Tactile sensing of prostate cancer : a resonance sensor method evaluated using human prostate tissue in vitro
Prostate cancer is the most frequent type of cancer in men in Europe and the USA. The methods presently used to detect and diagnose prostate cancer are inexact, and new techniques are needed. Prostate tumours can be regarded as harder than the surrounding normal healthy glandular tissue, and therefo...
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| Format: | Doctoral Thesis |
| Language: | English |
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Umeå universitet, Institutionen för tillämpad fysik och elektronik
2007
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1445 http://nbn-resolving.de/urn:isbn:978-91-7264-461-8 |
| Summary: | Prostate cancer is the most frequent type of cancer in men in Europe and the USA. The methods presently used to detect and diagnose prostate cancer are inexact, and new techniques are needed. Prostate tumours can be regarded as harder than the surrounding normal healthy glandular tissue, and therefore it is of interest to be able to reliably measure prostate tissue stiffness. In this dissertation the approach was to evaluate tactile resonance sensor technology and its ability to measure mechanical properties and to detect cancer in human prostate tissue. The tactile resonance sensor is based on a piezoelectric transducer element vibrating at its resonance frequency through a feedback circuit. A change in the resonance frequency is observed when the sensor contacts an object. This feature has been utilized to measure tissue stiffness variations due to various pathophysiological conditions. An impression-controlled tactile resonance sensor system was first used to quantify stiffness and evaluate performance on silicone. Then the sensor system was used on fresh human prostate tissue in vitro to measure stiffness using a combination of frequency change and force measurements. Significant differences in measured stiffness between malignant and healthy normal tissue were found, but there were large variations within the groups. Some of the variability was explained by prostate tissue histology using a tissue stiffness model. The tissue content was quantified at four depths in the tissue specimens with a microscope-image-based morphometrical method involving a circular grid. Numerical weights were assigned to the tissue data from the four depths, and the weighted tissue proportions were related to the measured stiffness through a linear model which was solved with a least-squares method. An increase in the proportion of prostate stones, stroma, or cancer in relation to healthy glandular tissue increased the measured stiffness. Stroma and cancer had the greatest effect and accounted for 90 % of the measured stiffness (45% and 45%, respectively). The deeper the sensor was pressed, the greater, i.e., deeper, volume it sensed. A sensing depth was extrapolated from the numerical weights for the measurements performed at different impression depths. Horizontal surface tissue variations were studied by altering the circular grid size relative to the contact area between the sensor tip and the tissue. The results indicated that the sensing area was greater than the contact area. The sensor registered spatial tissue variations. Tissue density-related variations, as measured by the frequency change, were weakly significant or non-significant. The measured force registered elastic-related tissue variations, to which stroma and cancer were the most important variables. A theoretical material-dependent linear relation was found between frequency change and force from theoretical models of frequency change and force. Tactile resonance sensor measurements on prostate tissue verified this at small impression depths. From this model, a physical interpretation was given to the parameters used to describe stiffness. These results indicate that tactile resonance sensor technology is promising for assessing soft tissue mechanical properties and especially for prostate tissue stiffness measurement with the goal of detecting prostate cancer. However, further studies and development of the sensor design must be performed to determine the full potential of the method and its diagnostic power. Preferably, measurements of tissue mechanical properties should be used in combination with other methods, such as optical methods, to increase the diagnostic power. |
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