ABSTRACT
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.
Subject(s)
Lymphatic Metastasis/diagnosis , Neural Networks, Computer , Stomach Neoplasms , Germany , Humans , Preoperative CareABSTRACT
By recording miniature excitatory junction potentials (mejps) intracellularly at two points from a multiterminally innervated muscle fibre it is possible to select mejps whose amplitudes are not substantially affected by electrotonic decay. Many amplitude histograms of such selected mejps from untreated locust jumping muscle show a bimodal distribution with a high proportion of small-amplitude mejps (sub-mejps). Most amplitudes of excitatory junction potentials (ejps) resulting from the release of a single transmitter quantum correspond to the large-mode mejps. Tetanic nerve stimulation, in high [Mg2+]o without Ca2+, greatly reduces the proportion of sub-mejps. It is concluded that there are two modes of spontaneous transmitter release from the motor nerve endings.
