Robotic palpation and mechanical property characterization for abnormal tissue localization

Tissue palpation in endoscopy using EIT and soft actuators

The integration of soft robots in medical procedures has significantly improved diagnostic and therapeutic interventions, addressing safety concerns and enhancing surgeon dexterity. In conjunction with artificial intelligence, these soft robots hold the potential to expedite autonomous interventions, such as tissue palpation for cancer detection. While cameras are prevalent in surgical instruments, situations with obscured views necessitate palpation. This proof-of-concept study investigates the effectiveness of using a soft robot integrated with Electrical Impedance Tomography (EIT) capabilities for tissue palpation in simulated in vivo inspection of the large intestine. The approach involves classifying tissue samples of varying thickness into healthy and cancerous tissues using the shape changes induced on a hydraulically-driven soft continuum robot during palpation. Shape changes of the robot are mapped using EIT, providing arrays of impedance measurements. Following the fabrication of an in-plane bending soft manipulator, the preliminary tissue phantom design is detailed. The phantom, representing the descending colon wall, considers induced stiffness by surrounding tissues based on a mass-spring model. The shape changes of the manipulator, resulting from interactions with tissues of different stiffness, are measured, and EIT measurements are fed into a Long Short-Term Memory (LSTM) classifier. Train and test datasets are collected as temporal sequences of data from a single training phantom and two test phantoms, namely, A and B, possessing distinctive thickness patterns. The collected dataset from phantom B, which differs in stiffness distribution, remains unseen to the network, thus posing challenges to the classifier. The classifier and proposed method achieve an accuracy of 93 % and 88.1 % on phantom A and B, respectively. Classification results are presented through confusion matrices and heat maps, visualising the accuracy of the algorithm and corresponding classified tissues.

A Minimally Invasive Robotic Tissue Palpation Device

OBJECTIVE

Robot-assisted minimally invasive surgery remains limited by the absence of haptic feedback, which surgeons routinely rely on to assess tissue stiffness. This limitation hinders surgeons' ability to identify and treat abnormal tissues, such as tumors, during robotic surgery.

METHODS

To address this challenge, we developed a robotic tissue palpation device capable of rapidly and non-invasively quantifying the stiffness of soft tissues, allowing surgeons to make objective and data-driven decisions during minimally invasive procedures. We evaluated the effectiveness of our device by measuring the stiffness of phantoms as well as lung, heart, liver, and skin tissues obtained from both rats and swine.

RESULTS

Results demonstrated that our device can accurately determine tissue stiffness and identify tumor mimics. Specifically, in swine lung, we determined elastic modulus (E) values of 9.1 ± 2.3, 16.8 ± 1.8, and 26.0 ± 3.6 kPa under different internal pressure of the lungs (PIP) of 2, 25, and 45 cmH2O, respectively. Using our device, we successfully located a 2-cm tumor mimic embedded at a depth of 5 mm in the lung subpleural region. Additionally, we measured E values of 33.0 ± 5.4, 19.2 ± 2.2, 33.5 ± 8.2, and 22.6 ± 6.0 kPa for swine heart, liver, abdominal skin, and muscle, respectively, which closely matched existing literature data.

CONCLUSION/SIGNIFICANCE

Results suggest that our robotic palpation device can be utilized during surgery, either as a stand-alone or additional tool integrated into existing robotic surgical systems, to enhance treatment outcomes by enabling accurate intraoperative identification of abnormal tissue.

Transparent Pneumatic Tactile Sensors for Soft Biomedical Robotics

Palpation is a simple but effective method to distinguish tumors from healthy tissues. The development of miniaturized tactile sensors embedded on endoscopic or robotic devices is key to achieving precise palpation diagnosis and subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. By utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration, combining pneumatic sensing and hydraulic actuating, also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibilities in the early detection of solid tumor as well as in the development of all-in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.

A roller scanning type prostate palpation sensor system using a cantilever beam type force measurement

Palpation has been used for centuries in medicine as a screening and diagnosis modality for a number of pathological conditions. However, this technique is subjective since palpation sensitivity depends on the skill and experience of a medical examiner. An objective approach to acquire quantitative tactile information is needed. A roller scanning type palpation sensor system consisting of an x-axis linear slider and a palpation probe for detecting lumps in a soft object such as the prostate tissue is proposed and fabricated in this study. By translating the slider and changing the palpation angle of the probe, a chrome steel ball which was the contact component indented and scanned the surface of soft silicone phantoms embedded with hard lumps. Lumps were detected by measuring reaction force waveform fluctuations. Fundamental characteristics of the proposed sensor system were validated by comparison with the ground truth load cell output and showed a strong linear correlation with r²=0.9985. On silicone samples with hard lumps, the system was able to detect lumps of various sizes embedded at the depth of 5mm. From the results, the proposed sensor system holds potential for tissue characterization.

