Delta-radiomics Entropy Based on Tumor Heterogeneity Concept – Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma

C. Mireștean, R. Iancu, D. Iancu
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引用次数: 0

Abstract

Although adjuvant radiotherapy in combination with Temozolomide administration has clearly demonstrated the benefit in improving the prognosis of patients with multiforme glioblastoma, radiotherapy as only treatment or in combination with systemic treatment is one of the best supportive in unresectable cases. For recurrent cases, the salvage radiotherapy option (re-irradiation) can be chosen in carefully selected cases so that the benefit is greater than the toxicities. Radiomics, a new subdomain of artificial intelligence (AI), relies on advanced analysis in high-resolution medical imaging to establish diagnostic, prognostic and predictive models for clinical medicine. The variation of the delta-radiomics parameters analyzed within a tumor volume may be via tumor heterogeneity indirectly correlated with the response to treatment. The aim of the study is to propose a delta-radiomic based on entropy algorithm to allow the non-invasive pre-therapeutic identification of patients with unresectable or recurrent multiform glioblastoma who will benefit from irradiation and/or salvage re-irradiation.
基于肿瘤异质性概念的δ放射组学熵——不可切除/复发胶质母细胞瘤辐照反应预测因子
虽然辅助放疗联合替莫唑胺治疗对改善多形性胶质母细胞瘤患者的预后有明显的好处,但在不能切除的病例中,放疗作为单独治疗或与全身治疗联合是最好的支持方法之一。对于复发病例,可以在精心挑选的病例中选择补救性放疗方案(再照射),以便获益大于毒性。放射组学是人工智能(AI)的一个新的子领域,它依赖于高分辨率医学成像的先进分析,为临床医学建立诊断、预后和预测模型。在肿瘤体积内分析的δ放射组学参数的变化可能是通过与治疗反应间接相关的肿瘤异质性。该研究的目的是提出一种基于熵的三角放射学算法,以允许对不可切除或复发的多形式胶质母细胞瘤患者进行无创治疗前识别,这些患者将从放疗和/或补补性再照射中受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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