The Fraunhofer Institute for Ceramic Technologies and Systems, Branch Materials Diagnostics (IKTS-MD) covers also some fields of biosensing and nanotechnology, from basic research towards applications. This talk will especially address optically based methods for sensing applications: starting from analysis of the fractal dimension of time-resolved auto-fluorescence spectroscopy, to time-resolved luminescence measurements on upconversion phosphors for electron beam monitoring and last a refractive index sensing with a CCD chip technology based on localized SPR sensing. For all discussed methods the possible application will be discussed on examples of demonstrators in the fields of cancer diagnostics, medical surface sterilization process and biosensing.
Goals: Improving cancer diagnosis is one of the important challenges at this time. The precise differentiation
between benign and malignant tissue is in the oncology and oncologic surgery of the utmost significance. A new
diagnostic system, that facilitates the decision which tissue has to be removed, would be appreciated. In previous
studies many attempts were made to use tissue fluorescence for cancer recognition. However, no clear correlation
was found between tissue type and fluorescence parameters like time and wavelength dependent fluorescence
intensity I(t, λ). The present study is focused on cooperative behaviour of cells in benign or malignant prostates
tissue reflecting differences in their metabolism.
Material and Methods: 50 prostate specimens were obtained directly after radical prostatectomy and from
each specimen 6 punch biopsies were taken. Time-resolved fluorescence spectra were recorded for 4 different
measurement points for each biopsy. The pathologist evaluated each measurement point separately. An algorithm
was developed to determine a relevant parameter of the time dependent fluorescence data (fractal dimension DF ).
The results of the finding and the DF -value were correlated for each point and then analysed with statistical
Results: A total of 1200 measurements points were analysed. The optimal algorithm and conditions for
discrimination between malignant and non-malignant tissue areas were found. The correct classification could
be stated in 93.4% of analysed points. The ROC-curve (AUC = 0.94) confirms the chosen statistical method as
well as it informs about the specificity (0.94) and sensitivity (0.90).
Conclusion: The new method seems to offer a very helpful diagnostic tool for pathologists as well as for