# Association of [18F]-FDG PET/CT-Derived Radiomic Features with Clinical Outcomes and Genomic Profiles in Patients with Chronic Lymphocytic Leukemia

## TL;DR

This study shows that PET/CT radiomic features can predict outcomes and genetic mutations in chronic lymphocytic leukemia patients using machine learning.

## Contribution

The study introduces a novel application of PET/CT radiomics combined with machine learning to predict genetic profiles and clinical outcomes in CLL.

## Key findings

- Random Forest models achieved high accuracy in predicting disease progression and TP53 and NOTCH1 mutations.
- PET/CT radiomic features showed strong correlations with clinical and genetic outcomes in CLL patients.
- IGVH mutation prediction using radiomic features had lower performance compared to TP53 and NOTCH1.

## Abstract

Background: The role of PET/CT imaging in chronic lymphoproliferative syndromes (CLL) is debated. This study examines the potential of PET/CT radiomics in predicting outcomes and genetic profiles in CLL patients. Methods: A retrospective analysis was conducted on 50 CLL patients treated at Policlinico Tor Vergata, Rome, and screened, at diagnosis, with [18F]-FDG PET/CT. Potentially pathological lymph nodes were semi-automatically segmented. Genetic mutations in TP53, NOTCH1, and IGVH were assessed. Eight hundred and sixty-five radiomic features were extracted, with the cohort split into training (70%) and validation (30%) sets. Four machine learning models, each with Random Forest, Stochastic Gradient Descent, and Support Vector Machine learners, were trained. Results: Progression occurred in 10 patients. The selected radiomic features from CT and PET datasets were correlated with four models of progression and mutations (TP53, NOTCH1, IGVH). The Random Forest models outperformed others in predicting progression (AUC = 0.94/0.88, CA = 0.87/0.75, TP = 80.00%/87.50%, TN = 72.70%/87.50%) and the occurrence of TP53 (AUC = 0.94/0.96, CA = 0.87/0.80, TP = 87.50%/90.21%, TN = 85.70%/90.90%), and NOTCH1 (AUC = 0.94/0.85, CA = 0.87/0.67, TP = 80.00%/88.90%, TN = 80.00%/83.30%)mutations. The IGVH models showed poorer performance. Conclusions: ML models based on PET/CT radiomic features effectively predict outcomes and genetic profiles in CLL patients.