Computer A ided Detection of Tumours in Mammograms(IJIGSP-V6-N4-7)
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实用临床医药杂志 Journal of Clinical Medicine in Practice
第25卷
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1资料与方法
1.1 一般资料 回顾性分析本院2218年6月一2019年12月
手术病理检查显示为乳腺病变的226例女性患者 的临床资料，患者均为单发病灶。恶性病变 149 例，年龄 30 ~64 岁，平均（54.6 ±12.5）岁； 病理类型包括浸润性癌134例、导管内癌6例、黏 液腺癌5例、导管内乳头状癌2例、包裹性乳头状 癌2例。良性病变147例，年龄28~79岁，平均 （49.1±6.6）岁，病理类型包括纤维腺瘤29例、 乳腺增生14例、炎症6例、导管内乳头状瘤6例、 囊肿4例。患者术前均行乳腺摄影检查，均为病 变头足位（CC）及内外斜位（MLO）,且有完整的 临床资料。排除标准：因摄影质量影响病变观察 及感兴趣区（ROI）勾画者；摄片前已行穿刺、新 辅助化疗或手术者。 1.6乳腺X线摄影
差为0的变量被排除，然后对特征参数采用均值 替换的方法对异常值进行处理，进一步对特征参
数进行标准化处理，再进一步对特征参数进行标准
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胡黄连苷Ⅱ对非小细胞肺癌恶性进展的影响
胡黄连苷Ⅱ对非小细胞肺癌恶性进展的影响Δ郭寰宇 1＊，王卫芳 2 #，徐丽伟 3，董文博 4（1.长春中医药大学临床医学院实验中心，长春 130117；2.长春中医药 大学临床医学院生物化学教研室，长春 130117；3.长春中医药大学附属医院肿瘤血液科，长春 130112； 4.吉林大学第一医院二部检验科，长春 130061）中图分类号 R 965 文献标志码 A 文章编号 1001-0408（2024）04-0430-06DOI 10.6039/j.issn.1001-0408.2024.04.09摘要 目的 探讨胡黄连苷Ⅱ对非小细胞肺癌（NSCLC ）恶性进展的影响及机制。
方法 将A 549细胞分组为对照组，胡黄连苷Ⅱ低、中、高浓度组，K 6PC-5[鞘氨醇激酶1（SPHK 1）激活剂]组，胡黄连苷Ⅱ高剂量+K 6PC-5组，检测细胞增殖、迁移、侵袭情况，以及细胞中增殖细胞核抗原（PCNA ）、基质金属蛋白酶2（MMP-2）、MMP-9、SPHK 1、1-磷酸鞘氨醇受体3（S 1PR 3）及胞外信号调节激酶1/2（ERK 1/2）蛋白的表达情况。
以BALB/c 裸鼠为对象，通过皮下接种A 549细胞悬液建立NSCLC 裸鼠移植瘤模型，并将其分为裸鼠-对照组，裸鼠-胡黄连苷Ⅱ低、中、高剂量组，裸鼠-K 6PC-5组，裸鼠-胡黄连苷Ⅱ高剂量+K 6PC-5组（每组5只），考察胡黄连苷Ⅱ对其瘤体质量及体积的影响。
结果 与对照组比较，胡黄连苷Ⅱ低、中、高浓度组的细胞OD 450值、EdU 阳性细胞率、划痕愈合率、细胞侵袭数及PCNA 、MMP-2、MMP-9、SPHK 1、S 1PR 3、ERK 1/2蛋白的相对表达量均显著降低；与裸鼠-对照组比较，裸鼠-胡黄连苷Ⅱ低、中、高剂量组裸鼠体内的瘤体质量及体积均显著降低或缩小，上述指标均呈浓度/剂量依赖性变化（P ＜0.05）；K 6PC-5组细胞和裸鼠-K 6PC-5组裸鼠对应指标的变化趋势则相反（P ＜0.05）。
2020CSCO胆道系统肿瘤诊疗指南
中国临床肿瘤学会（CSCO）胆道恶性肿瘤诊疗指南GUIDELINES OF CHINESE SOCIETY OF CLINICAL ONCOLOGY （CSCO）BILIARY TRACT CANCER2020.V1.0中国临床肿瘤学会指南工作委员会组织编写人民卫生出版社中国临床肿瘤学会指南工作委员会组长梁后杰沈锋秦叔逵副组长（以姓氏汉语拼音为序）毕锋戴广海李恩孝刘基巍刘秀峰钦伦秀王理伟朱陵君中国临床肿瘤学会（CSCO）胆道恶性肿瘤诊疗指南2020. V1.0组长梁后杰沈锋秦叔逵副组长毕锋戴广海李恩孝刘基巍刘秀峰钦伦秀王理伟朱陵君秘书组郭婧谢赣丰郑怡专家组成员（以姓氏汉语拼音为序）（*主要执笔人）白苇空军军医大学西京医院毕锋*四川大学华西医院陈骏*南京大学医学院附属鼓楼医院陈小兵河南省肿瘤医院陈志宇陆军军医大学西南医院仇金荣海军军医大学东方肝胆外科医院戴广海*解放军总医院邓薇首都医科大学附属北京友谊医院方维佳*浙江大学附属第一医院顾康生安徽医科大学第一附属医院顾艳宏*江苏省人民医院郭婧*青岛大学附属医院郭增清福建省肿瘤医院何宇陆军军医大学西南医院何义富安徽省肿瘤医院黄云中南大学湘雅医院焦洋安徽医科大学第一附属医院李俊海军军医大学东方肝胆外科医院李勇南昌大学第一附属医院李恩孝*西安交通大学第一附属医院李富宇四川大学华西医院梁后杰*陆军军医大学西南医院廖峰*解放军东部战区总医院刘波山东省肿瘤医院刘宏鸣陆军军医大学特色医学中心刘基巍*大连医科大学附属第一医院刘先领中南大学湘雅二医院刘秀峰*解放军东部战区总医院柳江新疆自治区人民医院娄长杰哈尔滨医科大学附属肿瘤医院卢进四川省肿瘤医院陆明北京大学肿瘤医院陆菁菁*北京和睦家医院栾巍内蒙古自治区人民医院罗嘉湖南省肿瘤医院吕红英青岛大学附属医院马虹*华中科技大学协和医院马惠文重庆市肿瘤医院欧娟娟陆军军医大学西南医院彭永海*解放军联勤保障部队第九〇〇医院钦伦秀*复旦大学附属华山医院秦宝丽辽宁省肿瘤医院秦叔逵南京金陵医院秦艳茹郑州大学第一附属医院邱文生*青岛大学附属医院尚培中陆军第八十一集团军医院沈丽达云南省肿瘤医院石焕山东省肿瘤医院寿佳威浙江大学医学院附属邵逸夫医院滕赞中国医科大学附属第一医院田伟军天津医科大学总医院王斌吉林省肿瘤医院王坚上海交通大学附属第六人民医院王欣云南省第一人民医院王馨厦门大学附属中山医院王阿曼大连医科大学附属第一医院王理伟*上海交通大学医学院附属仁济医院王文玲贵州医科大学附属肿瘤医院吴田田北京大学国际医院吴胤瑛西安交通大学第一附属医院谢赣丰*陆军军医大学西南医院许瑞莲深圳市人民医院殷保兵*复旦大学附属华山医院殷先利湖南省肿瘤医院应杰儿浙江省肿瘤医院臧远胜海军医科大学附属长征医院张倜天津医科大学肿瘤医院张翠英内蒙古自治区人民医院张永杰淮安市第二人民医院赵达兰州大学第一医院郑璐陆军军医大学新桥医院郑怡*浙江大学医学院附属第一医院郑振东解放军北部战区总医院周航遵义医学院附属医院周军*北京大学肿瘤医院周俊同济大学附属东方医院周琪重庆市涪陵中心医院周云河南省人民医院周福祥武汉大学中南医院周建炜河南省人民医院朱青四川大学华西医院朱陵君*江苏省人民医院前言基于循证医学证据，兼顾诊疗产品的可及性，积极吸收精准医学新进展，制定中国常见癌症的诊断和治疗指南，是中国临床肿瘤学会（CSCO）的基本任务之一。
文献检索题(1)
单选1。
《中国图书馆分类法》将所有学科分为（）个基本大类。
A。
15B.18C.80D。
222.( ）是记录有知识的一切载体.A。
信息B.情报C.文献D。
知识3。
以下数据来源中经过主题标引的是( )A.MedlineB。
preMedlineC。
PSCD。
publine4.医学文献检索领域里最具权威性的主题词表是美国国立医学图书馆编制的( ）.A。
MeSHB。
MedLineC.IMD.PubMed5.下列检索出文献量最少的检索式是( ）A.A and BB。
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起着扩大检索范围的逻辑运算符是（）A。
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括号(）8。
按照文献获取的难易程度分,内容完全不公开的信息资源属于（）文献 A。
无色B.白色C.灰色D.黑色9。
按照文献的加工程度分,《医学文献信息检索实用教程》属于（ )A。
零次文献B。
一次文献C.二次文献D。
三次文献10.下列哪种文献属于二次文献（ ).（1分）A.专利文献B。
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会议文献D.目录11。
以下检索语言中基于文献内容特征的是（）。
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刊名12。
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刊名途径13。
下列属于文献外表特征的是（）。
（1分）A。
文献的分类号B。
文献的主题词C。
文献的关键词D.文献的作者14.（）是指能代表文献或课题的主要内容,具有实质意义，规范化的词或词组。
A.标题词B。
主题词C。
副主题词D。
关键词15。
( ）是指能代表文献或课题主要内容或主要内容的某一方面,具有一定实质意义的词或词组。
A.标题词B。
主题词C.叙词D。
关键词16。
Clin Cancer Res-2009(乳腺癌)
