Misleading terminology in pathology: lack of definitions hampers communication.

Accurate terminology is the basis for clear communication among specialists and relies upon precise definitions, indispensable for the WHO Classification of Tumours. We identified a number of potentially misleading terms in use in the recently published WHO Classification of Tumours, 5th edition. From a list of common sources that might be consulted by specialists in the pathology field, we searched for definitions of the terms. Where at least two sources provided definitions for a term, we assessed their level of agreement using an ad hoc developed scale. We identified 26 potentially misleading terms from the 5th edition Digestive System and Breast Tumour Books, and 16 sources. The number of definitions provided by the sources ranged from no definition (for four terms) to ten (for two terms). No source had definitions for all terms. We found only 111 (27%) of a possible 416 definitions. Where two or more definitions were present for a term, the level of agreement between them was judged to be high. There was a paucity of definitions for potentially misleading terms in the sources consulted, but there was a good agreement when two or more definitions were present. In a globalized world where healthcare workers and learners in many fields may access these sources to learn about terminology with which they are unfamiliar, the lack of definitions is a hindrance to a precise understanding of classification in the speciality of pathology and to clear communication between specialist groups.

Counting Mitoses With Digital Pathology in Breast Phyllodes Tumors.

CONTEXT.­: Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification. OBJECTIVE.­: To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis. DESIGN.­: Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted. RESULTS.­: Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size. CONCLUSIONS.­: Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.

Multiplex immunohistochemistry/immunofluorescence (mIHC/IF) for PD-L1 testing in triple-negative breast cancer: a translational assay compared with conventional IHC.

BACKGROUND: Programmed death-ligand 1 (PD-L1) monoclonal antibody therapy has recently gained approval for treating metastatic triple-negative breast cancer (TNBC) -, in particular in the PD-L1+ patient subgroup of the recent IMpassion130 trial. The SP142 PD-L1 antibody clone was used as a predictive assay in this trial, but this clone was found to be an outlier in previous harmonisation studies in lung cancer. AIMS: To address the comparability of PD-L1 clones in TNBC, we evaluated the concordance between conventional immunohistochemistry (IHC) and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) that allowed simultaneous quantification of three different PD-L1 antibodies (22C3, SP142 and SP263). METHODS: Our cohort comprised 25 TNBC cases, 12 non-small-cell lung carcinomas and 8 other cancers. EpCAM labelling was used to distinguish tumour cells from immune cells. RESULTS: Moderate-to-strong correlations in PD-L1 positivity were found between results obtained through mIHC/IF and IHC. Individual concordance rates in the study ranged from 67% to 100%, with Spearman's rank correlation coefficient values up to 0.88. CONCLUSIONS: mIHC/IF represents a promising tool in the era of cancer immunotherapy, as it can simultaneously detect and quantify PD-L1 labelling with multiple antibody clones, and allow accurate evaluation of tumour and immune cells. Clinicians and pathologists require this information to predict patient response to anti-PD-1/PD-L1 therapy. The adoption of this assay may represent a significant advance in the management of therapeutically challenging cancers. Further analysis and assay harmonisation are essential for translation to a routine diagnostic setting.

Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer.

BACKGROUND: Ki67 positivity in invasive breast cancers has an inverse correlation with survival outcomes and serves as an immunohistochemical surrogate for molecular subtyping of breast cancer, particularly ER positive breast cancer. The optimal threshold of Ki67 in both settings, however, remains elusive. We use computer assisted image analysis (CAIA) to determine the optimal threshold for Ki67 in predicting survival outcomes and differentiating luminal B from luminal A breast cancers. METHODS: Quantitative scoring of Ki67 on tissue microarray (TMA) sections of 440 invasive breast cancers was performed using Aperio ePathology ImmunoHistochemistry Nuclear Image Analysis algorithm, with TMA slides digitally scanned via Aperio ScanScope XT System. RESULTS: On multivariate analysis, tumours with Ki67 ≥14% had an increased likelihood of recurrence (HR 1.941, p=0.021) and shorter overall survival (HR 2.201, p=0.016). Similar findings were observed in the subset of 343 ER positive breast cancers (HR 2.409, p=0.012 and HR 2.787, p=0.012 respectively). The value of Ki67 associated with ER+HER2-PR<20% tumours (Luminal B subtype) was found to be <17%. CONCLUSION: Using CAIA, we found optimal thresholds for Ki67 that predict a poorer prognosis and an association with the Luminal B subtype of breast cancer. Further investigation and validation of these thresholds are recommended.