Prognostic value of pseudotime from texture parameters of FDG PET/CT in locally advanced rectal cancer with neoadjuvant concurrent chemoradiotherapy

Texture analysis generates image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters correlate with tumor biology and clinical attributes, their types and implications can be complex. To overcome this limitation, pseudotime analysis was applied to texture parameters to estimate changes in individual sample characteristics, and the prognostic significance of the estimated pseudotime of primary tumors was evaluated. Our subjects were 224 rectal cancer patients who underwent pretherapeutic FDG PET/CT and neoadjuvant concurrent chemoradiation therapy (CCRT). Texture parameters of the primary tumor were extracted from FDG PET/CT images acquired before neoadjuvant CCRT. The pseudotime of the primary tumor was successfully derived from texture parameters using the Phenopath tool. Clinico-pathologic features were obtained and survival analysis was performed. Pseudotime, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) demonstrated significant associations with overall survival. Unlike MTV and TLG, pseudotime was an independent prognostic factor for overall survival (hazard ratio = 4.807, p-value = 0.037). Pseudotime analysis integrates various metabolic texture parameters into a single metric. Pseudotime estimated from FDG PET/CT images of primary tumors shows superior prognostic significance to conventional PET parameters in patients with locally advanced rectal cancer treated with tri-modality therapy.

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Publication Details

Journal
Scientific Reports
Published
2024-12-28
DOI
https://doi.org/10.1038/s41598-024-83109-9
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.21

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article

Prognostic value of pseudotime from texture parameters of FDG PET/CT in locally advanced rectal cancer with neoadjuvant concurrent chemoradiotherapy

Young Suk Park, Hee Chul Park, Yong Beom Cho, Hyunjong Lee et al.
3 citations
Scientific Reports
Radiomics and Machine Learning in Medical Imaging
1.21
article

Prognostic value of pseudotime from texture parameters of FDG PET/CT in locally advanced rectal cancer with neoadjuvant concurrent chemoradiotherapy

Young Suk Park, Hee Chul Park, Yong Beom Cho, Hyunjong Lee, Joon Young Choi, Hee Cheol Kim, Seung Hyup Hyun
article en
3 citations

Abstract

Texture analysis generates image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters correlate with tumor biology and clinical attributes, their types and implications can be complex. To overcome this limitation, pseudotime analysis was applied to texture parameters to estimate changes in individual sample characteristics, and the prognostic significance of the estimated pseudotime of primary tumors was evaluated. Our subjects were 224 rectal cancer patients who underwent pretherapeutic FDG PET/CT and neoadjuvant concurrent chemoradiation therapy (CCRT). Texture parameters of the primary tumor were extracted from FDG PET/CT images acquired before neoadjuvant CCRT. The pseudotime of the primary tumor was successfully derived from texture parameters using the Phenopath tool. Clinico-pathologic features were obtained and survival analysis was performed. Pseudotime, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) demonstrated significant associations with overall survival. Unlike MTV and TLG, pseudotime was an independent prognostic factor for overall survival (hazard ratio = 4.807, p-value = 0.037). Pseudotime analysis integrates various metabolic texture parameters into a single metric. Pseudotime estimated from FDG PET/CT images of primary tumors shows superior prognostic significance to conventional PET parameters in patients with locally advanced rectal cancer treated with tri-modality therapy.

Scientific ReportsVol. 14(1)
Samsung Medical Center (KR), Sungkyunkwan University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Samsung
Openalex Percentile: Top 19%
Radiomics and Machine Learning in Medical Imaging
1.21
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