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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., (© 2020 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

An instrumented palpation sensor, designed for measuring the dynamic modulus of tissue in vivo, has been developed and trialled on ex vivo whole prostate glands. The sensor consists of a flexible membrane sensor/actuator with an embedded strain gauge and is actuated using a dynamically varying airflow at frequencies of 1 and 5 Hz. The device was calibrated using an indentation stiffness measurement rig and gelatine samples with a range of static modulus similar to that reported in the literature for prostate tissue. The glands were removed from patients with diagnosed prostate cancer scheduled for radical prostatectomy, and the stiffness was measured within 30 min of surgical removal. Each prostate was later examined histologically in a column immediately below each indentation point and graded into one of the four groups; normal, benign prostatic hyperplasia, cancerous and mixed cancer and benign prostatic hyperplasia. In total, 11 prostates were assessed using multiple point probing, and the complex modulus at 1 and 5 Hz was calculated on a point-by-point basis. The device yielded values of quasi-static modulus of 15 ± 0.5 kPa and dynamic modulus of 20 ± 0.5 kPa for whole prostates, and a sensitivity of up to 80% with slightly lower specificity was achieved on diagnosis of prostate cancer using a combination of mechanical measures. This assessment did not take into account some obvious factors such as edge effects, overlap and clinical significance of the cancer, all of which would improve performance. The device, as currently configured, is immediately deployable in vivo. A number of improvements are also identified which could improve the sensitivity and specificity in future embodiments of the probe.

Computational modeling has become a successful tool for scientific advances including understanding the behavior of biological and biomedical systems as well as improving clinical practice. In most cases, only general models are used without taking into account patient-specific features. However, patient specificity has proven to be crucial in guiding clinical practice because of disastrous consequences that can arise should the model be inaccurate. This paper proposes a framework for the computational modeling applied to the example of the male pelvic cavity for the purpose of prostate cancer diagnostics using palpation. The effects of patient specific structural features on palpation response are studied in three selected patients with very different pathophysiological conditions whose pelvic cavities are reconstructed from MRI scans. In particular, the role of intrabladder pressure in the outcome of digital rectal examination is investigated with the objective of providing guidelines to practitioners to enhance the effectiveness of diagnosis. Furthermore, the presence of the pelvic bone in the model is assessed to determine the pathophysiological conditions in which it has to be modeled. The conclusions and suggestions of this work have potential use not only in clinical practice and also for biomechanical modeling where structural patient-specificity needs to be considered. © 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd., (© 2015 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

Although palpation has been successfully employed for centuries to assess soft tissue quality, it is a subjective test, and is therefore qualitative and depends on the experience of the practitioner. To reproduce what the medical practitioner feels needs more than a simple quasi-static stiffness measurement. This paper assesses the capacity of dynamic mechanical palpation to measure the changes in viscoelastic properties that soft tissue can exhibit under certain pathological conditions. A diagnostic framework is proposed to measure elastic and viscous behaviors simultaneously using a reduced set of viscoelastic parameters, giving a reliable index for quantitative assessment of tissue quality. The approach is illustrated on prostate models reconstructed from prostate MRI scans. The examples show that the change in viscoelastic time constant between healthy and cancerous tissue is a key index for quantitative diagnostics using point probing. The method is not limited to any particular tissue or material and is therefore useful for tissue where defining a unique time constant is not trivial. The proposed framework of quantitative assessment could become a useful tool in clinical diagnostics for soft tissue., (Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.)