Identification of tumor nodule in soft tissue: An inverse finite-element framework based on mechanical characterization.,International Journal for Numerical Methods in Biomedical Engineering

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Identification of tumor nodule in soft tissue: An inverse finite-element framework based on mechanical characterization.
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.2 ) Pub Date : 2020-06-15 , DOI: 10.1002/cnm.3369
Antonio Candito ₁ , Javier Palacio-Torralba ₁ , Elizabeth Jiménez-Aguilar ₂ , Daniel W Good _{3,

4} , Alan McNeill _{3,

4} , Robert L Reuben ₁ , Yuhang Chen ₁

Affiliation

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.

中文翻译：

软组织中肿瘤结节的识别：基于力学表征的逆有限元框架。

软组织中结节的识别和表征，包括它们的大小、形状和位置，为肿瘤识别提供了基础。本研究提出了一种基于逆有限元 (FE) 的计算框架，用于表征检查组织样本的大小并使用仪器触诊检测嵌入的肿瘤结节的存在，而无需先验解剖知识。逆向分析应用于模型系统，人类前列腺，并基于可以通过经直肠机械探测获得的反作用力和来自等效 FE 模型的反作用力，该模型通过最小化之间的误差函数进行迭代优化两种情况，朝着目标解。肿瘤结节可以通过其对前列腺的压力状态的影响来识别。使用从磁共振 (MR) 图像重建的真实前列腺模型进一步验证了所提出方法的有效性。结果表明，所提出的框架能够表征前列腺的关键几何指标并识别癌性结节的存在。因此，当与仪器触诊相结合时，它有可能用于前列腺癌的初步诊断，并可能用于其他类型软组织中的实体瘤。

更新日期：2020-06-15

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