霍奇金病R－S细胞形态计量、判别分析和预测价值的初探

用自动图像分析技术，检测了７４个典型Ｒ－Ｓ细胞，７１个ＩＢ细胞，３５个红细胞的六项形态学参数值，建立了四个判别函数式。四个判别函数对确诊的Ｒ－Ｓ细胞和ＩＢ细胞判别符合率分别为７１．２３％、７３．６１％、８６．１１／、７９．１７％，以判别函数三判别效果最好。     

霍奇金病R—S细胞形态计量,判别分析和预测价值的初探

用自动图像分析技术，检测了７４个典型Ｒ－Ｓ细胞，７个ＩＢ细胞，３５个红细胞的六项形态学参数值，建立了四个判别函数式。四个判别函数对确诊的Ｒ－Ｓ细胞和ＩＢ细胞２判别符合率分别为７１．２３％，７３．６１％，８６．１１％，７９．１７％，以判别函数三判别效果最好。     

血清肿瘤标志物对SCLC和NSCLC的Fisher判别分析

目的：探讨血清肿瘤标志物对SCLC和NSCLC的鉴别诊断价值。方法：采用电化学发光免疫法检测25例患者血清中CEA、CYFRA21—1、NSE、CA125、CA199及SCCAg的水平；对NSCLC组和SCLC组间统计检验有差异（P〈0．05）的血清肿瘤标志物用Fisher判别方法建立判别函数鉴别诊断模型；用该模型回代相应变量对SCLC和NSCLC进行预测分组，检验该判别函数模型的鉴别诊断效果。结果：6种血清肿瘤标志物在NSCLC组和SCLC组间有统计学差异（P〈0．05）的变量分别是：CYFRA21—1、NSE和SCCAG；建立的判别函数鉴别诊断模型，在NSCLC组判别正确率89．87％，SCLC组判别正确率87．37％，两组合计判别正确率88．93％。结论：应用Fisher判别法对血清肿瘤标志物建立的判别函数鉴别诊断模型可简便、快速、有效地对NSCLC和SCLC进行鉴别诊断，值得临床借鉴。     

急性髓系白血病细胞异常免疫表型表达的研究

应用免疫酶标染色法检测了59例急性髓系白血病（AML）患者的白血病细胞免疫表型，结果表明CD2、CD5、CD7、CD10、CD19、CD22淋系抗原的表达率分别为16．9％（10／59）、119％（7／59）、16．9％（10／59）、15．3％（9／59）、102％（6／59）和6．8％（4／59）。进一步分析结果表明，在M3病例细胞中，CD2、CD10和CD19抗原表达阳性率明显高于M5组，而CD7抗原表达阳性率则明显低于M5组。结合临床，CD2、CD19阳性的AML病例对化疗治疗及应优于CD2、CD19阴性的AML病例；CD7阳性的AML病例的疗效与预后则比CD7阴性的AML病例差。提示部分AML病例的白血病细胞存在不同程度异常免疫表型的表达，且与疗效及预后有一定关系。     

TE T2基因阳性急性髓细胞白血病38例分析

目的：分析TET2基因阳性急性髓细胞白血病（AML）患者的临床及实验室特点,探讨可能影响治疗效果的因素。方法：收集38例TET2基因突变阳性AML患者的临床及实验室资料,并回顾性分析其中可能影响治疗效果的因素,TET2基因检测采用实时定量PCR方法。结果：38例患者中21例接受化疗,获得完全缓解（CR）12例（57．14％）,未缓解（NR）5例（23．81％）,疾病进展（PD）4例（19．05％）。应用不同化疗方案治疗后缓解率不同,应用去甲基化治疗的4例第一个疗程治疗后均达到完全缓解,未应用去甲基化治疗的17例中CR 8例（47．06％）、NR 5例（29．41％）、PD 4例（23．53％）。白血病细胞免疫表型CD34阴性、CD13阴性、CD33阳性者化疗后CR率更高,差异具有统计学意义（P＜0．05）。TET2基因阳性AML患者的CR率与年龄、性别、发病时白细胞计数、血红蛋白、血小板计数、白血病细胞免疫表型（CD56、CD9、HLA－DR）、是否伴有其他预后基因及复杂染色体核型无明显相关性。结论：TET2基因阳性AML患者的CR率与化疗方案及白血病细胞免疫表型CD34、CD13及CD33相关。去甲基化治疗可提高TET2基因阳性AML患者的CR率。影响TET2基因阳性AML患者疗效及长期生存的因素尚需进一步探讨。     

