microvessel density as a prognostic factor in women with breast … · (3). among all of these...

[CANCER RESEARCH 64, 2941–2955, May 1, 2004]

Review

Microvessel Density as a Prognostic Factor in Women with Breast Cancer:A Systematic Review of the Literature and Meta-Analysis

Bernard Uzzan,1 Patrick Nicolas,1 Michel Cucherat,2 and Gérard-Yves Perret1

1Laboratoire de Pharmacologie. Hôpital Avicenne, Bobigny, Assistance Publique–Hôpitaux de Paris and Unité de Formation et de Recherche Léonard de Vinci, Bobigny, and2EA 643, Faculté de Médecine Laënnec, Lyon, France

Abstract

We performed a meta-analysis of all 87 published studies linkingintratumoral microvessel density (MVD), reflecting angiogenesis, torelapse-free survival (RFS) and overall survival (OS). With median MVDas cutoff, MVD impact was measured by risk ratio (RR) between the twosurvival distributions. Seventeen studies did not mention survival data orfit inclusion criteria. Twenty-two were multiple publications of the sameseries, leaving 43 independent studies (8936 patients). MVD was assessedby immunohistochemistry, using antibodies against factor VIII (27 stud-ies; n � 5262), CD31 (10 studies; n � 2296), or CD34 (8 studies; n � 1726).MVD might be a better prognostic factor when assessed by CD31 or CD34versus factor VIII (P � 0.11). For RFS, statistical calculations wereperformed in 25 studies (6501 patients). High MVD significantly predictedpoor survival [RR � 1.54 for RFS and OS with the same 95% confidenceinterval (CI), 1.29–1.84]. Twenty-two studies analyzed separately lymphnode-negative patients (n � 3580), for whom predictors of poor survivalare requested. This latter meta-analysis included 15 studies for RFS (2727patients) and 11 for OS (1926 patients). High MVD significantly predictedpoor survival [RR � 1.99 for RFS (95% CI, 1.33–2.98) and RR � 1.54 forOS (95% CI, 1.01–2.33)]. Between-study variations could result frompatient selection criteria, techniques to stain and count microvessels, andcutoff selection. MVD was a significant although weak prognostic factor inwomen with breast cancer. Standardization of MVD assessment is needed.

Introduction

Breast cancer is the most frequent neoplasm in women and the mostfrequent cause of death in women between 35 and 55 years of age.Five-year overall survival (OS) is estimated to be 65%, with largedisparities between stages. Several prognostic indicators have beendemonstrated, including tumor size, axillary lymph node status, his-tological grade, tumor type, vascular invasion, and estrogen receptorstatus (1, 2). These prognostic factors allow a better comprehension ofthe natural history of the disease and the identification of homogene-ous populations of patients with a similar outcome profile. Numerousputative markers have been reported, yet very few have actuallygained widespread clinical use. Recent developments in cytogeneticsand molecular biology have provided new ways to assess prognosis(3). Among all of these prognostic factors, lymph node status remainsthe most important. In the United States Surveillance, Epidemiology,and End Results database, the 5-year OS was 92% in node-negativepatients, 81% in patients with one to three positive axillary lymphnodes, and 57% in those with more than four involved nodes (4).However, 20–30% of all lymph node-negative patients would stilldevelop a recurrence within 10 years of initial treatment of theprimary tumor (5). It would be crucial to identify such patients, whoshould benefit from adjuvant chemotherapy, which has sometimes

been shown to improve survival (6), although this issue is stillcontroversial, especially in older women with estrogen receptor-positive breast cancer (7). This strategy would avoid such adjuvanttreatment to be generalized to all of the node-negative patients,considering its heavy burden of side effects and its uselessness inmany patients.

Angiogenesis (or neovascularization) consists in the formation ofnew blood vessels from the endothelium of the existing vasculature.When a new tumor reaches the size of 1–2 mm, its ulterior growthrequires the induction of new blood vessels, which may consequentlylead to the development of metastases, via the penetration of malig-nant cells into the circulation. Vascular endothelial growth factor(VEGF), platelet-derived endothelial cell growth factor, and basicfibroblast growth factor, produced by tumor and stromal cells, arepotent inducers of the angiogenic switch. However, angiogenesis isnecessary but not sufficient to the development of metastases. Angio-genic activity is heterogeneous within a given tumor type. Concerningthe relationship between angiogenesis and clinical outcome, breastcancer has been the most studied tumor. For �10 years, microvesseldensity (MVD), a surrogate marker of tumoral angiogenesis, has beenproposed to identify patients at high risk of recurrence more preciselythan classical indicators, particularly in node-negative patients. Theidentification of such patients at an early stage of their disease wouldbe of great interest, allowing for a more appropriate and effectivetreatment (by adjuvant chemotherapy or in the near future by specificantiangiogenic drugs) of patients at higher risk and possibly predictingthe activity of these latter drugs.

Microvessel density assessment is the most commonly used tech-nique to quantify intratumoral angiogenesis in breast cancer. It wasfirst developed by Weidner et al. (8) in 1991 and uses panendothelialimmunohistochemical staining of blood microvessels [mainly withFactor VIII antigen (F. VIII Ag or von Willebrand’s factor), CD31,PECAM-1, or CD34; rarely with integrin �v�3, CD105, or type IVcollagen]. The first step in Weidner’s approach is the identification bylight microscopy of the area of highest neovessel density (the so called“hot spot”), by scanning the whole tumoral section at low power.Then, individual microvessels are counted at a higher power (�200field) in an adequate area (e.g., 0.74 mm2 per field using �20objective lens and �10 ocular). Any stained endothelial cell or clus-ters separate from adjacent vessels are counted as a single microves-sel, even in the absence of vessel lumen. Each single count is ex-pressed as the highest number of microvessels identified at the hotspot. Some authors use Chalkley count or computerized image anal-ysis systems, both aimed to minimize the subjectivity in the quanti-fication of MVD. The Chalkley count consists of applying a 25-pointeyepiece graticule on several hot spots (usually 3). The graticule isoriented to allow the maximum number of points to hit on or withinthe areas of stained microvessel profiles (Chalkley grid area: 0.196mm2; Ref. 9).

Recent evidence suggests that tumor angiogenesis is associatedwith patient outcome in a number of cancers, especially breast and

Received 7/2/03; revised 10/1/03; accepted 11/5/03.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 with18 U.S.C. Section 1734 solely to indicate this fact.

