“Radiological Grading” for Preoperative Assessment of Central Cartilaginous Tumors

Read full text
Authors: Shinji Miwa, Katsuhiro Hayashi, Takashi Higuchi, Hirotaka Yonezawa, Sei Morinaga, Yohei Asano, Satoru Demura
Cited by: 0
References: 37

Abstract

In the treatment of musculoskeletal tumors, surgeons sometimes experience discrepancies in the histological grade between the preoperative biopsy and resected tumor specimen, and the frequency of the histological discrepancies in cartilaginous tumors is higher than that in other bone tumors. For cartilaginous tumors, new diagnostic tools or methods for the prediction of histological grades are required to determine appropriate surgical procedures for each patient. Several radiological findings have been reported to be useful in differentiating benign cartilaginous tumors, atypical cartilaginous tumors/grade 1 chondrosarcomas, and high-grade chondrosarcomas. Furthermore, recent studies have shown the high accuracy of radiological scoring systems that integrate several radiological findings to predict the histological grades of cartilaginous tumors. Radiomics, which converts features in radiological images into quantitative data, enables the comprehensive analysis of cartilaginous tumors. Recent reports suggest that radiological diagnoses are highly reliable compared with preoperative histological diagnoses for predicting the final histological diagnosis. Based on previous reports, "radiological grading," i.e., the prediction of histological aggressiveness using radiological modalities, can be important for determining the surgical procedure, in addition to "histological grading." This review article discusses radiological findings, integrated radiological scoring systems, and radiomics-based predictions of histological grades in cartilaginous tumors.

Fields of Study & Subjects

Bone Tumor Diagnosis and Treatments Radiomics and Machine Learning in Medical Imaging Musculoskeletal synovial abnormalities and treatments

Publication details

Journal / source
Cancers
Volume
18
Issue
2
Pages
193–193
Publication date
7 January 2026
Language
EN