AIMC Topic: Soft Tissue Neoplasms

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Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis.

Academic radiology Apr 13, 2024
RATIONALE AND OBJECTIVES: To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs).
Humans Deep Learning Diagnosis, Differential Soft Tissue Neoplasms Sensitivity and Specificity Radiomics
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Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning.

Frontiers in immunology Jan 9, 2024
BACKGROUND: Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have cruc...
Prognosis RNA, Long Noncoding Humans Extracellular Traps Sarcoma Machine Learning Soft Tissue Neoplasms
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Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study.

BMC medicine Oct 26, 2023
BACKGROUND: Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differe...
Diagnosis, Differential Soft Tissue Neoplasms Deep Learning Ultrasonography Retrospective Studies Humans Sensitivity and Specificity
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A Deep Learning-Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors.

The American journal of pathology Apr 15, 2023
The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hema...
Humans Prospective Studies Eosine Yellowish-(YS) Ovarian Neoplasms Female Soft Tissue Neoplasms Hematoxylin Deep Learning Gastrointestinal Stromal Tumors
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Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases.

Scientific reports Nov 16, 2022
Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-b...
Diagnosis, Differential Sarcoma Soft Tissue Neoplasms Sensitivity and Specificity Deep Learning Leiomyoma Uterine Neoplasms Pelvic Neoplasms Humans Female
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Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

European radiology experimental Sep 8, 2022
OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep l...
Soft Tissue Neoplasms Lipoma Neoplasms, Adipose Tissue Machine Learning Deep Learning Humans Magnetic Resonance Imaging
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Research on imbalance machine learning methods for MRWI soft tissue sarcoma data.

BMC medical imaging Aug 26, 2022
BACKGROUND: Soft tissue sarcoma is a rare and highly heterogeneous tumor in clinical practice. Pathological grading of the soft tissue sarcoma is a key factor in patient prognosis and treatment planning while the clinical data of soft tissue sarcoma ...
Soft Tissue Neoplasms Humans Algorithms Sarcoma Machine Learning
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Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning.

Physics in medicine and biology Dec 7, 2021
Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (STSs). Distant metastases (DM) are the leading cause of death in STS patients and early detection is i...
Soft Tissue Neoplasms Humans Deep Learning Neural Networks, Computer Positron Emission Tomography Computed Tomography Sarcoma
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Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Nov 19, 2021
BACKGROUND AND PURPOSE: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. D...
Deep Learning Sarcoma Radiotherapy Planning, Computer-Assisted Observer Variation Soft Tissue Neoplasms Humans Reproducibility of Results
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Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.

Radiation oncology (London, England) Oct 14, 2021
OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
Deep Learning Soft Tissue Neoplasms Humans Cone-Beam Computed Tomography Phantoms, Imaging Image Processing, Computer-Assisted Neural Networks, Computer Radiotherapy Dosage Prognosis Organs at Risk Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Lung Neoplasms Bone Neoplasms
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