Requests for reprints: Patrick Nicolas, Laboratoire de Pharmacologie. Hôpital Avi-cenne, 125 route de Stalingrad, Bobigny, 93009 France.

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on June 9, 2021. © 2004 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from

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lung (10, 11). Many observational studies (either prospective orretrospective studies) have concluded that MVD is a prognostic factorin invasive breast cancer, but others reached the opposite conclusion(10). The data are so confounded that, in his review of prognosticfactors in breast cancer, Hayes et al. (2) did not recommend the use ofMVD as a basis for making clinical decisions. To determine whetherangiogenesis, assessed by its surrogate end point MVD, is a prognos-tic factor in breast cancer, we undertook a systematic review of theliterature with a meta-analysis. Although meta-analyses of observa-tional studies offer much more challenges than meta-analyses ofrandomized controlled trials, due to inherent biases and differences instudy design, they may provide a useful tool for helping to understandand quantify sources of variability in results across studies (12). Theaim of our meta-analysis was to test the hypothesis that initiallyassessed MVD would be able to predict OS (breast cancer-relateddeath) and/or relapse-free survival (RFS, recurrence at any site), eitherin the global population of operated breast cancer women withoutmetastases or exclusively in lymph node-negative patients. By doingso, we tried to contribute to convert MVD from candidate to acceptedprognostic factor in breast cancer (2). Actually, we performed fourdifferent meta-analyses, the first two including studies involvingeither lymph node-negative or node-positive patients or both (onemeta-analysis for OS the other one for RFS), the two others restrictedto studies with a majority (�75%) of or only node-negative patients

This article aims to comply with the recommendations for reportingmeta-analyses of observational studies (12).

Materials and Methods

Publication Selection. We did this meta-analysis according to a predeter-mined written protocol. To be eligible for our meta-analysis, studies had todeal with breast cancer only, with no distant metastases at inclusion, toevaluate the correlation between microvessel count or density and survival,to measure MVD in the primary tumor, and to be published in English, French,or German languages. We also excluded studies when their recruitment camefrom two distinct retrospective cohorts with a different prognosis (13–15),because we deemed their results could be biased.

Studies (full articles and abstracts) published between January 1991 andDecember 31, 2002, were identified by an electronic search using onlinePubmed, with the following key words used simultaneously, breast cancer,neovascularization, prognosis, and no special limits except time. Last querywas updated on September 4, 2003. We also screened references from therelevant literature, including all of the identified studies, but also reviews(10, 16) and editorials (17–19). We tried carefully to avoid duplication ofdata, by examining for each publication the names of all authors and thedifferent medical centers involved. When additional data were needed forstatistical analysis, the authors were kindly requested by letter or e-mail tosend them. Abstracts (n � 6) were only included in a descriptive analysis.

Methodological Assessment. Information was carefully extracted fromall of the publications in duplicate by two of us (B. U. and P. N.), using astandardized data collection form, with the following items, completereference of the publication, original publication or update of a formerpublication, mode of making up of the series of cases, median duration offollow up, number of women included in the study, mean or median age,menopausal status, anticancer treatment(s) during follow up, histologicaltype (ductal invasive or lobular), tumor size, nodal status, optical readingwith or without Chalkley count or image analysis system, number ofreaders, blinded reading, type(s) of immunohistochemical staining, numberof hot spots examined, magnification used, area of the field read, cutoffvalue for MVD (median MVD, unless otherwise stated), number of eventsin each category of MVD, RFS or OS or both, survival curves for the globalpopulation or separately for node-negative patients, and results of uni- andmultivariate analyses. Disagreements were resolved by consensus betweenthe two readers. In case of persistent disagreement, the final decision wasmade by our experts (G. P. for clinical evaluation, and M. C. for method-ological and statistical evaluation of data). There was only one disagree-

ment between the two readers on clinical data, but several on the statisticalworking of the data, resolved by M. C.

We did not set a predefined minimal number of patients for a study to beincluded in our meta-analysis, nor a minimal duration of median follow-up.We did not weigh each study by a quality score, because no such score hasreceived general agreement for use in a meta-analysis, especially of observa-tional studies, making more difficult the evaluation of its quality (20). Studieswere not blinded to the readers, but decisions of exclusion were always takenwithout knowledge of the global result of each study.

When duplicate studies were retrieved, we included in our systematicreview the study involving the highest number of patients from which datacould be extracted (usually the latest). This was done to avoid overlappingbetween cohorts, with two exceptions, the study by Toi et al. (21) wasincluded, as was the study by Gasparini et al. (22) published in 1998, a largemulticenter study which only included 140 Japanese patients, possibly mem-bers of the Japanese cohort of Toi, and the two studies of Guidi et al. (23, 24),because the authors confirmed that they corresponded to distinct protocols andpatients.

Studies were usually retrospective, but sometimes consisted of a cohort ofconsecutive patients. Although their methodological quality and the reliabilityof their conclusions were variable, their design was almost similar, a favorablecondition for our meta-analysis. One study included the complete population ofbreast cancer patients from the area of Odense University Hospital (25, 26).Usually, observers (i.e., readers of stained sections of tumor blocks) wereunaware of the clinical outcome of patients.

Statistical Methods. A study was considered significant when the P forthe statistical test comparing survival distributions between the groups withhigh and low MVD (usually with median MVD as cutoff) was inferior to0.05 in univariate analysis. The same threshold was adopted for multiva-riate analysis. A study was termed “positive” or conclusive when a highMVD predicted poorer survival and “negative” or inconclusive when a highMVD did not predict a poor survival, or even once predicted a bettersurvival (27).

For the quantitative aggregation of survival results, we measured the impactof MVD on survival by estimating the risk ratio (RR) between the high or lowMVD groups. For each trial, this RR was estimated by a method depending onthe data provided in the publication. The simplest method consisted in thedirect collection of RR, hazard ratio, or odds ratio, and their 95% confidenceinterval (CI) from the original article (5 studies only). If not available, welooked at the total numbers of events and the numbers of patients at risk in eachgroup to determine the RR estimate. When data were only available asgraphical survival plots, the calculations were done only if the number of stepson the curves equaled the number of events given in the publication, assumingthat the rate of censored patients was constant during the study follow-up (28).In 5 studies, the data were obtained directly from the authors (see Table 1).Whenever possible, the parameter MVD was dichotomized by using its ob-served median.

