TY - JOUR A1 - Weidl, Galia A1 - Röhrl, Norbert A1 - Rozas, Javier T1 - Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features JF - Histology and Histopathology N2 - This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading. KW - malignancy KW - pathology KW - Histologie KW - Hirntumor Y1 - 2007 UR - https://pubmed.ncbi.nlm.nih.gov/17523072/ U6 - https://doi.org/10.14670/HH-22.947 VL - 2007 IS - 22/9 SP - 947 EP - 962 ER - TY - CHAP A1 - Röhrl, Norbert A1 - Iglesias-Rozas, José R. A1 - Weidl, Galia T1 - Computer Assisted Classification of Brain Tumors T2 - Data Analysis, Machine Learning and Applications N2 - The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space. KW - brain KW - brain tumor KW - tumor KW - Hirntumor Y1 - 2007 UR - https://link.springer.com/chapter/10.1007/978-3-540-78246-9_7 U6 - https://doi.org/10.1007/978-3-540-78246-9_7 SP - 55 EP - 60 PB - Springer ER - TY - JOUR A1 - Röhrl, Norbert A1 - Iglesias-Rozas, José R. A1 - Weidl, Galia T1 - A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools JF - Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology N2 - To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas. KW - Cytologie KW - Histologie KW - Hirntumor Y1 - 2008 UR - https://pubmed.ncbi.nlm.nih.gov/18459585/ VL - 2008 IS - 30/1 SP - 33 EP - 8 ER -