DiviK: Divisive intelligent K-means for hands-free unsupervised clustering in biological big data

Investigation of molecular heterogeneity provides insights about tumor origin and metabolomics. Increasing amount of data gathered makes manual analyses infeasible. Automated unsupervised learning approaches are exercised for this purpose. However, this kind of analysis requires a lot of experience with setting its hyperparameters and usually an upfront knowledge about the number of expected substructures. Moreover, numerous measured molecules require additional step of feature engineering to provide valuable results. In this work we propose DiviK: a scalable auto-tuning algorithm for segmentation of high-dimensional datasets, and a method to assess the quality of the unsupervised analysis. DiviK is validated on two separate high-throughput datasets acquired by Mass Spectrometry Imaging in 2D and 3D. Proposed algorithm could be one of the default choices to consider during initial exploration of Mass Spectrometry Imaging data. With comparable clustering quality, it brings the possibility of focusing on different levels of dataset nuance, while requires no number of expected structures specified upfront. Finally, due to its simplicity, DiviK is easily generalizable to even more flexible framework, with other clustering algorithm used instead of k-means. Generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik.Comment: 8 pages, 7 figure

Long-Term Evaluation of Visual Outcomes and Patient Satisfaction after Binocular Implantation of a Bioanalogic Lens

Purpose. Long-term evaluation of the visual refractive outcomes and the quality of life after implantation of the WIOL-CF (Medicem, Czech Republic) in both eyes. Design. retrospective, nonrandomized noncomparative case series. Methods. 50 eyes of 25 patients, including 11 women (44%) and 14 men (56%). The age range of the patients was 38 to 77 years (mean age 55.48 ± 10.97 years). All patients underwent bilateral implantation of the WIOL-CF. Exclusion criteria were previous ocular surgeries except for cataract surgery and refractive lens exchange, irregular corneal astigmatism of >1.0 diopter, and ocular pathologies or corneal abnormalities. Postoperative examinations were performed at 14 days and 3, 6, 12 months of surgery; the last follow-up was between 24 and 36 months after the procedure. All exams included manifest refraction, monocular uncorrected visual acuity (UCVA) and distance-corrected visual acuity (DCVA) in 5 m (Snellen), monocular uncorrected visual acuity in 70 cm and 40 cm (Jeager) and binocular UCVA, DCVA in 5 m, 70 cm, and 40 cm, binocular contrast sensitivity (CS) under photopic conditions, binocular defocus curves, high-order aberrations, quality-of-vision VF-14 questionnaire, and spectacle independence. Results. Significant improvement in monocular visual acuity at all distances was demonstrated; the mean postoperative spherical equivalent was 0.32 ± 0.45D. The postoperative means of binocular distance UCVA and BCVA were also improved (p < .001) and so were the mean uncorrected intermediate VA (2.053 ± 1.268) and near uncorrected VA (2.737 ± 1.447). There was a significant improvement in contrast sensitivity at all spatial frequencies and higher-order aberration, compared to preoperative results. Conclusions. The evaluation of a WIOL-CF showed good distance, intermediate, and near visual acuity. Contrast sensitivity increased after surgery in all spatial frequencies. Patient satisfaction was high despite some optical phenomena. The rate of postoperative spectacle independence also turned out high. Financial Disclosure. No author has a financial or proprietary interest in any material or method mentioned

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies&mdash;papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer&mdash;and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset