Machine learning-based prediction of breast cancer growth rate in vivo.

Abstract:

BACKGROUND:Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen. METHODS:A serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort. RESULTS:SM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours. CONCLUSION:Our Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications.

journal_name

Br J Cancer

authors

Bhattarai S,Klimov S,Aleskandarany MA,Burrell H,Wormall A,Green AR,Rida P,Ellis IO,Osan RM,Rakha EA,Aneja R

doi

10.1038/s41416-019-0539-x

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

497-504

issue

6

eissn

0007-0920

issn

1532-1827

pii

10.1038/s41416-019-0539-x

journal_volume

121

pub_type

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