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 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.

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

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.

scholarly journals The Classification of Bladder Tumours

1963 ◽  
Vol 17 (1) ◽  
pp. 41-45
Author(s):  
T J Deeley ◽  
V J Desmet
Keyword(s):  
Bladder Tumours

Classification of Bladder Tumours

1985 ◽  
pp. 117-139 ◽  
Author(s):  
G. H. Jacobi ◽  
U. Engelmann ◽  
R. Hohenfellner
Keyword(s):  
Bladder Tumours

Histological Classification of Urinary Bladder Tumours

1999 ◽  
pp. 3-5 ◽  
Author(s):  
F. K. Mostofi ◽  
C. J. Davis ◽  
I. A. Sesterhenn ◽  
L. H. Sobin
Keyword(s):  
Urinary Bladder ◽  
Histological Classification ◽  
Bladder Tumours
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