Classification of gliomas and medulloblastomas using the new TNM system for cerebral tumors

1989 ◽  
Vol 12 (3) ◽  
pp. 233-238 ◽  
Author(s):  
Ulrich Szuwart ◽  
Harald Bennefeld ◽  
Hildegund Behr
Keyword(s):  
Tnm System ◽  
Cerebral Tumors ◽  
Classification Of Gliomas

scholarly journals Multiple PET tracers for use in the classification of gliomas according to the 2016 WHO criteria

2020 ◽  
Author(s):  
Keisuke Miyake ◽  
Kenta Suzuki ◽  
Tomoya B Ogawa ◽  
Daisuke Ogawa ◽  
Tetsuhiro Hatakeyama ◽  
...  
Keyword(s):  
Who Classification ◽  
Standardized Uptake Value ◽  
Metabolic Tumor Volume ◽  
Who Criteria ◽  
Pet Tracers ◽  
Pet Tracer ◽  
Positron Emission ◽  
18F Fdg ◽  
Classification Of Gliomas

Abstract Background The molecular diagnosis of gliomas such as isocitrate dehydrogenase (IDH) status (wild-type [wt] or mutation [mut]) is especially important in the 2016 WHO classification. Positron emission tomography (PET) has afforded molecular and metabolic diagnostic imaging. The present study aimed to define the interrelationship between the 2016 WHO classification of gliomas and the integrated data from PET images using multiple tracers, including 18F-fluorodeoxyglucose ( 18F-FDG), 11C-methionine ( 11C-MET), 18F-fluorothymidine ( 18F-FLT), and 18F-fluoromisonidazole ( 18F-FMISO). Methods This retrospective, single-center study comprised 113 patients with newly diagnosed glioma based on the 2016 WHO criteria. Patients were divided into four glioma subtypes (Mut, Codel, Wt, and glioblastoma multiforme [GBM]). Tumor standardized uptake value (SUV) divided by mean normal cortical SUV (tumor-normal tissue ratio [TNR]) was calculated for 18F-FDG, 11C-MET, and 18F-FLT. Tumor-blood SUV ratio (TBR) was calculated for 18F-FMISO. To assess the diagnostic accuracy of PET tracers in distinguishing glioma subtypes, a comparative analysis of TNRs and TBR as well as the metabolic tumor volume (MTV) were calculated by Scheffe’s multiple comparison procedure for each PET tracer following the Kruskal–Wallis test. Results The differences in mean 18F-FLT TNR and 18F-FMISO TBR were significant between GBM and other glioma subtypes (p < 0.001). Regarding the comparison between Gd-T1WI volumes and 18F-FLT MTVs or 18F-FMISO MTVs, we identified significant differences between Wt and Mut or Codel (p < 0.01). Conclusion Combined administration of four PET tracers might aid in the preoperative differential diagnosis of gliomas according to the 2016 WHO criteria.

WHO 2016 Classification of gliomas

2018 ◽  
Vol 44 (2) ◽  
pp. 139-150 ◽  
Author(s):  
P. Wesseling ◽  
D. Capper
Keyword(s):  
Classification Of Gliomas

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

A New Multiparametric Classification in Lung Cancer Patients - S.M.I.G.

1983 ◽  
Vol 69 (5) ◽  
pp. 437-443 ◽  
Author(s):  
Claudio Modini ◽  
Mario Albertucci ◽  
Franco Cicconetti ◽  
Donatella Tirindelli Danesi ◽  
Renzo Cristiani ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Growth Rate ◽  
Mortality Rate ◽  
Cancer Patients ◽  
Cell Type ◽  
Tnm System ◽  
Lung Cancer Patients ◽  
The Difference

The classification of bronchogenic carcinoma as a function of the prognosis is still an open field. The evaluation of stage, by use of the TNM system, and histologic cell type is not sufficient to guarantee a correct prognosis. The growth rate of the neoplasm is another important parameter. We propose a classification that takes into account the stage (S), histologic cell type (M), immune status (I) and the growth rate of the primary tumor (G): S.M.I.G. We studied 90 lung cancer patients according to the S.M.I.G. classification and we observed that their prognoses were directly correlated with their S.M.I.G. scores (the higher the score, the higher the 10-month mortality rate). The mortality rates within the first 10 months of follow-up were respectively 0%, 0%, 36.36%, 68%, 90.9% for the 5 groups obtained by S.M.I.G. The difference is statistically significant (P < 0.0075) and there is a linear correlation between the mortality rate and the score assigned to each group (R = 0.943; P < 0.05). The S.M.I.G. classification can predict the prognosis more efficiently than the usual classification (TNM) and histological cell type.

Experiences Based on the Classification of Bladder Tumours according to the TNM System

1975 ◽  
Vol 1 (2) ◽  
pp. 93-95 ◽  
Author(s):  
H. Vogler ◽  
M. Rothkopf ◽  
M. Mebel
Keyword(s):  
Tnm System ◽  
Bladder Tumours

scholarly journals RAPID GENETIC CLASSIFICATION OF GLIOMAS USING RAMAN SPECTROSCOPY

2018 ◽  
Vol 20 (suppl_5) ◽  
pp. v346-v346
Author(s):  
Laurent James Livermore ◽  
Martin Isabelle ◽  
Ian Bell ◽  
Puneet Plaha ◽  
Claire Vallance ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Genetic Classification ◽  
Classification Of Gliomas

scholarly journals Cellular Plasticity and Tumor Microenvironment in Gliomas: The Struggle to Hit a Moving Target

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1622 ◽  
Author(s):  
Ricardo Gargini ◽  
Berta Segura-Collar ◽  
Pilar Sánchez-Gómez
Keyword(s):  
Tumor Cells ◽  
Point Of View ◽  
Complete Surgical Resection ◽  
Current Therapies ◽  
The Subject ◽  
Traditional Therapies ◽  
New Treatments ◽  
Classification Of Gliomas

Brain tumors encompass a diverse group of neoplasias arising from different cell lineages. Tumors of glial origin have been the subject of intense research because of their rapid and fatal progression. From a clinical point of view, complete surgical resection of gliomas is highly difficult. Moreover, the remaining tumor cells are resistant to traditional therapies such as radio- or chemotherapy and tumors always recur. Here we have revised the new genetic and epigenetic classification of gliomas and the description of the different transcriptional subtypes. In order to understand the progression of the different gliomas we have focused on the interaction of the plastic tumor cells with their vasculature-rich microenvironment and with their distinct immune system. We believe that a comprehensive characterization of the glioma microenvironment will shed some light into why these tumors behave differently from other cancers. Furthermore, a novel classification of gliomas that could integrate the genetic background and the cellular ecosystems could have profound implications in the efficiency of current therapies as well as in the development of new treatments.

Evolving evidence on tumor and germline genetic classification of gliomas: implications for etiology and survival studies

2016 ◽  
Vol 35 (01) ◽  
pp. 31-37 ◽  
Author(s):  
Kimberly J. Johnson ◽  
Michael E. Scheurer ◽  
Adelheid Woehrer ◽  
Joseph Wiemels
Keyword(s):  
Genetic Classification ◽  
Survival Studies ◽  
Classification Of Gliomas
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