Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

Godoy, Sebastián; Hayat, Majeed M.; Ramírez, David; Myers, Stephen; Padilla, R. Steven; Krishna, S.

doi:10.1364/boe.8.002301

Cited by 49 publications

(47 citation statements)

References 40 publications

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“…The reason for choosing only two different stages, an early stage Clark II and a later stage Clark IV tumour, is to show the difference in thermal contrast obtained by dynamic thermography, as well as how the thermoregulation response affects the temperature contrast. From a diagnostic perspective it is desired to identify the skin lesion in its early stage to improve the survival rate [9,14,15,19,68]. We gathered the material properties and thicknesses for the different layers in Table 1, taken from [10,14,34].…”

Section: Computational Examplesmentioning

confidence: 99%

“…Early detection of the skin tumour is very important for the survival rate of patients, especially in the case of malignant melanoma [14][15][16][17][18]. Skin lesions have different physiological and thermal properties like blood perfusion rate, metabolic heat generation, thermal diffusivity from the healthy skin due to the pathological change of the tissue (vascularisation or angiogenesis) [8,9,[19][20][21][22], which is reflected in different skin temperatures that can be detected using IRT imaging. The temperature difference between the tumour region and the surrounding healthy skin can be small, especially for an early stage lesion under steady-state conditions, where the measurement and background noise produced by the subcutaneous tissues or vessels have a great effect on the thermal image contrast.…”

Section: Introductionmentioning

confidence: 99%

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Numerical modelling of skin tumour tissue with temperature-dependent properties for dynamic thermography

Iljaž

Wrobel

Hriberšek

et al. 2019

Computers in Biology and Medicine

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Dynamic thermography has been clinically proven to be a valuable diagnostic technique for skin tumor detection as well as for other medical applications, and shows many advantages over static thermography. Numerical modelling of heat transfer phenomena in biological tissue during dynamic thermography can aid the technique by improving process parameters or by estimating unknown tissue parameters based on measurement data. This paper presents a new non-linear numerical model of multilayer skin tissue containing a skin tumour together with thermoregulation response of the tissue during the cooling-rewarming process of dynamic thermography. The thermoregulation response is modelled by temperature-dependent blood perfusion rate and metabolic heat generation. The aim is to describe bioheat transfer more realistically. The model is based on the Pennes bioheat equation and solved numerically using a subdomain BEM approach treating the problem as axisymmetrical. The paper includes computational tests for Clark II and Clark IV tumours, comparing the models using constant and temperature-dependent properties which showed noticeable differences and highlighted the importance of using a local thermoregulation model. Results also show the advantage of using dynamic thermography for skin tumour screening and detection at an early stage. One of the contributions of this paper is a complete sensitivity analysis of 56 model parameters based on the gradient of the surface temperature difference between tumour and healthy skin. The analysis shows that size of the tumour, blood perfusion rate, thermoregulation coefficient of the tumour, body core temperature and density and specific heat of the skin layers in which the tumour is embedded are important for modelling the problem, and so have to be determined more accurately to reflect realistic skin response of the investigated tissue, while metabolic heat generation and its thermoregulation are not.

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Section: Computational Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical modelling of skin tumour tissue with temperature-dependent properties for dynamic thermography

Iljaž

Wrobel

Hriberšek

et al. 2019

Computers in Biology and Medicine

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“…With respect to the medical applications of this sensor, we can find applications in the field of human physiology and in the detection of some pathologies that have a clear thermal component. This is currently the case with the digital thermography technique [24] that has allowed for the diagnosis of skin tumours [25] and monitoring of the evolution of different interventions such as knee replacements [26], inflammations [27], allergies [28], tendinitis [29], etc.…”

Section: Introductionmentioning

confidence: 99%

A Method to Determine Human Skin Heat Capacity Using a Non-Invasive Calorimetric Sensor

Rivera

Socorro

Rivera

et al. 2020

Sensors

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A calorimetric sensor has been designed to measure the heat flow dissipated by a 2 × 2 cm2 skin surface. In this work, a non-invasive method is proposed to determine the heat capacity and thermal conductance of the area of skin where the measurement is made. The method consists of programming a linear variation of the temperature of the sensor thermostat during its application to the skin. The sensor is modelled as a two-inputs and two-outputs system. The inputs are (1) the power dissipated by the skin and transmitted by conduction to the sensor, and (2) the power dissipated in the sensor thermostat to maintain the programmed temperature. The outputs are (1) the calorimetric signal and (2) the thermostat temperature. The proposed method consists of a sensor modelling that allows the heat capacity of the element where dissipation takes place (the skin) to be identified, and the transfer functions (TF) that link the inputs and outputs are constructed from its value. These TFs allow the determination of the heat flow dissipated by the surface of the human body as a function of the temperature of the sensor thermostat. Furthermore, as this variation in heat flow is linear, we define and determine an equivalent thermal resistance of the skin in the measured area. The method is validated with a simulation and with experimental measurements on the surface of the human body.

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“…These dynamic thermal imaging (DTI) techniques apply a hot or cold thermal stimulus to tissue and observe its rate of recovery. Leveraging the different rates of thermal recovery in healthy versus diseased tissue following the application of thermal stimulus 5 , DTI has demonstrated improved tumor detection accuracy 5 – 8 . However, label-free DTI fails to fully capture high-resolution thermal tissue heterogeneity, which can highlight subtle differences for distinguishing malignant, benign, or inflamed tissue 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Focal dynamic thermal imaging for label-free high-resolution characterization of materials and tissue heterogeneity

O’Brien

Meng

Shmuylovich

et al. 2020

Sci Rep

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Evolution from static to dynamic label-free thermal imaging has improved bulk tissue characterization, but fails to capture subtle thermal properties in heterogeneous systems. Here, we report a label-free, high speed, and high-resolution platform technology, focal dynamic thermal imaging (FDTI), for delineating material patterns and tissue heterogeneity. Stimulation of focal regions of thermally responsive systems with a narrow beam, low power, and low cost 405 nm laser perturbs the thermal equilibrium. Capturing the dynamic response of 3D printed phantoms, ex vivo biological tissue, and in vivo mouse and rat models of cancer with a thermal camera reveals material heterogeneity and delineates diseased from healthy tissue. The intuitive and non-contact FDTI method allows for rapid interrogation of suspicious lesions and longitudinal changes in tissue heterogeneity with high-resolution and large field of view. Portable FDTI holds promise as a clinical tool for capturing subtle differences in heterogeneity between malignant, benign, and inflamed tissue.

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Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

Cited by 49 publications

References 40 publications

Numerical modelling of skin tumour tissue with temperature-dependent properties for dynamic thermography

Numerical modelling of skin tumour tissue with temperature-dependent properties for dynamic thermography

A Method to Determine Human Skin Heat Capacity Using a Non-Invasive Calorimetric Sensor

Focal dynamic thermal imaging for label-free high-resolution characterization of materials and tissue heterogeneity

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