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

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...

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Veröffentlicht in:Cancers 2022-11, Vol.14 (23), p.5881
Hauptverfasser: Celli, Veronica, Guerreri, Michele, Pernazza, Angelina, Cuccu, Ilaria, Palaia, Innocenza, Tomao, Federica, Di Donato, Violante, Pricolo, Paola, Ercolani, Giada, Ciulla, Sandra, Colombo, Nicoletta, Leopizzi, Martina, Di Maio, Valeria, Faiella, Eliodoro, Santucci, Domiziana, Soda, Paolo, Cordelli, Ermanno, Perniola, Giorgia, Gui, Benedetta, Rizzo, Stefania, Della Rocca, Carlo, Petralia, Giuseppe, Catalano, Carlo, Manganaro, Lucia
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Sprache:eng
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Zusammenfassung: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.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14235881