Bengt Dahlqvist

PREDICTING BREAST CANCER RECURRENCE

The prognostic value of 435 cytochemical, cytometrical, morphological, epidemiological, and clinical variables was analyzed in a prospective study of 179 breast cancer patients followed for five years after mastectomy. A variable reduction was obtained by first selecting variables correlated with recurrence rate in direct (Students t test) or correlation analysis with consideration of the type of variable analyzed (nominal, interval, ordinal). The 20 variables most strongly correlated with recurrence were analyzed by logistic stepwise regression analysis in order to find out what combination of variables had most discriminatory power in predicting recurrence. It was found that axillary metastization as such was correlated with a combination of variables describing mitotic frequency, size of primary tumor and differentiation of the primary tumor (average cluster size in fine‐needle biopsies). It was also found that there was a strong time dependency in the predictive power of the variables, so that different variable combinations predicted the recurrence rate during the first 2.5 year period (size of axillary metastases and primary tumor, number of lymphocytes around the tumor, mitotic frequency, and degree of differentiation) compared with the second 2.5 year period (variance of DNA content among tumor cell nuclei, number of lymphocytes around the tumor, occurrence of multiple tumors in the operated breast and occurrence of breast cancer among relatives). While other factors previously shown to be correlated with risk of recurrence were also found to be positively correlated here, they were neither as highly predictive as, nor did they increase the predictive value of the above mentioned combined variables. The current study strongly emphasizes that, at the present time, studies of recurrence prediction in human breast cancer should be based on an optimal combination of a number of variables which, independently, influence the prognosis. Further, the current study indicates that prerequisite methods for predicting breast cancer recurrence exist today.

Object Oriented Cell Image Segmentation

A correct segmentation of cell images into nucleus, cytoplasm and background is a prerequi-site for a working automatic pre-screening device for cervical cytology. This paper presents an algorithm for determining the segmentation thresholds. It is based on a-priori assumptions about a cells shape and size and works on one object at a time, disregarding everything else in the image. The algorithm is capable of verifying that the isolated object really looks like a cell; an essential feature in an automatic system. The nucleus and cytoplasm thresholds are decided upon almost independently of each other. The algorithm works by tracking iso-density contours around the object to be isolated and its execution time is thus proportional to the length of the contour rather than the area of the image. Some preliminary results are given and the possibility of efficiently implementing the algorithm in hardware is discussed.

A program for logistic prediction modelling

A computer program has been developed that can be used for analysing a binary outcome variable and a set of regressors of type interval with a logistic (i.e. nonlinear) model.

EFAM: A FORTRAN IV Program for Correlation Coefficients between Variables of more than One Scale Type

A program for computing correlation coefficients of variables from different scale types is presented. The computations are based on the concept of the E-correlation family of coefficients. Such a general coefficient is invariant over permitted transformations of the variables for their respective scale types. The program EFAM is able to consider the E-correlations from interval, ordinal, and nominal scales.

CORALL-2: A Fortran Program for Correlation and Similarity Measures:

This new version of the CORALL program contains around 50 various measures of similarity and correlation-as well as some other elementary statistical measures.

HCLA: A Computer Program for Hierarchical Cluster Analysis

The computer program HCLA for hierarchical cluster analysis is described. It gives the user various possibilities both for strategies and distance and similarity measures.

Computer Assisted Scanning Microscopy In Cytology

The design of an appropriate scanning and digitization equipment for an image analysis system involves many important trade-offs between image quality, scanning speed and system complexity. In the field of cytometry and automated cytology several different kinds of scanners have been proposed and constructed by various research groups. In this paper the basic physical and technical factors that limit the achievable scanner performance are examined and some examples of recent scanner designs are discussed.