scholarly journals Can the classification of low-grade endometrial stromal tumors still be improved?

2018 ◽  
Vol 473 (6) ◽  
pp. 663-664 ◽  
Author(s):  
Xavier Matias-Guiu ◽  
Ben Davidson ◽  
Sigurd F. Lax
Keyword(s):  
Low Grade ◽  
Stromal Tumors ◽  
Endometrial Stromal Tumors

Low-Grade Endometrial Stromal Tumors of the Uterus

2011 ◽  
Vol 16 (3) ◽  
pp. 130-138
Author(s):  
Charles J. Zaloudek
Keyword(s):  
Low Grade ◽  
Stromal Tumors ◽  
Endometrial Stromal Tumors

scholarly journals Low grade endometrial stromal sarcoma – A series of 4 cases and review of relevant literature

2015 ◽  
Vol 5 (9) ◽  
pp. 774-777 ◽  
Author(s):  
P Vijayan ◽  
LM Ilias ◽  
A Ponniah ◽  
BA Mohammed
Keyword(s):  
Smooth Muscle ◽  
Light Microscopy ◽  
Relevant Literature ◽  
Endometrial Stromal Sarcoma ◽  
Low Grade ◽  
Stromal Tumors ◽  
Radiological Features ◽  
Stromal Sarcoma ◽  
Smooth Muscle Tumors ◽  
Endometrial Stromal Tumors

Endometrial stromal tumors are rare uterine malignancies composed of cells resembling those of proliferative endometrial stroma. Because of their indistinct clinical and radiological features, these malignancies are very rarely diagnosed preoperatively. They are mostly often diagnosed as fibroid uteruspreoperatively and as a result managed conservatively many a time. Usually distinguishing these tumors from benign smooth muscle tumors of the uterus are not problematic by light microscopy, however, difficulties arise in distinction between highly cellular leiomyomas and endometrial stromal sarcomas with smooth muscle differentiation. Here we present four consecutive cases of low grade endometrial stromal sarcomaswith emphasis on their clinical, histopathological and immunohistochemical features. By this study, we have attempted to document the histologic features of LGESS with discussion of the differential diagnoses to contribute to this rare spectrum of uterine neoplasms.Journal of Pathology of Nepal (2015) Vol. 5, 774-777

Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

2019 ◽  
Author(s):  
S. Gitto ◽  
D. Albano ◽  
V. Chianca ◽  
R. Cuocolo ◽  
L. Ugga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Low Grade ◽  
High Grade ◽  
Machine Learning Classification

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

Outcome and Prognostic Factors in Endometrial Stromal Tumors: A Rare Cancer Network Study

2012 ◽  
Vol 82 (5) ◽  
pp. e757-e763 ◽  
Author(s):  
Ulrike Schick ◽  
Yasmin Bolukbasi ◽  
Juliette Thariat ◽  
Roxolyana Abdah-Bortnyak ◽  
Abraham Kuten ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Rare Cancer ◽  
Stromal Tumors ◽  
Cancer Network ◽  
Endometrial Stromal Tumors

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 Malignant Gastrointestinal Stromal Tumour with Metastasis

2015 ◽  
Vol 01 (01) ◽  
pp. 031-033
Author(s):  
Bhushita Lakhar ◽  
Nilesh Guru
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Gastrointestinal Stromal Tumour ◽  
Anatomic Site ◽  
Stromal Tumors ◽  
Mesenchymal Neoplasms ◽  
Imaging Characteristics ◽  
The World ◽  
Mitotic Frequency ◽  
Malignant Lesions

AbstractGastrointestinal stromal tumors (GISTs) are the most usual mesenchymal neoplasms of the gastrointestinal tract. Ever since the classification of GIST as an entity distinct from leiomyoma's, leiomyosarcomas, etc., there has been an increased concern in defining their imaging characteristics. It is estimated that approximately 5000-10,000 people are affected per year by this tumor all over the world. Most GISTs are benign (70-80%). However, these tumors have a spectrum ranging from benign to malignant lesions, depending on its anatomic site, tumor size, and mitotic frequency. We report a case of multiple malignant GIST with metastasis into Liver.

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.

Sign in / Sign up

Export Citation Format

Share Document