Local property characterization of prostate glands using inhomogeneous modeling based on tumor volume and location analysis
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- Kim, Y., Ahn, B., Lee, J.W. et al. Med Biol Eng Comput (2013) 51: 197. doi:10.1007/s11517-012-0984-7
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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.
KeywordsLocal mechanical propertiesFEMProstate
The mechanical properties of biological tissues are important indicators of the tissues’ pathological states. For instance, prostate adenocarcinomas have higher cellular densities than normal tissue, and the pathological findings from prostate adenocarcinoma demonstrate that these tumors have well-defined gland patterns and are denser than normal tissue. Thus, these changes in the tissue increase the mechanical properties of the tumor [13, 21]. Mechanical diagnosis is one possible approach for detecting tumors using the mechanical property differences between normal and tumor tissues. Palpation is the most frequently used clinical diagnostic method used by urologists to distinguish between malignant and benign tumors. However, the success rate of this technique is low , and it is not able to provide quantitative and precise information due to its dependence on the clinician’s skill. Mechanical characterization, based on well-defined biomechanical models, could address these limitations and provide more accurate and significant information to clinicians.
A great deal of research on mechanical characterization has been performed. Krouskop et al.  measured the mechanical properties of prostate tissue and showed that the elastic modulus of prostate tumor tissue is greater than that of normal tissue. Yang et al.  performed in vitro macro- and micro-indentation tests to measure the dynamic mechanical properties of human prostate tissue. Fearing et al.  used a probing system in silicone phantoms with inclusions and conducted a statistical analysis with the existence of these inclusions. Ahn et al.  localized the inclusions within silicone phantoms using the sweeping palpation system and the FEM-based mechanical property characterization. Tanaka et al. performed in vivo tests with human prostates, and the results were characterized according to three different firmness levels (soft, firm, and hard) to distinguish between cancerous and normal tissues [7, 25, 26]. Liu et al. used the internal structure of the prostate with rolling mechanical imaging to determine the approximate location, shape, and size of cancerous nodules [16, 22]. Ahn et al. [2, 4] and Carson et al.  characterized the mechanical properties of human prostate tissue in ex vivo experiments. The property characterization methods suggested by the previous studies were built on overly simplified assumptions that prostate tissue is linear and homogeneous, which limits the validity of mechanical property characterization. The inhomogeneous characteristics of normal and tumor tissues should be statistically analyzed for tumor location and volume, and this information should be incorporated into tissue modeling to more accurately characterize the tissue properties. Therefore, we obtained pathological information (location and volume) from prostate tumors and developed a novel, tumor-containing tissue model. Another important issue that should be included in modeling is the local property variance due to the multi-focal nodules of prostate tumors. Previous models did not consider boundary conditions and surface geometry in local mechanical stimulation experiments and thus do not include the local property variance of prostate glands. It is, therefore, necessary to develop local inhomogeneous prostate models that cover all sections of the prostate. For each local section, the location and volume information of each prostate tumor was determined by uro-pathologist and was statistically analyzed. Based on this information, six local inhomogeneous prostate models were developed as an enhanced numerical model strategy that included the inhomogeneous nature of the internal prostate structure.
2 Materials and methods
2.1 Indentation experiments 
2.2 Pathological location and volume analyses of tumor lesions
2.3 Finite element model
Tumor depth and diameter analysis results for six sections
0 ± 0
1 ± 1.29
1.61 ± 1.75
0.64 ± 0.93
0.4 ± 0.89
1 ± 1.15
1.14 ± 0.89
1.20 ± 0.92
1.03 ± 0.89
1.12 ± 1.06
1.08 ± 0.96
1.08 ± 1.03
Case number according to tumor location, tumor volume and Gleason score
Normal tissue (n)
Cancer tissue (n)
2.4 Quantitative parameter analysis
The optimized Young’s moduli from the TCP model estimation and the Hertz–Sneddon model estimation 
TCP model estimate
Normal prostate region
Hertz–Sneddon model estimate
Normal prostate region
Parameter study of simulations using different diameters, depths, and property ratios of tumors
0.0739 × (diameter) + 0.6306
−0.1765 × (depth) + 1.0197
0.1483 × (property ratio) + 0.8756
0.0337 × (diameter) + 0.6767
−0.0182 × (depth) + 0.9997
0.1597 × (property ratio) + 0.8547
0.0287 × (diameter) + 0.7993
−0.0193 × (depth) + 0.9902
0.0905 × (property ratio) + 0.9095
0.1019 × (diameter) + 0.9226
−0.0133 × (depth) + 1.0082
0.1363 × (property ratio) + 0.9423
0.0555 × (diameter) + 0.7223
−0.0692 × (depth) + 1.0141
0.0847 × (property ratio) + 0.9953
0.0127 × (diameter) + 0.9182
−0.0253 × (depth) + 1.0242
0.1437 × (property ratio) + 0.8625
This paper presents the TCP models to obtain more precise mechanical properties of normal tissue and tumor lesion. The models include prostate geometries reconstructed by sequential CT images and inhomogeneous structure based on the tumor volume and location information. Ahn et al. estimated the elastic moduli of the normal and tumor-contained tissues of the human prostate using the Hertz–Sneddon model whose elastic moduli of normal tissue and tumor are 17.0 and 24.1 kPa, respectively . In our research, we consider the volume and location information of tumor lesion statistically and develop the TCP models to estimate more precise elastic moduli of local sections. The estimated elastic moduli of the normal tissue and tumor were 14.7 and 41.6 kPa, respectively. These results are supported by the previous research that the elasticity of the cancerous cell and tissue is reported to be about 2–3 times higher than that of the normal cells and tissues [9, 12]. A recent study by Zhang et al. reported that the elastic moduli of normal prostate tissue and prostate tissue with greater than 60 % of the total prostate volume being cancerous were 15.9 and 40.4 kPa, respectively. Therefore, we assert that the estimated properties of normal tissue and cancerous tissue used in this model are more precise than those obtained by other researchers.
Although we obtained more precise measurements of the properties of prostates, our model still has some limitations. It assumes homogeneity and isotropicity within both regions and neglects the detailed network of biological tissue structures [8, 11]. The prostate is ~70 % glandular elements and 30 % fibromuscular stroma. The relative proportions of glandular elements and fibromuscular stroma differs between individuals and might indicate different elasticities. Therefore, consideration of the structural characteristics of each region is required for more accurate analyses.
In this research, we developed the six section TCP models based on prostate geometries (tumor volume and location information). In addition, these models were used to estimate more precise properties of prostate normal tissue and tumor lesion, which can provide the quantitative and precise diagnostic information to surgeons. The obtained results can also be used as the baseline property criteria for the diagnosis and localization of prostate tumors.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-0001007).