Quality-assured Efficient Engineering of Feedforward Neural Networks (QUEEN)

 

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Summary

Objectives: Lymph node metastasis (LNM) is an important prognostic indicator in patients with gastric carcinoma. However, the methods that have been established for preoperative diagnosis of LNM show insufficient accuracy.

Methods: This study describes the use of the QUality Assured Efficient Engineering of Feedforward Neural Networks with Supervised Learning (QUEEN) technique to attempt optimization of the preoperative diagnosis of lymph node metastasis in patients with gastric carcinoma. The results were compared with the Maruyama Diagnostic System (MDS) for preoperative prediction of LNM, established at the National Cancer Center in Tokyo.

Results: QUEEN is able to extract predictive variables from a case-based database. The combination of a development method, a special type of neural network and the corresponding encoding yielded an accuracy of 72.73%, which is notably higher than that of the MDS. Our system produced a nearly ten per cent higher sensitivity and around eighteen per cent higher specificity than MDS.

Conclusion: Our results show that QUEEN is a reasonable method for the development of ANNs. We used the QUEEN system for prediction of LNM in gastric cancer. This system may allow more meaningful preoperative planning by gastric surgeons.

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