Preoperative Magnetic Resonance Imaging Results Are Concordant with Pathology Staging in Rectal Cancer
Dursun Burak Ozdemir, Serdar Senol, Mirsad Yalcinkaya

TL;DR
This study compares MRI results with actual pathology findings in rectal cancer patients after treatment, finding MRI moderately reliable for staging.
Contribution
The study evaluates MRI reliability in rectal cancer staging after neoadjuvant therapy, identifying subgroup-specific performance.
Findings
MRI showed moderate agreement with pathology for lymph node staging (N stage) but not for tumor depth (T stage) or CRM.
Subgroup analysis revealed better MRI performance for lower rectal tumors and specific N stage predictive value.
MRI demonstrated 88.6% specificity for N stage prediction with an AUC of 0.776.
Abstract
Background and Objectives: Magnetic resonance imaging (MRI) is the gold standard for rectal cancer staging; however, its reliability after neoadjuvant therapy (NAT) remains controversial due to treatment-induced tissue changes. This study aimed to compare preoperative MRI findings with postoperative pathologic results in rectal cancer patients following NAT and to assess MRI reliability across clinical subgroups. Materials and Methods: This single-center retrospective study included 47 adult patients with locally advanced rectal adenocarcinoma who received NAT followed by elective rectal resection, with preoperative pelvic MRI and postoperative pathology results. Clinical features, MRI (T stage, N stage, and circumferential resection margin [CRM]), and pathologic staging were recorded. The endpoints were defined as concordance (via kappa coefficients) and predictive performance (via ROC…
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 4Peer 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
TopicsColorectal Cancer Surgical Treatments · Colorectal and Anal Carcinomas · Radiomics and Machine Learning in Medical Imaging
