Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net

Computational hepatocellular carcinoma tumor grading based on cell nuclei classification

Hepatocellular carcinoma (HCC) is the most common histological type of primary liver cancer. HCC is graded according to the malignancy of the tissues. It is important to diagnose low-grade HCC tumors because these tissues have good prognosis. Image interpretation-based computer-aided diagnosis (CAD) systems have been developed to automate the HCC grading process. Generally, the HCC grade is determined by the characteristics of liver cell nuclei. Therefore, it is preferable that CAD systems utilize only liver cell nuclei for HCC grading. This paper proposes an automated HCC diagnosing method. In particular, it defines a pipeline-path that excludes nonliver cell nuclei in two consequent pipeline-modules and utilizes the liver cell nuclear features for HCC grading. The significance of excluding the nonliver cell nuclei for HCC grading is experimentally evaluated. Four categories of liver cell nuclear features were utilized for classifying the HCC tumors. Results indicated that nuclear texture is the dominant feature for HCC grading and others contribute to increase the classification accuracy. The proposed method was employed to classify a set of regions of interest selected from HCC whole slide images into five classes and resulted in a 95.97% correct classification rate.

Brain tumor classification using AFM in combination with data mining techniques

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Improving morphology-based malignancy grading schemes in astrocytic tumors by means of computer-assisted techniques

We propose an original methodology which improves the accuracy of the prognostic values associated with conventional morphologically-based classifications in supratentorial astrocytic tumors in the adult. This methodology may well help neuropathologists, who must determine the aggressiveness of astrocytic tumors on the basis of morphological criteria. The proposed methodology comprises two distinct steps, i.e. i) the production of descriptive quantitative variables (related to DNA ploidy level and morphonuclear aspects) by means of computer-assisted microscopy and ii) data analysis based on an artificial intelligence-related method, i.e. the decision tree approach. Three prognostic problems were considered on a series of 250 astrocytic tumors including 39 astrocytomas (AST), 47 anaplastic astrocytomas (ANA) and 164 glioblastomas (GBM) identified in accordance with the WHO classification. These three problems concern i) variations in the aggressiveness level of the high-grade tumors (ANA and GBM), ii) the detection of the aggressive as opposed to the less aggressive low-grade astrocytomas (AST), and iii) the detection of the aggressive as opposed to the less aggressive anaplastic astrocytomas (ANA). Our results show that the proposed computer-aided methodology improves conventional prognosis based on conventional morphologically-based classifications. In particular, this methodology enables some reference points to be established on the biological continuum according to the sequence AST-->ANA-->GBM.

Advanced soft computing diagnosis method for tumour grading

OBJECTIVE

To develop an advanced diagnostic method for urinary bladder tumour grading. A novel soft computing modelling methodology based on the augmentation of fuzzy cognitive maps (FCMs) with the unsupervised active Hebbian learning (AHL) algorithm is applied.

MATERIAL AND METHODS

One hundred and twenty-eight cases of urinary bladder cancer were retrieved from the archives of the Department of Histopathology, University Hospital of Patras, Greece. All tumours had been characterized according to the classical World Health Organization (WHO) grading system. To design the FCM model for tumour grading, three experts histopathologists defined the main histopathological features (concepts) and their impact on grade characterization. The resulted FCM model consisted of nine concepts. Eight concepts represented the main histopathological features for tumour grading. The ninth concept represented the tumour grade. To increase the classification ability of the FCM model, the AHL algorithm was applied to adjust the weights of the FCM.

RESULTS

The proposed FCM grading model achieved a classification accuracy of 72.5%, 74.42% and 95.55% for tumours of grades I, II and III, respectively.

CONCLUSIONS

An advanced computerized method to support tumour grade diagnosis decision was proposed and developed. The novelty of the method is based on employing the soft computing method of FCMs to represent specialized knowledge on histopathology and on augmenting FCMs ability using an unsupervised learning algorithm, the AHL. The proposed method performs with reasonably high accuracy compared to other existing methods and at the same time meets the physicians' requirements for transparency and explicability.

