NEURAL-NETWORK ANALYSIS OF QUANTITATIVE HISTOLOGICAL FACTORS TO PREDICT PATHOLOGICAL STAGE IN CLINICAL STAGE-I NONSEMINOMATOUS TESTICULAR CANCER

A great deal of controversy exists in staging clinical stage I (CSI) n onseminomatous testicular germ cell tumors (NSGCT) because of the diff iculty of distinguishing true stage I patients from those with occult retroperitoneal or distant metastases. The goal of this study was to q uantitate primary tumor histologic factors and to apply these in a neu ral network computer analysis to determine if more accurate staging co uld be achieved. All available primary tumor histological slides from 93 CSI NSGCT patients were analyzed for vascular invasion (VI), lympha tic invasion (LI), tunical invasion (TI) and quantitative determinatio n 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 datase ts for neural network analysis; one experienced researcher used custom Kohonen and back propagation programs and one less experienced resear cher used a commercially available program. For each experiment, a sub set of data was used for training, and subsets were blindly used to te st the accuracy of the networks. In the custom back propagation networ k, 86 of 93 patients were correctly staged for an overall accuracy of 92% (sensitivity 88%, specificity 96%). Using Neural Ware commercial s oftware 74 of 93 (79.6%) were accurately staged when all 7 input varia bles 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 provid e clinically useful information in the CSI NSGCT population; however, the expertise of the network researcher appears to be important, and c ommercial software in general use may not be superior to standard regr ession analysis. Prospective testing of expert methodology should be i nstituted to confirm its utility.