Biomedical subjects

R Marcelpoil

Publications and source records attributed to R Marcelpoil.

6 recordsLinked to original sources

PubMed2000

Prognostic value of graph theory-based tissue architecture analysis in carcinomas of the tongue.

Several studies on oral squamous cell carcinomas (OSCC) suggest that the clinical value of traditional histologic grading is limited both by poor reproducibility and by low prognostic impact. However, the prognostic potential of a strictly quantitative and highly reproducible assessment of the tissue architecture in OSCC has not been evaluated. Using image analysis, in 193 cases of T1-2 (Stage I-II) OSCC we retrospectively investigated the prognostic impact of two graph theory-derived structural features: the average Delaunay Edge Length (DEL_av) and the average homogeneity of the Ulam Tree (ELH_av). Both structural features were derived from subgraphs of the Voronoi Diagram. The geometric centers of the cell nuclei were computed, generating a two-dimensional swarm of point-like seeds from which graphs could be constructed. The impact on survival of the computed values of ELH_av and DEL_av was estimated by the method of Kaplan and Meier, with relapse-free survival and overall survival as end-points. The prognostic values of DEL_av and ELH_av as computed for the invasive front, the superficial part of the carcinoma, the total carcinoma, and the normal-appearing oral mucosa were compared. For DEL_av, significant prognostic information was found in the invasive front (p < 0.001). No significant prognostic information was found in superficial part of the carcinoma (p = 0.34), in the carcinoma as a whole (p = 0.35), or in the normal-appearing mucosa (p = 0.27). For ELH_av, significant prognostic information was found in the invasive front (p = 0.01) and, surprisingly, in putatively normal mucosa (p = 0.03). No significant prognostic information was found in superficial parts of the carcinoma (p = 0.34) or in the total carcinoma (p = 0.11). In conclusion, strictly quantitative assessment of tissue architecture in the invasive front of OSCC yields highly prognostic information.

Aged↗

PubMed2000

New algorithms based on the Voronoi Diagram applied in a pilot study on normal mucosa and carcinomas.

An adequate reproducibility in the description of tissue architecture is still a challenge to diagnostic pathology, sometimes with unfortunate prognostic implications. To assess a possible diagnostic and prognostic value of quantitiative tissue architecture analysis, structural features based on the Voronoi Diagram (VD) and its subgraphs were developed and tested. A series of 27 structural features were developed and tested in a pilot study of 30 cases of prostate cancer, 10 cases of cervical carcinomas, 8 cases of tongue cancer and 8 cases of normal oral mucosa. Grey level images were acquired from hematoxyline-eosine (HE) stained sections by a charge coupled device (CCD) camera mounted on a microscope connected to a personal computer (PC) with an image array processor. From the grey level images obtained, cell nuclei were automatically segmented and the geometrical centres of cell nuclei were computed. The resulting 2-dimensional (2D) swarm of pointlike seeds distributed in a flat plane was the basis for construction of the VD and its subgraphs. From the polygons, triangulations and arborizations thus obtained, 27 structural features were computed as numerical values. Comparison of groups (normal vs. cancerous oral mucosa, cervical and prostate carcinomas with good and poor prognosis) with regard to distribution in the values of the structural features was performed with Student's t-test. We demonstrate that some of the structural features developed are able to distinguish structurally between normal and cancerous oral mucosa (P = 0.001), and between good and poor outcome groups in prostatic (P = 0.001) and cervical carcinomas (P = 0.001). We present results confirming previous findings that graph theory based algorithms are useful tools for describing tissue architecture (e.g., normal versus malignant). The present study also indicates that these methods have a potential for prognostication in malignant epithelial lesions.

Algorithms↗

PubMed2000

Caveats: numerical requirements in graph theory based quantitation of tissue architecture.

