ArticleLa Radiologia medica2023
MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- Evaluation of Magnetic Resonance Imaging Features to Distinguish Lipomas from Atypical Lipomatous Tumours in the Extremities and Back.Diagnostics (Basel, Switzerland) · 2026Article
- Fat-containing soft-tissue tumors: Imaging findings and pathologic correlation.Skeletal radiology · 2026Review
- Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Automated detection of primary soft tissue sarcomas of the extremities using artificial intelligence and ChatGPT.Frontiers in oncology · 2026Article
- Article
- From Validation to Refinement: Optimising a Diagnostic Score for Atypical Lipomatous Tumours and Lipomas.Diagnostics (Basel, Switzerland) · 2025Article
- Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Review
- An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Automated Detection of Necrotizing Soft Tissue Infection Features by Computed Tomography.Diagnostics (Basel, Switzerland) · 2025Article
- Atypical Lipomatous Tumours vs. Lipomas: A Multimodal Diagnostic Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review.Frontiers in oncology · 2025Review
- Evaluation of the GATIS score for predicting prognosis in rectal neuroendocrine neoplasms.World journal of gastroenterology · 2024Article
- GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience.World journal of gastroenterology · 2024Article
- Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor.Journal of imaging informatics in medicine · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 9 institutions in 1 country.
Funding
Abstract
purposeTo determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities. MATERIAL AND
methodsThis retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort.
resultsEight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474).
conclusionMRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.