ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations

Simple Summary Tumour-infiltrating lymphocytes (TILs) reflect the host's response against tumours. TILs have a strong prognostic effect in the so-called triple-negative (oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 negative) subset of breast cancers and predict a better response when primary systemic (neoadjuvant) treatment is administered. Although they are easy to assess, their quantitative assessment is subject to some inter-observer variation. ONEST (Observers Needed to Evaluate Subjective Tests) is a new way of analysing inter-observer variability and helps in estimating the number of observers required for a more reliable estimation of this phenomenon. This aspect of reproducibility for TILs has not been explored previously. Our analysis suggests that between six and nine pathologists can give a good approximation of inter-observer agreement in TIL assessments. Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation. This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists. In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2-11) are most commonly needed to give a robust estimate of reproducibility. In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses

The Petersen prognostic index revisited in Dukes B colon cancer - Inter-institutional differences

A prognostic index (Petersen index, PI) was created for patients with pT3-4 pN0 M0 (Stage II, Dukes' B) colon cancers to distinguish between patients with better and worse outcome, and to help in recommending adjuvant chemotherapy for high risk patients in this stage. The prognostic value of the PI was evaluated in two independent retrospective series of stage II (Dukes' B) colon cancer patients. The parameters defining the PI (venous invasion, peritoneal involvement, circumferential margin involvement, perforation through the tumour) and performance of the PI were compared in two institutions. The two series of patients consisted of 127 and 87 patients. Venous invasion was more frequently detected at one of the centres (p<0.01) and tumour perforation was more frequent at the other (p<0.01). There were no significant differences in the 5-year survival estimates of all patients (p=0.19), and of either the low PI value groups (p=0.52) or that of the high PI value groups (p=0.99) between the two sites. In contrast, there were significant differences in the survival estimates between patients of the low PI category and those of the high PI category altogether (p<0.01) and in either centre. Although, it was expected that differences in the frequency of the parameters involved in the PI would influence its performance, this was not confirmed by the data. Our results suggest that using the PI may be of value in prognostic factor based therapy selection of colon carcinoma patients

Unifocal, multifocal and diffuse carcinomas: A reproducibility study of breast cancer distribution

Multifocality of invasive breast carcinoma has been associated with prognostic disadvantage. Unifocal, multifocal and diffuse distributions have been recently defined for both inasive carcinomas and in situ components, and these have been combined into categories of prognostic relevance. Eight observers analyzed the same series of 30 megaslides from 29 carcinomas, and had to classify the lesions into the three distribution patterns of unifocal, multifocal or diffuse (or not present/non influential). The reproducibility of the distribution patterns of invasive carcinomas was better than that of the in situ carcinoma components, but was still only fair to moderate on the basis of kappa values. The reproducibility of DCIS was poor to slight with some kappa values reflecting agreement by chance only. The results suggest the definitions of these distribution patterns require refinements for a more reliable and reproducible diagnosis if one wants to associate prognostic information with this variable. © 2012 Elsevier Ltd

International multicenter tool to predict the risk of four or more tumor-positive axillary lymph nodes in breast cancer patients with sentinel node macrometastases

Recently, many centers have omitted routine axillary lymph node dissection (ALND) after metastatic sentinel node biopsy in breast cancer due to a growing body of literature. However, existing guidelines of adjuvant treatment planning are strongly based on axillary nodal stage. In this study, we aim to develop a novel international multicenter predictive tool to estimate a patient-specific risk of having four or more tumor-positive axillary lymph nodes (ALN) in patients with macrometastatic sentinel node(s) (SN). A series of 675 patients with macrometastatic SN and completion ALND from five European centers were analyzed by logistic regression analysis. A multivariate predictive model was created and validated internally by 367 additional patients and then externally by 760 additional patients from eight different centers. All statistical tests were two-sided. Prevalence of four or more tumor-positive ALN in each center's series (P = 0.010), number of metastatic SNs (P < 0.0001), number of negative SNs (P = 0.003), histological size of the primary tumor (P = 0.020), and extra-capsular extension of SN metastasis (P < 0.0001) were included in the predictive model. The model's area under the receiver operating characteristics curve was 0.766 in the internal validation and 0.774 in external validation. Our novel international multicenter-based predictive tool reliably estimates the risk of four or more axillary metastases after identifying macrometastatic SN(s) in breast cancer. Our tool performs well in internal and external validation, but needs to be further validated in each center before application to clinical use

International Multicenter Tool to Predict the Risk of Nonsentinel Node Metastases in Breast Cancer.

BackgroundAxillary treatment of breast cancer patients is undergoing a paradigm shift, as completion axillary lymph node dissections (ALNDs) are being questioned in the treatment of patients with tumor-positive sentinel nodes. This study aims to develop a novel multi-institutional predictive tool to calculate patient-specific risk of residual axillary disease after tumor-positive sentinel node biopsy.MethodsBreast cancer patients with a tumor-positive sentinel node and a completion ALND from five European centers formed the original patient series (N = 1000). Statistically significant variables predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other centers. All statistical tests were two-sided.ResultsNine tumor- and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series.ConclusionsWe present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use