The prognostic value of angiogenesis by Chalkley counting in a confirmatory study design on 836 breast cancer patients.

This study addresses the prognostic value of estimating angiogenesis by Chalkley counting in breast cancer. A population-based group consisting of 836 patients with operated primary, unilateral invasive breast carcinomas was included from a predefined region and period of time. The median follow-up time was 11 years and 4 months. The microvessels were immunohistochemically stained by antibodies against CD34. The Chalkley count was obtained by a 25-point grid within three, subjectively selected, vascular tumor areas of highest microvessel density. The Chalkley count was analyzed in three categories using predefined Chalkley cutoff points at five and seven. There were significant correlations between high Chalkley counts and axillary lymph node metastasis, large tumor size, high histological malignancy grade, and histological type. A high Chalkley count showed lower probabilities of recurrence-free survival (P < 0.0001) and overall survival (P < 0.0001). In the Cox multivariate analysis, the hazard ratio (and 95% confidence interval) showed that the increased risk to die were: 1.55 (1.19-2.03) with Chalkley counts between 5 and 7; 2.26 (1.72-2.98) with counts > or =7 compared with counts < or =5; and 1.46 (1.14-1.87) with counts > or =7 compared with counts between 5-7. The study confirmed that estimation of angiogenesis by Chalkley counting had independent prognostic value in breast cancer patients. The Chalkley count could be useful to stratify node-negative patients for adjuvant treatment.

[1]  F. Harrell,et al.  Regression models for prognostic prediction: advantages, problems, and suggested solutions. , 1985, Cancer treatment reports.

[2]  H. Storm,et al.  Survival of Danish cancer patients, 1943-1987. , 1993, APMIS. Supplementum.

[3]  J. Folkman Tumor angiogenesis. , 1985, Advances in cancer research.

[4]  F. B. Sørensen,et al.  Angiogenesis in breast cancer: a comparative study of the observer variability of methods for determining microvessel density. , 1998, Laboratory investigation; a journal of technical methods and pathology.

[5]  H. W. Chalkley Method for the Quantitative Morphologic Analysis of Tissues , 1943 .

[6]  F Pozza,et al.  Tumor angiogenesis: a new significant and independent prognostic indicator in early-stage breast carcinoma. , 1992, Journal of the National Cancer Institute.

[7]  D. G. Altman,et al.  Statistical aspects of prognostic factor studies in oncology. , 1994, British Journal of Cancer.

[8]  N. Weidner,et al.  Angiogenesis in breast cancer. , 1996, Cancer treatment and research.

[9]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[10]  Stephen B. Fox,et al.  COMMENTARY Tumour angiogenesis and prognosis , 1997, Histopathology.

[11]  D.,et al.  Regression Models and Life-Tables , 2022 .

[12]  F Pozza,et al.  Evaluating the potential usefulness of new prognostic and predictive indicators in node-negative breast cancer patients. , 1993, Journal of the National Cancer Institute.

[13]  R. Weller,et al.  International Histological Classification of Tumours , 1981 .

[14]  R. W. Scarff,et al.  Histological typing of breast tumors. , 1982, Tumori.

[15]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[16]  A. Harris,et al.  Quantitation and prognostic value of breast cancer angiogenesis: Comparison of microvessel density, Chalkley count, and computer image analysis , 1995, The Journal of pathology.

[17]  H. Mouridsen,et al.  Danish Breast Cancer Cooperative Group--DBCG. , 1977, Ugeskrift for laeger.

[18]  S. Fox,et al.  Quantification of angiogenesis in solid human tumours: an international consensus on the methodology and criteria of evaluation. , 1996, European journal of cancer.

[19]  S. Fox Tumour angiogenesis and prognosis. , 1997, Histopathology.

[20]  D. Schoenfeld,et al.  Sample-size formula for the proportional-hazards regression model. , 1983, Biometrics.

[21]  Giampietro Gasparini,et al.  Erratum: “Evaluating the Potential Usefulness of New Prognostic and Predictive Indicators in Node-Negative Breast Cancer Patients,” , 1993 .

[22]  Robert Gray,et al.  Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis , 1992 .

[23]  H. Bloom,et al.  Histological Grading and Prognosis in Breast Cancer , 1957, British Journal of Cancer.