TTF-1/p63-positive poorly differentiated NSCLC: A histogenetic hypothesis from the basal reserve cell of the terminal respiratory unit

TTF-1 is expressed in the alveolar epithelium and in the basal cells of distal terminal bronchioles. It is considered the most sensitive and specific marker to define the adenocarcinoma arising from the terminal respiratory unit (TRU). TTF-1, CK7, CK5/6, p63 and p40 are useful for typifying the majority of non-small-cell lung cancers, with TTF and CK7 being typically expressed in adenocarcinomas and the latter three being expressed in squamous cell carcinoma. As tumors with coexpression of both TTF-1 and p63 in the same cells are rare, we describe different cases that coexpress them, suggesting a histogenetic hypothesis of their origin. We report 10 cases of poorly differentiated non-small-cell lung carcinoma (PD-NSCLC). Immunohistochemistry was performed by using TTF-1, p63, p40 (∆Np63), CK5/6 and CK7. EGFR and BRAF gene mutational analysis was performed by using real-time PCR. All the cases showed coexpression of p63 and TTF-1. Six of them showing CK7+ and CK5/6− immunostaining were diagnosed as “TTF-1+ p63+ adenocarcinoma”. The other cases of PD-NSCLC, despite the positivity for CK5/6, were diagnosed as “adenocarcinoma, solid variant”, in keeping with the presence of TTF-1 expression and p40 negativity. A “wild type” genotype of EGFR was evidenced in all cases. TTF1 stained positively the alveolar epithelium and the basal reserve cells of TRU, with the latter also being positive for p63. The coexpression of p63 and TTF-1 could suggest the origin from the basal reserve cells of TRU and represent the capability to differentiate towards different histogenetic lines. More aggressive clinical and morphological features could characterize these “basal-type tumors” like those in the better known “basal-like” cancer of the breast

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Purpose: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods: Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results: In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. Conclusions: The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the syste

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Content-based CT image retrieval system using deep learning: Preliminary assessment of its accuracy for classifying lesion patterns and retrieving similar cases among patients with diffuse lung diseases

Practical image retrieval systems must fully use image databases. We investigated the accuracy of our content-based computer tomography (CT) image retrieval system (CB-CTIRS) for classifying lesion patterns and retrieving similar cases in patients with diffuse lung diseases. The study included 503 individuals, with 328 having diffuse lung disease and 175 having normal chest CT scans. Among the former, we randomly selected ten scans that revealed one of five specific patterns [consolidation, ground-glass opacity (GGO), emphysema, honeycombing, or micronodules: two cases each]. Two radiologists separated the squares into six categories (five abnormal patterns and one normal pattern) to create a reference standard. Subsequently, each square was entered into the CB-CTIRS, and the F-score used to classify squares was determined. Next, we selected 15 cases (three per pattern) among the 503 cases, which served as the query cases. Three other radiologists graded the similarity between the retrieved and query cases using a 5-point grading system, where grade 5 = similar in both the opacity pattern and distribution and 1 = different therein. The F-score was 0.71 for consolidation, 0.63 for GGO, 0.74 for emphysema, 0.61 for honeycombing, 0.15 for micronodules, and 0.67 for normal lung. All three radiologists assigned grade 4 or 5 to 67.7% of retrieved cases with consolidation, emphysema, or honeycombing, and grade 2 or 3 to 67.7% of the retrieved cases with GGO or micronodules. The retrieval accuracy of CB-CTIRS is satisfactory for consolidation, emphysema, and honeycombing but not for GGO or micronodules

A MIQE-Compliant Real-Time PCR Assay for Aspergillus Detection

PMCID: PMC3393739This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Focal Spot, Spring 1977

https://digitalcommons.wustl.edu/focal_spot_archives/1017/thumbnail.jp