Considering the many sources of heterogeneity between studies and conse-quently between their individual RR estimates, we calculated the overall RR byusing a random-effect model (Der Simonian and Laird’s method). By conven-tion, an observed RR �1 implied a worse prognosis in the high MVD group.The detrimental impact of angiogenesis on survival was deemed statisticallysignificant when the lower boundary of the 95% CI of the overall RR was �1.

Comparisons of proportions of studies with or without various characteris-tics were made by �2 tests.

The statistical calculations for our meta-analyses were performed withEasyMA.net Internet distributed application (Department of Clinical Pharma-cology, Cardiology Hospital, Lyons, France;3 Ref. 29).

Results

Studies Selection and Characteristics. Our electronic search re-trieved a total of 237 references (see Appendix 1). Our global litera-ture search collected 87 studies (8, 13–15, 21–27, 30–105) including6 abstracts (43, 90, 91, 96, 104, 105). Only 58 came from theelectronic database (8, 13, 15, 21–27, 30, 31, 33, 34, 36–42, 44–50,

3 Internet address: http://www.spc.univ-lyon1.fr/easyma.net.

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MVD, A PROGNOSTIC FACTOR IN BREAST CANCER



52, 55–57, 61, 63, 64, 69–75, 77, 79–82, 84, 86, 89, 92, 94, 95, 97,99, 101, 102, 106). One publication was written in German with anEnglish abstract (95), but we did not include it because there were noseparate results by nodal status, and it included only 52 node-positivepatients, and finally the series of 54 node-negative patients wascompleted and later published by the same authors (87). No articleused French language. One publication was excluded because it waswritten in Czech language, and its English abstract did not mentionsurvival data (36). All of the other articles were written in Englishlanguage. One reference was actually a chapter of a book we could notobtain (98), but it very probably corresponded to the subsequent fullarticle by Fregene et al. (93).

Among all of these 87 studies, 17 did not mention survival dataor were out of the scope of our systematic review and wereexcluded (8, 31, 32, 36, 39, 47, 53, 57, 63, 65, 67, 70, 72, 86, 89,97, 104). One of these 17 studies was rejected because it includedmetastatic cancers (32). These excluded studies are listed in Ap-pendix 2, with informations on the cause(s) of rejection. Twenty-

two studies corresponded to multiple publications of the sameseries of cases and were also excluded (26, 45, 51, 56, 68, 69, 71,76 –78, 81– 83, 85, 90, 92, 94, 95, 98, 99, 103, 106). Three studiesincluded patients coming from two distinct retrospective cohortsand were excluded (13–15). Two studies were rejected becausemeta-analytic calculations were obviously impossible (54, 101).Forty-three independent studies fulfilled our inclusion criteria,representing 8936 patients (21–25, 27, 30, 33, 35, 37, 38, 40 – 44,46, 48 –50, 52, 55, 58 – 62, 64, 66, 73–75, 79, 80, 84, 87, 88, 91, 93,96, 100, 102, 105). These studies included at least 3616 post-menopausal women from the 27 studies providing a description ofthe clinical characteristics of 5917 patients. The main features ofthe eligible studies are summarized in Table 1. The study designwas more often a retrospective (trohoc; n � 18) than a prospectivecohort study (n � 12). In 13 studies, the study design could not beinferred. In several studies, a lot of patients from the initial serieswere excluded because of insufficient histological material, insuf-ficient clinical data, or other reasons, leading to a potential recruit-

Table 1 Main characteristics of the 43 studies fulfilling the inclusion criteria

First author (ref) Year

Studyfrom

PubMedStudydesign n

Medianfollow-up

(y)Blindedreading

Reader(n)

MVDa

assessment Antibody

Countedarea

(mm2) RR estimation Analysis Result

Bosari (102)b 1992 Yes Retro. 120 9 ? � 1 Optical FVIII ? Data-extrapolated RFS, RFS� PositiveSneige (105) 1992 No ? 138 ? ? ? Optical FVIII ? Missing RFS� PositiveVan Hoef (100) 1993 No Retro. 93 13 ? 1 Chalkley FVIII 0.476 Missing OS, RFS NegativeConner (91) 1994 No Retro. 92 16 ? ? Optical FVIII ? Missing OS NegativeFregene (93)b 1994 No Retro. 316 8 Yes � 1 Optical FVIII 0.190 Data-extrapolated RFS, RFS� NegativeSightler (96) 1994 No ? 84 ? ? ? Optical �

AutomatedCD31 0.747 Missing OS Negative

Axelsson (75)b 1995 Yes Prosp. 220 11.5 Yes � 1 Optical FVIII 0.340 Data-extrapolated OS, RFS, OS�, RFS� NegativeCostello (79)b 1995 Yes ? 87 ? ? ? Optical FVIII 0.220 Data-extrapolated OS, RFS, OS�, RFS� NegativeFox (80) 1995 Yes Retro. 211 3.5 ? � 1 Optical �

ChalkleyCD31 0.155 Missing OS Positive

Goulding (84) 1995 Yes Retro. 165 12 ? 1 Optical �Automated

CD31 0.170 Missing OS Negative

Obermair (87)b 1995 No Prosp. 230 6 ? ? Optical FVIII 0.250 Data-extrapolated RFS, RFS� PositiveOgawa (88)b 1995 No ? 155 7 Yes � 1 Optical FVIII 0.785 Data-extrapolated OS, RFS, OS�, RFS� PositiveToi (21)b 1995 Yes Retro. 328 4.5 Yes ? Optical FVIII ? Survival curves RFS, RFS� PositiveMorphopoulos

(73)b1996 Yes Retro. 160 5 Yes 1 Optical FVIII 0.785 Survival curves OS Negative

Simpson (74)b 1996 Yes Prosp. 178 6 — — Automated CD34 0.740 Data-extrapolated OS, RFS PositiveCharpin (60)b 1997 No ? 167 9.5 — — Automated CD31 ? Data-extrapolated OS, RFS, OS�, RFS� PositiveKarelia (61) 1997 Yes ? 51 5 ? ? Optical FVIII ? Missing OS PositiveLee (62)b 1997 No Prosp. 87 8 ? � 1 Optical FVIII ? Data-extrapolated RFS, RFS� NegativeMartin (64) 1997 Yes Prosp. 174 7 Yes ? Optical CD34 0.680 Missing OS PositiveSterns (66) 1997 No Prosp. 50 ? Yes 1 Optical FVIII ? Missing OS, RFS NegativeAcenero (50, 51) 1998 Yes ? 112 5 ? ? Optical �

