Rationale and Objectives
Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence,
which is not informative for therapeutic decision-making. We sought to develop and
independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic
re-stratification of NHG 2 tumors.
Materials and Methods
Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans
were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training
and independent test cohort, with the NHG 2 as the prognostic validation set. From
MRI image features, a radiomics-based signature predictive of the histological grade
was built by use of the LASSO logistic regression algorithm. The model was developed
for identifying NHG 1 and 3 radiological patterns, followed with re-stratification
of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes
using the learned patterns, and the prognostic value was assessed in terms of recurrence-free
survival (RFS).
Results
The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors,
where RG2-high had an increased risk for recurrence (HR 2.20, 1.10–4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low
shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high
with NHG 3, revealing that the model captures radiomic features in NHG 2 that are
associated with different aggressiveness. The prognostic value of Rad-Grade was further
validated in the NHG2 ER+ (HR 2.53, 1.13–5.56, p = 0.023) and NHG 2 ER+LN– (HR 5.72, 1.24–26.44, p = 0.025) subgroups, and in specific treatment contexts.
Conclusion
The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising
alternative to gene expression profiling for tumor grading and thus may improve clinical
decisions.
Key words
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Article info
Publication history
Published online: January 04, 2023
Accepted:
December 6,
2022
Received in revised form:
December 6,
2022
Received:
October 13,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 Published by Elsevier Inc. on behalf of The Association of University Radiologists.
