Oncotarget

Research Papers:

Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

Marco Rengo, Simona Picchia, Simona Marzi, Davide Bellini, Damiano Caruso, Mauro Caterino, Maria Ciolina, Domenico De Santis, Daniela Musio, Vincenzo Tombolini and Andrea Laghi _

PDF  |  HTML  |  How to cite  |  Order a Reprint

Oncotarget. 2017; 8:114746-114755. https://doi.org/10.18632/oncotarget.21778

Metrics: PDF 429 views  |   HTML 617 views  |   ?  


Abstract

Marco Rengo1, Simona Picchia1, Simona Marzi2, Davide Bellini1, Damiano Caruso1, Mauro Caterino4, Maria Ciolina1, Domenico De Santis1, Daniela Musio3, Vincenzo Tombolini3 and Andrea Laghi1

1Department of Radiological Sciences, Oncology and Pathology. “Sapienza” - University of Rome, Diagnostic Imaging Unit - I.C.O.T. Hospital, Latina, Italy

2Medical Physics Laboratory, Regina Elena National Cancer Institute, Rome, Italy

3Department of Radiological Sciences, Oncology and Pathology. “Sapienza” - University of Rome, Radiotherapy Unit, Policlinico Umberto I, Rome, Italy

4Radiology Unit, Regina Elena National Cancer Institute, Rome, Italy

Correspondence to:

Andrea Laghi, email: andrea.laghi@uniroma1.it

Keywords: rectal cancer, neoadjuvant therapy, magnetic resonance imaging, prognosis

Received: August 03, 2017     Accepted: September 21, 2017     Published: October 10, 2017

ABSTRACT

This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible.


Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.
PII: 21778