A neural network approach to the biopsy diagnosis of early acute renal transplant rejection

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Abstract
To develop and test a neural network to assist in the histological diagnosis of early acute renal allograft rejection. We used three sets of biopsies to train and test the network: 100 ‘routine’ biopsies from Leicester; 21 selected difficult biopsies which had already been evaluated by most of the renal transplant pathologists in the UK, in a study of the Banff classification of allograft pathology and 25 cases which had been classified as ‘borderline’ according to the Banff classification in a review of transplant biopsies from Oxford. The correct diagnosis for each biopsy was defined by careful retrospective clinical review. Biopsies where this review did not provide a clear diagnosis were excluded. Each biopsy was graded for 12 histological features and the data was entered into a simple single layer perception network, designed using the MATLAB neural network toolbox. Results were compared with logistic regression using the same data, and with ‘conventional’ histological diagnosis. If the network was trained only with the 100 ‘routine’ cases, its performance with either of the other sets was poor. However, if either of the ‘difficult’ sets was added to the training group, testing with the other ‘difficult’ group improved dramatically; 19 of the 21 ‘Banff’ study cases were diagnosed correctly. This was achieved using observations made by a trainee pathologist. The result is better than was achieved by any of the many experienced pathologists who had previously seen these biopsies (maximum 18/21 correct), and is considerably better than that achieved by using logistic regression with the same data. A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.