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Machine Learning for Cancer Diagnosis and Prognosis;Machine Learning for Cancer Diagnosis and Prognosis;This page describes various linear-programming-based machine learning;approaches which have been applied to the diagnosis and prognosis of;breast cancer. This work is the result of a collaboration at the;University of Wisconsin-Madison between;Prof. Olvi L. Mangasarian;of the Computer Sciences Department and;Dr. William H. Wolberg;of the departments of Surgery and Human Oncology.;Here is a copy of the;press release;distributed at the American Cancer Society Science Writers seminar in;March of 1994. It provides a good overview of this research.;Table of Contents; Diagnosis; Prognosis; Bibliography; Citation in the Popular Press; Local Related Links; Other Related Links;Diagnosis;This work grew out of the desire by Dr. Wolberg to accurately diagnose;breast masses based solely on a Fine Needle Aspiration (FNA). He;identified nine visually assessed characteristics of an FNA sample which;he considered;relevant to diagnosis. In collaboration with Prof. Mangasarian and;two of his graduate students, Rudy Setiono and;Kristin Bennett, a;classifier was constructed using the multisurface method (MSM) of pattern;separation on these nine features that;successfully diagnosed 97% of new cases. The resulting data set is;well-known as the;Wisconsin Breast Cancer Data.;The image analysis work began in 1990 with the addition of;Nick Street;to the research team. The goal was to diagnose the sample based on a;digital image of a small section of the FNA slide. The results of;this research have been consolidated into a software system known as;Xcyt, which is currently used by Dr. Wolberg in his clinical;practice. The diagnosis process is now performed as follows:; An FNA is taken from the breast mass. This material is then;mounted on a microscope slide and stained to highlight the cellular;nuclei. A portion of the slide in which the cells are;well-differentiated is then scanned using a digital camera and a;frame-grabber board.; The user then isolates the individual nuclei using Xcyt.;Using a mouse pointer, the user draws the approximate boundary of;each nucleus. Using a computer vision approach known as "snakes",;these approximations then converge to the exact nuclear boundaries.;This interactive process takes between two and five minutes per slide.;Here is an image showing;Xcyt in use.; Once all (or most) of the nuclei have been isolated in this;fasion, the program computes values for each of ten characteristics of;each nuclei, measuring size, shape and texture. The mean, standard;error and extreme values of these features are computed, resulting in;a total of 30 nuclear features for each sample.; Based on a training set of 569 cases, a linear classifier was;constructed to differentiate benign from malignant samples. This;classifier consists of a single separating plane in the space of three;of the features: Extreme Value of Area, Extreme Value of Smoothness,;and Mean Value of Texture. By projecting all the cases onto the;normal of this separating plane, approximate;probability densities of;the benign and;malignant points were constructed. These allow a simple Bayesian;computation of probability of malignancy for new patients. These;densities are shown to the patient, allowing her to judge the;"confidence" of her diagnosis by comparison to hundreds of previous samples.;To date, this system has correctly diagnosed 176 consecutive new;patients (119 benign, 57 malignant). In only eight of those cases did;Xcyt return a "suspicious" diagnosis (that is, an estimated;probability of malignancy between 0.3 and 0.7).;A small subset of the source images used in this research can be found; here. These are very good;test cases for;image segmentation or object recognition algorithms. If your pet;segmentation algorithm can automatically identify all of the nuclei in;these images, please email me (street@cs.wisc.edu) and let's work together.;Prognosis;The second problem considered in this research is that of prognosis,;the prediction of the long-term behavior of the disease. We have;approached prognosis as a function-approximation problem, using input;features -- including those computed by Xcyt;-- to predict a;time of recurrence in malignant patients, using right-censored data.;Our solution is termed;the Recurrence Surface Approximation method (RSA), and utilizes a linear;program to construct a surface which predicts time of recurrence for;new patients. By examining the actual recurrence of those training cases;with similar predicted recurrence times, we can plot the probability of;disease-free survival for various times (out to 10 years) for an;individual patient. This capability has been incorporated into;Xcyt and an example is shown;here.;These survival curves plot the probability of disease-free survival versus;time (in years).;The black disease-free survival curve represents all patients in our;original study; the red curve represents the probability of;disease-free survival for the sample case. This particular case therefore;has an above-average prognosis, with a probability of being disease-free;after 10 years equal to about 80%.;The RSA procedure can also be used to compare the predictive power of;various prognostic factors. Our results indicate that precise,;detailed cytological information of the type provided by Xcyt;gives better prognostic accuracy than the traditional factors Tumor;Size and Lymph Node Status. If corroborated by other researchers,;this result could remove the need for the often