Multiscale mechano-morphology of soft tissues : a computational study with applications to cancer diagnosis and treatment

Cooperation of engineering and biomedical sciences has produced significant advances in healthcare technology. In particular, computational modelling has led to a faster development and improvement of diagnostic and treatment techniques since it allows exploring multiple scenarios without additional complexity and cost associated to the traditional trial-and-error methodologies. The goal of this thesis is to propose computational methodologies to analyse how the changes in the microstructure of soft tissues, caused by different pathological conditions, influence the mechanical properties at higher length scales and, more importantly, to detect such changes for the purpose of quantitative diagnosis and treatment of such pathologies in the scenario of drug delivery. To achieve this objective different techniques based on quasi-static and dynamic probing have been established to perform quantitative tissue diagnosis at the microscopic (tissue) and macroscopic (organ) scales. The effects of pathologies not only affect the mechanical properties of tissue (e.g. elasticity and viscoelasticity), but also the transport properties (e.g. diffusivity) in the case of drug delivery. Such transport properties are further considered for a novel multi-scale, patient-specific framework to predict the efficacy of chemotherapy in soft tissues. It is hoped that this work will pave the road towards non-invasive palpation techniques for early diagnosis and optimised drug delivery strategies to improve the life quality of patientsJames-Watt Scholarship, Heriot-Watt Annual Fund and the Institute of Mechanical, Process and Energy Engineering (IMPEE) Grant

Using Visual Cues to Enhance Haptic Feedback for Palpation on Virtual Model of Soft Tissue

This paper explores methods that make use of visual cues aimed at generating actual haptic sensation to the user, namely pseudo-haptics. We propose a new pseudo-haptic feedback based method capable of conveying 3D haptic information and combining visual haptics with force feedback to enhance the user’s haptic experience. We focused on an application related to tumor identification during palpation and evaluated the proposed method in an experimental study where users interacted with a haptic device and graphical interface while exploring a virtual model of soft tissue, which represented stiffness distribution of a silicone phantom tissue with embedded hard inclusions. The performance of hard inclusion detection using force feedback only, pseudo-haptic feedback only, and the combination of the two feedbacks were compared with the direct hand touch. The combination method and direct hand touch had no significant difference in the detection results. Compared with the force feedback alone, our method increased the sensitivity by 5%, the positive predictive value by 4%, and decreased detection time by 48.7%. The proposed methodology has great potential for robot-assisted minimally invasive surgery and in all applications where remote haptic feedback is needed

Tactile Sensing System for Lung Tumour Localization during Minimally Invasive Surgery

Video-assisted thoracoscopie surgery (VATS) is becoming a prevalent method for lung cancer treatment. However, VATS suffers from the inability to accurately relay haptic information to the surgeon, often making tumour localization difficult. This limitation was addressed by the design of a tactile sensing system (TSS) consisting of a probe with a tactile sensor and interfacing visualization software. In this thesis, TSS performance was tested to determine the feasibility of implementing the system in VATS. This was accomplished through a series of ex vivo experiments in which the tactile sensor was calibrated and the visualization software was modified to provide haptic information visually to the user, and TSS performance was compared using human and robot palpation methods, and conventional VATS instruments. It was concluded that the device offers the possibility of providing to the surgeon the haptic information lost during surgery, thereby mitigating one of the current limitations of VATS

Multi-axis Stiffness Sensing Device for Medical Palpation

This paper presents an innovative hand-held device able to compute stiffness when interacting with a soft object. The device is composed of four linear indenters and a USB camera. The stiffness is computed in real-time, tracking the movements of spherical features in the image of the camera. Those movements relate to the movements of the four indenters when interacting with a soft surface. Since the indenters are connected to springs with different spring constants, the displacement of the indenters varies when interacting with a soft object. The proposed multi-indenting device allows measuring the object's stiffness as well as the pan and tilt angles between the sensor and the surface of the soft object. Tests were performed to evaluate the accuracy of the proposed palpation mechanism against commercial springs of known stiffness. Results show that the accuracy and sensitivity of the proposed device increases with the softness of the examined object. Preliminary tests with silicon show the ability of the sensing mechanism to characterize phantom soft tissue for small indentation. It is noted that the results are not affected by the orientation of the device when probing the surface. The proposed sensing device can be used in different applications, such as external palpation for diagnosis or, if miniaturized, embedded on an endoscopic camera and used in Minimally Invasive Surgery (MIS)

Development of a clinical decision model for thyroid nodules

<p>Abstract</p> <p>Background</p> <p>Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery.</p> <p>Methods</p> <p>Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules.</p> <p>Results</p> <p>Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively.</p> <p>Conclusion</p> <p>An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.</p

Computational framework for identification of cancerous nodules in prostate based on instrumented palpation

The interplay between engineering and medical research plays a major role in advancing the healthcare technologies. Novel medical devices have been developed to improve the diagnosis and treatment plans for patients with pathological conditions such as prostate cancer (PCa). In this context, in silico modelling has been a valuable tool as it is complementary to traditional trial-and-error approaches, particularly in the area of nodule identification in soft tissue. The goal of this thesis is to develop a computational framework of detecting and characterizing the presence of PCa, based on instrumented probing. The proposed methodologies involve Finite-Element simulations, inverse analysis and probability-based methods, using models reconstructed from medical imaging and histological data. The proposed methods are later validated using experimental measurements from instrumented probing on ex-vivo prostates. It is expected that the in-silico framework can serve as a complementary tool to the medical devices and to improve the effectiveness of current methods for early PCa diagnosis.James-Watt ScholarshipHeriot-Watt University - Annual Fund Gran