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

Autor: Ciucci D; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Cassano B; Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy. bartolomeo.cassano@gmail.com., Donatiello S; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Martire F; Tor Vergata Postgraduate School of Medical Physics, University of Rome, Rome, Italy., Napolitano A; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Polito C; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Solfaroli Camillocci E; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Cervino G; Roma 3 University of Rome, Rome, Italy., Pungitore L; Roma 3 University of Rome, Rome, Italy., Altini C; Nuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Villani MF; Nuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Pizzoferro M; Nuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Garganese MC; Nuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy., Cannatà V; Medical Physics Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
Jazyk: angličtina
Zdroj: EJNMMI physics [EJNMMI Phys] 2024 Feb 22; Vol. 11 (1), pp. 19. Date of Electronic Publication: 2024 Feb 22.
DOI: 10.1186/s40658-024-00623-5
Abstrakt: 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 [ 131 I]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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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