Evaluation of an early regression index (Eritcp) as predictor of pathological complete response in cervical cancer: A pilot-study

Davide Cusumano, Francesco Catucci, Angela Romano, Luca Boldrini, Antonio Piras, Sara Broggi, Claudio Votta, Lorenzo Placidi, Matteo Nardini, Giuditta Chiloiro, Alessia Nardangeli, Viola De Luca, Bruno Fionda, Maura Campitelli, Rosa Autorino, Maria Antonietta Gambacorta, Luca Indovina, Claudio Fiorino, Vincenzo Valentini

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Recent studies have highlighted the potentialities of a radiobiological parameter, the early regression index (ERITCP), in the treatment response prediction for rectal cancer patients treated with chemoradiotherapy followed by surgery. The aim of this study is to evaluate the performance of this parameter in predicting pathological complete response (pCR) in the context of low field MR guided radiotherapy (MRgRT) for cervical cancer (CC). Methods: A total of 16 patients affected by CC were enrolled. All patients underwent a MRgRT treatment, with prescription of 50.6 Gy in 22 fractions. A daily MR acquisition was performed at simulation and on each treatment fraction. Gross tumor volume (GTV) was delineated on the MR images acquired at the following biological effective dose (BED) levels: 14, 28, 42, 54 and 62 Gy. The ERITCP was calculated at the different BED levels and its predictive performance was quantified in terms of receiver operating characteristic (ROC) curve. Results: pCR was observed in 11/16 cases. The highest discriminative power of ERITCP was reported when a BED value of 28 Gy is reached, obtaining an area under curve (AUC) of 0.84. Conclusion: This study confirmed ERITCP as a promising response biomarker also for CC, although further studies with larger cohort of patients are recommended.

Original languageEnglish
Article number8001
Pages (from-to)1-10
Number of pages10
JournalApplied Sciences (Switzerland)
Volume10
Issue number22
DOIs
Publication statusPublished - Nov 2 2020

Keywords

  • Cervical cancer
  • MR-guided radiotherapy
  • Predictive models

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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