Four-year follow-up study in a NF1 Boy with a focal pontine hamartoma

Neurofibromatosis is a collective name for a group of genetic conditions in which benign tumours affect the nervous system. Type 1 is caused by a genetic mutation in the NF1 gene (OMIM 613113) and symptoms can vary dramatically between individuals, even within the same family. Some people have very mild skin changes, whereas others suffer severe medical complications. The condition usually appears in childhood and is diagnosed if two of the following are present: six or more café-au-lait patches larger than 1.5 cm in diameter, axillary or groin freckling, 2 or more Lisch nodules (small pigmented areas in the iris of the eye), 2 or more neurofibromas, optic pathway gliomas, bone dysplasia, and a first-degree family relative with Neurofibromatosis type 1. The pattern of inheritance is autosomal dominant, however, half of all NF1 cases are 'sporadic' and there is no family history. Neurofibromatosis type 1 is an extremely variable condition whose morbidity and mortality is largely dictated by the occurrence of the many complications that may involve any of the body systems. We describe a family affected by NF1 in whom genetic molecular analysis identified the same mutation in the son and father. Routine MRI showed pontine focal lesions in the eight-year-old son, though not in the father. We performed a four years follow-up study and at follow-up pontine hamartoma size remained unchanged in the son, and the father showed still no brain lesions, confirming thus an intra-familial phenotype variability

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

In vivo investigation of hyperpolarized [1,3-13C2]acetoacetate as a metabolic probe in normal brain and in glioma.

Dysregulation in NAD+/NADH levels is associated with increased cell division and elevated levels of reactive oxygen species in rapidly proliferating cancer cells. Conversion of the ketone body acetoacetate (AcAc) to β-hydroxybutyrate (β-HB) by the mitochondrial enzyme β-hydroxybutyrate dehydrogenase (BDH) depends upon NADH availability. The β-HB-to-AcAc ratio is therefore expected to reflect mitochondrial redox. Previous studies reported the potential of hyperpolarized 13C-AcAc to monitor mitochondrial redox in cells, perfused organs and in vivo. However, the ability of hyperpolarized 13C-AcAc to cross the blood brain barrier (BBB) and its potential to monitor brain metabolism remained unknown. Our goal was to assess the value of hyperpolarized [1,3-13C2]AcAc in healthy and tumor-bearing mice in vivo. Following hyperpolarized [1,3-13C2]AcAc injection, production of [1,3-13C2]β-HB was detected in normal and tumor-bearing mice. Significantly higher levels of [1-13C]AcAc and lower [1-13C]β-HB-to-[1-13C]AcAc ratios were observed in tumor-bearing mice. These results were consistent with decreased BDH activity in tumors and associated with increased total cellular NAD+/NADH. Our study confirmed that AcAc crosses the BBB and can be used for monitoring metabolism in the brain. It highlights the potential of AcAc for future clinical translation and its potential utility for monitoring metabolic changes associated with glioma, and other neurological disorders

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Classification of brain tumours from MR spectra: the INTERPRET collaboration and its outcomes.

The INTERPRET project was a multicentre European collaboration, carried out from 2000 to 2002, which developed a decision-support system (DSS) for helping neuroradiologists with no experience of MRS to utilize spectroscopic data for the diagnosis and grading of human brain tumours. INTERPRET gathered a large collection of MR spectra of brain tumours and pseudo-tumoural lesions from seven centres. Consensus acquisition protocols, a standard processing pipeline and strict methods for quality control of the aquired data were put in place. Particular emphasis was placed on ensuring the diagnostic certainty of each case, for which all cases were evaluated by a clinical data validation committee. One outcome of the project is a database of 304 fully validated spectra from brain tumours, pseudotumoural lesions and normal brains, along with their associated images and clinical data, which remains available to the scientific and medical community. The second is the INTERPRET DSS, which has continued to be developed and clinically evaluated since the project ended. We also review here the results of the post-INTERPRET period. We evaluate the results of the studies with the INTERPRET database by other consortia or research groups. A summary of the clinical evaluations that have been performed on the post-INTERPRET DSS versions is also presented. Several have shown that diagnostic certainty can be improved for certain tumour types when the INTERPRET DSS is used in conjunction with conventional radiological image interpretation. About 30 papers concerned with the INTERPRET single-voxel dataset have so far been published. We discuss stengths and weaknesses of the DSS and the lessons learned. Finally we speculate on how the INTERPRET concept might be carried into the future.Funding from project MARESCAN (SAF2011-23870) from Ministerio de Economia y Competitividad in Spain. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008-2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. JRG acknowledges support from Cancer Research UK, the University of Cambridge and Hutchison Whampoa Ltd.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/nbm.343

Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization

Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.Peer ReviewedPostprint (author's final draft