Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features

Autor: Xiaofen Li, Jingkun Zhang, Yinping Leng, Jiaqi Liu, Linlin Li, Tianyi Wan, Wentao Dong, Bing Fan, Lianggeng Gong
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Medical Imaging, Vol 24, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1471-2342
DOI: 10.1186/s12880-024-01330-4
Popis: Abstract Background Distinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma. Methods Approximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA). Results Ultimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p
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