胃粘膜癌前病变细胞的核和核仁组成区嗜银蛋白图像分析

以银染方法，用ＶＤＰ－１７５０型自动图像分析仪（美国ＶＩＣＯＭ公司），对１２５例胃粘膜细胞的核和核仁组成嗜银蛋白，进行２０项几何学参数，构造参数测量。结果经多元判别分析，选择判别作用较大的参数组成判别函数，建立判别模式，取得了与原诊断较好的正确符合率，其回代正确率高达９３．６％；刀切法正确率达９０．４％。     

急性髓细胞白血病早期病死高危因素及高白细胞髓性白血病首交化疗策 …

目的 :探讨急性髓细胞白血病 (acutemyelogenousleukemia ,AML)早期病死 (earlydeath ,ED)的高危因素以及高白细胞髓性白血病 (hyperleukocyticacutemyelogenousleukemia ,HAML)的首次化疗策略。 方法 :采用回顾性病例对照、多因素分析的方法对 30 1例AML、31例HAML进行研究。结果 :白细胞计数 (≥ 10 0× 10 9/L)、骨髓增生度 (极度活跃 )、体温 (≥ 39℃ )、血小板计数 (≤ 2 0× 10 9/L)及白血病类型 (M5)等指标进入Logistic多元回归方程 (P =0 0 0 0 0 ) ,OR值分别约 5 0、4 9、3 3、3 2和 2 2 ;HAML的首次化疗 ,采用常规剂量 (平均相对剂量强度≥ 0 5 )、含蒽环类方案 (DA)化疗发生ED的危险性分别为小剂量 (平均相对剂量强度 <0 5 )、其它方案 (HA或HOAP)化疗的 12倍 (P =0 0 34 6 )和 7 5倍 (P =0 0 479)。结论 :白细胞计数 (≥ 10 0× 10 9/L)、骨髓增生度 (极度活跃 )、体温 (≥ 39℃ )、血小板计数 (≤ 2 0× 10 9/L)及白血病类型 (M5)等 5个指标为AMLED的高危因素 ,其中以HAML最为重要 ,而HAML首次化疗 ,采用小剂量、非蒽环类方案化疗 ,可能有助于降低ED率。     

化疗患者口腔炎的高危因素及应用数学模型对口腔炎的预测

目的：研究癌症患者化疗后口腔炎发生的高危因素，建立数学模型，对口腔炎作出预测。方法：对83例癌症患者进行研究，按口腔炎发生与否分为口腔炎组和对照组，对15例临床指标行单因素，多因素Logistic回归分析及判别分析。结果：多因素Logistic回归分析提示持续灌注、体力状态，白细胞计数及口腔pH值4项指标为口腔炎的高危因素；据高危因素所建立的判别函数，阳性预测值为85.2%，阴性预测值为94.6%。判别准确率为91.6%。结论：持续灌注、体力状态，白细胞计数及口腔pH值4项指标为口腔炎的高危因素；据此所建立的判别函数模型简单，能较准确地预测口腔炎的发生，有一定的临床参考价值。     

