Nucleotyping

Nucleotyping is a unique prognostic marker for cancer based on proprietary image analysis methods, inital results show its application as a prognostic marker where no other useful markers currently exist.

We have strong indications of the usefulness of our test in Prostate, Breast and gynaecological cancers, and are presently carrying out larger retrospective studies on Breast (500 patients), Prostate (300 patients), Sarcomas in the Uterus (>400) and colorectal (>600 patients) cancers – to be completed by 2007-2009. Prospective studies are required for “complete medical evidence” and they could start in 2007 and results would start coming in from 2009. Early acceptors should start using this method in clinical routine in 2008 and the market will open up from 2009.

Selected References

Premalignant changes in DNA organization in mouse liver after diethylnitrosamine treatment. Danielsen HE. University of Oslo, 1992.

Nuclear texture analysis: a new prognostic tool in metastatic prostate cancer. Jorgensen T, Yogesan K, Tveter
KJ, Skjorten F, Danielsen, HE. Cytometry 1996; 24(3):277-283.

Entropy-based texture analysis of chromatin structure in advanced prostate cancer. Yogesan K, Jorgensen T, Albregtsen F, Tveter KJ, Danielsen HE. Cytometry 1996; 24(3):268-276.

Prognostic factors in patients with metastatic (stage D2) prostate cancer: experience from the Scandinavian
Prostatic Cancer Group Study-2 . Jorgensen T, Kanagasingam Y, Kaalhus O, Tveter KJ, Bryne M, Skjorten F et al.
J Urol 1997; 158(1):164-170.

A review of caveats in statistical nuclear image analysis. Schulerud H, Kristensen GB, Liestol K, Vlatkovic L, Reith A, Albregtsen et al. Analytical Cellular Pathology 1998; 16(2):63-82.

The use of fractal features from the periphery of cell nuclei as a classiﬁcation tool. Nilsen B, Albregtsen F, Danielsen HE. Analytical Cellular Pathology – 2000; 19(1):21-37

Adaptive gray level run length features obtained from class distance matrices. F. Albregtsen, B. Nielsen, H.E. Danielsen Pattern Recognition 2000; 3:738-741.

Prognostic classiﬁcation of early ovarian cancer based on very low dimensionality adaptive texture feature vectors from cell nuclei from monolayers and histological sections. B. Nielsen, F. Albregtsen, W. Kildal and H.E. Danielsen. Anal. Cell Pathol 2001; 23(2):75-88..

Low dimensionality adaptive texture feature vectors from class distance and class difference matrices. B. Nielsen, F. Albregtsen, H.E. Danielsen. IEEE Transactions on medical imaging, 23(1): 73-84, 2004