Fit of biokinetic data in molecular radiotherapy: a machine learning approach

Abstract

Abstract 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, $$\Delta \tau$$ Δ τ can reach down to − 67%, while using ML $$\Delta \tau$$ Δ τ 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.

Keywords

• Machine learning
• Akaike information criterion
• F-test
• Fit function
• Biokinetic curves