New Algorithms Based on the Voronoi Diagram Applied in a Pilot Study on Normal Mucosa and Carcinomas

Abstract

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 tis‐ sue architecture (e.g., normal versus malignant). The present study also indicates that these methods have a potential for prognostication in malignant epithelial lesions.

Prognostic Value of Graph Theory-Based Tissue Architecture Analysis in Carcinomas of the Tongue

“…In the present paper, we demonstrate that, in particular, the invasive front of OSCC contains morphologic information with high prognostic value. The results in the present study are consistent with our previous findings in a pilot study, in which we found that graph theory-based quantitation of tissue architecture was well suited for prognostication of several types of carcinomas (Sudbø et al, 2000).…”

“…Alignment of the fields of view relied on the mechanical precision of the microscope stage. After compiling 36 to 81 fields of view, areas of interest were digitally defined as closed contours using a previously described algorithm (Sudbø et al, 2000). Only cellular elements within the digitally defined areas were included in the computations (Fig.…”

“…In carcinomas of the prostate, the computed weighted sum of a minimal spanning tree structure correlates well with Gleason grading of the carcinomas, indicating a possible prognostic value of this graph theory-based structural feature (Wetzel et al, 1997). In a pilot study, we developed and tested a number of VD-derived structural features and applied them to data from 8 cases of carcinomas of the tongue, 30 cases (15 with a good and 15 with a poor outcome) of carcinoma of the prostate, and 10 cases of carcinomas of the cervix (Sudbø J et al, 2000). In this pilot study, two structural features, the average Delaunay Edge Length (DEL_av) and the average homogeneity of the Ulam Tree (ELH_av) were found to have a prognostic impact.…”

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

“…Accuracy of the model is stable over much of the parameter range, with lower accuracy only occurring where lambda forces few to no features to be included in the model. Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Monaco and Tomaszewski, 2008;Kong et al, 2007;Kwak et al, 2011;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Naik et al, 2008Naik et al, , 2007Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sertel et al, 2010;Sparks and Madabhushi, 2013;Sudbø et al, 2000;Tabesh and Teverovskiy, 2006;Tabesh et al, 2007Tabesh et al, , 2005Tahir and Bouridane, 2006;Tasoulis et al, 2003;Teverovskiy et al, 2004;Thiran and Macq, 1996;Tsai et al, 2009;Xu et al 2014aXu et al , 2014bYang et al, 2009). These approache...…”

“…Even before WSI was practical, researchers captured a single frame of an entire slide using a standard microscope creating a subset of the image for computational analysis (Adiga et al, 2006;Aiad et al, 2009;Altunbay et al, 2010;Baak et al, 1981;Brook et al, 2007;Esgiar et al, 2002Esgiar et al, , 2002Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Kong et al, 2007;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Ozolek et al, 2014;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sudbø et al, 2000;Tahir and Bouridane, 2006;Tasoulis et al, 2003;Thiran and Macq, 1996;Tsai et al, 2009). For many researchers doing pathology image analysis today, this is still the best option, either because they do not have access to a WSI system, or because they can capture data not possible with a WSI system, such as confocal microscopy images.…”