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Background: Histopathological evaluation is a critical component in the management of patients with colorectal cancer (CRC). It is the single most powerful prognostic indicator in CRC and determines if adjuvant chemotherapy is indicated. The aim of this study was to assess if the introduction of a comprehensive standardized pathology proforma improved the quality of histopathology reporting., Methods: A standardized pathology proforma, based on the 1996 minimum dataset for colorectal histopathology reporting, was introduced in our pathology department in 1998. Pathology reports for all colonic resection specimens for 1996 (n = 85) and 2000 (n = 86) were identified, retrieved and entered on to database. Comparison was made with the minimum dataset published in the 1996 guidelines for the management of colorectal cancer., Results: Demographic details were complete in all cases. Clinical data was incomplete in 57 (67%) patients in 1996 and 63 (73%) in 2000 (ns; chi2). There were 24 (28%) (7 Abdomino-perineal resections (APER)) and 40 (47%) (17 APER's) rectal specimens for 1996 and 2000, respectively. The presence or absence of pathological background abnormalities were commented on in 18 (21%) reports in 1996 and 80 (93%) reports in 2000 (P < 0.01; Fishers exact test (Fisher)). Histological differentiation was commented on in 73 (86%) and 86 (100%) in 1996 and 2000, respectively (P < 0.01; Fisher). Dukes' stage was stated in 33 (39%) reports in 1996 and 86 (100%) in 2000 (P < 0.01; Fisher) but Dukes' stage was calculable in 84 (99%) in 1996 and 86 reports (100%) for 2000 (ns; Fisher). The apical node was commented on in 34 (40%) reports in 1996 and 85 (99%) reports in 2000 (P < 0.01; Fisher). The median (IQR) number of nodes assessed in 1996 was 8 (5-12) compared to 12 (8-17) in 2000 (P < 0.001; Mann-Whitney (MW)). Complete resection was mentioned in 74 (87%) reports in 1996 and 86 (100%) in 2000 (P < 0.01; Fisher). Regarding rectal specimens, the circumferential resection margin (CRM) was commented on in 19 of 24 specimens in 1996 and 38 of 40 specimens in 2000 (ns; Fisher). Relationship to the peritoneal reflection was commented on in 1 (1%) rectal specimen in 1996 and 30 (35%) in 2000 (P < 0.001; Fisher)., Conclusion: The introduction of a standardized proforma for reporting CRC resection specimens improves the quality of histopathological reporting. This aids decision-making regarding adjuvant chemotherapy or radiotherapy and further surveillance.

Objective: To develop an approach to the prediction of survival in patients with colorectal cancer using nearest neighbor analysis and case-based reasoning., Study Design: A total of 216 patients with full clinicopathologic records and five-year follow-up were the subjects of this study. They were divided into a core database of 162 cases and a test group of 54 cases, with follow-up on all patients. When the patient was still alive at the end of the follow-up period, censored survival time was used. For each of the test cases, the four closest neighbors from the database were retrieved and their median survival time recorded and used as the predicted estimate of survival. Case matching was based on a Euclidean multivariate distance measure for the three best predictor variables: patient age, Dukes stage and tubule configuration. Cases with the smallest distance from the test case were considered to be the most similar. The predicted survival times for the test cases were compared with the actual, observed survival in the test cases to determine the success of this approach., Results: The results showed reasonable concordance between observed and predicted survival figures, although there was a large degree of spread. Classification of cases into < or = 60 and > 60 months' survival showed a correct classification rate of 63%. For the prediction of survival time, the distribution of differences between observed and predicted survival times for the uncensored test cases had a median value of--5 months but also showed a wide dispersion of values. Correlation of observed and predicted survival times, while not reaching statistical significance at P < .05, did show a strong positive association., Conclusion: Case-based approaches to the prediction of survival times in cancer patients are important. The results of the current study illustrate the difficulties in applying this approach to survival data and highlight the complexity of patient information and the inability to accurately predict patient outcome on a small subset of clinicopathologic features. While extensive work needs to be carried out to improve prediction power, this study illustrates the potential for case-based analyses. The ability to retrieve feature-matched cases from hospital patient databases has clear, independent advantages in patient management, but the ability to provide reliable, targeted prognostic estimates on individual cases should be a common goal in medical research.