基于痰涂片巴氏染色色度学特征的肺癌脱落细胞的模式识别研究

目的探讨基于巴氏染色色度学参数的痰涂片肺癌脱落细胞的模式识别方法,为肺癌脱落细胞的计算机诊断奠定基础。方法研究分为学习组和评价组,两组均进一步分为正常组和肺癌组;两个正常组又均分为正常的柱状上皮细胞、鳞状上皮细胞和组织细胞组;两个肺癌组又均分为鳞癌、腺癌、大细胞癌及小细胞癌细胞组。选取的参数包括上述细胞的红、绿、蓝三基色及三基色系数,核的红、绿、蓝三基色及其三基色系数。根据学习组测试结果建立判别函数,计算回代判别符合率;用评价组细胞判别诊断的准确率。结果所建立的判别函数对学习组正常与肺癌细胞判别的符合率分别为62.5%、95.9%,对评价组两组细胞判别诊断的准确率分别为63.4%、95.5%;对学习组鳞癌、腺癌、大细胞癌和小细胞癌癌细胞判别的符合率分别为72.5%、46.2%、81.2%、56.5%,对评价组四类癌细胞判别的准确率分别为71.8%、44.7%、82.7%、57.9%;对正常组柱状上皮细胞、组织细胞及鳞状上皮细胞判别的符合率分别为100.0%、95.8%、94.2%,对评价组三类正常细胞判别诊断的准确率分别为100%、95.8%、94.2%。学习组的判别符合率与评价组的诊断准确率两者间差异无显著性。结论用整个细胞及核的色度学参数建立的逐步判别函数对于判别痰涂片中的正常细胞与肺癌细胞,正常的柱状上皮细胞、组织细胞和鳞状上皮细胞,以及肺的鳞癌、腺癌、小细胞癌和大细胞癌四类亚型肺癌细胞有重要的价值,对于肺的小细胞癌及腺癌细胞的判别效果不佳;基于所建立的判别函数,提出了痰涂片肺癌脱落细胞色度学定量判别分析路径。     

急性髓系白血病患者外周血细胞中FLT3基因表达与FLT3／ITD突变的关系及其意义

背景与目的：研究表明FLT3／ITD突变的急性髓性白血病（acutemyeloid leukemia,AML）患者预后差,但关于AML患者FLT3基因的表达水平在预后中的作用及其与FLT3／ITD突变关系的研究尚不充分。本研究探讨初治AML患者FLT3基因的表达水平与FLT3／ITD突变的关系及其临床意义。方法：建立实时荧光定量PCR检测FLT3基因表达水平及PCR检测FLT3／ITD突变的方法。分析79例初治AML患者FLT3基因水平、FLT3／ITD突变及与预后的关系。结果：22．7％（18／79）的AML患者存在FLT3／ITD突变。92．4％（73／79）的患者标本中可检测到FLT3基因表达,FLT3基因表达水平为0-7320,中位数为312,正常对照组未检测到FLT3基因的表达。FLT3基因高表达及FLT3／ITD突变AML组的白细胞计数及骨髓白血病细胞均显著高于低表达和无突变AML组（P〈0．05）。FLT3基因高表达的AML组FLT3／ITD突变率（25．6％）同低表达的AML组（20．0％）相比,差异无统计学意义（P〉0．05）,FLT3／ITD突变AML患者FLT3基因表达中位数与无突变组比较差异也无统计学意义。FLT3／ITD突变组的完全缓解率（58．8％）显著低于无FLT3／ITD突变组（82．1％）（P〈0．05）;FLT3基因高表达AML组FLT3／ITD的完全缓解率（68．6％）同低表达AML组（84．2％）相比差异无统计学意义（P〉0．05）,但无FLT3／ITD突变组中,FLT3基因高表达组完全缓解率（69．2％）显著低于低表达组（93-3％）（P〈0．05）。结论：FLT3高表达与FLT3／ITD突变之间无明显相关性,FLT3高表达对于无FLT3／ITD突变AML患者可能是一个预后不良的指标。     

化疗患者口腔炎的高危因素及应用数学模型对口腔炎的预测

目的 :研究癌症患者化疗后口腔炎发生的高危因素 ,建立数学模型 ,对口腔炎作出预测。方法 :对 83例癌症患者进行研究 ,按口腔炎发生与否分为口腔炎组和对照组 ,对 15例临床指标行单因素、多因素Logistic回归分析及判别分析。结果 :多因素Logistic回归分析提示持续灌注、体力状态、白细胞计数及口腔pH值 4项指标为口腔炎的高危因素 ;据高危因素所建立的判别函数 ,阳性预测值为 85 2 %、阴性预测值为 94 6%、判别准确率为 91 6%。结论 :持续灌注、体力状态、白细胞计数及口腔pH值 4项指标为口腔炎的高危因素 ;据此所建立的判别函数模型简单 ,能较准确地预测口腔炎的发生 ,有一定的临床参考价值     

