Morphological assessment of the degree of differentiation has been shown in numerous studies to provide useful prognostic information in breast cancer, but until recently histological grading has not been accepted as a routine procedure, mainly because of perceived problems with reproducibility and consistency. In the Nottingham/Tenovus Primary Breast Cancer Study the most commonly used method, described by Bloom & Richardson, has been modified in order to make the criteria more objective. The revised technique involves semiquantitative evaluation of three morphological features--the percentage of tubule formation, the degree of nuclear pleomorphism and an accurate mitotic count using a defined field area. A numerical scoring system is used and the overall grade is derived from a summation of individual scores for the three variables: three grades of differentiation are used. Since 1973, over 2200 patients with primary operable breast cancer have been entered into a study of multiple prognostic factors. Histological grade, assessed in 1831 patients, shows a very strong correlation with prognosis; patients with grade I tumours have a significantly better survival than those with grade II and III tumours (P less than 0.0001). These results demonstrate that this method for histological grading provides important prognostic information and, if the grading protocol is followed consistently, reproducible results can be obtained. Histological grade forms part of the multifactorial Nottingham prognostic index, together with tumour size and lymph node stage, which is used to stratify individual patients for appropriate therapy.

Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.

Depending on the initiating stimulus, cancer cell death can be immunogenic or nonimmunogenic. Immunogenic cell death (ICD) involves changes in the composition of the cell surface as well as the release of soluble mediators, occurring in a defined temporal sequence. Such signals operate on a series of receptors expressed by dendritic cells to stimulate the presentation of tumor antigens to T cells. We postulate that ICD constitutes a prominent pathway for the activation of the immune system against cancer, which in turn determines the long-term success of anticancer therapies. Hence, suboptimal regimens (failing to induce ICD), selective alterations in cancer cells (preventing the emission of immunogenic signals during ICD), or defects in immune effectors (abolishing the perception of ICD by the immune system) can all contribute to therapeutic failure. We surmise that ICD and its subversion by pathogens also play major roles in antiviral immune responses.

Immunogenic Cell Death in Cancer Therapy

Pathological prognostic factors in breast cancer.

In 1982 we constructed a prognostic index for patients with primary, operable breast cancer This index was based on a retrospective analysis of 9 factors in 387 patients Only 3 of the factors (tumour size, stage of disease, and tumour grade) remained significant on multivariate analysis The index was subsequently validated in a prospective study of 320 patients We now present the results of applying this prognostic index to all of the first 1,629 patients in our series of operable breast cancer up to the age of 70 We have used the index to define three subsets of patients with different chances of dying from breast cancer: 1) good prognosis, comprising 29% of patients with 80% 15-year survival; 2) moderate prognosis, 54% of patients with 42% 15-year survival; 3) poor prognosis, 17% of patients with 13% 15-year survival The 15-year survival of an age-matched female population was 83%

The Nottingham Prognostic Index in primary breast cancer.

Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database . The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.

A Dataset for Breast Cancer Histopathological Image Classification

Cardiac glycosides and breast cancer.

The inter- and intra-observer reproducibility of three grading systems for breast cancer has been analysed: those of the World Health Organisation, of Black, and of Hartveit. In addition, the components forming the basis of these grading systems were analysed separately with regard to reproducibility and interrelationship. In this analysis, degree of differentiation was also included as a parameter. The grading systems of WHO and of Black gave a stratification of the material into three categories of tumours, but that of Hartveit did not. All three systems had a low inter- and intra-observer reproducibility. Truncated component analysis indicated that the grading systems of WHO and of Black are closely related to each other and to the 'nuclear lobulation' component of Hartveit's system. The components 'pleomorphism', 'mitotic frequency', and 'hyperchromasia' of the WHO system were closely interrelated. Descriptors such as 'differentiation' and 'tubule formation' were interrelated but acted in a different direction from the others.

https://jcp.bmj.com/content/jclinpath/32/10/979.full.pdf

Analysis of reproducibility of subjective grading systems for breast carcinoma.

Cardiac glycosides and breast cancer, revisited.

A new method for measuring differences in nuclear detail in chrome alum gallocyanin-stained nuclei of cells from human breast cancers was compared with conventional subjective grading and classification systems. The new method, termed computerized nuclear morphometry (CNM), gives a multivariate numerical score that correlates well with nuclear atypia and gives a higher reproducibility of classification than do subjective observations with conventional histological preparations. When 100 individual nuclei from each of 137 breast cancers were examined by CNM, there was a broad CNM score variation between patients but a good reproducibility for each tumor. When different parts of the same tumor were sampled, there was good reproducibility between samples, indicating that some breast cancers at least are "geometrically monoclonal." When these cancers were compared by the grading systems of WHO and Black, correlations of 0.43 and 0.48, respectively, were found. There was a poor correlation between CNM and classifications of tumor type, but in general there were high values for CNM in medullary tumors and low values in mucous tumors. Correlations between CNM and tumor progression and prognosis await future study of patients participating in the study.

Computerized Nuclear Morphometry as an Objective Method for Characterizing Human Cancer Cell Populations

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 (Student's 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.

https://acsjournals.onlinelibrary.wiley.com/doi/pdf/10.1002/1097-0142%2819821215%2950%3A12%3C2884%3A%3AAID-CNCR2820501231%3E3.0.CO%3B2-K

Björn Stenkvist

Papers

Cardiac glycosides and breast cancer.

Analysis of reproducibility of subjective grading systems for breast carcinoma.

Cardiac glycosides and breast cancer, revisited.

Computerized Nuclear Morphometry as an Objective Method for Characterizing Human Cancer Cell Populations

Predicting breast cancer recurrence.