Data processing: artificial intelligence – Miscellaneous
Reexamination Certificate
2006-10-13
2008-12-02
Holmes, Michael B (Department: 2129)
Data processing: artificial intelligence
Miscellaneous
C706S021000, C435S007230
Reexamination Certificate
active
07461048
ABSTRACT:
Methods and systems are provided that use clinical information, molecular information and computer-generated morphometric information in a predictive model for predicting the occurrence (e.g., recurrence) of a medical condition, for example, cancer. In an embodiment, a model that predicts prostate cancer recurrence is provided, where the model is based on features including seminal vesicle involvement, surgical margin involvement, lymph node status, androgen receptor (AR) staining index of tumor, a morphometric measurement of epithelial nuclei, and at least one morphometric measurement of stroma. In another embodiment, a model that predicts clinical failure post prostatectomy is provided, wherein the model is based on features including biopsy Gleason score, lymph node involvement, prostatectomy Gleason score, a morphometric measurement of epithelial cytoplasm, a morphometric measurement of epithelial nuclei, a morphometric measurement of stroma, and intensity of androgen receptor (AR) in racemase (AMACR)-positive epithelial cells.
REFERENCES:
patent: 5993388 (1999-11-01), Kattan et al.
patent: 6409664 (2002-06-01), Kattan et al.
patent: 6410043 (2002-06-01), Steiner et al.
patent: 6413533 (2002-07-01), Steiner et al.
patent: 6413535 (2002-07-01), Steiner et al.
patent: 6545034 (2003-04-01), Carson et al.
patent: 6545139 (2003-04-01), Thompson et al.
patent: 6828429 (2004-12-01), Srivastava et al.
patent: 6906320 (2005-06-01), Sachs et al.
patent: 6949342 (2005-09-01), Golub et al.
patent: 7052908 (2006-05-01), Chang
patent: 7071303 (2006-07-01), Lin
patent: 7105560 (2006-09-01), Carson et al.
patent: 7105561 (2006-09-01), Carson et al.
patent: 7129262 (2006-10-01), Carson et al.
patent: 7151100 (2006-12-01), Carson et al.
patent: 7189752 (2007-03-01), Carson et al.
patent: 7211599 (2007-05-01), Carson et al.
patent: 7229774 (2007-06-01), Chinnaiyan et al.
patent: 7245748 (2007-07-01), Degani et al.
patent: 7321881 (2008-01-01), Saidi et al.
patent: 7332290 (2008-02-01), Rubin et al.
patent: 7361680 (2008-04-01), Carson et al.
patent: 7393921 (2008-07-01), Lin
Raf kinase inhibitor protein: a putative molecular target in prostate cancer Singh, S.; Malik, B.K.; Sharma, D.K.; India Annual Conference, 2004. Proceedings of the IEEE INDICON 2004. First Dec. 20-22, 2004 pp. 406-409 Digital Object Identifier 10.1109/INDICO.2004.1497783.
Improving risk grouping rules for prostate cancer patients with optimization Churilov, L.; Bagirov, A.M.; Schwartz, D.; Smith, K.; Dally, M.; System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on Jan. 5-8, 2004 pp. 9 pp. Digital Object Identifier 10.1109/HICSS.2004.1265355.
MRI alone simulation for conformal radiation therapy of prostate cancer: technical aspects Pasquier, D.; Betrouni, N.; Vermandel, M.; Lacornerie, T.; Lartigau, E.; Rousseau, J.; Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE Aug. 2006 pp. 160-163.
A Model-Aided Segmentation in Urethra Identification Based on an Atlas Human Autopsy Image for Intensity Modulated Radiation Therapy Yan Song; Muller, B.; Burman, C.; Mychalcazk, B.; Yulin Song; Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE Aug. 22-26, 2007 pp. 3532-3535.
Automatic detection of three radio-opaque markers for prostate targeting using EPID during external radiation therapy Pouliot, S.; Zaccarin, A.; Laurendeau, D.; Pouliot, J.; Image Processing, 2001. Proceedings. 2001 International Conference on vol. 2, Oct. 7-10, 2001 pp. 857-860 vol. 2 Digital Object Identifier 10.1109/ICIP.2001.958629.
MRI alone simulation for conformal radiation therapy of prostate cancer: technical aspects Pasquier, D.; Betrouni, N.; Vermandel, M.; Lacornerie, T.; Lartigau, E.; Rousseau, J.; Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE Aug. 30, 2006-Sep. 3, 2006 pp. 160-163.
