Omission of Histologic Grading From Clinical Decision Making May Result in Overuse of Adjuvant Therapies in Breast Cancer: Results From a Nationwide Study

• Published in: Journal of clinical oncology Vol. 19; no. 1; pp. 28 - 36
• Main Authors: Lundin, J, Lundin, M, Holli, K, Kataja, V, Elomaa, L, Pylkkänen, L, Turpeenniemi-Hujanen, T, Joensuu, H
• Format: Journal Article
• Language: English
• Published: Baltimore, MD American Society of Clinical Oncology 01.01.2001; Lippincott Williams & Wilkins
• Subjects: Adult; Aged; Aged, 80 and over; Biological and medical sciences; Breast Neoplasms - mortality; Breast Neoplasms - pathology; Breast Neoplasms - therapy; Decision Making; Disease-Free Survival; Female; Finland - epidemiology; Gynecology. Andrology. Obstetrics; Humans; Logistic Models; Mammary gland diseases; Medical sciences; Middle Aged; Neural Networks (Computer); Prognosis; Proportional Hazards Models; Survival Rate; Tumors; Finland; Histological grading; Human; Postoperative; Mammary gland diseases; Prognosis; Decision making; Surgery; Prediction; Adjuvant treatment; Female; Mammary gland; Malignant tumor
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2001.19.1.28

To investigate the influence of routinely performed histologic grading on breast cancer outcome prediction and patient selection for adjuvant therapy. The analysis is based on a cohort of 2,842 women diagnosed with breast cancer and comprising 91% of all breast cancers diagnosed in five defined geographical regions in Finland in 1991 through 1992. Data on clinicopathologic factors and follow-up were collected from hospital case records and national registries. Histologic grade assessed at diagnosis and other clinicopathologic data were available for 1,554 operable unilateral invasive carcinomas. The relative value of grade with respect to competing prognostic factors was estimated with the Cox proportional hazards model and logistic regression. Interactions and nonlinearity of factors were accounted for by using an artificial neural network. Histologic grade was correlated strongly with survival in the entire series and in all subgroups studied. Women with well-differentiated node-negative cancer had a 97% 5-year distant disease-free survival rate as compared with 78% for women with poorly differentiated cancer. Grade was an independent prognostic factor in multivariate models and increased the predictive accuracy of a neural network model. Inclusion of grade data in a Cox multivariate model based on tumor size and hormone receptor status in node-negative cancer increased the proportion of patients with 5% or less risk for distant recurrence at 5 years from 15% to 54%. Even when assessed by pathologists who have no special training in breast cancer pathology, histologic grade has substantial and independent prognostic value in breast cancer. Omission of grading from clinical decision making may result in considerable overuse of adjuvant therapies.