PURPOSE: The aim of this study was to estimate the mechanical properties (elasticity) of normal and cancer prostate tissues and to develop a tissue elasticity map for the diagnosis and localization of prostate cancer. MATERIALS AND METHODS: A total of 735 sites from 35 radical prostatectomy specimens were used in the experiments using a robotic palpation system, and the elasticities of the specimens were estimated by a tissue characterization algorithm. The estimated elasticities from 21 regions were separated into normal and cancer tissues using the pathological information, and a tissue elasticity map was developed using numerical functions and a nonlinear surface-fitting method. RESULTS: The mean elastic moduli of the normal and cancer tissues were 15.25 ± 5.88 and 28.80 ± 11.20 kPa, respectively. The base region had the highest elasticity, followed by the medial and apex regions. These results demonstrated the ability to separate the cancer tissue from the normal tissue based on its elastic modulus. The tissue elasticity mapping was carried out using the estimated elasticity and nonlinear surface fitting. The proposed map showed the elasticity and was used to estimate the elastic modulus of the prostate at any given region. CONCLUSION: Tissue elasticity may be an important indicator of prostate cancer because the pathologic changes alter the tissue properties, including cell integrity and intercellular matrix. This work provides quantitative and objective information for the diagnosis of prostate cancer. In addition, these results may have implications for the localization of prostate cancers.
PURPOSE: The aim of this study was to estimate the mechanical properties (elasticity) of normal and cancer prostate tissues and to develop a tissue elasticity map for the diagnosis and localization of prostate cancer. MATERIALS AND METHODS: A total of 735 sites from 35 radical prostatectomy specimens were used in the experiments using a robotic palpation system, and the elasticities of the specimens were estimated by a tissue characterization algorithm. The estimated elasticities from 21 regions were separated into normal and cancer tissues using the pathological information, and a tissue elasticity map was developed using numerical functions and a nonlinear surface-fitting method. RESULTS: The mean elastic moduli of the normal and cancer tissues were 15.25 ± 5.88 and 28.80 ± 11.20 kPa, respectively. The base region had the highest elasticity, followed by the medial and apex regions. These results demonstrated the ability to separate the cancer tissue from the normal tissue based on its elastic modulus. The tissue elasticity mapping was carried out using the estimated elasticity and nonlinear surface fitting. The proposed map showed the elasticity and was used to estimate the elastic modulus of the prostate at any given region. CONCLUSION: Tissue elasticity may be an important indicator of prostate cancer because the pathologic changes alter the tissue properties, including cell integrity and intercellular matrix. This work provides quantitative and objective information for the diagnosis of prostate cancer. In addition, these results may have implications for the localization of prostate cancers.
Authors: Seok Chang Ryu; Zhan Fan Quek; Je-Sung Koh; Pierre Renaud; Richard J Black; Behzad Moslehi; Bruce L Daniel; Kyu-Jin Cho; Mark R Cutkosky Journal: IEEE Trans Robot Date: 2015-02 Impact factor: 5.567
Authors: Qiangqiang Ouyang; Juan Wu; Songping Sun; Jake Pensa; Ahmad Abiri; Erik Dutson; James Bisley Journal: IEEE Trans Biomed Eng Date: 2021-09-20 Impact factor: 4.756
Authors: Ahmad Abiri; Yen-Yi Juo; Anna Tao; Syed J Askari; Jake Pensa; James W Bisley; Erik P Dutson; Warren S Grundfest Journal: Surg Endosc Date: 2018-09-05 Impact factor: 4.584