DOI] [PubMed] [Google Scholar]
- 25.Chen H, Wu Q, Zhang Y, et al. Nomograms based on the novel platelet index score predict postoperative prognosis in endometrial cancer. Gynecol Oncol. 2020;158(3):689–697. doi: 10.1016/j.ygyno.2020.05.040. [DOI] [PubMed] [Google Scholar]
- 26.Kawakita Y, Motoyama S, Sato Y, et al. Prognostic significance of combined platelet distribution width and C-reactive protein score in esophageal cancer. Anticancer Res. 2020;40(10):5715–5725. doi: 10.21873/anticanres.14586. [DOI] [PubMed] [Google Scholar]
- 27.蒋 慧云, 李 小毛, 王 佳, et al. 术前血小板分布宽度在子宫内膜癌诊断预测中的价值. 实用医学杂志. 2018;34(7):1188–1190. [Google Scholar]
- 28.Vagdatli E, Gounari E, Lazaridou E, et al. Platelet distribution width: a simple, practical and specific marker of activation of coagulation. Hippokratia. 2010;14(1):28–32. [PMC free article] [PubMed] [Google Scholar]
- 29.张 翔, 庄 瑞. 血小板分布宽度对鼻咽癌患者预后的影响. 国际肿瘤学杂志. 2018;45(5):257–261. [Google Scholar]
- 30.Huang Y, Cui MM, Huang YX, et al. Preoperative platelet distribution width predicts breast cancer survival. Cancer Biomark. 2018;23(2):205–211. doi: 10.3233/CBM-181267. [DOI] [PubMed] [Google Scholar]
- 31.张 林楠, 刘 玉峰, 苏 淑芳, et al. 血小板分布宽度对神经母细胞瘤预后的预测价值. 中华实用儿科临床杂志. 2020;35(6):440–444. [Google Scholar]