# Integrating PET and MRI Radiomics for Staging and Prognostic Stratification in Anal Canal Cancer

## TL;DR

This study shows that combining PET and MRI radiomic features can improve staging and predict outcomes in anal canal cancer patients.

## Contribution

The study introduces a two-feature PET radiomic model that improves long-term prognostic prediction in anal squamous cell carcinoma.

## Key findings

- A PET-derived shape feature (SHAPE_Volume(log)) strongly predicted progression-free survival.
- A two-feature model incorporating GLZLM_SZE improved long-term predictive performance (5-year AUC: 0.79).
- Eleven PET- and MRI-derived features reliably distinguished early from advanced-stage anal canal cancer.

## Abstract

This study examined whether radiomic features extracted from PET and MRI can enhance prognostication and staging in anal squamous cell carcinoma. A PET-derived shape feature, SHAPE_Volume(vx)(log), was a strong predictor of progression-free survival, and a two-feature model incorporating a heterogeneity measure (GLZLM_SZE) further improved long-term predictive performance. Additionally, eleven PET- and MRI-derived features reliably distinguished early (stage I/II) from advanced (stage III/IV) disease. These findings support the potential of integrated PET/MRI radiomics as non-invasive biomarkers for refined staging and individualized risk stratification, offering clinically meaningful information that may assist treatment planning and patient management.

Introduction: While fluorine-18 fluorodeoxyglucose positron emission tomography combined with computed tomography (18F-FDG PET/CT = PET) and magnetic resonance imaging (MRI) is used for staging patients with anal squamous cell carcinoma (ASCC), the role of radiomics in predicting patients’ outcomes remains underexplored. This study aimed to assess whether PET- and MRI-derived radiomic features could improve stage discrimination and predict outcomes in ASCC. Methods: We retrospectively included 129 patients with biopsy-proven ASCC (2012–2023) staged with PET; 67 also underwent MRI at diagnosis. Both MRI and PET radiomic features were extracted from tumor volumes using LifeX. Cox proportional hazards models identified features predictive of progression-free survival (PFS). A two-feature prognostic model was developed using top-performing PET features. Features were also evaluated for their ability to distinguish early (I/II) from advanced (III/IV) stages according to the American Joint Committee on Cancer version 9 (AJCC v9). Time-dependent area under the curve (AUC) analyses assessed model performance over 1–5 years. Results: Twelve PET-derived radiomic features differentiated early-stage from advanced-stage tumors and predicted PFS, though none predicted overall survival (OS). A one-feature model based on tumor volume (SHAPE_Volume[log]) was significantly associated with PFS (HR: 1.85; p < 0.001), and a two-feature model incorporating gray-level zone length matrix—short-zone emphasis (GLZLM_SZE) improved long-term prediction (5-year AUC: 0.79 vs. 0.72). Twenty MRI features significantly discriminated between stage groups, but none were independently prognostic for survival. Notably, GLZLM_SZE showed significant differences across stage groups in both modalities, highlighting its potential as a cross-modality biomarker. Conclusions: The study highlights the potential of PET and MRI radiomics to aid staging and prognostication in ASCC patients. PET radiomic features are associated with survival outcomes, while PET and MRI features are associated with tumor stage.