Background: The most common radionuclide procedures for parathyroid imaging are ^99mTc-MIBI/ ^99mTc pertechnetate subtraction scintigraphy and ^99mTc-MIBI double-phase imaging, with estimation of MIBI wash-out rate. Those two methods are by some authors regarded as complementary techniques, yielding the best evaluation of parathyroid gland if performed conjointly. By such an approach it seems reasonable to substitute the visual assessment of neck scintigrams and semiquantitative evaluation of MIBI wash-out rate with a single, common procedure. The aim of this study was application of the Artificial Neural Network (ANN) simulated by the computer program to detection and localisation of pathological parathyroid tissue in the planar neck scintigrams.
Material and methods methods: The applied algorithm was based on simultaneous data processing in sets of 3 single pixels, each of them belonging to one of the three consecutive neck scintigrams generated 20 min. after ^99mTcO₄ - administration, 10 min. after ^99mTc-MIBI injection and 120 min. after ^99mTc-MIBI injection, respectively. Those scintigrams were aligned which each other according to the same vertical and horizontal co-ordinates. The training patterns were obtained from 25 patients by searching for maximum count numbers within small ROIs drawn in selected scintigraphic areas, arbitrarily classified and coded in a numerical scale. In 10 pts the results of ANN simulation were compared with those obtained by common conventional assessment of two radionuclide parathyroid examinations: subtraction method and ^99mTc-MIBI double-phase imaging.
Results: The training patterns processed by the neural network showed a close relationship with the results of visual assessment of original neck scintigrams, with R square coefficient R² = 0.717, and standard error equal to 0.243. Similar comparison between original data and results of multidimensional regression analysis yielded weaker relationship, with R² = 0.543 and standard error 0.567. Parametric images obtained by the neural network presented regions with homogeneously distributed, relatively high activity, greater than or equal to 750 cts/pixel, visualized in areas of confirmed abnormal parathyroid location. In all 10 patients with suspected parathyroid adenoma results obtained by ANN simulation agreed with those by conventional methods. In five of these cases no parathyroid abnormalities were found. In the remaining 5 subjects results of both approaches were positive but the abnormalities were depicted more distinctly and visualised more clearly in parametric images received by ANN than in original scans.
Conclusions: Application of trained ANN enables objective and quantitative detection and localisation of parathyroid adenoma and is a good alternative for conventional radionuclide imaging procedures used in diagnosing parathyroid abnormality. Including in neural network simulation not only scintigraphic data, but also clinical symptoms and/or some other indicators of parathyroid abnormality, parathormone level first of all, should be a next step in developing a procedure for assessing parathyroid abnormality, of high diagnostic accuracy.