AutomatedFVIII 0.740 Missing RFS Positive

Chu (52)b 1998 Yes Retro. 163 5 Yes ? Optical CD31 0.740 Data-extrapolated OS, RFS PositiveGasparini (22) 1998 Yes Retro. 531 6.5 Yes � 1 Optical CD31 0.740 Data-extrapolated OS, RFS, OS� PositiveHeimann (55)b 1998 Yes ? 167 15 ? ? Optical CD34 0.145 Data-extrapolated OS, RFS, OS�, RFS� PositiveMayers (58)b 1998 No Prosp. 519 6 ? � 1 Optical CD31 ? Reported in text OS, RFS, OS�, RFS� NegativeNarita (59)b 1998 No ? 100 7.5 ? ? Optical FVIII 0.250 Data-extrapolated OS PositiveDe Placido (42)b 1999 Yes Prosp. 233 11.5 ? ? Optical FVIII 0.370 Reported in text OS, OS� PositiveGerson (43) 1999 No ? 63 2.5 ? ? Optical CD34 ? Missing OS, RFS NegativeJitsuiki (44)b 1999 Yes Retro. 147 3 ? � 1 Optical CD31 0.384 Data-extrapolated OS, RFS PositiveKumar (46)b 1999 Yes ? 106 5 Yes 1 Optical CD105 ? Data-extrapolated OS, RFS PositiveTynninen (48)b 1999 Yes Retro. 84 10.5 ? ? Optical FVIII 1.300 Data-extrapolated OS, OS� NegativeViens (49)b 1999 Yes Retro. 135 5 Yes 1 Optical CD31 ? Reported in text OS, RFS PositiveFridman (38)b 2000 Yes Retro. 216 6 ? ? Optical �

AutomatedFVIII 0.630 Given by author OS, RFS Negative

Guidi (24)b 2000 Yes Prosp. 47 16 Yes 1 Optical FVIII 0.740 Given by author OS, RFS NegativeHansen (25, 26)b 2000 Yes Prosp. 836 11 Yes 1 Optical �

ChalkleyCD34 0.196 Data-extrapolated OS, RFS, OS�, RFS� Positive

Medri (27)b 2000 Yes Prosp. 378 6 Yes � 1 Optical FVIII 0.720 Data-extrapolated RFS, RFS� NegativeTakei (40) 2000 Yes ? 110 10.5 ? ? Optical FVIII 0.740 Missing OS, RFS NegativeTas (41)b 2000 Yes Retro. 120 3.5 Yes 1 Optical CD34 0.720 Reported in text OS PositiveKato (33)b 2001 Yes Retro. 377 10 Yes � 1 Optical FVIII ? Reported in text OS, RFS PositiveLee (35)b 2001 No ? 82 4 — — Automated CD34 ? Given by author RFS, RFS� NegativeVincent-Salomon

(37)b2001 Yes Retro. 685 11 Yes ? Optical FVIII 1.200 Given by author OS, RFS Negative

Guidi (23)b 2002 Yes Prosp. 577 9.5 Yes 1 Optical FVIII 0.740 Given by author OS, RFS NegativeGunel (30) 2002 Yes Retro. 42 4 Yes � 1 Optical FVIII ? Missing OS, RFS Negative

a MVD, microvessel density; RR, risk ratio; Retro., retrospective study (trohoc); Prosp., prospective study (cohort); OS, overall survival; RFS, relapse-free survival; positive, inverserelationship between MVD and survival.

b Study included in the final meta-analyses.

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ment bias. A great majority of tumors, the histological type ofwhich was mentioned, were ductal invasive cancers (4834 of 5693;85%). Among these 43 studies, 4 were in abstract form and wereonly included in the descriptive analysis (43, 91, 96, 105). Thenumber of patients included in the studies ranged from 50 (66) to836 (Ref. 25; mean number, 208). Thirty-two studies (74% of thetotal number of independent studies) were published between years1995 and 2000, including 19 positive studies.

Twenty-two independent studies (n � 3580) included only lymphnode-negative patients or gave separate results for such patients (21,22, 25, 27, 34, 42, 48, 52, 55, 58–60, 62, 74, 75, 79, 87, 88, 92, 93,100, 102).

Studies Results Reports. Twenty-two studies (4779 patients)found an inverse relationship between survival and MVD (so-calledpositive studies), whereas 21 studies (4157 patients) found no relation(negative studies). All of the studies were dispatched into three classesaccording to the length of median follow-up: �5 years (n � 11), 5–10years (n � 18), and �10 years (n � 11). The proportion of positivestudies was not higher among those with short follow-up. Among the9 largest studies (�300 patients each), representing 4547 patients(half of the global population), 4 were positive. There were signifi-cantly more negative studies among those including �100 patients(10 negative versus 2 positive studies), than among those including�100 patients (11 versus 20; P � 0.005 by �2 test). This findingargues against a major publication bias. The proportion of positivestudies was exactly the same whether the study mentioned explicitlymore than one reader of the immunostained slides or not (5 of 10versus 16 of 31). Nine studies (2367 patients) used a graticule (Chalk-ley count; Refs. 26, 46, 52, 58, 64, 68, 79, 80, 100). The proportion ofpositive studies was similar whether or not a graticule was used (6 of9 versus 17 of 34). Too few studies (3 positive and 5 negative; Refs.35, 38, 51, 60, 64, 74, 84, 96) used computerized image analysis todraw any firm conclusion. The area of the field examined optically didnot influence the results (7 of 14 positive studies with a field area� 0.384 mm2 versus 4 of 15 with a field area � 0.384 mm2; P � notsignificant). Immunostaining was obtained by use of antibodies di-rected against F. VIII Ag in 27 studies (5262 patients; Refs. 21, 23, 24,27, 30, 33, 37, 38, 40, 42, 48, 50, 59, 61, 62, 64, 66, 73, 75, 79, 87,88, 91, 93, 100, 102, 105), CD31 in 10 studies (2296 patients; Refs.22, 44, 49, 52, 58, 60, 64, 80, 84, 96), and CD34 in 8 studies (1726patients; Refs. 25, 35, 41, 43, 46, 55, 64, 74). When pooling the resultsfrom CD31 and CD34, the proportion of positive studies was some-what higher compared with that of F. VIII Ag (11 of 18 versus 10 of27; P � 0.11 by �2 test).

