Classification of Low-Grade Copper-Nickel Ore and Mining Waste by Ecological Hazard and Hydrometallurgical Processability

2020 ◽  
Vol 56 (2) ◽  
pp. 275-282
Author(s):  
A. V. Svetlov ◽  
P. V. Pripachkin ◽  
V. A. Masloboev ◽  
D. V. Makarov
Keyword(s):  
Mining Waste ◽  
Low Grade ◽  
Copper Nickel ◽  
Ecological Hazard

Classification of low-grade neuroendocrine tumors of midgut and unknown origin

2002 ◽  
Vol 33 (11) ◽  
pp. 1126-1132 ◽  
Author(s):  
Susanne van Eeden ◽  
Pascal F.H.J. Quaedvlieg ◽  
Babs G. Taal ◽  
G.Johan A. Offerhaus ◽  
Cornelis B.H.W. Lamers ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Unknown Origin ◽  
Low Grade

scholarly journals Interobserver reproducibility of The Paris System for Reporting Urinary Cytology

CytoJournal ◽  
2017 ◽  
Vol 14 ◽  
pp. 17 ◽  
Author(s):  
Theresa Long ◽  
Lester J. Layfield ◽  
Magda Esebua ◽  
Shellaine R. Frazier ◽  
D. Tamar Giorgadze ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Urinary Cytology ◽  
Low Grade ◽  
High Grade ◽  
Urothelial Cells ◽  
Urothelial Carcinomas ◽  
Absolute Agreement ◽  
Potential Impact ◽  
Urine Specimens

Background: The Paris System for Reporting Urinary Cytology represents a significant improvement in classification of urinary specimens. The system acknowledges the difficulty in cytologically diagnosing low-grade urothelial carcinomas and has developed categories to deal with this issue. The system uses six categories: unsatisfactory, negative for high-grade urothelial carcinoma (NHGUC), atypical urothelial cells, suspicious for high-grade urothelial carcinoma, high-grade urothelial carcinoma, other malignancies and a seventh subcategory (low-grade urothelial neoplasm). Methods: Three hundred and fifty-seven urine specimens were independently reviewed by four cytopathologists unaware of the previous diagnoses. Each cytopathologist rendered a diagnosis according to the Paris System categories. Agreement was assessed using absolute agreement and weighted chance-corrected agreement (kappa). Disagreements were classified as low impact and high impact based on the potential impact of a misclassification on clinical management. Results: The average absolute agreement was 65% with an average expected agreement of 44%. The average chance-corrected agreement (kappa) was 0.32. Nine hundred and ninety-nine of 1902 comparisons between rater pairs were in agreement, but 12% of comparisons differed by two or more categories for the category NHGUC. Approximately 15% of the disagreements were classified as high clinical impact. Conclusions: Our findings indicated that the scheme recommended by the Paris System shows adequate precision for the category NHGUC, but the other categories demonstrated unacceptable interobserver variability. This low level of diagnostic precision may negatively impact the applicability of the Paris System for widespread clinical application.

scholarly journals Classification of Brain Tumor IDH Status using MRI and Deep Learning

2019 ◽  
Author(s):  
Sahil Nalawade ◽  
Gowtham Murugesan ◽  
Maryam Vejdani-Jahromi ◽  
Ryan A. Fisicaro ◽  
Chandan Ganesh Bangalore Yogananda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Validation Data ◽  
Mutation Status ◽  
Radiologic Imaging ◽  
Address Data

AbstractIsocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

scholarly journals Classification of mucinous appendiceal neoplasm and pseudomyoxma peritonei

2018 ◽  
Vol 8 (1) ◽  
pp. 1301-1307
Author(s):  
Arnab Ghosh
Keyword(s):  
Goblet Cell ◽  
Wide Spectrum ◽  
Low Grade ◽  
Controversial Issues ◽  
High Grade ◽  
Goblet Cell Carcinoid ◽  
Signet Ring Cells ◽  
Mucinous Neoplasm ◽  
Signet Ring

Mucinous appendiceal tumors are uncommon and include a wide spectrum of tumors whose classification remained controversial. Some of these mucin producing appendiceal tumors can disseminate to the peritoneal cavity leading to pseudomyxoma peritonei (PMP). Despite several attempts to classify mucinous tumors of appendix and PMP by different authors in the past, no universally accepted classification system was present. The controversial issues were discussed at the 2012 World Congress of the Peritoneal Surface Oncology Group International (PSOGI) in Berlin. A panel of 71 experts from 13 different countries was formed under the lead co-ordinator Norman J. Carr. A total of 4 rounds of questionnaires and one meeting were held. The opinion of the majority was taken into account. Importance of intactness of muscularis mucosae, pushing invasion and infiltrative invasion were emphasized. The entities Low grade appendiceal mucinous neoplasm (LAMN) and High grade appendiceal mucinous neoplasm (HAMN) were defined.. The terminologies suggested for Goblet cell carcinoid and adenoneuroendocrine carcinoma were goblet cell tumor and adenocarcinoma ex goblet cell carcinoid. Acellular mucin in peritoneum was not classified under PMP which was classified into 3 categories depending upon low grade , high grade cytologic features and presence of signet ring cells. It was suggested to report the extent of mucin and cells separately. A reporting format solely for mucinous appendiceal tumors was formulated by the panel. However, there are some grey areas which may have to be addressed in future.

Clinical importance of molecular markers of adult diffuse glioma

2019 ◽  
Vol 19 (5) ◽  
pp. 412-416 ◽  
Author(s):  
Emanuela Molinari ◽  
Olimpia E Curran ◽  
Robin Grant
Keyword(s):  
Molecular Markers ◽  
Molecular Genetic ◽  
Clinical Importance ◽  
Response To Treatment ◽  
Low Grade ◽  
Diffuse Glioma ◽  
Wild Type ◽  
Genetic Classification ◽  
Cns Tumours

In 2016, the WHO incorporated molecular markers, in addition to histology, into the diagnostic classification of central nervous system (CNS) tumours. This improves diagnostic accuracy and prognostication: oligo-astrocytoma no longer exists as a clinical entity; isocitrate dehydrogenase (IDH) mutant and 1p/19q co-deleted oligodendroglioma is a smaller category with better prognosis; IDH wild-type ‘low-grade’ glioma has a much poorer prognosis; and glioblastoma is divided into IDH mutant (with an better prognosis than pre-2016 glioblastoma) and IDH wild type (with a poorer prognosis). Previous advice based on phenotype alone will change with respect to median survival, best management plan and response to treatment. There are implications for routine neuropathology reporting and future trial design. Cases that are difficult to classify may need more advanced molecular genetic classification through DNA methylation-based classification of CNS tumours (Heidelberg Classifier). We discuss the practical implications.

Leaching of a low-grade, copper–nickel sulfide ore

2009 ◽  
Vol 98 (1-2) ◽  
pp. 66-72 ◽  
Author(s):  
M. Maley ◽  
W. van Bronswijk ◽  
H.R. Watling
Keyword(s):  
Nickel Sulfide ◽  
Low Grade ◽  
Copper Nickel ◽  
Sulfide Ore
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