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.

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.

It is well known that the changes in tissue microstructure associated with certain pathophysiological conditions can influence its mechanical properties. Quantitatively relating the tissue microstructure to the macroscopic mechanical properties could lead to significant improvements in clinical diagnosis, especially when the mechanical properties of the tissue are used as diagnostic indices such as in digital rectal examination and elastography. In this study, a novel method of imposing periodic boundary conditions in non-periodic finite-element meshes is presented. This method is used to develop quantitative relationships between tissue microstructure and its apparent mechanical properties for benign and malignant tissue at various length scales. Finally, the inter-patient variation in the tissue properties is also investigated. Results show significant changes in the statistical distribution of the mechanical properties at different length scales. More importantly the loss of the normal differentiation of glandular structure of cancerous tissue has been demonstrated to lead to changes in mechanical properties and anisotropy. The proposed methodology is not limited to a particular tissue or material and the example used could help better understand how changes in the tissue microstructure caused by pathological conditions influence the mechanical properties, ultimately leading to more sensitive and accurate diagnostic technologies.

SummaryComputational 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.

Variation in mechanical properties is a useful marker for cancer in soft tissue and has been used in clinical diagnosis for centuries. However, to develop such methods as instrumented palpation, there remain challenges in using the mechanical response during palpation to quantify tumor load. This study proposes a computational framework of identification and quantification of cancerous nodules in soft tissue without a priori knowledge of its geometry, size, and depth. The methodology, using prostate tissue as an exemplar, is based on instrumented palpation performed at positions with various indentation depths over the surface of the relevant structure (in this case, the prostate gland). The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. The methodology is first demonstrated using computational models and then validated using tissue-mimicking gelatin phantoms, where the depth and volume of the tumor nodule is estimated with good accuracy. The proposed framework is capable of quantifying a tumor nodule in soft tissue without a priori information about its geometry, thus presenting great promise in clinical palpation diagnosis for a wide variety of solid tumors including breast and prostate cancer. Highlights• To propose a novel and effective computational framework to identify and quantify the tumor nodule in soft tissue, in terms of its location, size, and volume, based on mechanical measurement without a priori knowledge of its geometry. • To demonstrate the clinical relevance of the proposed research and solutions in tumor quantification-the need of noninvasiveness using primary diagnostic approaches. • To validate such a methodology using tissue-mimicking phantoms, which demonstrate the effectiveness and the sensitivity of the proposed method in clinical applications.Electronic supplementary material The online version of this article (https://doi.