Diagnostic reproducibility of tumour budding in colorectal cancer: a multicentre,multinational study using virtual microscopy

Puppa G, Senore C, Sheahan K, Vieth M, Lugli A, Zlobec I, Pecori S, Wang L M, Langner C, Mitomi H, Nakamura T, Watanabe M, Ueno H, Chasle J, Conley S A, Herlin P, Lauwers G Y & Risio M
(2012) Histopathology  61, 562–575 Diagnostic reproducibility of tumour budding in colorectal cancer: a multicentre, multinational study using virtual microscopy Aims: Despite the established prognostic relevance of tumour budding in colorectal cancer, the reproducibility of the methods reported for its assessment has not yet been determined, limiting its use and reporting in routine pathology practice. Methods and results: A morphometric system within telepathology was devised to evaluate the reproducibility of the various methods published for the assessment of tumour budding in colorectal cancer. Five methods were selected to evaluate the diagnostic reproducibility among 10 investigators, using haematoxylin and eosin (H&E) and AE1‐3 cytokeratin‐immunostained, whole‐slide digital scans from 50 pT1–pT4 colorectal cancers. The overall interobserver agreement was fair for all methods, and increased to moderate for pT1 cancers. The intraobserver agreement was also fair for all methods and moderate for pT1 cancers. Agreement was dependent on the participants’ experience with tumour budding reporting and performance time. Cytokeratin immunohistochemistry detected a higher percentage of tumour budding‐positive cases with all methods compared to H&E‐stained slides, but did not influence agreement levels. Conclusions: An overall fair level of diagnostic agreement for tumour budding in colorectal cancer was demonstrated, which was significantly higher in early cancer and among experienced gastrointestinal pathologists. Cytokeratin immunostaining facilitated detection of budding cancer cells, but did not result in improved interobserver agreement.     

Integrating multiple-domain rules for disease classification

In psychiatry, clinicians use criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to diagnose mental disorders. Most criteria sets have several symptom domains, and in order to be diagnosed, an individual must meet the minimum number of symptoms required by each domain. Some efforts are now focused on adding biomarkers to these symptom domains to facilitate the detection of and highlight the neurobiological basis of psychiatric disorders. Thus, a new criteria set may consist of both clinical symptom counts in several domains and continuous biomarkers. In this paper, we propose a method to integrate classification rules from multiple data sources to estimate an optimal criteria set. Each domain-specific rule can be counts of symptoms, a linear function of symptoms, or even nonparametric. The overall classification rule is the intersection of these domain-specific rules. Based on examining the expected population loss function, we propose two iterative algorithms using either support vector machines or logistic regression to fit intersection rules consistent with the Diagnostic and Statistical Manual of Mental Disorders. In simulation studies, these proposed methods are comparable with the true decision rule. The methods are applied to the motivating study to construct a criteria set for complicated grief. The developed criteria set shows a substantial improvement in sensitivity and specificity compared to the current standards on an independent validation study.     

Moderately Hypofractionated Helical IMRT,FDG–PET/CT-guided,for Progressive Malignant Pleural Mesothelioma in Patients With Intact Lungs

Introduction

The objective of this study was to present the outcomes of moderately hypofractionated helical intensity-modulated radiation therapy (HT) with/without simultaneous integrated boost (SIB) on fluorodeoxyglucose-positron emission tomography (FDG-PET) positive areas (gross tumor volume [GTV]-PET) for patients with progressive malignant pleural mesothelioma (MPM) after previous treatments.