Algerian Journal of Health Sciences

Predictive Factors Of Pathological Complete Response (pcr) In Locally Advanced Breast Cancer

Auteurs : Kouadri Nassima . Mokrani Aida . Boudersa Asma . Filali Taha .

Résumé

Introduction. Breast cancer is the 1st cancer and the 1st cause of cancer death in women. The proportion of Locally advanced breast cancer (LABC) is highly variable across the world and its prognosis is reserved. In addition to operability, the goal of Neoadjuvant chemotherapy (NACT) is pathological response, in particular complete response (pCR) considered as good prognostic factor. The heterogeneity of the histological response points to the study of predictive factors in order to adapt treatments. The aim of this study is to determine the predictive factors of pCR after NACT for LABC. Patients and methods. A prospective, multicenter study focused on LABC (stage III), treated by 8 cycles of NACT. After surgery, histological response is evaluated and cases of pCR noted. Data analysis is first descriptive : admission data, treatment, clinical, radiological and histological response. Then analytic study to determine the factors statistically associated with pCR. Results. 86 patients, median age 47.8 (29-65 years). More than 55% are T4 and 10% T4d. More than 20% N2. Stage IIIB represents 55.4%. The HER-2 positive group is the most frequent (53%), the luminal group 30% and Triple-negative 17%. The rate of pCR is 35.6%, in addition to 18.4% of npCR. This rate is different depending on the molecular group. The main predictors of pCR are: radiological size of T, ER level, histology, and last radiologic response. Conclusion. Our study has shown that certain factors are associated with pCR. Their integration into a predictive score and its prospective validation would make it possible to orient the personalization and intensification of the NACT in order to improve the pCR rates, since it is directly linked to the prognosis.

Mots clés

Breast cancer ; Pathological complete response (pCR) ; Neoadjuvant chemotherapy ; Predictive factors