CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites: Initial Results

Avi Ben-Cohen*, Eyal Klang, Idit Diamant, Noa Rozendorn, Stephen P. Raskin, Eli Konen, Michal Marianne Amitai, Hayit Greenspan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale and Objectives This study aimed to provide decision support for the human expert, to categorize liver metastases into their primary cancer sites. Currently, once a liver metastasis is detected, the process of finding the primary site is challenging, time-consuming, and requires multiple examinations. The proposed system can support the human expert in localizing the search for the cancer source by prioritizing the examinations to probable cancer sites. Materials and Methods The suggested method is a learning-based approach, using computed tomography (CT) data as the input source. Each metastasis is circumscribed by a radiologist in portal phase and in non-contrast CT images. Visual features are computed from these images, combined into feature vectors, and classified using support vector machine classification. A variety of different features were explored and tested. A leave-one-out cross-validation technique was conducted for classification evaluation. The methods were developed on a set of 50 lesion cases taken from 29 patients. Results Experiments were conducted on a separate set of 142 lesion cases taken from 71 patients with four different primary sites. Multiclass categorization results (four classes) achieved low accuracy results. However, the proposed system was found to provide promising results of 83% and 99% for top-2 and top-3 classification tasks, respectively. Moreover, when compared to the experts’ ability to distinguish the different metastases, the system shows improved results. Conclusions Automated systems, such as the one proposed, show promising new results and demonstrate new capabilities that, in the future, will be able to provide decision and treatment support for radiologists and oncologists, toward more efficient detection and treatment of cancer.

Original languageEnglish
Pages (from-to)1501-1509
Number of pages9
JournalAcademic Radiology
Volume24
Issue number12
DOIs
StatePublished - Dec 2017

Keywords

  • CAD
  • CT
  • Cancer
  • deep learning
  • primary site

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