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.

Robot-Aided Prostate Cancer Diagnosis with Fiber Optic Sensing: A Validation Study on Phantoms and Ex-Vivo Tissues

Despite technological progress in instrumental diagnostic investigations of the last decade, prostate cancer remains one of the most frequent malignant tumors and the second leading cause of cancer death among men. Although prostate biopsy remains the reference among all diagnosis procedures, it still exposes patients to the risk of developing complications. In this paper, the authors present a novel robotic system for prostate cancer diagnosis aimed at improving the current diagnostic procedures and reducing their undesired effects. The purpose of this work is to validate the proposed methodology by considering experimental analysis on both phantom and ex-vivo prostate tissues.

Analyzing Liver Surface Indentation for In Vivo Refinement of Tumor Location in Minimally Invasive Surgery

Manual palpation to update the position of subsurface tumor(s) is a normal practice in open surgery, but is not possible through the small incisions of minimally invasive surgery (MIS). This paper proposes a method that has the potential to use a simple constant-force indenter and the existing laparoscopic camera for tumor location refinement in MIS. The indenter floats with organ movement to generate a static surface deformation on the soft tissue, resolving problems of previous studies that require complicated measurement of force and displacement during indentation. By analyzing the deformation profile, we can intraoperatively update the tumor's location in real-time. Indentation experiments were conducted on healthy and "diseased" porcine liver specimens to obtain the deformation surrounding the indenter site. An inverse finite element (FE) algorithm was developed to determine the optimal material parameters of the healthy liver tissue. With these parameters, a computational model of tumorous tissue was constructed to quantitatively evaluate the effects of the tumor location on the induced deformation. By relating the experimental data from the "diseased" liver specimen to the computational results, we estimated the radial distance between the tumor and the indenter, as well as the angular position of the tumor relative to the indenter.

Mechanical characterization of porcine liver properties for computational simulation of indentation on cancerous tissue

An accurate characterization of soft biological tissue properties is essential for a realistic simulation of surgical procedures. Unconfined uniaxial compression tests with specimens affixed to the fixtures are often performed to characterize the stress-stretch curves of soft biological tissues, with which the material parameters can be obtained. However, the constrained boundary condition causes non-uniform deformation during the uniaxial test, posing challenges for accurate measurement of tissue deformation. In this study, we measured the deformation locally at the middle of liver specimens and obtained the corresponding stress-stretch curves. Since the effect of the constrained boundary condition on the local deformation of specimen is minimized, the stress-stretch curves are thus more realistic. Subsequently, we fitted the experimental stress-stretch curves with several constitutive models and found that the first-order Ogden hyperelastic material model was most suitable for characterizing the mechanical properties of porcine liver tissues. To further verify the characterized material properties, we carried out indentation tests on porcine liver specimens and compared the experimental data with computational results by using finite element simulations. A good agreement was achieved. Finally, we constructed computational models of liver tissue with a tumor and investigated the effect of the tumor on the mechanical response of the tissue under indentation. The computational results revealed that the liver specimen with tumor shows a stiffer response if the distance between the tumor and the indenter is small.

Evaluation of Artificial Tactile Sense in Mass Detection in Silicone Phantom for the Diagnosis of Prostate Tumor

We analyzed the kinetic and kinematic variables of artificial tactile and artificial vibrotactile sensing test for mass detection in silicon phantom to determine tactile intensity and speed to obtain the best result in detecting the type and location of the mass. This study has utilized Artificial Tactile Sensing Instrument for Mass Detection (ATSIMD) in cylindrical silicone phantoms. The masses embedded in these samples were inserted in axial and environmental, deep and surface positions. The loading velocity, probe location, and the frequency of the applied force were considered as the independent variables in this study. It was found that for superficial mases the accuracy of detection at low speed 5 mm/sec, although dependent on the probe, but was 50% higher than under other conditions. For deep masses, with increasing mass depth, the accuracy of detection at medium speed of 8 mm/sec was 30% higher than at low speed. Mass detection by ATSIMD used in this study showed maximum efficiency at medium loading velocity. At low and high loading velocities, the dependence of mass detection on the probe location is related to the interaction of the testing method, tissue, and viscoelastic properties of the tissue.

