Academic Journal
Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study.
| Τίτλος: | Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study. |
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| Συγγραφείς: | Chouchen, Mariem, Barki, Chamseddine, Dergaa, Ismail, Ceylan, Halil İbrahim, de Giorgio, Andrea, Bragazzi, Nicola Luigi, Rahmouni, Hanene Boussi |
| Πηγή: | Bioengineering (Basel); Jun2026, Vol. 13 Issue 6, p696, 17p |
| Θεματικοί όροι: | Machine learning, Thyroid cancer, Nuclear medicine, Disease risk factors, Occupational exposure, Public health surveillance, Prediction models, Dose-response relationship (Radiation) |
| Περίληψη: | Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk assessment in occupational exposure settings. This study examined whether machine learning algorithms can serve as high-precision computational surrogates for excess relative risk estimation in NMHP. Aim: The study aimed to (i) develop and validate three machine learning algorithms for predicting the excess relative risk per unit absorbed dose for radiation-induced solid thyroid cancer (ERR/Gy.RST), (ii) characterize relationships between dosimetric and demographic features and predicted risk, and (iii) identify the optimal algorithm for deployment in occupational health surveillance. Methods: A dataset of 4657 observations was constructed from Life Span Study-derived ERR/Gy parameters, adapted to occupational low-dose conditions, using a dose-and-dose-rate effectiveness factor of 2.0, per ICRP Publication 103. Five features (gender, age at exposure, current age, distance from the I-131 source, and cumulative absorbed dose in the thyroid) were used to train a decision tree regressor (dtcr), a random forest regressor (rfr), and a multilayer perceptron (MLP) neural network algorithm. Results: Cumulative absorbed dose in the thyroid correlated positively with ERR/Gy.RST (r = 0.63, p < 0.01), while radiation source distance demonstrated a strong inverse association (r = −0.38, p < 0.01). The MLP algorithm achieved R2 score = 0.999, MSE = 0.002, and MAE = 0.010, substantially outperforming the rfr (R2 score = 0.700, MSE = 0.410, MAE = 0.295) and the dtcr (R2 score = 0.510, MSE = 0.654, MAE = 0.289). Conclusions: The MLP algorithm provides a high-fidelity surrogate for established ERR/Gy.RST projection tools in the NMHP context, enabling computationally efficient, feature-integrated occupational radiation-induced thyroid cancer risk quantification. These findings suggest that machine learning-based surrogate modeling is a practical, scalable complement for occupational health practitioners and radiation protection officers to support individualized surveillance of radiation-induced thyroid cancer risk in nuclear medicine departments. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chouchen%2C+Mariem%22">Chouchen, Mariem</searchLink><br /><searchLink fieldCode="AR" term="%22Barki%2C+Chamseddine%22">Barki, Chamseddine</searchLink><br /><searchLink fieldCode="AR" term="%22Dergaa%2C+Ismail%22">Dergaa, Ismail</searchLink><br /><searchLink fieldCode="AR" term="%22Ceylan%2C+Halil+İbrahim%22">Ceylan, Halil İbrahim</searchLink><br /><searchLink fieldCode="AR" term="%22de+Giorgio%2C+Andrea%22">de Giorgio, Andrea</searchLink><br /><searchLink fieldCode="AR" term="%22Bragazzi%2C+Nicola+Luigi%22">Bragazzi, Nicola Luigi</searchLink><br /><searchLink fieldCode="AR" term="%22Rahmouni%2C+Hanene+Boussi%22">Rahmouni, Hanene Boussi</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bioengineering (Basel); Jun2026, Vol. 13 Issue 6, p696, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Thyroid+cancer%22">Thyroid cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+medicine%22">Nuclear medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+exposure%22">Occupational exposure</searchLink><br /><searchLink fieldCode="DE" term="%22Public+health+surveillance%22">Public health surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Dose-response+relationship+%28Radiation%29%22">Dose-response relationship (Radiation)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk assessment in occupational exposure settings. This study examined whether machine learning algorithms can serve as high-precision computational surrogates for excess relative risk estimation in NMHP. Aim: The study aimed to (i) develop and validate three machine learning algorithms for predicting the excess relative risk per unit absorbed dose for radiation-induced solid thyroid cancer (ERR/Gy.RST), (ii) characterize relationships between dosimetric and demographic features and predicted risk, and (iii) identify the optimal algorithm for deployment in occupational health surveillance. Methods: A dataset of 4657 observations was constructed from Life Span Study-derived ERR/Gy parameters, adapted to occupational low-dose conditions, using a dose-and-dose-rate effectiveness factor of 2.0, per ICRP Publication 103. Five features (gender, age at exposure, current age, distance from the I-131 source, and cumulative absorbed dose in the thyroid) were used to train a decision tree regressor (dtcr), a random forest regressor (rfr), and a multilayer perceptron (MLP) neural network algorithm. Results: Cumulative absorbed dose in the thyroid correlated positively with ERR/Gy.RST (r = 0.63, p < 0.01), while radiation source distance demonstrated a strong inverse association (r = −0.38, p < 0.01). The MLP algorithm achieved R<superscript>2</superscript> score = 0.999, MSE = 0.002, and MAE = 0.010, substantially outperforming the rfr (R<superscript>2</superscript> score = 0.700, MSE = 0.410, MAE = 0.295) and the dtcr (R<superscript>2</superscript> score = 0.510, MSE = 0.654, MAE = 0.289). Conclusions: The MLP algorithm provides a high-fidelity surrogate for established ERR/Gy.RST projection tools in the NMHP context, enabling computationally efficient, feature-integrated occupational radiation-induced thyroid cancer risk quantification. These findings suggest that machine learning-based surrogate modeling is a practical, scalable complement for occupational health practitioners and radiation protection officers to support individualized surveillance of radiation-induced thyroid cancer risk in nuclear medicine departments. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bioengineering (Basel) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/bioengineering13060696 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 696 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Thyroid cancer Type: general – SubjectFull: Nuclear medicine Type: general – SubjectFull: Disease risk factors Type: general – SubjectFull: Occupational exposure Type: general – SubjectFull: Public health surveillance Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Dose-response relationship (Radiation) Type: general Titles: – TitleFull: Machine Learning Prediction of Excess Relative Risk for Radiation-Induced Solid Thyroid Cancer Among Nuclear Medicine Healthcare Professionals: A Computational Modeling Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chouchen, Mariem – PersonEntity: Name: NameFull: Barki, Chamseddine – PersonEntity: Name: NameFull: Dergaa, Ismail – PersonEntity: Name: NameFull: Ceylan, Halil İbrahim – PersonEntity: Name: NameFull: de Giorgio, Andrea – PersonEntity: Name: NameFull: Bragazzi, Nicola Luigi – PersonEntity: Name: NameFull: Rahmouni, Hanene Boussi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23065354 Numbering: – Type: volume Value: 13 – Type: issue Value: 6 Titles: – TitleFull: Bioengineering (Basel) Type: main |
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