Among the 22 lymph node-negative studies (3580 patients), 13were positive studies (2185 patients; 61% of node-negative patients;Refs. 21, 22, 25, 34, 42, 52, 55, 59, 74, 87, 88, 92, 102) and 14 studiesused F. VIII Ag (7 positive and 7 negative studies; 2098 patients;Refs. 21, 27, 34, 42, 48, 59, 62, 75, 79, 87, 88, 93, 100, 102).

Meta-Analysis. Data extraction for meta-analytic calculations wasimpossible in 14 studies (22, 30, 40, 43, 50, 61, 64, 66, 80, 84, 91, 96,100, 105), even after writing to the authors for complementary infor-mation, leaving 33 studies (7738 patients) available for the finalmeta-analyses (21, 23–25, 27, 33–35, 37, 38, 41, 42, 44, 46, 48, 49,52, 55, 58–60, 62, 68, 73–75, 79, 82, 87, 88, 92, 93, 102). These 33studies include duplicate studies by Kato et al. (34), Gasparini et al.(68, 82), and Fox et al. (92), which were not included in the corpus of43 independent eligible studies. This is the reason why the sum of the14 studies not suitable for meta-analytic calculations and of the 33studies available for the final meta-analyses makes 47, i.e., 4 morethan 43. Of the 14 studies not suitable for calculations, 6 were positive(22, 50, 61, 64, 80, 105) and 8 were negative studies, so that theirexclusion did not bias the sample of studies. We could not include in

our calculations a large study (22), because MVD was used as acontinuous variable, and we could not obtain reformulated data withMVD expressed as a binary variable. Fortunately, we could analyzefrom the same author a smaller study of node-positive patients (68),and another one of node-negative patients (82). For the study by Foxet al. (80), we could not convert the table of events provided bytertiles into two classes of MVD and instead used another study fromthe same author (92). We added a second study by Kato et al. (34),specifically devoted to node-negative patients, including a greaternumber of these patients than his global study (33). Only 15 among 22eligible studies could be included in our meta-analysis of lymphnode-negative patients, representing 2727 patients (21, 25, 27, 34, 55,58, 60, 62, 75, 79, 87, 88, 92, 93, 102).

The four main meta-analyses (global population/node-negative pa-tients, RFS/OS) are shown on Figs. 1-4. The largest (global popula-tion, RFS; Fig. 1) included 25 studies (6501 patients) [RR � 1.54(95% CI, 1.29–1.85; P � 0.001)]; the smallest (lymph node-negative,OS; Fig. 4) included only 11 studies (1926 patients) [RR � 1.54 (95%CI, 1.01–2.33; P � 0.04)]. In the global population, RR for OS (Fig.2) was 1.54 (95% CI, 1.29–1.84). The highest RR (1.99; 95% CI,1.33–2.98; P � 0.001) was found for RFS in node-negative patients(Fig. 3). All four of the meta-analyses gave statistically significantresults, favoring a link between high MVD and poor survival.

Fourteen studies (21, 23, 24, 27, 33, 37, 38, 62, 75, 79, 87, 88, 93,102) using factor VIII as the immunohistochemical marker could beincluded in a separate meta-analysis for RFS (Fig. 5). There was animportant heterogeneity between studies. Only 6 studies using CD31were available for meta-analysis and 5 studies using CD34.

We performed a sensitivity testing by subtracting the study with thehighest sample size (25), first from the global analysis, then from thenode-negative subgroup. This testing did not modify the estimatedRRs. Because we had decided not to assess the quality of each studyby scoring, we did not perform sensitivity testing by subtractingstudies of lower quality. Anyway, such assessment is controversial inmeta-analyses of observational studies (12).

Fig. 1. Meta-analysis of 25 studies evaluating microvessel density with relapse-freesurvival in the global population [risk ratio (RR) estimated with DerSimonian and Laird’srandom model]. CI, confidence interval.

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Discussion

Our overview and meta-analysis of all published studies fromwhich statistical data could be obtained or calculated shows thatMVD, a marker of angiogenesis, does indeed predict poor survival inwomen with invasive breast cancer, especially in node-negative pa-tients. Our findings are in agreement with a recent meta-analysis on

the link between MVD and survival in patients with non-small celllung cancer (11). However, our conclusions should be tempered forseveral reasons. First, the overall statistical link we elicited betweenMVD and survival was rather weak, with a global RR of 1.54 for OSand RFS [even lesser than the hazard ratios found by Meert et al. (11)in lung cancer: 1.80–1.99]. As a rule of the thumb, a prognostic factorwith a RR �2 is of limited practical use (2).

In addition, we relied on published reports of aggregate data and

Fig. 2. Meta-analysis of 24 studies evaluating microvessel density with overall survivalin the global population [risk ratio (RR) estimated with DerSimonian and Laird’s randommodel]. CI, confidence interval.

Fig. 3. Meta-analysis of 15 studies evaluating microvessel density with relapse-freesurvival in the node-negative population [risk ratio (RR) estimated with DerSimonian andLaird’s random model]. CI, confidence interval.

Fig. 4. Meta-analysis of 11 studies evaluating microvessel density with overall survivalin the node-negative population [risk ratio (RR) estimated with DerSimonian and Laird’srandom mode]. CI, confidence interval.

Fig. 5. Meta-analysis of 14 studies evaluating MVD measured by Factor VIII withrelapse free survival in the global population [risk ratio (RR) estimated with DerSimonianand Laird’s random mode]. CI, confidence interval.

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were unable to do a survival analysis making full use of the time-to-relapse of individual patients. Finally, the studies included in ourmeta-analysis were observational studies, more prone to many biasesthan prospective randomized controlled studies (107). We attemptedto minimize publication bias by making our literature search ascomplete as possible. However, we could not take into account thefew studies published in abstract form only or in Japanese language;this selection might have favored the “positive” studies, because theyare more often published in English whereas the “negative” ones tendto be reported more often in native languages (108). Obviously, wecould not account for unpublished studies. However, the discrepanciesin the conclusions of various studies could have encouraged research-ers to publish their data whatever the results, thus limiting suchpublication bias. Duplication of patients between studies from thesame group was sometimes difficult to avoid. Studies may havediffered in the baseline characteristics of patients included (age,menopausal status, tumor type, and size), the adjuvant treatment theymight have received for their cancer, the number of patients, theduration of follow-up, the immunohistochemical marker used to as-sess MVD, or the method of microvessel count itself. All of thesesources of variability represent potential selection biases. Observerswere not blinded to outcome data in all of the studies (informationbias). Multivariate analyses tried to control for different factors,minimizing confounding bias, but the factors controlled for were fewand differed between studies. Finally, the discrepancy between theconclusions of a meta-analysis of epidemiological studies relating theuse of hormone replacement therapy to the risk of coronary heartdisease (halved, a statistically robust conclusion; Ref. 109) and thefindings of three recent randomized controlled trials showing nobenefit of this therapy should be kept in mind (110).

