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

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Moul, JW; Snow, PB; Fernandez, EB; Maher, PD; Sesterhenn, IA

Published Date

  • May 1995

Published In

Volume / Issue

  • 153 / 5

Start / End Page

  • 1674 - 1677

PubMed ID

  • 7715008

International Standard Serial Number (ISSN)

  • 0022-5347

Language

  • eng

Conference Location

  • United States