DNA Methylation Markers Predict Outcome in Node-Positive,Estrogen Receptor-Positive Breast Cancer with Adjuvant Anthracycline-Based ChemotherapyOliver Hartmann,1Frede rique Spyratos,4Nadia Harbeck,2Dimo Dietrich,1Anne Fassbender,1Manfred Schmitt,2Serenella Eppenberger-Castori,3VincentVuaroqueaux,3Florence Lerebours,4KatrinWelzel,1Sabine Maier,1Achim Plum,1Stephan Niemann,1John A.Foekens,5Ralf Lesche,1and JohnW.M.Martens 5AbstractPurpose:We have shown that DNAmethylation of the PI TX2gene predicts risk of distant recurrence in steroid hormone receptor-positive,node-negative breast cancer.Here,we present results from a multicenter study investigating whether PI TX2and other candidate DNAmeth-ylation markers predict outcome in node-positive,estrogen receptor-positive,HER-2-negative breast cancer patients who received adjuvant anthracycline-based chemotherapy.Experimental Design:Using a microarray platform,we analyzed DNAmethylation in regu-latory regions of PITX2and 60additional candidate genes in 241breast cancer ing Cox regression analysis,we assessed the predictive power of the individual marker/marker panel candidates.Clinical endpoints were time to distant metastasis,disease-free sur-vival,and overall survival.Anested bootstrap/cross-validation strategy was applied to identify and validate marker panels.Results:DNAmethylation of PI TX2and 14other genes was correlated with clinical outcome.In multivariate models,each methylation marker added significant information to established clinical factors.Afour-marker panel including PI TX2,BMP4,FGF4,and C20orf55was identified that resulted in improvement of outcome prediction compared with PITX2alone.Conclusions:This study provides further evidence for the PITX2biomarker,which has now been successfully confirmed to predict outcome among different breast cancer patient populations.We further identify new DNAmethylation biomarkers,three of which can be combined into a panel with PITX2to increase the outcome prediction performance in our anthracycline-treated primary breast cancer population.Our results show that a well-defined panel of DNAmethylation markers enables outcome prediction in lymph node-positive,HER-2-negative breast cancer patients treated with anthracycline-based chemotherapy.I n breast cancer,currently available methods are inadequate todetermine precisely the aggressiveness of the disease and the likelihood of response to a certain treatment in individual patients.Therefore,biomarkers predicting the tumor’s meta-static potential and its response to a specific treatment are urgently needed.This is particularly true for patients for whom current guidelines (1)recommend anthracycline-based chemo-therapy.These patients comprise a clinically distinct interme-diate-to high-risk subgroup,for which in view of the short and long-term toxicity as well as the availability of new effective anthracycline-free chemotherapy options (2),the widespread use of adjuvant anthracyclines is currently under debate (3).Unfortunately,reliable biomarkers for benefit from anthracy-cline therapy are still lacking.DNA methylation of CpG dinucleotides within gene regulatory regions,associated with suppression of gene expression,is a common and early event in cancer (4–6).Specific DNA methylation patterns for tumor subtypes including breast cancer have been reported and associated with clinical outcome (7–16).Cancer Therapy:ClinicalAuthors’Affiliations:1Epigenomics AG,Berlin,Germany;2Department of Obstetrics and Gynecology,T echnical University Munich,Munich,Germany;3Stiftung T umorbank,Basel,Switzerland;4Centre ReneHuguenin,St.Cloud,France;and 5Erasmus Medical Center,Rotterdam,The Netherlands Received 1/22/08;revised 8/25/08;accepted 9/2/08.Grant support:Epigenomics and European Union Sixth Framework Program grant LSHC-CT-2003-504586(J.W.M.Martens,J.A.Foekens,S.Eppenberger-Castori,F.Spyratos,M.Schmitt,and N.Harbeck).The costs of publication of this article were defrayed in part by the payment of page charges.This article must therefore be hereby marked advertisement in accordance with 18U.S.C.Section 1734solely to indicate this fact.Note:Supplementary data for this article are available at Clinical Cancer Research Online (/).Preliminary results of this study were presented as a poster presentation at 29th San Antonio Breast Cancer Symposium,December 14-17,2006[Lesche et al.,Breast Cancer ResT reat 2006;100:A6009].Requests for reprints:John W.M.Martens,Department of Medical Oncology,Erasmus Medical Center,P.O.Box 2040,3000CARotterdam,The Netherlands.Phone:31-10-7044372;Fax:31-10-7044377;E-mail:j.martens @erasmusmc.nl.F 2009American Association for Cancer Research.doi:10.1158/R-08-0166www Clin Cancer Res 2009;15(1)January 1,2009315We have recently found and validated that DNA hyper-methylation of the PITX2promoter is associated with a high risk of recurrence in node-negative,steroid