AI-supported modified risk staging for multiple myeloma cancer useful in real-world scenario.

ABSTRACT:

Introduction

An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin.

Materials and methods

MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 cl

SUBMITTER: Farswan A 

PROVIDER: S-EPMC8278429 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature