Mortality simulator of breast cancer patients

We have created a machine learning model to evaluate the mortality in breast cancer patients.

This model uses clinical, expresion and mutational data from almost two thousands of breast cancer patients, as described in Breast cancer mortality prediction using machine learning.

Patient's data:

Hormone therapy:
ER status measured by IHC:
Neoplasm histologic grade:
HER status measured by SNP6:
Lymph nodes examined positive:
Tumor size:
Tumor stage:
BARD1 expression:
MLH1 expression:
MSH6 expression:
CCNE1 expression:
CDK2 expression:
CDC25A expression:
CCND1 expression:
CDK6 expression:
E2F4 expression:
E2F5 expression:
SRC expression:
STAT1 expression:
STAT5B expression:
ADAM17 expression:
CIR1 expression:
DLL3 expression:
DTX3 expression:
KDM5A expression:
NOTCH1 expression:
PSEN2 expression:
HEY2 expression:
AURKA expression:
BMPR1B expression:
BRAF expression:
CASP10 expression:
CASP3 expression:
CHEK1 expression:
DLEC1 expression:
EIF4EBP1 expression:
ERBB2 expression:
ERBB3 expression:
GSK3B expression:
HIF1A expression:
IGF1R expression:
KRAS expression:
MAP2K4 expression:
MAP3K1 expression:
MAP3K4 expression:
MAP3K5 expression:
MMP12 expression:
MMP7 expression:
MMP9 expression:
NFKB1 expression:
PDGFB expression:
RHEB expression:
RPS6KB2 expression:
SLC19A1 expression:
SMAD6 expression:
TGFB3 expression:
GATA3 expression:
RUNX1 expression:
TBX3 expression:
ABCC10 expression:
MAP2 expression:
MAPT expression:
TUBB4B expression:
AHNAK expression:
ARID2 expression:
CHD1 expression:
FANCD2 expression:
FLT3 expression:
LAMA2 expression:
NACOA3 expression:
NEK1 expression:
NR3C1 expression:
NRAS expression:
PRKCQ expression:
RPGR expression:
SIAH1 expression:
AR expression:
CDK8 expression:
CYP21A2 expression:
HES6 expression:
HSD17B2 expression:
HSD17B8 expression:
NRIP1 expression:
SERPINI1 expression:
SRD5A3 expression:
TP53 mutated:
Overall mortality: 6.74%

For the previous patient the mortality at 5 years is: 6.74%.

Please note that it is impossible to predict the future with certainty, and a physician must always interpret these predictions to make a diagnosis.