hormone receptor-positive breast cancer who received tamoxifen as their only systemic adjuvant therapy(13,17).These studies further showed that DNA methylation can be successfully measured in formalin-fixed,paraffin-embedded breast cancer specimens, which is one of the strengths of DNA methylation technologies and considered a prerequisite for routine clinical application (17).A follow-up study revealed that PITX2methylation is a strong and pure prognostic factor in patients with node-negative,steroid hormone receptor-positive breast cancer who did not receive any adjuvant systemic treatment(15).In the present study,we have used a microarray technology to study whether methylation of PITX2and60other candidate genes could be predictors for outcome in breast cancer patients with node-positive,steroid hormone receptor-positive,and HER-2-negative tumors receiving adjuvant anthracycline-based chemotherapy.In our study,we focused on patients with lymph node-positive,estrogen receptor(ER)-positive,and HER-2-negative breast cancer,as these patients comprise a clinically distinct subgroup for which anthracycline-based adjuvant chemotherapy is currently recommended(1).Our study suggests that reliable prediction of clinical outcome for these breast cancer patients may be possible using a well-defined panel of DNA methylation markers.Materials and MethodsPatients and samplesThe study cohort comprised384node-positive breast cancer patients whose tumor samples were recruited from four clinical centers:Erasmus Medical Center;Centre Rene´Huguenin;Stiftung Tumorbank;and Department of Obstetrics and Gynecology,Technical University of Munich.Appropriate consent,according to institutional requirements, has been obtained for all patients.The study protocol was approved by the local ethics committees.To be eligible for this study,patients had to fulfill all of the following criteria:(a)histologically confirmed invasive breast cancer,(b)primary tumor stage pT1to pT3,(c)histologically confirmed lymph node involvement(pN>1),(d)surgery before2002(potential follow-up of at least5years),(e)standard adjuvant anthracycline-based chemother-apy[no dose-dense therapy,no other primary systemic chemotherapy (except hormonal therapy),and no additional taxane],and(f) availability of clinical follow-up data.Among the384patients recruited for the study,284were reported as ER-positive.HER-2status was determined in all samples using the Light Cycler HER-2/neu DNA quantification Kit(Roche Applied Science). HER-2-positive patients(n=43)who nowadays would receive trastuzumab treatment were then excluded,leaving a total of241 node-positive,ER-positive,and HER-2-negative breast cancer patients available for analysis.For the whole cohort,the percentages of patients with distant metastasis at5and10years were estimated as70.1% (64.3-76.3%)and56.1%(48.5-65.0%),respectively;5-and10-year disease-free survival(DFS)were64.5%(58.6-71.0%)and47.4%(40.0-64.2%),respectively;and5-and10-year overall survival(OS)were 85.8%(81.4-90.5%)and67.7%(59.8-79.1%),respectively.The median follow-up81.5months.DNA extraction and bisulfite treatmentGenomic DNA was extracted from snap-frozen tumor tissue or tumor cell nuclei pelleted at100,000Âg as described(14).Bisulfite conversion of the DNA was done using the EpiTect Kit(Qiagen).Candidate gene selectionThe61candidate genes(listed in the Supplementary Material)to be analyzed on the microarray were selected based on(a)genome-wide screens for prognostic markers in ER-positive and ER-negative breast cancer,6(b)P TX2pathway analyses,and(c)literature.PCR amplification and microarray hybridizationPCR amplification and microarray hybridization were done as described previously(7,14).In total,64PCR amplificates representing 61genes were pooled and hybridized to the microarray on which detection oligonucleotides for methylated(CG)and nonmethylated (TG)gene copies were spotted.This allowed for simultaneous quantitative measurement of unmethylated and methylated copies of the genes.Microarrays included4oligonucleotide pairs for each of the 64PCR amplificates(total of256pairs).Each probe pair covered between one and three CpG dinucleotides in the regulatory regions of the respective candidate gene.The methylation score for each CpG site was calculated from the fluorescence intensity values of the methylated (FI m)and unmethylated(F I u)oligonucleotides.To stabilize the