Identification of Tumor Nodule in Soft Tissue - An Inverse Finite-element Framework based on Mechanical Characterization

Antonio Candito, Javier Palacio-Torralba, Elizabeth Jiménez-Aguilar, Daniel W. Good, S. Alan McNeill, Robert L. Reuben, Yuhang Chen

Research output: Contribution to journalArticle

Abstract

Identification and characterization of nodules in soft tissue, including their size, shape, and location, provide a basis for tumor identification. This study proposes an inverse finite-element (FE) based computational framework, for characterizing the size of examined tissue sample and detecting the presence of embedded tumor nodules using instrumented palpation, without a priori anatomical knowledge. The inverse analysis was applied to a model system, the human prostate, and was based on the reaction forces which can be obtained by trans-rectal mechanical probing and those from an equivalent FE model, which was optimized iteratively, by minimizing an error function between the two cases, toward the target solution. The tumor nodule can be identified through its influence on the stress state of the prostate. The effectiveness of the proposed method was further verified using a realistic prostate model reconstructed from magnetic resonance (MR) images. The results show the proposed framework to be capable of characterizing the key geometrical indices of the prostate and identifying the presence of cancerous nodules. Therefore, it has potential, when combined with instrumented palpation, for primary diagnosis of prostate cancer, and, potentially, solid tumors in other types of soft tissue.

Original languageEnglish
JournalInternational Journal for Numerical Methods in Biomedical Engineering
Early online date25 May 2020
DOIs
Publication statusE-pub ahead of print - 25 May 2020

Keywords

  • instrumented palpation
  • inverse analysis
  • prostate cancer
  • tissue mechanics
  • tumor identification

ASJC Scopus subject areas

  • Software
  • Biomedical Engineering
  • Modelling and Simulation
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

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