Abstract
Mechanical property characterization of prostate tumors can enhance the results obtained by palpation by providing quantitative and precise diagnostic information to surgeons. The multi-focal characteristics of prostate tumors cause inhomogeneity and local property variance in the prostate glands, which is one reason for inaccurate property characterizations of the tumors. Therefore, biomechanical models should include inhomogeneity and local property variance by taking into consideration the anatomical information (location and volume) of the tumors. We developed six inhomogeneous local prostate models using the finite element method, which takes into account the location and volume information of prostate tumors. The models were divided into six different sections: lateral apex, lateral mid, lateral base, medial apex, medial mid and medial base tumors. Information on the location and volume of prostate tumors was obtained using pathological analysis. The mechanical properties of prostate tumors were estimated using the developed model simulation and the ex vivo indentation experiment results from the human resected prostates. The results showed that the mean elastic moduli of the normal and tumoral regions were 14.7 and 41.6 kPa, respectively. Our models provided more reliable estimates of the elastic moduli than the conventionally used Hertz–Sneddon model, and the results from our model were more closely correlated with previous studies due to the inclusion of the anatomical information via inhomogeneous modeling. These six local models provide baseline property criteria for the diagnosis and localization of prostate tumors using the optimized elastic moduli of normal prostate tissues.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-0001007).
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Y. Kim and B. Ahn contributed equally.
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Kim, Y., Ahn, B., Lee, J.W. et al. Local property characterization of prostate glands using inhomogeneous modeling based on tumor volume and location analysis. Med Biol Eng Comput 51, 197–205 (2013). https://doi.org/10.1007/s11517-012-0984-7
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DOI: https://doi.org/10.1007/s11517-012-0984-7