variance,the score was transformed using the generalized log transformation(g LOG):methylation score=g LOG(FI m/FI u)(18). For statistical analysis,methylation scores for each amplificate were determined by averaging measurements from all probe pairs belonging to one amplificate using the median.Multiple amplificates from the same candidate gene entered data analysis independently.Valid microarray results were obtained for all samples.Clinical endpointsThe primary clinical endpoint in this study was time to distant metastasis(TDM).Secondary endpoints were DFS and OS.For TDM, only distant recurrences were considered.Ipsilateral and locoregional recurrences were not considered as events or censoring events. Contralateral breast cancer,development of other primary tumors, death(from any cause)without observed recurrence,and loss for follow-up were considered censoring events.For DFS,all recurrences were considered as events,whereas death(from any cause)before recurrence,contralateral breast cancer,and development of other 6Unpublisheddata.Cancer Therapy:Clinical Clin Cancer Res2009;15(1)January1,2009316primary tumors or loss for follow-up were considered censoring events. For OS,only death(from any cause)was considered as event and patients were censored only when lost for follow-up.Statistical analysesThe relation between clinical endpoints and DNA methylation score was analyzed for each amplificate by linear univariate Cox proportional hazards models(19,20).Here,likelihood ratio tests(LRT)were done to test for a significant relationship of methylation scores of each amplificate with clinical endpoints.Hazard ratios(HR)for continuous variables were calculated relative to an increment of the interquartile range of that variable(for the increment from the25%quantile to the 75%quantile of the measurements;ref.20).Multivariate regression analysis,testing the association between clinical endpoint and multiple methylation scores and/or clinical variables,was done by linear Cox proportional hazards models.In that case,LRT was done to test for a significant association of the outcome variables(TDM,DFS,and OS) with the derived model consisting of clinical variables and/or methylation scores.In addition,Wald tests(testing the hypothesis that the variable in question provides significant information to the multivariate model)were calculated.Survival curves were plotted according to Kaplan-Meier(21).Log-rank tests were used to test for differences between survival curves.To describe and compare the predictive performance of each variable and each multivariate model, the concordance index(C index;refs.20,22)was calculated.To penalize overfitting by entering multiple factors,the bootstrap-corrected version of the C index is given(20,22).To correct for multiple testing in univariate testing(64amplificates), significance levels were adjusted using false discovery rate correction using the linear step-up procedure proposed in ref.23.Marker panel feature selection.A nested cross-validation/bootstrap procedure was applied to perform feature selection and panel validation (24,25).A schematic illustration of the methodology is shown in the Supplementary Material.To identify a marker panel for outcome prediction,the63amplificates[all but the amplificate designed in the promoter of transcripts A and B for PITX2(PITX2P2)]were first ranked by univariate performance(Cox proportional hazards model,LRT). Then,marker panels containing PITX2P2and increasing numbers of the best19single markers were evaluated with respect to their prognostic predictability,which was estimated using the bootstrap-corrected C index with B=200bootstrap runs(20).A gene was selected for the final predictor model if the C index of the model including this marker was larger than that of the model excluding the marker.A C index was considered‘‘larger’’if at least an increase of1%over the model containing PITX2P2was observed.The extensive search was limited to a maximum of20amplificates altogether,balancing the critical sample size to build a reasonable regression model(number of distant metastases)on the one side and the request to find markers complementing previously included ones(and therefore not necessarily good univariate markers)on the other.Marker panel validation.The cross-validated prediction score is an unbiased estimate of the performance of the marker panel in a future data set.It