The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer

Author:

Curcean Sebastian12ORCID,Curcean Andra3ORCID,Martin Daniela2,Fekete Zsolt12,Irimie Alexandru45,Muntean Alina-Simona2,Caraiani Cosmin6

Affiliation:

1. Department of Radiation Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, Romania

2. Department of Radiation Oncology, ‘Prof. Dr. Ion Chiricuta’ Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania

3. Department of Imaging, Affidea Center, 15c Ciresilor Street, 400487 Cluj-Napoca, Romania

4. Department of Oncological Surgery and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, 8 Victor Babes Street, 400012 Cluj-Napoca, Romania

5. Department of Oncological Surgery, ‘Prof. Dr. Ion Chiricuta’ Oncology Institute, 34-36 Republicii Street, 400015 Cluj-Napoca, Romania

6. Department of Medical Imaging and Nuclear Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania

Abstract

The role of magnetic resonance imaging (MRI) in rectal cancer management has significantly increased over the last decade, in line with more personalized treatment approaches. Total neoadjuvant treatment (TNT) plays a pivotal role in the shift from traditional surgical approach to non-surgical approaches such as ‘watch-and-wait’. MRI plays a central role in this evolving landscape, providing essential morphological and functional data that support clinical decision-making. Key MRI-based biomarkers, including circumferential resection margin (CRM), extramural venous invasion (EMVI), tumour deposits, diffusion-weighted imaging (DWI), and MRI tumour regression grade (mrTRG), have proven valuable for staging, response assessment, and patient prognosis. Functional imaging techniques, such as dynamic contrast-enhanced MRI (DCE-MRI), alongside emerging biomarkers derived from radiomics and artificial intelligence (AI) have the potential to transform rectal cancer management offering data that enhance T and N staging, histopathological characterization, prediction of treatment response, recurrence detection, and identification of genomic features. This review outlines validated morphological and functional MRI-derived biomarkers with both prognostic and predictive significance, while also exploring the potential of radiomics and artificial intelligence in rectal cancer management. Furthermore, we discuss the role of rectal MRI in the ‘watch-and-wait’ approach, highlighting important practical aspects in selecting patients for non-surgical management.

Publisher

MDPI AG

Reference186 articles.

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