3863 - Analysis of prognostic factors on overall survival in elderly women treated for early breast cancer using data mining and machine learning

Background

One third of early breast cancers (EBC) in women are diagnosed over 70 years (y) old. In this population, clinicians look not only at EBC characteristics but also antecedents and comorbidities to determine the best treatment.

Methods

ConSoRe is a new generation data analytics solution using natural language processing and perform advanced data mining. It was used for data extraction from electronic patient record of 2048 patients (pts) of more than 70 years, operated for EBC in Centre Leon Berard since 1997. Patient and tumor characteristics, treatment and survival were extracted.

Results

Mean age was 75.5 y (range 70-100 y). Main comorbidities described were coronary heart disease: 340 pts (16,6%) and diabetes: 321 pts (15,7%). Distribution of body mass index (BMI) was under 18.5: 56 pts (3,3%), 18.5-25: 677 pts (39,6%); 25-30: 620 (36,3%) and over 30: 356 (20,8%). Mean tumor size was 23,5 mm (range 6 to 150), SBR grading was distributed as well: grade 1: 317 (17%) ; grade 2: 1007 (55%) ; grade 3: 476 (26%) while estrogen receptor were positive (>10%) in 1553 pts (86%), progesteron receptor positive (>10%) in 1299 pts (71%) and HER2 positive in 101 pts (6,3%). Histological nodes staging was N0 in 1497 pts (72%); N1 in 414 pts (20%); N2 in 95 pts (5%); N3 in 52 pts (2%). 295 pts (14%) were treated by chemotherapy, 80 pts (3,9%) by trastuzumab, 1544 pts (75%) by radiotherapy and 1543 (75%) by hormonotherapy. Despite a limited mean follow-up of 5,1 y (range 0.3-20,7y), we observe 83 local relapse (4%), 144 metastatic relapse (7%) and 261 deaths (12.7%) with a mean overall survival (OS) of 4,6 y (10 days to 21,4 y). In the multivariate analysis, BMI under 19 and HER 2 positive were independent predictors of OS (HR: 0,85; p = 2.49e-09). Using Multiple Correspondence Analysis, percentage of explained variances is very low with less than 10%. These results show the limit of classical approach by descriptive data analysis, that is why, we propose new method by machine learning and operational research to determine the best adjuvant treatment.

Conclusions

This elderly population has a poor prognosis for which taking BMI into account is important when defining the therapeutic strategy. Classical approaches by data analysis reach their limits.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Heudel Pierre.

Funding

Has not received any funding.

Disclosure

P. Heudel: Research grant / Funding (institution): AstraZeneca; Honoraria (institution): novartis; Honoraria (self): Pfizer. O. Tredan: Honoraria (self): Roche; Honoraria (self): Pfizer; Honoraria (self): novartis; Honoraria (self): AstraZeneca; Honoraria (self): lilly; Honoraria (self): BMS; Honoraria (self): MSD. All other authors have declared no conflicts of interest.