corrects for the optimism introduced due to feature selection within the same data set(24,25).A schematic illustration of the methodology is presented in the Supplementary Material.In brief,the generalization error for the marker panel selection was estimated by repeating the feature selection procedure using a20-fold cross-validation,starting with all64amplificates in each cross-validation run.We trained on95%of the samples and computed the cross-validated predictive index for the samples in the remaining5%of samples using the Cox model developed for the training set.To combine results from all runs,the predictive indices for all samples were transformed into risk percentiles within each run.The nested cross-validation procedure itself was replicated100times using random permutations of the data set to determine the estimation error of the cross-validated C index.As cross-validated risk percentiles are correlated among cases,the usual null distributions of the test statistics are not valid.To derive P values of both the likelihood ratio and Wald tests (Cox model)as well as the log-rank test(Kaplan-Meier analysis)for the cross-validated prediction score,the null distribution was estimated using100repetitions of the entire procedure for permutations of survival times and censoring indicators.To further illustrate the performance of the cross-validated prediction score,the score was analyzed in a similar way as described above for our individual amplificates.All results using a single cross-validated prediction score were generated using results of the first replicate of the nested cross-validation procedure.Statistical analysis was done using R version2.4.17and the R package‘‘Design’’version2.1. ResultsPatient and tumor characteristics.Patient and tumor char-acteristics(n=241)are given in Supplementary Material,and these are comparable with previously published cohorts with similar inclusion criteria(26).These clinical standards may differ somewhat from current guidelines as shown by the fact that only104(43%)patients received additional adjuvant endocrine treatment,although their tumors had positive steroid hormone receptor status.All patients had received adjuvant anthracycline-based chemotherapy according to clinical stand-ards at the time of their primary therapy.Tumor size(HR, 1.77;P=0.0225),grade(HR, 2.07; P=0.004),and adjuvant endocrine therapy(HR,0.49; P=0.0025)were significantly associated with TDM in this cohort(Table1).Adjuvant endocrine therapy,grade,and progesterone receptor(PgR)were significantly associated with DFS(data not shown).Tumor size,grade,endocrine therapy, and PgR status were significantly associated with OS(data not shown).For all clinical endpoints,risk of recurrence was lower for patients who received adjuvant endocrine therapy. Univariate analysis of methylation markers.Because we have observed DNA methylation of PITX2as a strong marker in previous studies,we first analyzed the performance of this marker.The methylation score measured with the amplificate designed for the promoter of transcripts A and B of PITX2 (PITX2P2),located upstream of the first transcriptional start, showed that PITX2P2hypermethylation was associated with a high risk of distant recurrence in this patient cohort[HR,1.66; 95%confidence interval(95%CI), 1.21-2.28;P=0.002;C index=0.624;see Table1and Fig.1].PITX2P2hyper-methylation was also associated with poor DFS(HR,1.47;95% CI,1.11-1.96;P=0.0084)and OS(HR,2.07;95%C,1.40-3.06;P=0.0003).However,methylation of the amplificate designed for the promoter regulating transcript C of PITX2 (P I TX2P1)was not associated with distant recurrence in this patient cohort(HR,1.23;95%CI,0.89-1.68;P=0.21). Among the other62tested amplificates,15amplificates, derived from14genes,were associated with TDM(false discovery rate<5%;Table2).Methylation of BMP4,FOXL2, LMX1A,C20orf55,BARX1,PLAU(both amplificates),FGF4, NR5A1,BRCA1,TLX3,NR2E1,LHX4,ZNF1A1,and CCND2 and BMP4,FOXL2,LMX1A,FGF4,and BRCA1showed C indices between0.58and0.63comparable with that of PITX2P2.For DFS,BMP4,FOXL2,and C20orf55and for OS all15amplificates described above were significantly associated with the respective clinical endpoint(false discovery rate<5%; 7DNA Methylation Markers and Anthracyclines Clin Cancer Res2009;15(1)January1,2009317data not shown).For all markers,with the exception of C20orf55,lower methylation scores were associated