应用“数学模型”预测临床T1—2N0M0乳腺癌患者腋窝巴结转移情况的研究

建立一个数学模型,预测临床Ｔ１－２Ｎ０Ｍ０乳腺癌患者腋窝淋巴结转移情况。方法：Ｃ地２５６例行根治术乳腺癌患者进行回顾性研究,按腋窝淋巴结转移情况分为两组,行多因素Ｌｏｇｉｓｔｉｃ回归分析及判别分析。结果Ｌｏｇｉｓｔｉｃ多元回归分析提示微淋巴管浸润、肿瘤大小、肿瘤部位、癌周浸润、间质浸润等５个指标为腋窝淋巴结转移的高危因素;据高危因素所建立的判别函数,阴性预测值高达８８．９％,阳性预测值７１．８％,     

鼻咽癌放射性口腔粘膜炎多因素分析及建立判别模型的临床价值

背景与目的口腔粘膜炎是鼻咽癌放疗中最常见且对患者影响最大的急性反应之一。本研究拟分析鼻咽癌患者在综合治疗中发生严重口腔粘膜炎的相关因素,筛查出发生严重口腔粘膜炎的高危因素,并建立数学判别模型,对其发生情况做出预测。方法对102例鼻咽癌患者进行研究,按口腔粘膜炎的严重程度分为严重组和轻度组,对17项临床及实验室等指标进行单因素分析、多因素Logistic分析及判别分析。结果多因素Logistic分析提示化疗、粘膜反应早期未应用抗生素、口腔卫生差、患病前有吸烟史为发生严重口腔粘膜炎的高危因素;根据高危因素建立对数优势线性判别函数模型,将102例患者的临床资料进行回代判别,判别灵敏度为83.0%,特异度为71.4%,阳性预测值为75.9%,阴性预测值为79.5%。结论化疗、口腔卫生差、有吸烟史是严重的口腔粘膜炎发生的高危因素;早期干预对其有一定预防作用。本研究建立的函数模型有助于预测严重口腔粘膜炎的发生。     

影响老年急性髓系白血病患者预后的危险因素分析

目的 探讨影响老年急性髓系白血病患者预后的危险因素.方法 回顾性分析121例老年急性髓系白血病患者的临床资料.对比不同临床资料患者的完全缓解率和中位生存期.通过多因素Cox模型分析统计影响老年急性髓系白血病患者预后的危险因素.结果 本研究患者的中位生存期为131 d(95%可信区间109~154 d),诱导化疗后的完全缓解率为29.75%.年龄≤70岁、PS评分﹤2分、原发急性髓系白血病、骨髓原始细胞比例≤50%、接受标准化疗以及白细胞CD34表达阴性患者的完全缓解率升高(P﹤0.05);年龄≤70岁、PS评分﹤2分、原发急性髓系白血病、初治时的白细胞计数≤50×109/L、骨髓原始细胞比例≤50%、接受标准化疗以及白细胞CD34表达阴性患者的中位生存期延长(P﹤0.05);多因素Cox模型分析结果显示,年龄、PS评分、初治时白细胞计数以及治疗方案是影响老年急性髓系白血病患者预后的危险因素(P﹤0.05).结论 年龄、PS评分、初治时白细胞计数以及治疗方案是影响老年急性髓系白血病患者预后的危险因素.临床应通过整体评估,制定个体化的化疗方案,以改善患者的预后.     

Clinical implications of angiogenic factors in patients with acute or chronic leukemia: hepatocyte growth factor levels have prognostic impact, especially in patients with acute myeloid leukemia.

