Casale Roberto, Varriano Giulia, Santone Antonella, Messina Carmelo, Casale Chiara, Gitto Salvatore, Sconfienza Luca Maria, Bali Maria Antonietta, Brunese Luca
Department of Radiology, Institut Jules Bordet-Université Libre de Bruxelles (ULB), Brussels, Belgium.
Department of Medicine and Health Sciences Vincenzo Tiberio, University of Molise, Campobasso, Italy.
JAMIA Open. 2023 Apr 12;6(2):ooad025. doi: 10.1093/jamiaopen/ooad025. eCollection 2023 Jul.
Soft-tissue sarcomas (STSs) of the extremities are a group of malignancies arising from the mesenchymal cells that may develop distant metastases or local recurrence. In this article, we propose a novel methodology aimed to predict metastases and recurrence risk in patients with these malignancies by evaluating magnetic resonance radiomic features that will be formally verified through formal logic models.
This is a retrospective study based on a public dataset evaluating MRI scans T2-weighted fat-saturated or short tau inversion recovery and patients having "metastases/local recurrence" (group B) or "no metastases/no local recurrence" (group A) as clinical outcomes. Once radiomic features are extracted, they are included in formal models, on which is automatically verified the logic property written by a radiologist and his computer scientists coworkers.
Evaluating the Formal Methods efficacy in predicting distant metastases/local recurrence in STSs (group A vs group B), our methodology showed a sensitivity and specificity of 0.81 and 0.67, respectively; this suggests that radiomics and formal verification may be useful in predicting future metastases or local recurrence development in soft tissue sarcoma.
Authors discussed about the literature to consider Formal Methods as a valid alternative to other Artificial Intelligence techniques.
An innovative and noninvasive rigourous methodology can be significant in predicting local recurrence and metastases development in STSs. Future works can be the assessment on multicentric studies to extract objective disease information, enriching the connection between the radiomic quantitative analysis and the radiological clinical evidences.
四肢软组织肉瘤（STS）是一组起源于间充质细胞的恶性肿瘤，可能发生远处转移或局部复发。在本文中，我们提出了一种新方法，旨在通过评估磁共振影像组学特征来预测这些恶性肿瘤患者的转移和复发风险，这些特征将通过形式逻辑模型进行正式验证。
这是一项基于公共数据集的回顾性研究，评估T2加权脂肪饱和或短tau反转恢复序列的MRI扫描，以及以“转移/局部复发”（B组）或“无转移/无局部复发”（A组）为临床结局的患者。一旦提取出影像组学特征，就将其纳入形式模型，由放射科医生及其计算机科学家同事编写的逻辑属性将在该模型上自动得到验证。
评估形式方法在预测STS远处转移/局部复发（A组与B组）方面的有效性，我们的方法显示敏感性和特异性分别为0.81和0.67；这表明影像组学和形式验证可能有助于预测软组织肉瘤未来的转移或局部复发情况。
作者讨论了相关文献，认为形式方法可作为其他人工智能技术的有效替代方法。
一种创新的、非侵入性的严谨方法在预测STS的局部复发和转移发展方面可能具有重要意义。未来的工作可以是进行多中心研究评估，以提取客观的疾病信息，丰富影像组学定量分析与放射学临床证据之间的联系。
