ArticleFrontiers in oncology2021
Radiomics Models for the Preoperative Prediction of Pelvic and Sacral Tumor Types: A Single-Center Retrospective Study of 795 Cases.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers.Journal of orthopaedic surgery and research · 2023Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Deep learning ensemble models for CT-based differentiation of malignant and benign sacral bone tumors: development and evaluation.Insights into imaging · 2026Article
- Assessing an Automated Noncontrast CT-based Pipeline for Sacral Tumor Classification Using a Hip Bone Reference Frame.Radiology. Imaging cancer · 2026Article
- Applying Radiomics to Predict Outcomes in Patients with High-Grade Retroperitoneal Sarcoma Treated with Preoperative Radiotherapy.Journal of imaging · 2025Article
- End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study.NPJ precision oncology · 2025Article
- Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial intelligence and chordoma: A scoping review of the current landscape and future directions.Brain & spine · 2025Review
- Comparison of Different Fusion Radiomics for Predicting Benign and Malignant Sacral Tumors: A Pilot Study.Journal of imaging informatics in medicine · 2024Article
- Preoperative prediction of high-grade osteosarcoma response to neoadjuvant therapy based on a plain CT radiomics model: A dual-center study.Journal of bone oncology · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- Article
- Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.European radiology · 2022Article
- An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.Insights into imaging · 2022Article
- Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT.Cancers · 2022Article
- State of the Art and New Concepts in Giant Cell Tumor of Bone: Imaging Features and Tumor Characteristics.Cancers · 2021Review
- Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures.Journal of computer assisted tomographyArticle
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7 authors.
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Abstract
purposeTo assess the performance of random forest (RF)-based radiomics approaches based on 3D computed tomography (CT) and clinical features to predict the types of pelvic and sacral tumors. MATERIALS AND
methodsA total of 795 patients with pathologically confirmed pelvic and sacral tumors were analyzed, including metastatic tumors (n = 181), chordomas (n = 85), giant cell tumors (n =120), chondrosarcoma (n = 127), osteosarcoma (n = 106), neurogenic tumors (n = 95), and Ewing's sarcoma (n = 81). After semi-automatic segmentation, 1316 hand-crafted radiomics features of each patient were extracted. Four radiomics models (RMs) and four clinical-RMs were built to identify these seven types of tumors. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models.
resultsIn total, 795 patients (432 males, 363 females; mean age of 42.1 ± 17.8 years) were consisted of 215 benign tumors and 580 malignant tumors. The sex, age, history of malignancy and tumor location had significant differences between benign and malignant tumors (
conclusionsThe RF-based clinical-radiomics models provided high discriminatory performance in predicting pelvic and sacral tumor types, which could be used for clinical decision-making.
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