Torsten Jarkrans

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.

Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried out from Feulgen stained histological tissue sections. The object based features were extracted from gray scale images, binary images obtained by thresholding the nuclei and several other images derived through image processing operations. The textural features were based on the spatial gray‐tone co‐occurrence probability matrices and the graph based features were extracted from the minimum spanning trees connecting all nuclei. The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. The results indicate reasonably good reproducibility for the best sets of features. In addition, image analysis based grading showed almost identical correlation to mitotic density and expression of p53 protein as subjective grading. It should thus be possible to use this kind of image analysis as a prognostic tool for bladder carcinoma.

Multifocal breast carcinoma

The present study comprises 173 breast cancers in women living in Sweden. Mastectomy was performed and the surgical specimens were thoroughly scrutinized histopathologically with special attention given to the mammary tissue outside the dominant mass. Fifty-two patients (30 percent) had multifocal growth in the same breast as the dominant breast cancer. The patients have been followed up for 3 years after operation and compared with age-matched controls. The multifocal growth was not correlated with age, size of the tumor or death of the patient within 3 years. A previous diagnosis of benign breast disease was significantly more common among the cancer patients than among the controls, although it was unrelated to multifocal growth. This study stresses the importance of considering the high incidence of multifocal growth of breast cancer when discussing treatment by operation less radical than mastectomy.

Segmentation of cervical cells: Detection of overlapping cell nuclei☆

Abstract A method for detecting overlapping cell nuclei in Pap-stained cervical smears is described. The algorithm uses information both from the nuclear contour and from the density profile of the nucleus. For the analysis of the nuclear contour the smoothed difference chain code is used. From this code any significant concavities along the contour are found and a number of features describing their size and relative location are computed. If these clearly indicate an overlap situation the object is classified as an overlap. Otherwise a density profile is generated along a line orthogonal to the line joining the two major concavities. This profile is checked for peaks and valleys indicative of an overlap situation and a new set of features are generated and used to classify the object as single or overlapping. The algorithm performed reasonably well when tested on an independent test set of about 240 cell images.

CELLO - An Interactive System for Image Analysis

CELLO is an interactive, command oriented image analysis system. It has been developed for applications in automated cytology i.e. for the development of microscopic cell image analysis algorithms but is very flexible and can be used for a wide range of image analysis applications.

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.

Minimum Spanning Trees (MST) as a Tool for Describing Tissue Architecture when Grading Bladder Carcinoma

In this pilot study we have investigated the possible use of minimum spanning trees, MST, as a way of quantitatively describing the tissue architecture when developing a computer program for malignancy grading of transitional cell bladder carcinoma. The MST was created by connecting the centre points of the nuclei in the tissue section image. These nuclei were found by thresholding the image at an automatically determined threshold followed by a connected component labeling and a watershed algorithm for separation of overlapping nuclei. Clusters were defined in the MST by thresholding the edge lengths. For these clusters geometric and densitometric features were measures. These features were compared by multivariate statistical methods to the subjective grading by the pathologists and the resulting correspondence was 85% on a material of 40 samples.

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.

ILIAD — a High Level Dialogue System for Picture Analysis

ILIAD is a comprehensive digital picture analysis system that interacts with the user, through a high level language. The system is constructed to be a general tool for picture processing and picture analysis in different environments. It runs on a general purpose computer but it also contains provisions for utilizing different kinds of special hardware.

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.