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Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer.

Publication ,  Journal Article
Moul, JW; Snow, PB; Fernandez, EB; Maher, PD; Sesterhenn, IA
Published in: J Urol
May 1995

A great deal of controversy exists in staging clinical stage I (CSI) nonseminomatous testicular germ cell tumors (NSGCT) because of the difficulty of distinguishing true stage I patients from those with occult retroperitoneal or distant metastases. The goal of this study was to quantitate primary tumor histologic factors and to apply these in a neural network computer analysis to determine if more accurate staging could be achieved. All available primary tumor histological slides from 93 CSI NSGCT patients were analyzed for vascular invasion (VI), lymphatic invasion (LI), tunical invasion (TI) and quantitative determination of percentage of the primary tumor composed of embryonal carcinoma (%EMB), yolk sac carcinoma (%YS), teratoma (%TER) and seminoma (%SEM). These patients had undergone retroperitoneal lymphadenectomy or follow-up such that final stage included 55 pathologic stage I and 38 stage II or higher lesions. Two investigators were provided identical datasets for neural network analysis; one experienced researcher used custom Kohonen and back propagation programs and one less experienced researcher used a commercially available program. For each experiment, a subset of data was used for training, and subsets were blindly used to test the accuracy of the networks. In the custom back propagation network, 86 of 93 patients were correctly staged for an overall accuracy of 92% (sensitivity 88%, specificity 96%). Using Neural Ware commercial software 74 of 93 (79.6%) were accurately staged when all 7 input variables were used; however, accuracy improved from 84.9 to 87.1% when 2, 4 and 5 of the variables were used. Quantitative histologic assessment of the primary tumor and neural network processing of data may provide clinically useful information in the CSI NSGCT population; however, the expertise of the network researcher appears to be important, and commercial software in general use may not be superior to standard regression analysis. Prospective testing of expert methodology should be instituted to confirm its utility.

Duke Scholars

Published In

J Urol

ISSN

0022-5347

Publication Date

May 1995

Volume

153

Issue

5

Start / End Page

1674 / 1677

Location

United States

Related Subject Headings

  • Urology & Nephrology
  • Testis
  • Testicular Neoplasms
  • Software
  • Sensitivity and Specificity
  • Regression Analysis
  • Neural Networks, Computer
  • Neoplasm Staging
  • Neoplasm Invasiveness
  • Male
 

Citation

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ICMJE
MLA
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Moul, J. W., Snow, P. B., Fernandez, E. B., Maher, P. D., & Sesterhenn, I. A. (1995). Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer. J Urol, 153(5), 1674–1677.
Moul, J. W., P. B. Snow, E. B. Fernandez, P. D. Maher, and I. A. Sesterhenn. “Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer.J Urol 153, no. 5 (May 1995): 1674–77.
Journal cover image

Published In

J Urol

ISSN

0022-5347

Publication Date

May 1995

Volume

153

Issue

5

Start / End Page

1674 / 1677

Location

United States

Related Subject Headings

  • Urology & Nephrology
  • Testis
  • Testicular Neoplasms
  • Software
  • Sensitivity and Specificity
  • Regression Analysis
  • Neural Networks, Computer
  • Neoplasm Staging
  • Neoplasm Invasiveness
  • Male