AI Article Synopsis
- The study aimed to create an AI clinical decision support system that predicts radiation doses to specific parts of the mandible using CT scans before planning radiation therapy for head and neck cancer.
- Using data from 106 patients, the AI model demonstrated impressive predictive capabilities, with a positive predictive value of 0.95 and a negative predictive value of 0.88, and showed strong correlation with estimates from an experienced oncologist.
- The findings indicate that the AI system improves the accuracy of predicting radiation dose to dental structures in patients undergoing radiation therapy, highlighting its potential role in enhancing treatment planning.
Article Abstract
Purpose: The aim was to develop a novel artificial intelligence (AI)-guided clinical decision support system, to predict radiation doses to subsites of the mandible using diagnostic computed tomography scans acquired before any planning of head and neck radiation therapy (RT).
Methods And Materials: A dose classifier was trained using RT plans from 86 patients with oropharyngeal cancer; the test set consisted of an additional 20 plans. The classifier was trained to predict whether mandible subsites would receive a mean dose >50 Gy. The AI predictions were prospectively evaluated and compared with those of a specialist head and neck radiation oncologist for 9 patients. Positive predictive value (PPV), negative predictive value (NPV), Pearson correlation coefficient, and Lin concordance correlation coefficient were calculated to compare the AI predictions to those of the physician.
Results: In the test data set, the AI predictions had a PPV of 0.95 and NPV of 0.88. For 9 patients evaluated prospectively, there was a strong correlation between the predictions of the AI algorithm and physician ( = .72, < .001). Comparing the AI algorithm versus the physician, the PPVs were 0.82 versus 0.25, and the NPVs were 0.94 versus 1.0, respectively. Concordance between physician estimates and final planned doses was 0.62; this was 0.71 between AI-based estimates and final planned doses.
Conclusion: AI-guided decision support increased precision and accuracy of pre-RT dental dose estimates.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977910 | PMC |
| http://dx.doi.org/10.1016/j.adro.2021.100886 | DOI Listing |
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