Correlation of in vitro infiltration with glioma histological type in organotypic brain slices

Diffuse invasion of the brain, an intrinsic property of gliomas, renders these tumours incurable, and is a principal determinant of their spatial and temporal growth. Knowledge of the invasive potential of gliomas is highly desired in order to understand their behaviour in vivo. Comprehensive ex vivo invasion studies including tumours of different histological types and grades are however lacking, mostly because reliable physiological invasion assays have been difficult to establish. Using an organotypic rodent brain slice assay, we evaluated the invasiveness of 42 grade II–IV glioma biopsy specimens, and correlated it with the histological phenotype, the absence or presence of deletions on chromosomes 1p and 19q assessed by fluorescent in situ hybridisation, and proliferation and apoptosis indices assessed by immunocytochemistry. Oligodendroglial tumours with 1p/19q loss were less invasive than astrocytic tumours of similar tumour grade. Correlation analysis of invasiveness cell proliferation and apoptosis further suggested that grade II–III oligodendroglial tumours with 1p/19q loss grow in situ as relatively circumscribed compact masses in contrast to the more infiltrative and more diffuse astrocytomas. Lower invasiveness may be an important characteristic of oligodendroglial tumours, adding to our understanding of their more indolent clinical evolution and responsiveness to therapy.

Distinguishing and grading human gliomas by IR spectroscopy

As a molecular probe of tissue composition, IR spectroscopy can potentially serve as an adjunct to histopathology in detecting and diagnosing disease. This study demonstrates that cancerous brain tissue (astrocytoma, glioblastoma) is distinguishable from control tissue on the basis of the IR spectra of thin tissue sections. It is further shown that the IR spectra of astrocytoma and glioblastoma affected tissue can be discriminated from one another, thus providing insight into the malignancy grade of the tissue. Both the spectra and the methods employed for their classification reveal characteristic differences in tissue composition. In particular, the nature and relative amounts of brain lipids, including both the gangliosides and phospholipids, appear to be altered in cancerous compared to control tissue. Using a genetic classification approach, classification success rates of up to 89% accuracy were obtained, depending on the number of regions included in the model. The diagnostic potential and practical applications of IR spectroscopy in brain tumor diagnosis are discussed.

Measurement of brain structures with artificial neural networks: two- and three-dimensional applications

PURPOSE

To evaluate the ability of an artificial neural network (ANN) to identify brain structures. This ANN was applied to postprocessed magnetic resonance (MR) images to segment various brain structures in both two- and three-dimensional applications.

MATERIALS AND METHODS

An ANN was designed that learned from experience to define the corpus callosum, whole brain, caudate, and putamen. Manual segmentation was used as a training set for the ANN. The ANN was trained on two-thirds of the manually segmented images and was tested on the remaining one-third. The reliability of the ANN was compared against manual segmentations by two technicians.

RESULTS

The ANN was able to identify the brain structures as readily and as well as did the two technicians. Reliability of the ANN compared with the technicians was 0.96 for the corpus callosum, 0.95 for the whole brain, 0.86 (right) and 0.93 (left) for the caudate, and 0.71 (right) and 0.88 (left) for the putamen.

CONCLUSION

The ANN was able to identify the structures used in this study as well as did the two technicians. The ANN could do this much more rapidly and without rater drift. Several other cortical and subcortical structures could also be readily identified with this method.

Tissue inhibitors of metalloproteinases level and collagenase activity in gingival crevicular fluid: the relevance to periodontal diseases

OBJECTIVES

To provide an overall assessment of levels of tissue inhibitors of metalloproteinases (TIMPs), collagenase activities, and of immuno-reactivities for matrix metalloproteinases (MMP)-1 and -8 in gingival crevicular fluid (GCF) obtained from healthy subjects, and gingivitis and periodontitis patients, and to analyse the relationships between periodontal tissue destruction and the GCF components in periodontal diseases by principal component analysis.

MATERIALS AND METHODS

GCF was sampled with sterile paper strips from 10 gingivitis and 11 periodontitis patients. Ten volunteers served as clinically healthy controls. TIMP-1 and -2 protein amounts in GCF were measured by ELISA, and active and APMA-activatable collagenase activities were determined by functional assays using image-analysis after SDS-PAGE.

RESULTS

GCF TIMP-1 level and both active and latent collagenase activities were significantly higher in the diseased groups than in the healthy group. TIMP-2 was detectable in only 29% of all subjects (mean: 2.06 ng). Western blot analysis showed that MMP-8 was the major interstitial collagenase in the GCF of the diseased groups. Principal component analysis using clinical parameters and the GCF components has indicated components one to three account for 87% of total variation when evaluating the relevance of their measurements to periodontal diseases.

CONCLUSIONS

We conducted the functional and immunological characterization of MMPs and TIMPs in the GCF of periodontally diseased patients. Principal component analysis indicated components one to three explaining 87% of total variation, and further suggested that higher collagenase activity (especially in active collagenase) would be an important marker in evaluating the pathogenesis of periodontitis. Consequently, these observations may have significant therapeutic and diagnostic implications.