Objective: To determine whether clinician or hospital caseload affects mortality from colorectal cancer., Design: Cohort study of cases ascertained between 1990 and 1994 by a region-wide colorectal cancer register., Outcome Measures: Mortality within a median follow up period of 54 months after diagnosis., Results: Of the 3217 new patients registered over the period, 1512 (48%) died before 31 December 1996. Strong predictors of survival both in a logistic regression (fixed follow up) and in a Cox's proportional hazards model (variable follow up) were Duke's stage, the degree of tumour differentiation, whether the liver was deemed clear of cancer by the surgeon at operation, and the type of intervention (elective or emergency and curative or palliative intent). In a multilevel model, surgeon's caseload had no significant effect on mortality at 2 years. Hospital workload, however, had a significant impact on survival. The odds ratio for death within 2 years for cases managed in a hospital with a caseload of between 33 and 46 cases per year, 47 and 54 cases per year, and >/=55 cases per year (compared to one with

Automation in histopathology is an attractive concept and recent advances in the application of computerized expert systems and machine vision have made automated image analysis of histological images possible. Systems capable of complete automation not only require the ability to segment tissue features and grade histological abnormalities, but, must also be capable of locating diagnostically useful areas from within complex histological scenes. This is the first stage of the diagnostic process. The object of this study was to develop criteria for the automatic identification of focal areas of colorectal dysplasia from a background of histologically normal tissue. Fields of view representing normal colorectal mucosa (n = 120) and dysplastic mucosa (n = 120) were digitally captured and subjected to image texture analysis. Two features were selected as being the most important in the discrimination of normal and adenomatous colorectal mucosa. The first was a feature of the co-occurrence matrix and the second was the number of low optical density pixels in the image. A linear classification rule defined using these two features was capable of correctly classifying 86 per cent of a series of training images into their correct groups. In addition, large histological scenes were digitally captured, split into their component images, analysed according to texture, and classified as normal or abnormal using the previously defined classification rule. Maps of the histological scenes were constructed and in most cases, dysplastic colorectal mucosa was correctly identified on the basis of image texture: 83 per cent of test images were correctly classified. This study demonstrates that abnormalities in low-power tissue morphology can be identified using quantitative image analysis. The identification of diagnostically useful fields advances the potential of automated systems in histopathology: these regions could than be scrutinized at high power using knowledge-guided image segmentation for disease grading. Systems of this kind have the potential to provide objectivity, unbiased sampling, and valuable diagnostic decision support.

Objective: To examine differences in epithelial cell nuclear texture along the length of normal, well-oriented colorectal glands., Study Design: Histologically normal mucosa was obtained from four patients who had a family history of cancer and no hyperproliferation as assessed by bromodeoxyuridine immunohistochemistry. Serial sections were stained using the Feulgen technique and viewed to identify glands that were sectioned along their entire length. Two glands were sampled from each patient. Each gland was divided into three compartments based on epithelial cell counts along the length of the gland. Within each compartment, 10 nuclei were randomly selected and their nuclear texture features measured using computerized image analysis., Results: Analysis of variance demonstrated that significant differences exist in the nuclear texture of cells in the different compartments of the gland. Discriminant analysis selected three variables that were important and independent in the quantitative discrimination of the gland compartments. These features were related to the elliptical shape, optical density distribution and cooccurrence structure of the chromatin texture within the epithelial nuclei., Conclusion: It is clear that these differences are associated with the spatial variation in proliferation and differentiation status found within colorectal mucosal glands. Nuclear measurements from colorectal epithelium with near-normal architecture should therefore be stratified in relation to position within the gland. Nuclear texture and size measurements within defined compartments may provide a sensitive means of detecting early malignant transformation.

The histopathologic grading of colorectal adenomatous dysplasia is a subjective process. In this study, automated image analysis using knowledge-guided software was used to quantitatively assess colorectal glandular characteristics. Cases of histologically normal mucosa and of low, moderate and high grade dysplasia were examined using the technique. A total of 19 morphometric and densitometric features were measured on each gland. These included gland shape, epithelial area, nuclear stratification and nuclear optical density. Discriminant analysis of the data revealed those morphometric features which provided the best discrimination between the various histologic groups. The area of epithelium occupied by nuclei was the strongest discriminating variable, and this, in combination with a discriminant function derived from the remaining variables, was used to plot cases in bivariate sample space. The plots revealed that the data for normal glands were consistently well separated from dysplastic gland data. Data sets belonging to the various grades of dysplasia showed varying degrees of separation, depending which two histologic groups the discriminant function was based on. This study showed that automated image analysis of complex histologic scenes is possible using knowledge-guided segmentation and that it can provide useful data for the objective classification of colorectal dysplasia.

Scene segmentation in images of sections of colorectal glands is guided by a knowledge-based system. The construction of the knowledge file, processing sequences and computation of a crowding index for adenomatous glands are described. The histologic sections were stained by hematoxylin and eosin, and the image segmentation was difficult. The knowledge-based system has to determine, on the basis of context and logic, which detected object groups belong to which histologic components. Image segmentation was followed by a reconstruction process. Acceptable segmentation was attained in approximately 85% of the glands processed.