Skip to main content

19.03.2020 | Original Paper

Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach

Journal of Digital Imaging
Ahmad Maaref, Francisco Perdigon Romero, Emmanuel Montagnon, Milena Cerny, Bich Nguyen, Franck Vandenbroucke, Geneviève Soucy, Simon Turcotte, An Tang, Samuel Kadoury
Wichtige Hinweise
This work was accepted and presented as an abstract for SIIM 2019, in Denver, CO.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


In developed countries, colorectal cancer is the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy and eligibility for surgery. However, while FOLFOX-based regimens are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight in the classification of liver metastases identified on diagnostic images. In this paper, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural networks to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLM undergoing chemotherapy, their response to a FOLFOX with Bevacizumab regimen as first-line of treatment. The ground truth for assessment of treatment response was histopathology-determined tumor regression grade. Our DCNN approach trained on 444 lesions from 202 patients achieved accuracies of 91% for differentiating treated and untreated lesions, and 78% for predicting the response to FOLFOX-based chemotherapy regimen. Experimental results showed that our method outperformed traditional machine learning algorithms and may allow for the early detection of non-responsive patients.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf

Jetzt e.Med zum Sonderpreis bestellen!

Sichern Sie sich jetzt Ihr e.Med-Abo und sparen Sie 50 %!

Weitere Produktempfehlungen anzeigen
Über diesen Artikel
  1. Sie können e.Med Radiologie 14 Tage kostenlos testen (keine Print-Zeitschrift enthalten). Der Test läuft automatisch und formlos aus. Es kann nur einmal getestet werden.