Classification tree analysis of second neoplasms in survivors of childhood cancer

BACKGROUND: Reports on childhood cancer survivors estimated cumulative probability of developing secondary neoplasms vary from 3,3% to 25% at 25 years from diagnosis, and the risk of developing another cancer to several times greater than in the general population. METHODS: In our retrospective study, we have used the classification tree multivariate method on a group of 849 first cancer survivors, to identify childhood cancer patients with the greatest risk for development of secondary neoplasms. RESULTS: In observed group of patients, 34 develop secondary neoplasm after treatment of primary cancer. Analysis of parameters present at the treatment of first cancer, exposed two groups of patients at the special risk for secondary neoplasm. First are female patients treated for Hodgkin's disease at the age between 10 and 15 years, whose treatment included radiotherapy. Second group at special risk were male patients with acute lymphoblastic leukemia who were treated at the age between 4,6 and 6,6 years of age. CONCLUSION: The risk groups identified in our study are similar to the results of studies that used more conventional approaches. Usefulness of our approach in study of occurrence of second neoplasms should be confirmed in larger sample study, but user friendly presentation of results makes it attractive for further studies

Probable neuroimmunological link between Toxoplasma and cytomegalovirus infections and personality changes in the human host

BACKGROUND: Recently, a negative association between Toxoplasma-infection and novelty seeking was reported. The authors suggested that changes of personality trait were caused by manipulation activity of the parasite, aimed at increasing the probability of transmission of the parasite from an intermediate to a definitive host. They also suggested that low novelty seeking indicated an increased level of the neurotransmitter dopamine in the brain of infected subjects, a phenomenon already observed in experimentally infected rodents. However, the changes in personality can also be just a byproduct of any neurotropic infection. Moreover, the association between a personality trait and the toxoplasmosis can even be caused by an independent correlation of both the probability of Toxoplasma-infection and the personality trait with the third factor, namely with the size of living place of a subject. To test these two alternative hypotheses, we studied the influence of another neurotropic pathogen, the cytomegalovirus, on the personality of infected subjects, and reanalyzed the original data after the effect of the potential confounder, the size of living place, was controlled. METHODS: In the case-control study, 533 conscripts were tested for toxoplasmosis and presence of anti-cytomegalovirus antibodies and their novelty seeking was examined with Cloninger's TCI questionnaire. Possible association between the two infections and TCI dimensions was analyzed. RESULTS: The decrease of novelty seeking is associated also with cytomegalovirus infection. After the size of living place was controlled, the effect of toxoplasmosis on novelty seeking increased. Significant difference in novelty seeking was observed only in the largest city, Prague. CONCLUSION: Toxoplasma and cytomegalovirus probably induce a decrease of novelty seeking. As the cytomegalovirus spreads in population by direct contact (not by predation as with Toxoplasma), the observed changes are the byproduct of brain infections rather than the result of manipulation activity of a parasite. Four independent lines of indirect evidence, namely direct measurement of neurotransmitter concentration in mice, the nature of behavioral changes in rodents, the nature of personality changes in humans, and the observed association between schizophrenia and toxoplasmosis, suggest that the changes of dopamine concentration in brain could play a role in behavioral changes of infected hosts

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Segmentation of Hippocampus in MRI Data

This project deals with application of graph-based methods in segmentation of low contrast image data, specifically hippocampus in MRI data. Using graph cuts for the segmentation allows the software to utilize high accuracy, robustness and an ability to interact with the user

Segmentation of Hippocampus in MRI Data

This project deals with application of graph-based methods in segmentation of low contrast image data, specifically hippocampus in MRI data. Using graph cuts for the segmentation allows the software to utilize high accuracy, robustness and an ability to interact with the user