背景:在文献中报道了不同的分析方法(AM)来选择适当的拟合模型并拟合时间-活动曲线(TAC)的数据。另一方面,机器学习算法(ML)越来越多地用于分类和回归任务。这项工作的目的是研究使用ML对最合适的拟合模型进行分类并预测曲线下面积(τ)的可能性。
方法:已经开发了两种不同的ML系统,用于对拟合模型进行分类并预测生物动力学参数。用合成的TACs对这两个系统进行了训练和测试,该合成的TACs模拟了受转移性分化型甲状腺癌影响的患者的全身部分注射活动,用[131I]I-NaI管理。试验性能,定义为分类精度(CA)和曲线下实际面积与估计面积之间的百分比差异(Δτ),将其与使用改变TAC的点数(N)的AM获得的那些进行比较。使用20名真实患者的数据进行AM和ML之间的比较。
结果:随着N的变化,CA对于ML保持恒定(约98%),虽然F检验(从62%提高到92%)和AICc(从50%提高到92%),随着N的增加。有了AM,[公式:见正文]可以达到-67%,而使用ML[公式:见文本]范围在±25%内。使用真正的TAC,用ML系统获得的τ与AM有很好的一致性。
结论:采用ML系统可能是可行的,具有更好的分类和更好的生物动力学参数估计。
BACKGROUND: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ).
METHODS: Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [131I]I-NaI. Test performances, defined as classification accuracy (CA) and percentage difference between the actual and the estimated area under the curve (Δτ), were compared with those obtained using AM varying the number of points (N) of the TACs. A comparison between AM and ML were performed using data of 20 real patients.
RESULTS: As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM.
CONCLUSIONS: The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.