with better clinical outcome.Multivariate analysis of methylation markers and clinical variables.PITX2P2methylation was a significant marker in the multivariate analysis including age at surgery,pathologic T stage and grade,PgR status,and adjuvant endocrine therapy (P=0.05;Table1).To show clinical usefulness of a biomarker, it is necessary that the marker increases the predictive performance of established clinical variables.For the multivar-iate model including PITX2P2,the bootstrap-corrected C index, which is an unbiased estimate of the effect size for regression models,is larger than that for the model including the clinical variables only(0.647compared with0.61,respectively;Table 3).Therefore,PITX2P2provides additional information for prediction of TDM independent of established clinical variables.Most of the15amplificates identified in univariate analysis were also a significant marker in multivariate analysis when combined with age,pathologic T stage,PgR status,and adjuvant endocrine therapy.The bootstrap-corrected C indices of the multivariate models(range,0.620-0.665)were larger than those of the multivariate model using only clinical variables(0.610;Table2).Marker panel selection and performance prediction.Having established the role of several DNA methylation markers(PITX2 and15other markers)for outcome prediction,we assessed if combining these markers into a panel resulted in improved performance compared with that of the individual markers alone.Overfitting becomes a critical issue if feature selection, model building,and performance estimation are done within one data set(27).Therefore,the bootstrapped-corrected C index is used in the feature selection step.To select relevant markers that could improve the performance of our already well-established marker PITX2,we evaluated the gain in estimated effect size(C index)by stepwise adding the best markers from univariate analysis to PITX2P2.Figure2A illustrates the results of the feature selection step.With PITX2P2as‘‘anchor,’’BMP4, C20orf55,and FGF4were selected to define a four-marker panel for prediction of TDM in the currentcohort.Fig.1.Prediction of TDM survivalprobabilities based on PITX2P2DNAmethylation.Kaplan-MeierTDM curves ofpatients categorized in subgroups basedon PI TX2P2DNAmethylation scores(P=0.00014).The methylation score wasgrouped into quartiles.Log-rank test wasused to test for significant separationbetween the groups.C index was0.624.Patients at risk for the different quartiles areindicated in12-mo intervals.Cancer Therapy:Clinical Clin Cancer Res2009;15(1)January1,2009318To estimate the performance regarding outcome prediction of the four-marker panel,feature selection was nested within a cross-validation procedure.This corrects for overfitting due to feature selection,and the resulting cross-validated C index is an unbiased estimate of the performance of the marker panel in a future data set.The C index of the cross-validated prediction score is0.668compared with0.624for PITX2P2alone (Table3).We replicated the estimation procedure100times, thereby generating a range of C indices for our four-marker panel.As a result,a median C index of0.654(range,0.622-0.676)was obtained for the prediction score,and in98of100 replications,the estimated performance of our four-marker panel was better than that of PITX2P2alone(Fig.2B).Based on these results,we conclude that a four-marker panel can indeed improve outcome prediction over a single marker.To further illustrate the performance of our outcome predictor,we report the results of the first cross-validation replicate in more detail.The four markers selected for our marker panel represent a stable selection:C20orf55was selected in all cross-validation runs,FGF4in16of20runs, and BMP4in14of20runs.Only two other markers were selected in any run:APC and BRCA1(Fig.2C).The C index of the cross-validated prediction score for a multivariate model including the cross-validated prediction score as well as age at surgery,pathologic T stage,PgR,and adjuvant endocrine therapy was0.695(P<0.01,LRT;Table3).The Wald P value for the cross-validated prediction score in the multivariate model was<0.01.Kaplan-Meier estimates illustrate the difference(P<0.01,log-rank test;Fig.2D)between the performance of the identified marker panel(Fig.3,left)and PITX2P2alone(Fig.1).For all patients,the5-year survival prediction of the quartile with