painful axillary lymph;node surgery.;Chronological Bibliography;Linked papers are provided in postscript format; if you don't have a;postscript viewer, you can download the file (e.g., shift-click in Netscape);and print it. Abstracts are ASCII text. To obtain papers which are not;linked, please contact the first author.; O.L. Mangasarian, R. Setiono and W.H. Wolberg.; Pattern Recognition via Linear Programming: Theory and; Application to Medical Diagnosis.; In; Proceedings of the Workshop on Large-Scale Numerical; Optimization,; 1989, pages 22-31, Philadelphia, PA. SIAM.; O.L. Mangasarian and W. H. Wolberg.; Cancer Diagnosis via Linear Programming. SIAM News,; Vol. 23, 1990, pages 1 & 18.; W.H. Wolberg and O.L. Mangasarian.; Multisurface Method of Pattern Separation for Medical; Diagnosis Applied to Breast Cytology.; Proceedings of the National Academy of Sciences, U.S.A.,; Vol. 87, 1990, pages 9193-9196.; W.N. Street.; Toward Automated Cancer Diagnosis: An Interactive; System for Cell Feature Extraction.; Technical Report 1052, Computer Sciences Department,; University of Wisconsin, October 1991.; W.H. Wolberg, K.P. Bennett and O.L. Mangasarian.; Brast Cancer Diagnosis and Prognostic Determination; from Cell Analysis.; Manuscript, 1992,; Departments of Surgery and Human Oncology and; Computer Sciences, University of Wisconsin, Madison, WI 53706.; W.H. Wolberg, W.N. Street, and O.L. Mangasarian.; Breast Cytology; Diagnosis via Digital Image Analysis.;Analytical and Quantitative Cytology and Histology,; Vol. 15 No. 6, pages 396-404, December 1993.; (abstract); W.N. Street, W.H. Wolberg and O.L. Mangasarian.;; Nuclear Feature Extraction For Breast Tumor Diagnosis.; In; IS&T/SPIE 1993 International Symposium on Electronic Imaging:; Science and Technology,; volume 1905, pages 861-870, San Jose, CA, 1993.; (abstract); W.H. Wolberg, W.N. Street, and O.L. Mangasarian.; Machine learning; techniques to diagnose breast cancer from fine-needle aspirates.;Cancer Letters; Vol. 77, pages 163-171, 1994.; (abstract); W. N. Street;; Cancer Diagnosis and Prognosis via; Linear-Programming-Based Machine Learning.; Ph.D. Dissertation, University of Wisconsin-Madison, August; 1994.; Available as UW Mathematical Programming Technical Report 94-14.; (abstract); W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian.;; Computerized breast cancer diagnosis and prognosis from fine needle; aspirates.; Archives of Surgery 1995; 130:511-516.; (abstract); W.H. Wolberg, W.N. Street, and O.L. Mangasarian.;; Image analysis and machine learning applied to breast cancer; diagnosis and prognosis.;Analytical and Quantitative Cytology and Histology,; Vol. 17 No. 2, pages 77-87, April 1995.; (abstract); W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian.;; Computer-derived Nuclear Features Distinguish Malignant from Benign; Breast Cytology.;Human Pathology,; Vol. 26, pages 792-796, 1995.; (abstract); W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian.;; Computer-derived Nuclear ``Grade'' and Breast Cancer Prognosis.;Analytical and Quantitative Cytology and Histology,; Vol. 17 No. 4, pages 257-264, August 1995.; (abstract); O.L. Mangasarian, W.N. Street and W.H. Wolberg.;; Breast cancer diagnosis and prognosis via linear programming.;Operations Research,; 43(4), pages 570-577, July-August 1995.; Available as UW Mathematical Programming Technical Report 94-10.; (abstract); W. N. Street, O. L. Mangasarian, and W.H. Wolberg.;; An inductive learning approach to prognostic prediction.;Proceedings of the Twelfth International Conference on; Machine Learning,; A. Prieditis and S. Russell, eds., pages 522-530,; Morgan Kaufmann, 1995.; (abstract); M. W. Teague, W. H. Wolberg, W. N. Street, O. L. Mangasarian,; S. C. Call and D. L. Page.; Indeterminate Fine Needle Aspiration of the Breast:; Image Analysis Aided Diagnosis.; Cancer,; submitted.; (abstract); W. N. Street, O. L. Mangasarian, and W. H. Wolberg.;; Individual and collective prognostic prediction.; Technical Report 96-01, Computer Sciences Department, University of; Wisconsin, Madison, WI, January 1996. Submitted to ICML and AAAI conferences.; (abstract);Citation in the Medical and Popular Press; News from Medicine segment,; CNN Prime News, March 12, 1994.; Breast Biopsy Without Surgery.; Tim Friend,; USA Today,; March 24, 1994.; Cancer Detection Imitates Oil Prospecting.; Joe Manning,; Milwaukee Sentinel,; March 24, 1994.; Analyzing Breast Cancer.; Detroit News,; March 28, 1994.; A High-tech Cancer Hunt.; Marilynn Marchione,; Milwaukee Journal,; March 28, 1994.; Computerized Interpretation of Breast FNA Biopsies: Progress Reported,; Oncology Times,; April 1994.; Computer Program Hunts Breast Cancer,; Ruth SoRelle,; Houston Chronicle,; April 22, 1994.; Computer Program May Improve Interpretation of Aspirate,; Oncology News International,; May 1994.; New Data Suggest Needle Biopsies Could Replace Surgical Biopsy; for Diagnosing Breast Cancer.; Journal of the American Medical Association,; Medical News & Perspectives column, June 9, 1994, Vol. 271, No. 22.; Diagnosis Via Image Analysis and Machine Learning,; Cope,; September/October 1994.; Computer Seeks Out Breast Cancer,; Madison Capital Times,; January 17, 1995.; Computer-Aided Cancer Prediction,; Los Angeles Times,; January 25, 1995.;Local Related Links;; UW Mathematical Programming Group;; UW Machine Learning Group;; UW Medical School;Other Related Links;; The National Library of Medicine (NLM);; University of Nevada Center for Biomedical Modeling Research;; OncoLink;; Washington University Institute for Biomedical Computing;paulb@cs.wisc.edu;