The present study evaluated the serum levels of known angiogenic factors and analysed their prognostic significance in patients with acute or chronic leukemia. Enzyme-linked immunosorbent assays (ELISAs) were performed to quantify the basic fibroblast growth factor (bFGF), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), tumor necrosis factor-alpha (TNF-alpha), angiogenin, and matrix metalloproteinase-9 (MMP-9) in stored samples obtained before treatment from patients with acute myeloid leukemia (AML; 30 patients), acute lymphoblastic leukemia (ALL; 10 patients), and chronic myelogenous leukemia (CML; 14 patients). The levels of VEGF, HGF, angiogenin, and MMP-9 were all significantly higher in patients with CML than in healthy individuals. The HGF levels were also higher in patients with AML than in healthy individuals, plus there was a significant correlation between the HGF level and the white blood cell count, monocyte count, and serum level of lactate dehydrogenase (LDH) in patients with AML. In a univariate analysis, age and HGF level were both found to be significant parameters predictive for an achievement of complete remission (CR) in patients with AML. Meanwhile, in a multivariate analysis using a logistic regression model, the HGF level was the only parameter strongly predictive for CR (P=0.047). The leukemia-free survival (LFS) rate for AML patients with a lower HGF concentration was better than that for AML patients with a higher HGF concentration (1 year LFS rates=75.0% vs. 37.5%, P=0.065). The HGF concentration was an independent prognostic factor for an achievement of CR, plus higher HGF concentrations were associated with a lower survival in patients with AML.     

Extramedullary infiltrates at diagnosis have no prognostic significance in children with acute myeloid leukaemia.

This retrospective study was designed to review the relative frequency and prognostic significance of extramedullary infiltrates in children with acute myeloid leukaemia (AML). The registration data and initial discharge summaries were reviewed for all children diagnosed with AML, and registered by the Dutch Childhood Leukaemia Study Group (DCLSG). Between 1972 and 1998, 477 children were diagnosed with AML. Of these patients, 120 (25.1%) had extramedullary leukaemia (EML) at diagnosis. Four categories of EML were found: skin, soft tissue or bone, gingival infiltration and central nervous system (CNS) involvement. Patients who presented with gingival infiltrates, were older than those without EML or those in the other EML subgroups, had a high initial WBC count and a high proportion of M4/M5 morphological variants. This type of presentation could indicate a special biological entity. Univariate analysis of prognostic factors in patients treated after 1985 with intensive protocols showed that initial WBC count and the presence of favourable cytogenetic findings were significant. The presence of EML at diagnosis had no significant effect on event-free survival. In a stepwise multiple regression analysis only favourable cytogenetic findings remained significant.     

Identification of patients at risk for early death after conventional chemotherapy in solid tumours and lymphomas

1-5% of cancer patients treated with cytotoxic chemotherapy die within a month after the administration of chemotherapy. Risk factors for these early deaths (ED) are not well known. The purpose of this study was to establish a risk model for ED after chemotherapy applicable to all tumour types. The model was delineated in a series of 1051 cancer patients receiving a first course of chemotherapy in the Department of Medicine of the Centre Léon Bérard (CLB) in 1996 (CLB-1996 cohort), and then validated in a series of patients treated in the same department in 1997 (CLB-1997), in a prospective cohort of patients with aggressive non-Hodgkin's lymphoma (NHL) (CLB-NHL), and in a prospective cohort of patients with metastatic breast cancer (MBC series) receiving first-line chemotherapy. In the CLB-1996 series, 43 patients (4.1%) experienced early. In univariate analysis, age > 60, PS > 1, lymphocyte (ly) count 1 (hazard ratio 3.9 (95% Cl 2.0-7.5)) and d1-ly count 相似文献   

骨髓增生异常综合征转化的急性髓系白血病52例临床观察

目的 探讨骨髓增生异常综合征(MDS)转化的急性髓系白血病(AML)的临床特征、疗效及影响因素.方法 回顾性分析52例MDS转化的AML患者转化时间、临床特点、MDS转化的AML亚型、疗效及其影响因素等临床资料.结果 52例患者转化为AML的中位时间为6.75个月.单因素分析显示,按中位年龄分层,≥46岁组转化为AML时间短于＜46岁组;男性短于女性;修订国际预后积分系统(IPSS-R)核型预后差组短于预后好组.多因素分析显示,年龄≥46岁、IPSS-R核型预后差为MDS较早转化为AML的独立危险因素.MDS转化的AML亚型为M2 35例(67.31％),M56例(11.54％),M4、M6各5例(9.62％),M1 1例(1.92％).髓外浸润发生率为32.69％(17/52),主要部位为肝、脾、淋巴结、牙龈、消化道及扁桃体.转化为AML后白细胞计数、中性粒细胞计数较初诊MDS时明显增高(P＜0.05).MDS转化的AML治疗完全缓解(CR)率为33.3％(8/24).结论 年龄大、IPSS-R核型预后差为MDS向AML转化的危险因素.MDS转化的AML临床亚型主要为M2,髓外浸润表现不少见,其治疗缓解率低,治疗相关死亡率高,预后差.     

