ArticleEuropean radiology2024
Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.Nature communications · 2026Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- A machine learning approach usingFrontiers in immunology · 2024Article
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
objectivesThe incidence of anal squamous cell carcinoma (ASCC) is increasing worldwide, with a significant proportion of patients treated with curative intent having recurrence. The ability to accurately predict progression-free survival (PFS) and overall survival (OS) would allow for development of personalised treatment strategies. The aim of the study was to train and external test radiomic/clinical feature derived time-to-event prediction models.
methodsConsecutive patients with ASCC treated with curative intent at two large tertiary referral centres with baseline FDG PET-CT were included. Radiomic feature extraction was performed using LIFEx software on the pre-treatment PET-CT. Two distinct predictive models for PFS and OS were trained and tuned at each of the centres, with the best performing models externally tested on the other centres' patient cohort.
resultsA total of 187 patients were included from centre 1 (mean age 61.6 ± 11.5 years, median follow up 30 months, PFS events = 57/187, OS events = 46/187) and 257 patients were included from centre 2 (mean age 62.6 ± 12.3 years, median follow up 35 months, PFS events = 70/257, OS events = 54/257). The best performing model for PFS and OS was achieved using a Cox regression model based on age and metabolic tumour volume (MTV) with a training c-index of 0.7 and an external testing c-index of 0.7 (standard error = 0.4).
conclusionsA combination of patient age and MTV has been demonstrated using external validation to have the potential to predict OS and PFS in ASCC patients. CLINICAL RELEVANCE STATEMENT: A Cox regression model using patients' age and metabolic tumour volume showed good predictive potential for progression-free survival in external testing. The benefits of a previous radiomics model published by our group could not be confirmed on external testing. KEY POINTS: • A predictive model based on patient age and metabolic tumour volume showed potential to predict overall survival and progression-free survival and was validated on an external test cohort. • The methodology used to create a predictive model from age and metabolic tumour volume was repeatable using external cohort data. • The predictive ability of positron emission tomography-computed tomography-derived radiomic features diminished when the influence of metabolic tumour volume was accounted for.
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