Predictive model of axillary lymph node involvement in women with small invasive breast carcinoma: axillary metastases in breast carcinoma.
BACKGROUND
Axillary lymph node involvement (ALNI) remains the most accurate predictive factor for recurrence risk and survival in patients with invasive breast carcinoma (IBC) and is an essential element in therapeutic decisions. However, axillary dissection (AD) is responsible for several side effects and is now discussed in small IBC. The objective of this study was to define a predictive model of ALNI by using clinical and histologic variables available before surgery.


METHODS
The authors studied 795 cases of IBC (T0, T1, T2 < or = 4 cm; N0; M0) treated between 1980 and 1997 by conservative surgery and radiation therapy. All cases had axillary dissection with at least 10 lymph nodes removed. A stepwise logistic regression analysis was performed to build a predictive model of ALNI. The authors then used the jackknife resampling technique to produce unbiased estimates of the probabilities of ALNI along with their confidence intervals.


RESULTS
The global ALNI rate was 25.7%. The final predictive model included clinical tumor size, location, and histologic subtype and grade as variables independently associated with ALNI. The estimated probability of ALNI varied from 6% to 45%, according to case characteristics for these variables.


CONCLUSIONS
These results show that the omission of AD in surgical procedures for these tumors is debatable. Even when ALNI rates were low, the superior bounds of the confidence intervals could be high. Consequently, we do not recommend to omit AD in women whose estimated risks are higher than 25%. Women with a risk of ALNI lower than 25% could benefit from the sentinel lymph node procedure with, likewise, a limited risk of false-negative.
Martin
Caroline
C
Department of Epidemiology and Biostatistics, Centre Paul Strauss, Strasbourg, France. caro.martin@libertysurf.fr
Cutuli
Bruno
B
Velten
Michel
M
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology* / Q000628:therapy; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis / Q000473:pathology; D008875:Middle Aged; D008962:Models, Theoretical; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011379:Prognosis; D012044:Regression Analysis; D012307:Risk Factors