Computerized analyses of morphology and proliferative activity differentiate hepatoblastoma from paediatric hepatocellular carcinoma

Abstract

These results indicate that computerized morphometric analysis can yield useful criteria to distinguish HB from pHCC in small biopsy specimens, and, compared with FHB, the poorer prognosis of EHB may result from its more undifferentiated (immature) and proliferative phenotype.

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.…”

Hepatoblastomas: Biology of Disease and Prognostic Factors

“…The PCNA labeling index was found to be significantly higher in the embryonal than in the fetal phenotype (Rugge et al 1998). The significant difference between fetal and embryonal cell proliferative activity assessed by immunohistochemistry was confirmed in a later study (Tsai et al 2009). The Ki-67/MIB-1 labeling index varied from 0 % to 39.5 % in one study, with a mean value of 13.5 %.…”

Automatic Segmentation of Molecular Pathology Images Using a Robust Mixture Model with Markov Random Fields

“…This allows the counting and classification into different components to adopt a more objective and less labour intensive way. Computer visual algorithms have been applied to individual cell images in the diagnosis of thyroid, breast, and liver cancers [1], [2], [3], [4]. Automatic segmentation of digital pathology images thus has a large potential to facilitate the assessment of clinical behaviour of many diseases including cancer, by providing a new tool to quantify and observe the molecular variation within tissue samples [5], [6].…”