the highest cross-validated prediction scores for the four-marker panel is<50%,whereas that of the quartile with the lowest cross-validated prediction scores is>85%.Similar to TDM,significant prediction scores were derived for DFS(P=0.01)and OS(P<0.01).Since today all patients with steroid hormone receptor-positive breast cancer will receive adjuvant endocrine therapy,we performed a subgroup analysis for those patients in our cohort who had received adjuvant endocrine treatment. Although the size of cohort was limited to104patients,a highly statistically significant separation of survival groups was observed when stratifying the patients according to the prediction score(Fig.3,right).DiscussionAccording to current guidelines,adjuvant chemotherapy needs to be considered for the majority of patients with primary breast cancer,with the exception of a small subgroup with very favorable outcome prediction based on clinical factors(1).In node-positive breast cancer,anthracycline-based adjuvant chemotherapy has become the standard of care during the1990s(28).Moreover,several studies showed that adding a taxane(29),or,more recently,administering dose-dense chemotherapy regimes(including anthracyclines;ref.30), further improves outcome in node-positive breast cancer. However,not all patients will benefit from such more aggressive therapies and therefore only face their more severe side effects,because these patients either have an excellent prognosis or have tumors that do not respond to certain chemotherapies.Thus,prognostic and predictive markers are urgently needed to tailor the best possible therapy regimen for the individualpatients.DNA Methylation Markers and Anthracyclines Clin Cancer Res2009;15(1)January1,2009319In previous studies,we found that DNA methylation of PITX2reliably predicts the risk of distant recurrence in node-negative breast cancer (13,15,17).These results prompted us to analyze PITX2methylation in node-positive breast cancer and to measure other DNA methylation markers,with the goal to develop a marker panel for improved outcome prediction.For this purpose,we have analyzed a cohort of 241patients with lymph node-positive,ER-positive,and HER-2-negative breast cancer,who had all received adjuvant anthracycline-based chemotherapy.We focused on patients with these tumor characteristics,because they constitute a clinically relevant subgroup for which standardized treatment guidelines are available (1,26).Among the node-positive breast cancer patients,our subpopulation is considered to have a relatively favorable outcome because of their positive steroid hormone receptor status and the absence of HER-2amplification.Therefore,we hypothesized that it might be possible to further stratify this group of patients so that a good prognosis group could be defined that would derive sufficient benefit from conventional anthracycline-based therapy and would not require more aggressive therapy such as,for example,dose-dense regimens or addition of taxanes.In the current study,we were able to show that PITX2DNA methylation also predicts outcome in node-positive breast cancer.Because clinical factors such as grade,tumor stage,or age have been described previously as outcome predictors in primary breast cancer,we first calculated a base model.The predictive accuracy of the multivariate Cox model with established clinical variables,quantified using the bootstrap-corrected C index as an unbiased measure of the predictive strength of a model,was 0.604.Adding PITX2to the base model resulted in a significantly improved outcome prediction (C index =0.651).This confirms the utility of PITX2as a marker for outcome prediction in primary breast cancerandFig.2.Marker panel feature selection and marker panel validation.A,marker panel selection.Result of the feature selection step using bootstrap-corrected C index to select markers for the panel to predictTDM.Marker models built using PITX2P2and the first n amplificates (X axis ,markers ranked according to univariate performance)areevaluated using the bootstrap-corrected C index (Yaxis ).Closed circles,selected markers.These are,from left to right ,amplificates for PITX2P2,BMP4,C20orf55,and FGF4.B ,histogram of cross-validated C index from 100repetitions.C index for model with PITX2P2alone was 0.624(thick vertical black line );98of 100repetitions for the four-marker panel are larger.C,stability of marker