ArticleNPJ precision oncology2024
Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- MRI features outperform histologic grade for outcome prediction in translocation-driven enriched soft-tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Single and multi-site CT-based radiogenomics analysis of metastatic lung adenocarcinoma and correlations with outcome.European radiology · 2026Article
- Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.NPJ precision oncology · 2026Article
- Review
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.Emerging topics in life sciences · 2025Article
- An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- A prediction method for radiation proctitis based on SAM-Med2D model.Scientific reports · 2025Article
- A CT-based deep learning model to predict local recurrence-free survival in primary retroperitoneal sarcoma.Frontiers in medicine · 2025Article
- A CT-based radiomics model for predicting lymph node metastasis in hepatic alveolar echinococcosis patients to support lymph node dissection.European journal of medical research · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
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Abstract
Our objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.
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