Frontiers in Oncology (Jun 2022)

Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

  • Leonardo Rundo,
  • Leonardo Rundo,
  • Lucian Beer,
  • Lucian Beer,
  • Lucian Beer,
  • Lorena Escudero Sanchez,
  • Lorena Escudero Sanchez,
  • Mireia Crispin-Ortuzar,
  • Mireia Crispin-Ortuzar,
  • Mireia Crispin-Ortuzar,
  • Marika Reinius,
  • Marika Reinius,
  • Marika Reinius,
  • Cathal McCague,
  • Cathal McCague,
  • Hilal Sahin,
  • Hilal Sahin,
  • Hilal Sahin,
  • Vlad Bura,
  • Vlad Bura,
  • Vlad Bura,
  • Roxana Pintican,
  • Roxana Pintican,
  • Marta Zerunian,
  • Stephan Ursprung,
  • Iris Allajbeu,
  • Iris Allajbeu,
  • Helen Addley,
  • Paula Martin-Gonzalez,
  • Paula Martin-Gonzalez,
  • Thomas Buddenkotte,
  • Naveena Singh,
  • Anju Sahdev,
  • Ionut-Gabriel Funingana,
  • Ionut-Gabriel Funingana,
  • Ionut-Gabriel Funingana,
  • Mercedes Jimenez-Linan,
  • Mercedes Jimenez-Linan,
  • Florian Markowetz,
  • Florian Markowetz,
  • James D. Brenton,
  • James D. Brenton,
  • James D. Brenton,
  • Evis Sala,
  • Evis Sala,
  • Evis Sala,
  • Ramona Woitek,
  • Ramona Woitek,
  • Ramona Woitek

DOI
https://doi.org/10.3389/fonc.2022.868265
Journal volume & issue
Vol. 12

Abstract

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BackgroundPathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.MethodsOmental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).ResultsThe performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.ConclusionsCT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

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