Modification of RECIST 1.1 criteria for assessing response in breast tumours treated with radiation therapy using multiparametric breast MRI: Radiology and oncology perspective
E. Durie, M. Morris, N.A. Healy, S. Salehi-Bird, A. Seth, P. Haria, N. Tunariu, S. Allen, Aditi Chandra, Shalini Sahu, Rakesh Anandarajan, Miklos Barta, Ioannis Roxanis, F.H. Cafferty, M.D. Blackledge, N. Somaiah

TL;DR
This paper proposes modified criteria for assessing breast tumor response to radiation therapy using multiparametric MRI to better distinguish fibrosis from residual disease.
Contribution
The paper introduces new MRI-based response criteria to address limitations of RECIST 1.1 in evaluating breast tumors treated with radiation.
Findings
RECIST 1.1 has limitations in assessing breast tumors treated with radiation due to fibrosis.
Multiparametric MRI can differentiate fibrosis and residual disease more effectively.
Modified criteria using T2W, CE T1W, and DWI sequences are proposed for response evaluation.
Abstract
The traditional, size-based, RECIST 1.1 guideline is the standard for assessing tumour response, but has notable limitations, particularly for breast tumours treated with radiotherapy (RT). RT often induces fibrosis, leading to a persisting measurable abnormality on T1W/T2W MRI sequences despite an underlying pathological response. As pre-operative RT trials expand and omission of surgery is being tested, having anatomico-functional MRI response criteria may facilitate more accurate response evaluation. We propose modified RECIST 1.1 criteria based on 3 sequences of multiparametric MRI (mpMRI): unenhanced T2W, contrast-enhanced (CE) T1W and diffusion-weighted imaging (DWI). Key recommendations include complete response defined as 1) resolution of tumour mass on all sequences, or 2) evidence of residual T2 abnormality with CE images showing no enhancement above background parenchymal…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMRI in cancer diagnosis · Radiomics and Machine Learning in Medical Imaging · Breast Cancer Treatment Studies
