Pretreatment Staging FDG PET-Derived Radiomic Score Predicts Progression-Free Survival in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy

Background/Objectives: Clinical outcomes in locally advanced rectal cancer (LARC) treated with total neoadjuvant therapy (TNT) remain heterogeneous, and available clinicopathological parameters provide limited prognostic accuracy. This study aimed to develop a radiomic score (RadScore) from baseline staging 18F-FDG PET/CT images using LASSO-penalized Cox regression, and to evaluate its ability to independently predict progression-free survival (PFS) in LARC patients treated with TNT followed by surgery. Methods: Eighty-five patients with LARC who underwent surgical resection after TNT between May 2018 and December 2024 were retrospectively analyzed. Primary tumor volumes were semi-automatically segmented on pretreatment FDG PET/CT images, and 140 radiomic features were extracted in accordance with Image Biomarker Standardisation Initiative (IBSI) guidelines. After correlation-based filtering, 23 non-redundant features were subjected to five-fold cross-validated LASSO-Cox regression for feature selection and RadScore construction. Internal validation used 1000-repetition bootstrap resampling. Results: Disease progression was recorded in 33 of 85 patients (mean PFS: 57.5 months). The final RadScore comprised two features—Intensity Histogram Kurtosis and GLCM Difference Entropy—and was identified as an independent prognostic factor in multivariable Cox analysis (HR = 1.81; 95% CI: 1.23–2.67; p = 0.003). The model achieved a C-index of 0.674, with a bootstrap-corrected C-index of 0.650 (95% CI: 0.568–0.741). Kaplan–Meier analysis demonstrated significantly shorter PFS in the high-risk group (log-rank p = 0.006). Conclusions: A pretreatment FDG PET-derived RadScore independently predicts PFS in LARC patients treated within a TNT framework, offering preliminary evidence for a potential imaging biomarker for preoperative risk stratification and individualized treatment planning—to our knowledge, among the first such evidence in a TNT-treated rectal cancer cohort.

Authors

Institutions

Publication Details

Journal
Diagnostics
Published
2026-07-22
DOI
https://doi.org/10.3390/diagnostics16142294
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Pretreatment Staging FDG PET-Derived Radiomic Score Predicts Progression-Free Survival in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy

Adem Şengül, Kadir Alper Küçüker, Ahmet Alacacıoğlu, Bülent Turgut et al.
Diagnostics
Radiomics and Machine Learning in Medical Imaging
article

Pretreatment Staging FDG PET-Derived Radiomic Score Predicts Progression-Free Survival in Locally Advanced Rectal Cancer Treated with Total Neoadjuvant Therapy

Adem Şengül, Kadir Alper Küçüker, Ahmet Alacacıoğlu, Bülent Turgut, Ayşegül Aksu, Onder Kaan Vezir
article en

Abstract

Background/Objectives: Clinical outcomes in locally advanced rectal cancer (LARC) treated with total neoadjuvant therapy (TNT) remain heterogeneous, and available clinicopathological parameters provide limited prognostic accuracy. This study aimed to develop a radiomic score (RadScore) from baseline staging 18F-FDG PET/CT images using LASSO-penalized Cox regression, and to evaluate its ability to independently predict progression-free survival (PFS) in LARC patients treated with TNT followed by surgery. Methods: Eighty-five patients with LARC who underwent surgical resection after TNT between May 2018 and December 2024 were retrospectively analyzed. Primary tumor volumes were semi-automatically segmented on pretreatment FDG PET/CT images, and 140 radiomic features were extracted in accordance with Image Biomarker Standardisation Initiative (IBSI) guidelines. After correlation-based filtering, 23 non-redundant features were subjected to five-fold cross-validated LASSO-Cox regression for feature selection and RadScore construction. Internal validation used 1000-repetition bootstrap resampling. Results: Disease progression was recorded in 33 of 85 patients (mean PFS: 57.5 months). The final RadScore comprised two features—Intensity Histogram Kurtosis and GLCM Difference Entropy—and was identified as an independent prognostic factor in multivariable Cox analysis (HR = 1.81; 95% CI: 1.23–2.67; p = 0.003). The model achieved a C-index of 0.674, with a bootstrap-corrected C-index of 0.650 (95% CI: 0.568–0.741). Kaplan–Meier analysis demonstrated significantly shorter PFS in the high-risk group (log-rank p = 0.006). Conclusions: A pretreatment FDG PET-derived RadScore independently predicts PFS in LARC patients treated within a TNT framework, offering preliminary evidence for a potential imaging biomarker for preoperative risk stratification and individualized treatment planning—to our knowledge, among the first such evidence in a TNT-treated rectal cancer cohort.

DiagnosticsVol. 16(14)
Izmir Kâtip Çelebi University (TR)
Good health and well-being
Openalex Percentile: Top 8%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.