Persistent homology for fast tumor segmentation in whole slide histology images

Automated tumor segmentation in Hematoxylin & Eosin stained histology images is an essential step towards a computer-aided diagnosis system. In this work we propose a novel tumor segmentation approach for a histology whole-slide image (WSI) by exploring the degree of connectivity among nuclei using the novel idea of persistent homology profiles. Our approach is based on 3 steps: 1) selection of exemplar patches from the training dataset using convolutional neural networks (CNNs); 2) construction of persistent homology profiles based on topological features; 3) classification using variant of k-nearest neighbors (k-NN). Extensive experimental results favor our algorithm over a conventional CNN

Stinkwort is rapidly expanding its range in California

Stinkwort (Dittrichia graveolens) is a Mediterranean native that has become a weed in areas of Europe as well as in Australia. This strongly aromatic weed was first reported in California in 1984 in Santa Clara County, and it had spread to 36 of the 58 California counties by 2012. Stinkwort is not palatable to animals, and can be poisonous to livestock and cause contact allergic dermatitis in humans. In California, this weed is found primarily along roadsides. However, the biology of this annual plant suggests that it could also invade open riparian areas and overgrazed rangelands. Stinkwort has an unusual life cycle among annual plants: Unlike most summer or late-season winter annuals, stinkwort flowers and produces seeds from September to December. Such basic biological information is critical to developing timely and effective control strategies for this rapidly expanding weed

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features

Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on the selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperforms competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis

Neuregulin signaling pathway in smoking behavior

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Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER: NCT03574454

Rare and Low Frequency Genomic Variants Impacting Neuronal Functions Modify the Dup7q11.23 Phenotype

© 2021, The Author(s). Background: 7q11.23 duplication (Dup7) is one of the most frequent recurrent copy number variants (CNVs) in individuals with autism spectrum disorder (ASD), but based on gold-standard assessments, only 19% of Dup7 carriers have ASD, suggesting that additional genetic factors are necessary to manifest the ASD phenotype. To assess the contribution of additional genetic variants to the Dup7 phenotype, we conducted whole-genome sequencing analysis of 20 Dup7 carriers: nine with ASD (Dup7-ASD) and 11 without ASD (Dup7-non-ASD). Results: We identified three rare variants of potential clinical relevance for ASD: a 1q21.1 microdeletion (Dup7-non-ASD) and two deletions which disrupted IMMP2L (one Dup7-ASD, one Dup7-non-ASD). There were no significant differences in gene-set or pathway variant burden between the Dup7-ASD and Dup7-non-ASD groups. However, overall intellectual ability negatively correlated with the number of rare loss-of-function variants present in nervous system development and membrane component pathways, and adaptive behaviour standard scores negatively correlated with the number of low-frequency likely-damaging missense variants found in genes expressed in the prenatal human brain. ASD severity positively correlated with the number of low frequency loss-of-function variants impacting genes expressed at low levels in the brain, and genes with a low level of intolerance. Conclusions: Our study suggests that in the presence of the same pathogenic Dup7 variant, rare and low frequency genetic variants act additively to contribute to components of the overall Dup7 phenotype

Vorinostat Combined with High-Dose Gemcitabine, Busulfan, and Melphalan with Autologous Stem Cell Transplantation in Patients with Refractory Lymphomas

AbstractMore active high-dose regimens are needed for refractory/poor-risk relapsed lymphomas. We previously developed a regimen of infusional gemcitabine/busulfan/melphalan, exploiting the synergistic interaction. Its encouraging activity in refractory lymphomas led us to further enhance its use as a platform for epigenetic modulation. We previously observed increased cytotoxicity in refractory lymphoma cell lines when the histone deacetylase inhibitor vorinostat was added to gemcitabine/busulfan/melphalan, which prompted us to clinically study this four-drug combination. Patients ages 12 to 65 with refractory diffuse large B cell lymphoma (DLCL), Hodgkin (HL), or T lymphoma were eligible. Vorinostat was given at 200 mg/day to 1000 mg/day (days −8 to −3). Gemcitabine was infused continuously at 10 mg/m2/minute over 4.5 hours (days −8 and −3). Busulfan dosing targeted 4000 μM-minute/day (days −8 to −5). Melphalan was infused at 60 mg/m2/day (days −3 and −2). Patients with CD20+ tumors received rituximab (375 mg/m2, days +1 and +8). We enrolled 78 patients: 52 DLCL, 20 HL, and 6 T lymphoma; median age 44 years (range, 15 to 65); median 3 prior chemotherapy lines (range, 2 to 7); and 48% of patients had positron emission tomography–positive tumors at high-dose chemotherapy (29% unresponsive). The vorinostat dose was safely escalated up to 1000 mg/day, with no treatment-related deaths. Toxicities included mucositis and dermatitis. Neutrophils and platelets engrafted promptly. At median follow-up of 25 (range, 16 to 41) months, event-free and overall survival were 61.5% and 73%, respectively (DLCL) and 45% and 80%, respectively (HL). In conclusion, vorinostat/gemcitabine/busulfan/melphalan is safe and highly active in refractory/poor-risk relapsed lymphomas, warranting further evaluation

Reference values of spirometry for Finnish adults

BackgroundDiagnostic assessment of lung function necessitates up-to-date reference values. The aim of this study was to estimate reference values for spirometry for the Finnish population between 18 and 80years and to compare them with the existing Finnish, European and the recently published global GLI2012 reference values. MethodsSpirometry was performed for 1380 adults in the population-based FinEsS studies and for 662 healthy non-smoking volunteer adults. Detailed predefined questionnaire screening of diseases and symptoms, and quality control of spirometry yielded a sample of 1000 native Finns (387 men) healthy non-smokers aged 18-83years. Sex-specific reference values, which are estimated using the GAMLSS method and adjusted for age and height, are provided. ResultsThe predicted values for lung volumes are larger than those obtained by GLI2012 prediction for the Caucasian subgroup for forced vital capacity (FVC) by an average 62% and 51% and forced expiratory volume in 1s (FEV1) by an average 42% and 30% in men and women, respectively. GLI2012 slightly overestimated the ratio FEV1/FVC with an age-dependent trend. Most reference equations from other European countries, with the exception of the Swiss SAPALDIA study, showed an underestimation of FVC and FEV1 to varying degrees, and a slight overestimation of FEV1/FVC. ConclusionThis study offers up-to-date reference values of spirometry for native Finns with a wide age range. The GLI2012 predictions seem not to be suitable for clinical use for native Finns due to underestimation of lung volumes.Peer reviewe