selection.Frequency of the indicated markers in cross-validation runs.In each of the 20cross-validation runs,featureselection is repeated on 95%of the samples.C20orf55was selected in all cross-validation runs,FGF4in 16of 20runs,and BMP4in 14of 20runs.Only two other markers were selected by this procedure:APC and BRCA1,which were both selected in only 3of 20runs.D,histogram of the null distribution of the log-rank test statistic of our data set.Histogram shows the distribution of log-rank test statistic of 100data sets,of which survival time and censoring indicator were randomly permutated.This permutated data set was used to derive P values of the LRT (Cox model)for the cross-validated prediction score.Thick vertical black line,log-rank statistic for the cross-validated prediction score of the Kaplan-MeierTDM curves shown in Fig.3(left panel ).Cancer Therapy:ClinicalClin Cancer Res 2009;15(1)January 1,2009320。
膀胱癌诊疗指南
膀胱癌诊断治疗指南（一）2007一、前言膀胱癌是我国临床上最常见的肿瘤之一，是一种直接威胁患者生存的疾病。
目前对膀胱癌的诊断、治疗等诸多方面尚缺乏比较统一的标准，有必要对膀胱癌的临床诊疗行为进行规范。
为了进一步统一膀胱癌诊断和治疗方法的选择，以利于对膀胱癌不同治疗方式的疗效判定以及与各地区膀胱癌诊疗结果的比较，提高我国膀胱癌的诊断治疗水平，中华医学会泌尿外科学分会组织有关专家组成编写组，在分会委员会的直接领导与组织下，以国内外循证医学资料为依据，参考《吴阶平泌尿外科学》、Campbell’s Urology和欧洲泌尿外科学会(EAU)、美国泌尿外科学会(AUA)、美国国立综合癌症网络(NCCN)等相关膀胱癌诊断治疗指南，结合国内临床实际，经过反复研讨，编写完成了中国《膀胱癌诊断治疗指南》(征求意见稿)，以便为我国不同医疗条件下泌尿外科医师选择合理的膀胱癌诊断方法与治疗手段提供相应有益的指导。
在《膀胱癌诊断治疗指南》编写过程中，通过PUBMED医学检索网、中华医学期刊网等对膀胱癌诊断治疗相关论文特别是近10~15年间的文献进行了检索。
根据论文可信度的评价，最后《膀胱癌诊断治疗指南》中共引用342条文献，其中由我国学者在国内或国际学术期刊中发表的论文共41条。
二、膀胱癌的流行病学和病因学（一）流行病学1．发病率和死亡率世界范围内，膀胱癌发病率居恶性肿瘤的第九位，在男性排名第六位，女性排在第十位之后[1]。
在美国，膀胱癌发病率居男性恶性肿瘤的第四位，位列前列腺癌、肺癌和结肠癌之后，在女性恶性肿瘤位居第九位[2]。
2002年世界膀胱癌年龄标准化发病率男性为10．1／10万，女性为2．5／10万，年龄标准化死亡率男性为4／10万，女性为1．1／10万。
美国男性膀胱癌发病率为24．1／10万，女性为6．4／10万[10。
美国癌症协会预测2006年美国膀胱癌新发病例数为61 420例(男44 690例，女16 730例)，死亡病例数为13 060例(男8 990例，女4 070例)[2]。
用Normalized Cut法自动提取乳腺超声图像中的肿瘤边缘
Ｄ ０３ ６ ／．ｓ ． ５— ２ ＂２ １ ． ． ８ Ｏｈ １ ． ９ ｊ ｓｎ０ ５８ ９ ． ０ ６０ ９ ｉ ２ ／０ ０ ０
用 Ｎｏ ｍａｉｅ ｔ 自动提 取乳腺 超声 图像 中的肿瘤边 缘 ｒ ｌ ｄ Ｃｕ 法 ｚ
苏燕妮 ， 汪源源
复 旦 大 学 电 子 工 程 系 ，上 海 ２ ０ ３ ０４ ３
ＳＵ ｎ ｎｉ Ｗ ＡＮＧ Ｙａ － ， Ｙｕａ ｙｕ ｎ ｎ— ａ
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ｂ ｕ ｄ ｒ ｒ ｔ ｎ ｌｒ ｓ ｌ ．Ｔｈ ｒ ｐｏ ｅ ｔ ｏ ｓ ａ ｐｌ ｄ ｔ ａ ａ ａ ｅ ｏ １ ｌ ｉ ａ ｌ ｒ ｏ ｉ ｒ ａ ｔ ｏ ｎ ａ ｙ ｆ ｈｅ ｆ ａ ｅ ｕ ｔ ｏ ｉ ｅ ｐ ｏ ｓ ｄ ｍｅ ｈ ｄ ｉ ｐ ｉ ｏ ａ ｄ ｔ ｂ ｓ ｆ １ ２ ｃｉ ｃ ｌｕ ｔ ａ ｎ ｃ ｂ ｅ ｅ ｎ ｓ ｓ
ｐ ｘ ｌ ｉ ｐ ｏ ｏ ｅ ｏ ａ ｔ ｍａ ｉａ ｌ ｅ ｍ ｅ ｔｔ ｅｂ ｅ ｓ ｕ ｏ ｓｏ ｌｒ ｏ ｃ ｉ ａ ｅ ．Ｔｈ ｅ ｈ ｄ ｐ ｒ ｉｉ ｎ ｉｅｓ ｓ ｒ ｐ ｓ ｄｔ ｕ ｏ ｔｃ ｌ ｓ ｇ ｎ ｈ ｒ ａ ｔｔ ｍ ｒｌ ｉ ｎ ｉ ｕ ｔ ａ ｎｉ ｙ ｅ ｎ ｓ ｍ ｇｓ ｅｍ ｔ ｏ ａ ｔｔｏ ｓ ａ ｂ ｅ ｔ ｕ ｔ ａ ｏ ｎｄ ｉ ａ ｅ ｉ ｔ ｌ ｓ ｅ ｓ ｗｉｈ Ｎｃ ｔ ｎ ｓ ｓ ｄ ｆ ｒ ｎ ｒ ｙ ｖ ｌ ｓ ａ ｄ ｔ ｅ ｓ ｔａ ｉｔ ｉ ｕ ｉ ｎ ｒ ａ ｌｒ ｕ ｍ ｇ ｎ ｏ ｃｕ ｔ ｒ ｔ ｕ ，ａ ｄ ｕ ｅ ｉ ｅ ｅ ｔ ｇ ａ ａ ｕｅ ｎ ｈ ｐａ ｉＪ ｄ ｓ ｒ ｂ ｔｏ ｓ ｓ ｏ ａ ｈ ｃ ｕ ｔ ｒ ｔ ｂ ａ ｎ ａ ｎ ｔ ａ ｏ ｔ ｕ ｆ ｔ ｅ ｔ ｔ ｍｏ ． Ｔｈ ｎ， ｏ ｍ ａ ｌｐｅ ｃ ｎ ａ ｅ ｏ ｎ ｃ ｕ ａ ｅ ｆ ｅ ｃ ｌ ｓ ｅ ｏ ｏ ｔ ｉ ｎ ｉ ｉｉ ｌｃ ｎ ｏ ｒ ｏ ｈｅ ｂｒ ａ ｕ ｓ ｒ ｅ ｆ ｒａ ｓ ｌ ｒ ｅ ｔ ｇ ｆｉ ａ ｃ ｒ ｔ
《2023年国际胰腺病协会京都指南：_胰腺导管内乳头状黏液性肿瘤的管理》意见要点
《2023年国际胰腺病协会京都指南：胰腺导管内乳头状黏液性肿瘤的管理》意见要点李家速，孙洪鑫，李兆申海军军医大学长海医院消化内科，消化内镜中心，上海 200433通信作者：李兆申，******************（ORCID： 0009-0008-4935-0462）；孙洪鑫，***********************（ORCID：0009-0005-5936-353X）摘要：近日，国际胰腺病协会发布了修订版管理胰腺导管内乳头状黏液性肿瘤（IPMN）的指南。
该指南主要聚焦“高危征象”和“担忧特征”的修订、未切除IPMN的监测、IPMN切除后的监测、对病理学方面的修订和对囊液分子标志物的研究5个方面，以期为临床实践提供最佳的循证依据，本文对其意见要点进行摘译。
关键词：胰腺导管内肿瘤；诊断；监测；指南Key points of the international Kyoto guidelines for the management of intraductal papillary mucinous neoplasm of the pancreas （2023）LI Jiasu， SUN Hongxin， LI Zhaoshen.（Department of Gastroenterology， Digestive Endoscopy Center， Changhai Hospital， Navy Medical University， Shanghai 200433， China）Corresponding authors： LI Zhaoshen，******************（ORCID：0009-0008-4935-0462）； SUN Hongxin，sunhongxin_0923@ （ORCID： 0009-0005-5936-353X）Abstract：Recently， the International Association of Pancreatology published a revised edition of the guidelines for the management of intraductal papillary mucinous neoplasm （IPMN）of the pancreas. The guidelines mainly focus on five topics，i.e.，revision of “high-risk stigmata” and “worrisome features”， surveillance of unresected IPMN， surveillance after resection of IPMN， revision of pathological aspects，and research on molecular markers in cyst fluid，in order to provide the best evidence-based reference for clinical practice. This article makes an excerpt of the key points in the guidelines.Key words：Pancreatic Intraductal Neoplasms； Diagnosis； Surveillance； Guidelines2023年12月，国际胰腺病协会（IAP）在线发布了修订版管理胰腺导管内乳头状黏液性肿瘤（IPMN）的京都指南［1］。
2000 Nature 乳腺癌分子分型的起始
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