An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN)

The histological grading of cervical intraepithelial neoplasia (CIN) remains subjective, resulting in inter- and intra-observer variation and poor reproducibility in the grading of cervical lesions. This study has attempted to develop an objective grading system using automated machine vision. The architectural features of cervical squamous epithelium are quantitatively analysed using a combination of computerized digital image processing and Delaunay triangulation analysis; 230 images digitally captured from cases previously classified by a gynaecological pathologist included normal cervical squamous epithelium (n = 30), koilocytosis (n = 46), CIN 1 (n = 52), CIN 2 (n = 56), and CIN 3 (n=46). Intra- and inter-observer variation had kappa values of 0.502 and 0.415, respectively. A machine vision system was developed in KS400 macro programming language to segment and mark the centres of all nuclei within the epithelium. By object-oriented analysis of image components, the positional information of nuclei was used to construct a Delaunay triangulation mesh. Each mesh was analysed to compute triangle dimensions including the mean triangle area, the mean triangle edge length, and the number of triangles per unit area, giving an individual quantitative profile of measurements for each case. Discriminant analysis of the geometric data revealed the significant discriminatory variables from which a classification score was derived. The scoring system distinguished between normal and CIN 3 in 98.7% of cases and between koilocytosis and CIN 1 in 76.5% of cases, but only 62.3% of the CIN cases were classified into the correct group, with the CIN 2 group showing the highest rate of misclassification. Graphical plots of triangulation data demonstrated the continuum of morphological change from normal squamous epithelium to the highest grade of CIN, with overlapping of the groups originally defined by the pathologists. This study shows that automated location of nuclei in cervical biopsies using computerized image analysis is possible. Analysis of positional information enables quantitative evaluation of architectural features in CIN using Delaunay triangulation meshes, which is effective in the objective classification of CIN. This demonstrates the future potential of automated machine vision systems in diagnostic histopathology. Copyright (C) 2000 John Wiley and Sons, Ltd.

New perspectives on neoplasia and the RNA world

Key tenets of modern biology are the central place of protein in cell regulation and the flow of genetic information from DNA to RNA to protein. However, it is becoming increasingly apparent that genomes are much more complex than hitherto thought with remarkably complex regulatory systems. The notion that the fraction of the genome involved in coding protein is all that matters is increasingly being questioned as the roles of non-coding RNA (ncRNA) in cellular systems becomes recognised. The RNA world, including microRNA (miRNA), small inhibitory RNA (siRNA) and other RNA species, are now recognised as being crucial for the regulation of chromatin structure, gene expression, mRNA processing and splicing, mRNA stability and translational control. Furthermore such ncRNA systems may be perturbed in disease states and most notably in neoplasia, including in haematological malignancies. Here the burgeoning evidence for a role of miRNA in neoplasia is reviewed and the importance of understanding the RNA world emphasised. Copyright (c) 2005 John Wiley & Sons, Ltd.

Clinical and immunohistochemical assessment of vulval intraepithelial neoplasia following photodynamic therapy using a novel bioadhesive patch-type system loaded with 5-aminolevulinic acid

Background: The work in this study appraised photodynamic treatment (PDT) as a treatment method for vulval intraepithelial neoplasia (VIN) using a novel bioadhesive patch to deliver aminolevulinic acid. An analysis of changes in expression of apoptotic and cell cycle proteins (p53, p21, Mdm2, Blc-2, Bax, Ki-67) in response to PDT was evaluated. Methods: PDT was performed using non-laser light, either as a one or two-cycle treatment, with clinical and pathological assessment following after 6 weeks. Twenty-three patients with 25 VIN lesions underwent 49 cycles of PDT Patches were designed to conform to uneven vulval skin and contained 38 mg cm(-2) aminolevulinic acid. Assessment was carried out at 6 weeks post-treatment. Patient-based treatment assessment, along with clinical and pathological changes, were monitored. Immunohistochemical staining was used to elucidate a possible biomolecular basis for induced cellular changes. Results: Most patients (52%) reported a symptomatic response, with normal pathology restored in 38% of lesions. The patch was easy to apply and remove, causing minimal discomfort. Fluorescence inspection confirmed protoporphyrin accumulation. Pain during implementation of PDT was problematic, necessitating some form of local analgesia. Changes in expression of cell cycle and apoptotic-related proteins suggested involvement of apoptotic pathways. Down regulation of p21 and inverse changes in Bcl-2 and Bax were key findings. Conclusion: Treatment of VIN lesions using a novel bioadhesive patch induced changes in cell cycle and apoptotic proteins in response to PDT with possible utilisation of apoptotic pathways. The efficacy of PDT in treating VIN could be improved by a better understanding of these apoptotic mechanisms, the influence of factors, such as HPV status, and of the need for effective pain management.

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

This paper introduces an automated computer- assisted system for the diagnosis of cervical intraepithelial neoplasia (CIN) using ultra-large cervical histological digital slides. The system contains two parts: the segmentation of squamous epithelium and the diagnosis of CIN. For the segmentation, to reduce processing time, a multiresolution method is developed. The squamous epithelium layer is first segmented at a low (2X) resolution. The boundaries are further fine tuned at a higher (20X) resolution. The block-based segmentation method uses robust texture feature vectors in combination with support vector machines (SVMs) to perform classification. Medical rules are finally applied. In testing, segmentation using 31 digital slides achieves 94.25% accuracy. For the diagnosis of CIN, changes in nuclei structure and morphology along lines perpendicular to the main axis of the squamous epithelium are quantified and classified. Using multi-category SVM, perpendicular lines are classified into Normal, CIN I, CIN II, and CIN III. The robustness of the system in term of regional diagnosis is measured against pathologists' diagnoses and inter-observer variability between two pathologists is considered. Initial results suggest that the system has potential as a tool both to assist in pathologists' diagnoses, and in training.