Small-cell carcinoma of the lung: derivation of a prognostic staging system.

Retrospective data on 22 pretreatment attributes were evaluated in 614 patients with small-cell carcinoma of the lung (SCCL). The series included 284 patients with limited disease (LD) and 328 patients with extensive disease (ED) managed between 1974 and 1986. Prognostic factors were evaluated by univariate analysis and by the Cox multivariate regression model. Recursive partition and amalgamation algorithm (RECPAM), two clustering methods well suited for obtaining strata and adapted for censoring survival data, were developed and used in the formulation of a new prognostic staging system. In univariate analysis, prognosis was significantly influenced by extent of disease (DE), the number of metastatic sites, and the detection of mediastinal spread in LD. Poor performance status (PS), male sex, and advanced age were negatively correlated with survival, as were increased serum levels of alkaline phosphates (AP), lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), total WBC count (WBCC), and low platelet count and low serum sodium. The Cox model identified plasma LDH and mediastinal spread as the only significant factors in LD; the influence of PS, number of metastatic sites, bone metastasis, brain metastasis, and platelet count were identified as significant in ED. The RECPAM model identified four distinct risk groups defined in a classification tree by the following eight attributes: DE, PS, serum AP, serum LDH, mediastinal spread, sex, WBCC, and liver metastasis. The four groups were distinguished by median survival times of 59, 49, 35, and 24 weeks, respectively (P = .0001). Interactions among prognostic factors are emphasized in the RECPAM classification model as evidenced by reassignment of patients across conventional staging barriers into alternate prognostic groups. The advantages of using RECPAM over the more conventional Cox regression techniques for a new staging system are discussed.     

A neurocomputational model for prostate carcinoma detection

BACKGROUND: Current guidelines for prostate carcinoma screening rely primarily on the digital rectal examination (DRE) and prostate specific antigen (PSA). Well described patient risk factors for prostate carcinoma also include age, ethnicity, family history, and complexed PSA. However, due to the nonlinear relation of each of these variables with prostate carcinoma, it is difficult to predict reliably each patient's risk based on linear univariate analysis. The authors investigated a neural network to model the risk of prostate carcinoma by seven readily available clinical features. METHODS: The database for the current study comprised 3268 men recently evaluated for the early detection of prostate carcinoma. The seven clinical features evaluated included age, race, family history, International Prostate Symptom Score (IPSS), DRE, and total and complexed PSA. Three hundred forty-eight subjects in the dataset included men with determined prostate biopsy outcomes and for whom at least 6 of 7 features were available. The dataset was divided randomly into a training set (60%) and a test set (40%), with n1/n2 cross-validation used to evaluate model accuracy, and was modeled with linear and quadratic discriminant function analysis and a neural computational system. After a model with acceptable goodness of fit was achieved, reverse regression analysis using Wilks's generalized likelihood ratio test was performed to evaluate the statistical significance of each input variable. RESULTS: The receiving operating characteristic (ROC) area for the neural computational system in the test set was 0.825, whereas total PSA and complexed PSA alone had ROC areas of 0.678 and 0.697, respectively. The ROC area of logistic regression in the test set was 0.510, linear discriminant function analysis was 0.674, and quadratic discriminant function analysis was 0.011. All were significantly less than the ROC area of the neural computational model (all Ps < 0.002). Reverse regression based on Wilks's generalized likelihood ratio test demonstrated each input feature to be highly significant to the model (all Ps < 0.000001). CONCLUSIONS: The authors modeled a combination of well described patient risk factors for prostate carcinoma using a neural computational system with acceptable goodness of fit. They demonstrated that each of the seven variates on which the model was based was critically significant to model performance. The authors presented this model for clinical use and suggested that clinicians use it in deciding to perform prostate biopsy.