Medical Science
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DOI: Combined Multimodal Clinical And Radiological Model For Classifying Soft Tissue Tumors
Xodjamova.G.A , Tashkent State Medical University, Uzbekistan Xodjibekov M.X , Tashkent State Medical University, UzbekistanAbstract
Soft tissue tumors (STTs) represent a heterogeneous group of mesenchymal neoplasms ranging from benign lesions to highly aggressive sarcomas. Accurate preoperative classification is essential for guiding biopsy planning, surgical strategy, and systemic therapy. However, conventional diagnostic workflows—based on clinical assessment and imaging interpretation—often face limitations due to overlapping morphological features across tumor subtypes.
A combined multimodal clinical and radiological model aims to improve diagnostic performance by integrating structured clinical data (e.g., age, sex, tumor location, growth rate, symptom duration, laboratory parameters) with advanced radiological features derived from imaging modalities such as MRI, CT, and occasionally PET. Radiological inputs may include conventional imaging characteristics (size, margins, signal intensity, contrast enhancement patterns) as well as quantitative radiomic features capturing tumor texture, shape, and heterogeneity.
Keywords
Soft tissue tumors, Soft tissue sarcoma, Benign and malignant differentiation
References
https://doi.org/10.3389/fonc.2024.1443029, Dai, X., Lu, H., Wang, X., Zhao, B., Liu, Z., Sun, T., Gao, F., Xie, P., Yu, H., & Sui, X. (2024). Development of ultrasound-based clinical, radiomics and deep learning fusion models for the diagnosis of benign and malignant soft tissue tumors.
https://doi.org/10.1007/978-3-030-60946-7_10 Neubauer, T. et al. (2020). Soft Tissue Sarcoma Co-segmentation in Combined MRI and PET/CT Data. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures.
Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk Yang, Miaomiao et al. Academic Radiology, Volume 32, Issue 5, 2838 – 2846
Hu, S., Yu, H., Tan, W., Zhang, C., Zhou, W., Xiao, B., Tao, X., & Zhang, J. (2025). A multimodal artificial intelligence system for the detection and diagnosis of solid pancreatic lesions under EUS. Endoscopic Ultrasound, 14(5), 274–281. https://doi.org/10.1097/eus.0000000000000145
Angeli, F, Bodega, F, Bergamaschi, L. et al. Multimodality Imaging in the Diagnostic Work-Up of Patients With Cardiac Masses: JACC: CardioOncology State-of-the-Art Review. J Am Coll Cardiol CardioOnc. 2024 Dec, 6 (6) 847–862. https://doi.org/10.1016/j.jaccao.2024.09.006
WHO classification of soft tissue tumours 2020: An update and simplified approach for radiologists Bansal, Abhinav et al. European Journal of Radiology, Volume 143, 109937, https://www.ejradiology.com/article/S0720-048X%2821%2900418-6/fulltext
Huan Yang, Minglei Yang, Jiani Chen, Guocong Yao, Quan Zou, Linpei Jia, Multimodal deep learning approaches for precision oncology: a comprehensive review, Briefings in Bioinformatics, Volume 26, Issue 1, January 2025, bbae699, https://doi.org/10.1093/bib/bbae699
Choi J. H., Ro J. Y. The 2020 WHO Classification of Tumors of Soft Tissue: Selected Changes and New Entities // Advances in Anatomic Pathology. 2020. Vol. 28. No. 1. pp. 44-58.
Molecular Pathology of Soft-Tissue Neoplasms and Its Role in Clinical Practic Evita B. Henderson-Jackson, MD, and Marilyn M. Bui, MD, PhD
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