Conditioned haptic perception for 3D localization of nodules in soft tissue palpation with a variable stiffness probe

This paper provides a solution for fast haptic information gain during soft tissue palpation using a Variable Lever Mechanism (VLM) probe. More specifically, we investigate the impact of stiffness variation of the probe to condition likelihood functions of the kinesthetic force and tactile sensors measurements during a palpation task for two sweeping directions. Using knowledge obtained from past probing trials or Finite Element (FE) simulations, we implemented this likelihood conditioning in an autonomous palpation control strategy. Based on a recursive Bayesian inferencing framework, this new control strategy adapts the sweeping direction and the stiffness of the probe to detect abnormal stiff inclusions in soft tissues. This original control strategy for compliant palpation probes shows a sub-millimeter accuracy for the 3D localization of the nodules in a soft tissue phantom as well as a 100% reliability detecting the existence of nodules in a soft phantom.

Identification of tumor nodule in soft tissue: An inverse finite-element framework based on mechanical characterization

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.

A novel palpation-based method for tumor nodule quantification in soft tissue-computational framework and experimental validation

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.

Graphical abstract

Design of a Drop-in EBI Sensor Probe for Abnormal Tissue Detection in Minimally Invasive Surgery

It is a common challenge for the surgeon to detect pathological tissues and determine the resection margin during a minimally invasive surgery. In this study, we present a drop-in sensor probe based on the electrical bioimpedance spectroscopic technology, which can be grasped by a laparoscopic forceps and controlled by the surgeon to inspect suspicious tissue area conveniently. The probe is designed with an optimized electrode and a suitable shape specifically for Minimally Invasive Surgery (MIS). Subsequently, a series of ex vivo experiments are carried out with porcine liver tissue for feasibility validation. During the experiments, impedance measured at frequencies from 1 kHz to 2 MHz are collected on both normal tissues and water soaked tissue. In addition, classifiers based on discriminant analysis are developed. The result of the experiment indicate that the sensor probe can be used to measure the impedance of the tissue easily and the developed tissue classifier achieved accuracy of 80% and 100% respectively.

Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation

Force sensing in robotic-assisted minimally invasive surgery (RMIS) is crucial for performing dedicated surgical procedures, such as bilateral teleoperation and palpation. Due to the bio-compatibility and sterilization requirements, a specially designed surgical tool/shaft is normally attached to the sensor while contacting the organ targets. Through this design, the measured force from the sensor usually contains uncertainties, such as noise, inertial force etc., and thus cannot reflect the actual interaction force with the tissue environment. Motivated to provide the authentic contact force between a robotic tool and soft tissue, we proposed a data-driven force compensation scheme without intricate modeling to reduce the effects of force measurement uncertainties. In this paper, a neural-network-based approach is utilized to automatically model the inertial force subject to noise during the robotic palpation procedure, then the exact contact force can be obtained through the force compensation method which cancels the noise and inertial force. Following this approach, the genuine interaction force during the palpation task can be achieved furthermore to improve the appraisal of the tumor surrounded by the soft tissue. Experiments are conducted with robotic-assisted palpation tasks on a silicone-based soft tissue phantom and the results verify the effectiveness of the suggested method.

INFRA-OBTURATOR TENSION SLING FOR URINARY INCONTINENCE TREATMENT

Stress incontinence occurs due to the inability to control an urge to urinate, which affects the quality of daily life. Although there is an existing therapeutics with polymer mesh tape, it is hard to fix around the urethra to restore the function of the pubourethral ligament. Therefore, in this research, an infra-obturator tension (IOT) sling was introduced to minimize the mobility of the bladder and urethra against intra-abdominal pressure. To verify the IOT performance, two different hypotheses were formulated: The hardness and volume of the IOT support the urethra and help prevent it from deforming by intra-abdominal pressure. The rotatory motion of the IOT can bend the urethra at the contact point and help restore the function of pubourethral ligament to increase vaginal tension. For the first hypothesis, a finite element simulation was conducted using three-dimensional geometrical model obtained by the computed tomography images of patients. For the second hypothesis, surgeons performed an IOT insertion operation and analyzed the sling rotation from the patients’ CT images (mean: 21∘). From the results, the mobility of the urethra was decreased because of IOT insertion. The mobility was also decreased because of the subsequent fibrotic changes from the encapsulation of the IOT.

