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Radiomics of multi-parametric MRI for the prediction of lung metastasis in soft-tissue sarcoma: a feasibility study.
Hu, Yue; Wang, Xiaoyu; Yue, Zhibin; Wang, Hongbo; Wang, Yan; Luo, Yahong; Jiang, Wenyan.
Afiliación
  • Hu Y; Department of Biomedical Engineering, China Medical University, Liaoning, 110122, China.
  • Wang X; Department of Radiology, Liaoning Cancer Hospital and Institute, Liaoning, 110042, China.
  • Yue Z; Department of Radiology, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, 450000, China.
  • Wang H; Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110004, China.
  • Wang Y; Department of Biomedical Engineering, China Medical University, Liaoning, 110122, China.
  • Luo Y; Department of Radiology, Liaoning Cancer Hospital and Institute, Liaoning, 110042, China.
  • Jiang W; Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, No. 44 Xiaoheyan Road, Liaoning, 110042, China. xiaoya83921@163.com.
Cancer Imaging ; 24(1): 119, 2024 Sep 05.
Article en En | MEDLINE | ID: mdl-39238054
ABSTRACT

PURPOSE:

To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS).

METHODS:

In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models.

RESULTS:

The margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model.

CONCLUSIONS:

This feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sarcoma / Estudios de Factibilidad / Nomogramas / Neoplasias Pulmonares Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Cancer Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sarcoma / Estudios de Factibilidad / Nomogramas / Neoplasias Pulmonares Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Cancer Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido