Using random forest for reliable classification and cost-sensitive learning for medical diagnosis

Background: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. Results: In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. Conclusion: The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class

Assesment of Stroke Risk Based on Morphological Ultrasound Image Analysis With Conformal Prediction

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid confidence measures to predictions of classical machine learning algorithms. We conduct experiments on a dataset which contains morphological features derived from ultrasound images of atherosclerotic carotid plaques, and we evaluate the results of four different Conformal Predictors (CPs). The four CPs are based on Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes classification (NBC), and k-Nearest Neighbours (k-NN). The results given by all CPs demonstrate the reliability and usefulness of the obtained confidence measures on the problem of stroke risk assessment

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia

Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN. Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN. Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified. Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features

Childhood leukemia and environmental factors

One of the Consulted experts: Paula Ambrósio, Lab. de Doenças Hematológicas Malignas, Unidade de Citogenética, Departamento de Genética, Instituto Nacional de Saúde Doutor Ricardo Jorge, Lisboa, Portugal (P. 129)Every year, about 80 children in Belgium and 140 children in the Netherlands are diagnosed with leukaemia. A longstanding question is which role environmental factors play in the occurrence of this disease. An extensive evaluation of the scientific knowledge on a wide range of possible factors, jointly undertaken by the Belgian Superior Health Council and the Dutch Health Council within the framework of the European Science Advisory Network for Health (EuSANH), shows in general limited evidence for causal links with leukaemia in children. The possibilities for protective measures are therefore also limited, especially given the complex interplay between genetic susceptibilities and environmental exposures, both natural and man-made. It is highly likely that most cases of leukaemia cannot be prevented, and it will probably never be possible to explain individual cases of childhood leukaemia

MEASURABLE RESIDUAL DISEASE AND LEUKEMIC STEM CELLS IN ACUTE MYELOID LEUKEMIA

Nearly all fit patients with acute myeloid leukemia (AML) receive intense chemotherapy, followed by consolidation therapy which can be either additional cycle(s) of chemotherapy, autologous stem cell transplantation or allogeneic stem cell transplantation. In this order, anti-leukemic efficacy increases together with toxicity. While, fortunately, most patients achieve complete remission, unfortunately, 40-50% of patients experience a relapse. Patients who relapse have a dismal prognosis since the relapse is mostly difficult to eradicate. A correct understanding of the risk to relapse is vital for selecting the correct therapy intensity. Risk stratification at diagnosis is based on factors such as age, white blood cell (WBC) count and genetic (mutations and cytogenetic aberrations) characteristics.1 This risk assessment at diagnosis does not suffice for an accurate estimation of patients that relapse, therefore, more specific and sensitive methods (both by flow cytometry and molecular techniques) are widely used to assess possible residual disease during and after therapy. When this residual disease (termed measurable residual disease or minimal residual disease, MRD) is present above a critical level, patients have a higher chance of experiencing a relapse. The overall aim of the studies described in this thesis is to investigate the role of measurable residual disease (MRD) and leukemic stem cells (LSC), and several initiatives to improve the MRD assessment to be used for relapse prediction for the individual patient. Chapter 2 covers a review on several aspects of LSCs in AML and its considered role in relapse progression. Moreover, it discusses how these relatively rare cells can be detected by flow cytometry, and furthermore discusses how this detection is currently used in clinical application. In chapter 3-4 we investigated if the LSC frequency harbors prognostic information for improved relapse prediction for AML. In chapter 3 we present the clinical significance of the presence and frequency of CD34+CD38- LSCs at time of diagnosis and in remission bone marrow in adult AML. In addition, the prognostic relevance of the combination of LSC-MRD and MFC-MRD is investigated. In chapter 4 we investigated whether detection of CD34+CD38- LSCs in BM of newly diagnosed pediatric AML bears similar prognostic relevance as shown in adult AML. In chapter 5-6 we elaborate on the importance of standardization of the flow cytometric MRD and LSC detection approaches. In chapter 5 we evaluated the technical and analytical feasibility of the previously designed eight‐color LSC single tube assay, as well as standardization of the process. In chapter 6 we present a new flow cytometric model for standardized and objective MRD calculation, retrospectively applied in a large clinical study. For this, we evaluate if the balance between neoplastic and normal progenitors in CR bone marrow has prognostic relevance. In chapter 7 we evaluate whether next-generation sequencing has clinical value for the prediction of relapse. Since measurements were simultaneously evaluated for MFC-MRD, we investigated whether NGS and MFC-MRD have independent and additive prognostic value. In addition, we studied whether MRD and LSC-MRD is a valid surrogate endpoint in AML. As shown in a recent clinical trial, the new therapeutic clofarabine has clinical beneficial effect in a subgroup of patients. In chapter 8 we investigated whether the prospectively defined MRD and LSC-MRD frequencies were different between patients with clofarabine and patients without clofarabine, and whether MRD levels mirrored the clinical outcome within this subgroup. Finally, in chapter 9 we summarize the results of this thesis and which implications these results may have for future AML relapse prediction. Furthermore, we evaluate the different techniques used in this thesis, discuss how each technique can be further optimized and elaborate on the optimal use for future clinical trials

Multivariate classification of gene expression microarray data

L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal