Computer-aided cytogenetic method of cancer diagnosis
AssigneeNew Jersey Institute of Technology
InventorsRoman Andrushkiw, Natalya V. Boroday, Dmitriy A. Klyushin, Yuriy I. Petunin
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Abstract
The present invention relates to noninvasive diagnostic systems for cancer detection comprising RGB-imaging of cancer cells, buccal epithelium cells and uses of the system for drug discovery. The present invention provides novel algorithms for the detection of malignancy associated changes of buccal epithelial cells based on RGB analysis.
Claims6
A method for diagnosis of breast cancer and fibroadenomatosis, the method comprising the steps:
- a) obtaining scanograms from a sample of buccal epithelium from a confirmed breast cancer patient and/or a confirmed fibroadenomatosis patient;
- b) assigning a green component and a red component for each scanogram;
- c) finding the center;
- d) constructing concentric squares;
- e) computing the average p-statistics between the squares in breast cancer training samples and fibroadenomatosis training samples;
- f) finding minimal p-statistics and maximal p-statistics,
- wherein for an investigated scanogram, compute N(P), wherein if N(P)>0, then breast cancer;
- wherein if N(P)=0, then do not make any decision;
- wherein if N(P)<0, then fibroadenomatosis;
- wherein N(P) is the difference between number of values that do not belong to their corresponding confidence interval for breast cancer and number of values that do not belong to their corresponding confidence interval for fibroadenomatosis;
- thereby determining a diagnosis for breast cancer or fibroadenomatosis.
A computer-controlled system comprising a digital imager that provides a scanogram of a cell, and an operably linked controller comprising computer-implemented programming implementing a method for diagnosis of breast cancer and fibroadenomatosis, the method comprising the steps:
- a) obtaining scanograms from a sample of buccal epithelium from a confirmed breast cancer patient and/or a confirmed fibroadenomatosis patient;
- b) assigning a green component and a red component for each scanogram;
- c) finding the center;
- d) constructing concentric squares;
- e) computing the average p-statistics between the squares in breast cancer training samples and fibroadenomatosis training samples;
- f) finding minimal p-statistics and maximal p-statistics,
- wherein for an investigated scanogram, compute N(P), wherein if N(P)>0, then breast cancer;
- wherein if N(P)=0, then do not make any decision;
- wherein if N(P)<0, then fibroadenomatosis;
- wherein N(P) is the difference between number of values that do not belong to their corresponding confidence interval for breast cancer and number of values that do not belong to their corresponding confidence interval for fibroadenomatosis;
- thereby determining a diagnosis for breast cancer or fibroadenomatosis.
Dependencies are inferred from the claim text.