MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer

医学 基因组学 子宫内膜癌 列线图 放射科 肿瘤科 计算生物学 内科学 癌症 生物 基因组 基因 遗传学
作者
Veronica Celli,Michele Guerreri,Angelina Pernazza,Ilaria Cuccu,Innocenza Palaia,Federica Tomao,Violante Di Donato,Paola Pricolo,Giada Ercolani,Sandra Ciulla,Nicoletta Colombo,Martina Leopizzi,Valeria Di Maio,Eliodoro Faiella,Domiziana Santucci,Paolo Soda,Ermanno Cordelli,Giorgia Perniola,Benedetta Gui,Stefania Rizzo,Carlo Della Rocca,Giuseppe Petralia,Carlo Catalano,Lucia Manganaro
出处
期刊:Cancers [Multidisciplinary Digital Publishing Institute]
卷期号:14 (23): 5881-5881 被引量:23
标识
DOI:10.3390/cancers14235881
摘要

High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by “ProMisE”. This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management.
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