Direct mechanical characterization of prostate tissue-a systematic review

BACKGROUND

Direct mechanical characterization of tissue is the application of engineering techniques to biological tissue to ascertain stiffness or elasticity, which can change in response to disease states. A number of papers have been published on the application of these techniques to prostate tissue with a range of results reported. There is a marked variability in the results depending on testing techniques and disease state of the prostate tissue. We aimed to clarify the utility of direct mechanical characterization of prostate tissue in identifying disease states.

METHODS

A systematic review of the published literature regarding direct mechanical characterization of prostate tissue was undertaking according to PRISMA guidelines.

RESULTS

A variety of testing methods have been used, including compression, indentation, and tensile testing, as well as some indirect testing techniques, such as shear-wave elastography. There is strong evidence of significant stiffness differences between cancerous and non-cancerous prostate tissue, as well as correlations with prostate cancer stage. There is a correlation with increasing prostate stiffness and increasing lower urinary tract symptoms in patients with benign prostate hyperplasia. There is a wide variation in the testing methods and protocols used in the literature making direct comparison between papers difficult. Most studies utilise ex-vivo or cadaveric tissue, while none incorporate in vivo testing.

CONCLUSION

Direct mechanical assessment of prostate tissue permits a better understanding of the pathological and physiological changes that are occurring within the tissue. Further work is needed to include prospective and in vivo data to aid medical device design and investigate non-surgical methods of managing prostate disease.

A novel method for rapid and quantitative mechanical assessment of soft tissue for diagnostic purposes: A computational study

Biological tissues often experience drastic changes in their microstructure due to their pathophysiological conditions. Such microstructural changes could result in variations in mechanical properties, which can be used in diagnosing or monitoring a wide range of diseases, most notably cancer. This paves the avenue for non-invasive diagnosis by instrumented palpation although challenges remain in quantitatively assessing the amount of diseased tissue by means of mechanical characterization. This paper presents a framework for tissue diagnosis using a quantitative and efficient estimation of the fractions of cancerous and non-cancerous tissue without a priori knowledge of tissue microstructure. First, the sample is tested in a creep or stress relaxation experiment, and the behavior is characterized using a single term Prony series. A rule of mixtures, which relates tumor fraction to the apparent mechanical properties, is then obtained by minimizing the difference between strain energy of a heterogeneous system and an equivalent homogeneous one. Finally, the percentage of each tissue constituent is predicted by comparing the observed relaxation time with that calculated from the rule of mixtures. The proposed methodology is assessed using models reconstructed from histological samples and magnetic resonance imaging of prostate. Results show that estimation of cancerous tissue fraction can be obtained with a maximum error of 12% when samples of different sizes, geometries, and tumor fractions are presented. The proposed framework has the potential to be applied to a wide range of diseases such as rectal polyps, cirrhosis, or breast and prostate cancer whose current primary diagnosis remains qualitative.

Histology-based homogenization analysis of soft tissue: application to prostate cancer

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.

Patient specific modeling of palpation-based prostate cancer diagnosis: effects of pelvic cavity anatomy and intrabladder pressure

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.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach.

Quantitative diagnostics of soft tissue through viscoelastic characterization using time-based instrumented palpation

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.

Optical-based artificial palpation sensors for lesion characterization

Palpation techniques are widely used in medical procedures to detect the presence of lumps or tumors in the soft breast tissues. Since these procedures are very subjective and depend on the skills of the physician, it is imperative to perform detailed a scientific study in order to develop more efficient medical sensors to measure and generate palpation parameters. In this research, we propose an optical-based, artificial palpation sensor for lesion characterization. This has been developed using a multilayer polydimethylsiloxane optical waveguide. Light was generated at the critical angle to reflect totally within the flexible and transparent waveguide. When a waveguide was compressed by an external force, its contact area would deform and cause the light to scatter. The scattered light was captured by a high-resolution camera and saved as an image format. To test the performance of the proposed system, we used a realistic tissue phantom with embedded hard inclusions. The experimental results show that the proposed sensor can detect inclusions and provide the relative value of size, depth, and Young's modulus of an inclusion.