ArticleCancers2021
Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication.Frontiers in oncology · 2025Pooled it
- Toward clinical value, not just accuracy: deep learning segmentation of soft tissue sarcoma.Skeletal radiology · 2026Article
- Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.Journal of imaging informatics in medicine · 2026Article
- Interpretable Deep Learning Radiomics Model for Preoperative Prediction of High-Grade Soft Tissue Sarcomas: A Multicenter MRI Study.Journal of imaging informatics in medicine · 2026Article
- Patient Perceptions of Artificial Intelligence in Orthopaedic Surgery: Identifying Potential Barriers to Acceptance and Disparities With Implementation.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Article
- Evaluation of Diagnostic Accuracy of Preoperative CT-Based Radiomics in Primary Retroperitoneal Sarcoma.Annals of surgical oncology · 2025Article
- Machine learning prediction of early reoperation following lower extremity tumor resection and endoprosthetic reconstruction: A PARITY trial secondary analysis.Journal of orthopaedic surgery and research · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review.Frontiers in oncology · 2025Review
- Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics.Diagnostics (Basel, Switzerland) · 2024Article
- Capped Plasmonic Gold and Silver Nanoparticles with Porphyrins for Potential Use as Anticancer Agents-A Review.Pharmaceutics · 2024Review
- A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma.NPJ precision oncology · 2024Article
- Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- Review
- Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors.Frontiers in radiology · 2024Review
- A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis.The Lancet. Oncology · 2023Article
- The importance of planning CT-based imaging features for machine learning-based prediction of pain response.Scientific reports · 2023Article
- Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 4 countries.
Funding
Abstract
backgroundIn patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging.
methodsContrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture.
resultsThe T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions.
conclusionsMRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.
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