Obviously, the choice of the endothelial marker for immunohisto-chemical staining may modify the conclusions. In his first study(1992) relating MVD to survival, Weidner et al. (106) used anantibody against factor VIII-related antigen, also termed von Will-ebrand’s factor, staining mainly mature vessels and cross-reactingwith lymphatic endothelium. This marker remains the most used in thestudies we reviewed. Some recent studies used antibodies directedagainst platelet endothelial cell adhesion molecule (also known asCD31) or CD34. JC-70, a monoclonal antibody against CD31, has theadvantage over F. VIII Ag of being present also on immature bloodvessels. Consequently, counts using this marker are �30% higherthan those using F. VIII Ag. However, CD31 can react mildly withfibroblasts and some plasma cells, and is rarely expressed quitestrongly: staining failure can reach 20% in routinely fixed breastspecimens. CD34 has the same characteristics as CD31, but withoutthe high rate of staining failure (16). The optimal marker has not beenidentified yet. One study compared F. VIII Ag, CD31, and CD34, andwas positive only for CD34 (64). Standardization with quality controlprograms involving multicenter studies is mandatory before use ofMVD as a surrogate marker of angiogenesis for prognostic or predic-tive purposes (111, 112).

Although most studies used a technique similar to that of Weidneret al. (8), many variations to the MVD assessment exist. The size ofthe area examined varied between studies. Some authors consideredthe mean or the highest value among three or more determinations ofMVD at different fields of the same hot spot. Some measured MVDas the mean or highest value at several hot spots. The cutoff value forMVD varied among studies. Many authors used the median value ofMVD as cutoff. Some divided the values of MVD by tertiles. Somechose the best cutoff available from the data, a questionable post hocstratification (113). Some used MVD as a continuous variable in theirmultivariate analysis (22, 74).

The Chalkley count is considered as a simple and acceptable

procedure for daily clinical use. Often, the Chalkley count for onetumor is taken as the mean value of three graticule counts. Its use issaid to increase the reproducibility of counts within a given hot spotand to reduce considerably the time needed to evaluate a hot spot.Although intended to be a surrogate estimate of MVD, it is actually arelative area estimate of immunostained vessels expressed by an indexwithout unit. The earlier studies analyzed the survival data with cutoffpoints at Chalkley counts 5 and 7, splitting at the tertiles.

To conclude, our meta-analysis found a statistically significantinverse relationship between angiogenesis, assessed by MVD, andsurvival, confirming that human invasive breast cancer is an angio-genesis-dependent malignancy. In view of our findings, the followingrecommendations should be made to future authors, include a largeseries of consecutive patients from a single cohort, stratify by lymphnode status, describe the clinical characteristics of the study popula-tion, use antibodies directed against either CD31 or rather CD34 forimmunostaining, express the results both in the global population andseparately by nodal status, and both as comparison of survival curvesand as multivariate regression analysis, and give a full description ofsurvival events to allow future calculations.

In the future, a better answer to the question raised would bedifficult to provide, because this epidemiological issue cannot beanswered directly by a randomized controlled trial (only interventionstudies, using antiangiogenic drugs, would provide an answer). Ameta-analysis of individual patient data would be hardly conceivablegiven the important dispersion of studies, and the time and resourcesneeded. The degree of vascularization has prognostic value both innode-positive and node-negative patients. We could not assess theprognostic role of MVD in the subgroup of lymph node-negativepatients with no initial chemotherapy, because most studies in node-negative patients did not stratify their results by the nature of thetreatment prescribed initially. We must already think of the bestmethodology to validate the approach of treating or not with adjuvantchemotherapy or specific antiangiogenic drugs node-negative patientsaccording to their MVD. To choose the patients to be treated by newspecific antiangiogenic drugs, and monitor such treatments, reliable,standardized, and useful predictive markers of angiogenesis areneeded. Some authors argued that MVD is possibly one of them.Conversely, Hlatky et al. (114) stated that it may not be a predictor oftreatment efficacy, because it is not a measure of the angiogenicdependence of a tumor, but rather reflects the metabolic burden of thesupported tumor cells. Consequently, MVD is not predictive of theresponse to antiangiogenic drugs, and, therefore, not useful for strat-ifying patients in clinical trials. However, MVD can now be addedrather confidently to the limited list of demonstrated prognostic fac-tors in invasive breast cancer, but how much it adds to classicalprognostic factors is debatable.

Acknowledgments

We thank all authors whose publications could be included in our meta-analysis, and especially those who provided us with complementary dataallowing meta-analytic calculations. We especially thank Professor Daniel F.Hayes and the Cancer and Leukemia Group B group for both completing theirdata concerning protocols 8541/8869 and 8082, and reviewing our manuscript.

Appendix

Appendix 1. Complete list of 237 publications retrieved by ourelectronic search (PubMed, indexed for MEDLINE). All articles in-clude related articles and links. For more information, write to theHelp Desk National Center for Biotechnology Information, NationalLibrary of Medicine, NIH.