Graph theory based methods represent one approach to an objective and reproducible structural analysis of tissue architecture. By these methods, neighborhood relations between a number of objects (e.g., cells) are explored and inherent to these methods are therefore certain requirements as to the number of objects to be included in the analysis. However, the question of how many objects are required to achieve reproducible values in repeated computations of proposed structural features, has previously not been adressed specifically. After digitising HE stained slides and storing them as grey level images, cell nuclei were segmented and their geometrical centre of gravity were computed, serving as the basis for construction of the Voronoi diagram (VD) and its subgraphs. Variations in repeated computations of structural features derived from these graphs were related to the number of cell nuclei included in the analysis. We demonstrate a large variation in the values of the structural features from one computation to another in one and the same section when only a limited number of cells (100-500) are included in the analysis. This variation decreased with increasing number of cells analyzed. The exact number of cells required to achieve reproducible values differ significantly between tissues, but not between separate cases of similar lesions. There are no significant differences between normal and malignantly changed tissues in oral mucosa with respect to how many cells must be included. For graph theory based analysis of tissue architecture, care must be taken to include an adequate number of objects; for some of the structural features we have tested, more than 3000 cells.

Biometry↗

PubMed1996

Cellular sociology applied to neuroendocrine tumors of the lung: quantitative model of neoplastic architecture.

This paper reports on cellular sociology, which consists of modeling tissular architecture based on graph theory. Voronoi's diagram was chosen to build the models. This diagram derives from a cell neighborhood concept and generates parameters which objectively represent tissue architecture. Minimal spanning tree (MST) is probably the more frequently used among graphs and successfully discriminates different grades of pathological process. However, Voronoi's diagram is more comprehensive and a more complete representation of architecture with the advantage of stability. The lung neuroendocrine tumor classification is far from being consensual, especially for lesions which don't fall in with typical carcinoid and small cell carcinoma groups. By comparing architectural models of 20 neuroendocrine tumors of the lung, this work supports the morphologic spectrum concept of these tumors and also supports the recently proposed concept of large-cell neuroendocrine tumors of the lung. Finally, architectural parameters separate small-cell-lung carcinomas from neuroendocrine non-small-cell lung carcinomas.

Carcinoid Tumor↗

PubMed1996

Interest of targeting AgNORs measurement in cycling cells: in vivo cell kinetic evaluation of non-small cell lung cancer.

This study investigated the actual growth rate of 30 low stage operable non-small cell lung carcinomas, including disease-free surviving and deceased patients. The actual growth rate was defined as the cell production rate and was calculated from the growth fraction and the cell cycle time of each tumor at the time of surgical resection. The growth fraction was assessed by the Ki67 index while the cell cycle time was assumed to be reflected by the AgNORs content in the cells positive for Ki67. AgNORs content was evaluated by means of image analysis of double-stained AgNOR-Ki67 tissue section. The actual growth rate did not discriminate between the disease-free surviving and deceased patients but the AgNORs content in Ki67 cells correlated with the survival time of those patients who died of the tumor. Patients expressing a small AgNORs content, which might indicate a long cell cycle, may die but later; patients with a high AgNORs content, which might indicate a short cell cycle, die early or will survive. A twilight curve was derived from this data and might provide new prognostic indicators.

Aged↗

PubMed1993

Normalization of the minimum spanning tree.

A problem of considerable interest in pattern recognition and data analysis is that of describing the spatial structure of a data set. In the field of biology this could be based on graph construction. Although the minimum spanning tree (MST), contains less information than the Relative Neighbourhood, Gabriel and Delaunay graphs [16], this graph has been frequently used [3-9]. The MST is a subgraph of all the preceding graphs. Two main types of parameters can be derived from a graph. Some of the parameters are derived from the structure of the graph (topological parameters), whereas others are based on the Euclidean metrics of the graph (edge lengths). Since these parameters are used to characterize the spatial structure of data sets, they have to be normalized so that different biological structures may be compared. A model for the normalization of the most common parameters derived from the MST is thus presented here. Two aspects of the problem are considered: (i) omission of the metrics associated dimension of the Euclidean parameters in order to compare biological structures at different scale factors and (ii) elimination of border effects to avoid border artefacts.

Data Interpretation, Statistical↗