Ultrasonic multifeature tissue characterization for the early detection of prostate cancer Scheipers, U.; Lorenz, A.; Pesavento, A.; Ermert, H.; Sommerfeld, H.-J.; Garcia-Schurmann, M.; Kuhne, K.; Senge, T.; Philippou, S.; Ultrasonics Symposium, 2001 IEEE vol. 2, Oct. 7-10, 2001 pp. 1265-1268 vol. 2.
Ablameyko, S., et al. “From cell image segmentation to differential diagnosis of thyroid cancer”, Pattern Recognition, 2002. Proceedings. 16thInternational Conference on Quebec City, Que., Canada Aug. 11-15, 2002, Los Alamitos, CA, USA, IEEE Compout. Soc, Us, vol. 1, Aug. 11, 2002, pp. 763-766.
Antonini, M., et al., “Image coding using wavelet transform,”IEEETrans. Image Process., vol. 1, pp. 205-220, 1992.
Baatz, M., et al., “Multiresolution Segmentation—An Optimization Approach for High Quality Multi-scale Image Segmentation,” InAngewandte Geographische InformationsverarbeitungXII, Strobl, J., Blaschke, T., Griesebner, G. (eds.), Wichmann—Verlag, Heidelberg, pp. 12-23, 2000.
Berry, DA, Cirrincione C, Henderson IC, et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer.Jama2006;295:1658-6714.
Biganzoli, E., et al. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach.Stat Med, 1998.
Brown, et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A 97:262-7, 2000.
Brown, S.F., et al. On the use of artificial neural networks for the analysis of survival data.IEEE Trans. on Neural Networks, 8(5):1071-1077, 1997.
Burke, H.B., et al. Artificial neural networks improve the accuracy of cancer survival prediction.Cancer, 97(4): pp. 857-862, 1997.
Camp, R., G. G. Chung, and D. L. Rimm, “Automated subcellular localization and quantification of protein expression in tissue microarrays,”Nature Medicine, vol. 8, pp. 1323-1327, 2002.
Chen, CD, Welsbie DS, Tran C, et al. Molecular determinants of resistance to antiandrogen therapy. Nat Med 2004; 10:33-91.
Cooperberg, MR, Broering JM, Litwin MS, et al. The contemporary management of prostate cancer in the United States: lessons from the cancer of the prostate strategic urologic research endeavor (CapSURE), a national disease registry. J Urol 2004;171:1393-4014.
Office Action corresponding to U.S. Appl. No. 10/991,240, mailed May 28, 2008, 22 pgs.
Daubechies, I.,Ten Lectures on Wavelets, SIAM, Philadelphia, PA, 1992, pp. 198-202 and pp. 254-256.
Davidow, E., et al. Advancing drug discovery through systems biology.Drug Discov Today, 8:175-183, 2003.
Definiens, Cellenger Architecture: A Technical Review, Apr. 2004.
deSilva, C.J.S., et al. Artificial neural networks and breast cancer prognosis.Australian Comput. J. 26:78-81, 1994.
Dhanasekaran, S.M., Barrette, T.R., Ghosh, D., Shah, R., Varambally, S., Kurachi, K., Pienta, K.J., Rubin, M.A., and Chinnaiyan, A.M. 2001. Delineation of prognostic biomarkers in prostate cancer. Nature 412:822-826.
Diamond, J., et al., “The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia,”Human Pathology, vol. 35, pp. 1121-1131, 2004.
Duda, R.O., et al.,Pattern Classification, 2nded. Wiley, New York, 2001, pp. 483-484.
Egmont-Petersen, M. et al., “Image Processing with Neural Networks-a-Review”, Pattern Recognition, Elsevier, Kidlington, GB, vol. 35, No. 10, Oct. 2002, pp. 2279-2301.
Eskelinen, M., Lipponen, P., Majapuro, R., and Syrjanen, K. 1991. Prognostic factors in prostatic adenocarcinoma assessed by means of quantitative histology. Eur Urol 19:274-278.
Fayyad, U.M., et al. Knowledge Discovery and Data Mining : Towards a unifying framework. InProceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, 1996. AAAI Press.
Freedland, S.J., Humphreys, E.B., Mangold, L.A., Eisenberger, M., Dorey, F.J., Walsh, P.C., and Partin, A.W. 2005. Risk of prostate cancer-specific mortality following biochemical recurre
Saidi Olivier
Teverovskiy Mikhail
Verbel David A.
Aureon Laboratories, Inc.
Holmes Michael B
Mintz Levin Cohn Ferris Glovsky & Popeo P.C.
LandOfFree
Systems and methods for treating, diagnosing and predicting... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Systems and methods for treating, diagnosing and predicting..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Systems and methods for treating, diagnosing and predicting... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4043339