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118: Takei H, Oyama T, Iino Y, et al. Clinical significance ofimmunohistochemical Bcl-2 expression in invasive breast carcinoma.Oncol Rep 1999;6:575–81. PMID: 10203594

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120: Toi M, Ishigaki S, Tominaga T. Metalloproteinases and tissueinhibitors of metalloproteinases. Breast Cancer Res Treat 1998;52:113–24. Review. PMID: 10066076

121: Tynninen O, von Boguslawski K, Aronen HJ, Paavonen T.Prognostic value of vascular density and cell proliferation in breastcancer patients. Pathol Res Pract 1999;195:31–7. PMID: 10048092

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123: Borgstrom P, Discipio R, Maione TE. Recombinant plateletfactor 4, an angiogenic marker for human breast carcinoma. Antican-cer Res 1998;18:4035–41. PMID: 9891442

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126: McLeskey SW, Tobias CA, Vezza PR, Filie AC, Kern FG,Hanfelt J. Tumor growth of FGF or VEGF transfected MCF-7 breastcarcinoma cells correlates with density of specific microvessels inde-pendent of the transfected angiogenic factor. Am J Pathol 1998;153:1993–2006. PMID: 9846989

127: Lee AH, Dublin EA, Bobrow LG, Poulsom R. Invasive lobularand invasive ductal carcinoma of the breast show distinct patterns ofvascular endothelial growth factor expression and angiogenesis.J Pathol 1998;185:394–401. PMID: 9828838

128: Gasparini G, Fox SB, Verderio P, et al. Determination ofangiogenesis adds information to estrogen receptor status in predict-ing the efficacy of adjuvant tamoxifen in node-positive breast cancerpatients. Clin Cancer Res 1996;2:1191–8. PMID: 9816287

129: Griffey SM, Verstraete FJ, Kraegel SA, Lucroy MD,Madewell BR. Computer-assisted image analysis of intratumoral ves-sel density in mammary tumors from dogs. Am J Vet Res 1998;59:1238–42. PMID: 9781454

130: Goede V, Fleckenstein G, Dietrich M, Osmers RG, Kuhn W,Augustin HG. Prognostic value of angiogenesis in mammary tumors.Anticancer Res. 1998;18:2199–202. PMID: 9703783

131: Delorme S, Huber S. Doppler sonography of breast tumors.Anticancer Res. 1998;18:2155–8. Review. PMID: 9703775

132: Nagaoka H, Iino Y, Takei H, Morishita Y. Platelet-derivedendothelial cell growth factor/thymidine phosphorylase expression inmacrophages correlates with tumor angiogenesis and prognosis ininvasive breast cancer. Int J Oncol 1998;13:449–54. PMID: 9683777

133: Heimann R, Ferguson DJ, Hellman S. The relationship be-tween nm23, angiogenesis, and the metastatic proclivity of node-negative breast cancer. Cancer Res 1998;58:2766–71. PMID:9661889

134: Lampe B, Hantschmann P, Dimpfl T. Prognostic relevance ofimmunohistology, tumor size and vascular space involvement in ax-illary node negative breast cancer. Arch Gynecol Obstet 1998;261:139–46. PMID: 9651658

135: Leek RD, Landers R, Fox SB, Ng F, Harris AL, Lewis CE.Association of tumor necrosis factor � and its receptors with thymi-dine phosphorylase expression in invasive breast carcinoma. Br JCancer 1998;77:2246–51. PMID: 9649140

136: Scott PA, Smith K, Poulsom R, De Benedetti A, Bicknell R,Harris AL. Differential expression of vascular endothelial growthfactor mRNA versus protein isoform expression in human breastcancer and relationship to eIF-4E. Br J Cancer 1998;77:2120–8.PMID: 9649123

137: Delorme S. Evaluating tumor vascularization with Dopplerultrasound. Radiologe 1998;38:335–43. Review. German. PMID:9646339

138: Gasparini G, Toi M, Verderio P, et al. Prognostic significanceof p53, angiogenesis, and other conventional features in operablebreast cancer: subanalysis in node-positive and node-negative pa-tients. Int J Oncol 1998;12:1117–25. PMID: 9538138

139: Charpin C, Bergeret D, Garcia S, et al. ELAM selectinexpression in breast carcinomas detected by automated and quantita-tive immunohistochemical assays. Int J Oncol 1998;12:1041–8.PMID: 9538126

140: Jacquemier JD, Penault-Llorca FM, Bertucci F, et al. Angio-genesis as a prognostic marker in breast carcinoma with conventionaladjuvant chemotherapy: a multiparametric and immunohistochemicalanalysis. J Pathol 1998;184:130–5. PMID: 9602702

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141: Weidner N. Tumoral vascularity as a prognostic factor incancer patients: the evidence continues to grow. J Pathol 1998;184:119–22. PMID: 9602700

142: Zhou JN, Ljungdahl S, Shoshan MC, Swedenborg J, Linder S.Activation of tissue-factor gene expression in breast carcinoma cellsby stimulation of the RAF-ERK signaling pathway. Mol Carcinog1998;21:234–43. PMID: 9585253

143: Acenero MJ, Gallego MG, Ballesteros PA, Gonzalez JF.Vascular density as a prognostic indicator for invasive ductal breastcarcinoma. Virchows Arch 1998;432:113–7. PMID: 9580076

144: Villena-Heinsen C, Ertan AK, Hollander M, Konig J, Tossou-nidis I, Schmidt W. Diagnostic and prognostic value of vascularresistance in breast tumors. Ultraschall Med 1998;19:10–5. German.PMID: 9577887

145: Ozer E, Canda T, Kurtodlu B. The role of angiogenesis,laminin and CD44 expression in metastatic behavior of early stagelow-grade invasive breast carcinomas. Cancer Lett 1997;121:119–23.PMID: 9570348

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147: Takei H, Iino Y, Horiguchi J, et al. Angiogenesis and stromalfibronectin expression in invasive breast carcinoma. Int J Oncol.1998;12:517–23. PMID: 9472087

148: Puglisi F, Federico E, Di Loreto C. Predictors of axillarylymph node metastases in patients with T1 breast carcinoma. Cancer1998;82:1410–2. No abstract available. PMID: 9529038

149: Jacobs TW, Siziopikou KP, Prioleau JE, et al. Do prognosticmarker studies on core needle biopsy specimens of breast carcinomaaccurately reflect the marker status of the tumor? Mod Pathol 1998;11:259–64. PMID: 9521472

150: Hyder SM, Murthy L, Stancel GM. Progestin regulation ofvascular endothelial growth factor in human breast cancer cells.Cancer Res 1998;58:392–5. PMID: 9458078

151: Ellis LM. Translational research in breast cancer angiogenesis.Ann Surg Oncol 1997;4:611–2. No abstract available. PMID:9416406

152: Karelia NH, Patel DD, Balar DB, et al. Prognostic significanceof tumor angiogenesis in advanced breast carcinoma: an Indian ex-perience. Neoplasma 1997;44:163–6. PMID: 9372857

153: Hunt KK, Ross MI. Changing trends in the diagnosis andtreatment of early breast cancer. Cancer Treat Res 1997;90:171–201.Review. No abstract available. PMID: 9367083

154: Martin L, Green B, Renshaw C, Lowe D, Rudland P, LeinsterSJ, Winstanley J. Examining the technique of angiogenesis assess-ment in invasive breast cancer. Br J Cancer 1997;76:1046–54. PMID:9376265

155: Haga S, Watanabe O, Shimizu T, et al. Analysis of the tumorstaining obtained by preoperative IV-DSA for breast cancer patients:density and metastasis correlation. Breast Cancer Res Treat 1997;43:129–35. PMID: 9131268

156: Gillett CE, Happerfield LC. Breast cancer–from clinic tolaboratory. Br J Biomed Sci 1997;54:47–56. PMID: 9167307

157: Relf M, LeJeune S, Scott PA, et al. Expression of the angio-genic factors vascular endothelial cell growth factor, acidic and basicfibroblast growth factor, tumor growth factor �-1, platelet-derivedendothelial cell growth factor, placenta growth factor, and pleiotro-phin in human primary breast cancer and its relation to angiogenesis.Cancer Res 1997;57:963–9. PMID: 9041202

158: Engels K, Fox SB, Whitehouse RM, Gatter KC, Harris AL.Distinct angiogenic patterns are associated with high-grade in situ

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159: Bertin N, Clezardin P, Kubiak R, Frappart L. Throm-bospondin-1 and -2 mRNA expression in normal, benign, and neo-plastic human breast tissues: correlation with prognostic factors, tu-mor angiogenesis, and fibroblastic desmoplasia. Cancer Res 1997;57:396–9. PMID: 9012463

160: Gasparini G, Toi M, Gion M, et al. Prognostic significance ofvascular endothelial growth factor protein in node-negative breastcarcinoma. J Natl Cancer Inst 1997;89:139–47. PMID: 8998183

161: Marinho A, Soares R, Ferro J, Lacerda M, Schmitt FC.Angiogenesis in breast cancer is related to age but not to otherprognostic parameters. Pathol Res Pract 1997;193:267–73. PMID:9258952

162: Eckhardt SG, Pluda JM. Development of angiogenesis inhib-itors for cancer therapy. Invest New Drugs 1997;15:1–3. Review.PMID: 9195284

163: Vermeulen PB, Libura M, Libura J, et al. Influence of inves-tigator experience and microscopic field size on microvessel densityin node-negative breast carcinoma. Breast Cancer Res Treat 1997;42:165–72. PMID: 9138605

164: Engels K, Fox SB, Harris AL. Angiogenesis as a biologicaland prognostic indicator in human breast carcinoma. EXS 1997;79:113–56. Review. PMID: 9002231

165: Donegan WL. Tumor-related prognostic factors for breastcancer. CA Cancer J Clin 1997;47:28–51. Review. PMID: 8996077

166: Kato T, Kimura T, Miyakawa R, et al. Clinicopathologic studyof angiogenesis in Japanese patients with breast cancer. World J Surg1997;21:49–56. PMID: 8943177

167: Heimann R, Ferguson D, Powers C, Recant WM, Weichsel-baum RR, Hellman S. Angiogenesis as a predictor of long-termsurvival for patients with node-negative breast cancer. J Natl CancerInst. 1996;88:1764–9. PMID: 8944007

168: Namer M, Ettore R, Pivot X, Ferrero JM, Teissier E. Value ofneo-angiogenesis evaluation in cancer of the breast. Bull Cancer1996;83:1041–2. French. No abstract available. PMID: 9116372

169: Pouillart P, Dieras V. Tissue parameters of prognosis in cancerof the breast. Bull Cancer 1996;83:1029–30. French. No abstractavailable. PMID: 9116369

170: Gasparini G. Clinical significance of the determination ofangiogenesis in human breast cancer: update of the biological back-ground and overview of the Vicenza studies. Eur J Cancer. 1996;32A:2485–93. Review. No abstract available. PMID: 9059337

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172: Leek RD, Lewis CE, Whitehouse R, Greenall M, Clarke J,Harris AL. Association of macrophage infiltration with angiogenesisand prognosis in invasive breast carcinoma. Cancer Res 1996;56:4625–9. PMID: 8840975

173: Morphopoulos G, Pearson M, Ryder WD, Howell A, HarrisM. Tumor angiogenesis as a prognostic marker in infiltrating lobularcarcinoma of the breast. J Pathol 1996;180:44–9. PMID: 8943814

174: Walker RA. Angiogenesis and breast cancer prognosis–acontinuing issue. J Pathol 1996;180:6–7. No abstract available.PMID: 8943808

175: Kozlov DV, Zorin NA, Golubev OA. Secondary neovascular-ization–one of the steps in the development and progression of breastcancer. Arkh Patol 1996;58:72–5. Review. Russian. PMID: 8967872

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180: Saji S, Toi M, Tominaga T. The importance of tumor angio-genesis as a prognostic indicator in primary breast cancer. NipponGeka Gakkai Zasshi 1996;97:368–74. Review. Japanese. PMID:8709939

181: Draghi F, Sabolla L, Campani R, Meloni G. Diagnosticimaging of phyllodes tumors: preliminary observations. Radiol Med(Torino) 1996;91:585–9. Italian. PMID: 8693124

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184: Prechtel K, Prechtel D. Breast carcinoma and breast savingtherapy–a critical comment from the viewpoint of the pathologist.Geburtshilfe Frauenheilkd 1996;56:184–9. German. PMID: 8682283

185: Sledge GW Jr. Implications of the new biology for therapy inbreast cancer. Semin Oncol 1996;23(1 Suppl 2):76–81. Review.PMID: 8614850

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189: Weidner N. Angiogenesis in breast cancer. Cancer Treat Res1996;83:265–301. Review. No abstract available. PMID: 8826652

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191: Harris AL, Zhang H, Moghaddam A, et al. Breast cancerangiogenesis–new approaches to therapy via antiangiogenesis, hy-poxic activated drugs, and vascular targeting. Breast Cancer Res Treat1996;38:97–108. Review. PMID: 8825127

192: Gasparini G. Re: Tumor angiogenesis as a prognostic assay forinvasive ductal breast carcinoma. J Natl Cancer Inst 1995;87:1799–801; author reply 1801–2. No abstract available. PMID: 7473839

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207: Wiggins DL, Granai CO, Steinhoff MM, Calabresi P. Tumorangiogenesis as a prognostic factor in cervical carcinoma. GynecolOncol 1995;56:353–6. PMID: 7535719

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microvessel density as a prognostic factor in women with breast … · (3). among all of these...

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