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. 2024 Aug 1;119(5):1569-1578.
doi: 10.1016/j.ijrobp.2024.02.021. Epub 2024 Mar 10.

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

Seyedmohammadhossein Hosseinian  1 , Mehdi Hemmati  2 , Cem Dede  3 , Travis C Salzillo  3 , Lisanne V van Dijk  4 , Abdallah S R Mohamed  5 , Stephen Y Lai  6 , Andrew J Schaefer  7 , Clifton D Fuller  8
Affiliations

Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

Seyedmohammadhossein Hosseinian et al. Int J Radiat Oncol Biol Phys. .

Abstract

Purpose: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.

Methods and materials: The analysis was conducted on retrospective data of 1259 patients with head and neck cancer treated at The University of Texas MD Anderson Cancer Center between 2005 and 2015. During a minimum 12-month posttherapy follow-up period, 173 patients in this cohort (13.7%) developed ORN (grades I to IV). The (structural) clusters of mandibular dose-volume histograms (DVHs) for these patients were identified using the K-means clustering method. A soft-margin support vector machine was used to determine the cluster borders and partition the dose-volume space. The risk of ORN for each dose-volume region was calculated based on incidence rates and other clinical risk factors.

Results: The K-means clustering method identified 6 clusters among the DVHs. Based on the first 5 clusters, the dose-volume space was partitioned by the soft-margin support vector machine into distinct regions with different risk indices. The sixth cluster entirely overlapped with the others; the region of this cluster was determined by its envelopes. For each region, the ORN incidence rate per preradiation dental extraction status (a statistically significant, nondose related risk factor for ORN) was reported as the corresponding risk index.

Conclusions: This study presents an unsupervised-learning analysis of a large-scale data set to evaluate the risk of mandibular ORN among patients with head and neck cancer. The results provide a visual risk-assessment tool for ORN (based on the whole DVH and preradiation dental extraction status) as well as a range of constraints for dose optimization under different risk levels.

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Figures

Fig. 1.
Fig. 1.
Inertia plot of the K-means clustering method.
Fig. 2.
Fig. 2.
Visualization of the identified clusters. (A) K = 5; (B) K = 6.
Fig. 3.
Fig. 3.
Cluster borders identified by soft-margin support vector machine. (A) K = 5: Cluster borders; (B) K = 6: Borders of the first 5 clusters; (C) K = 6: Envelopes of the sixth cluster.
Fig. 4.
Fig. 4.
Risk indices of dose-volume regions for K = 6. (A) No/edentulous dental extractions (PDE = 0). (B) With dental extractions (PDE = 1).
Fig. 5.
Fig. 5.
Different osteoradionecrosis incidences among dose-volume histograms with the same D30% value.
See this image and copyright information in PMC

Update of

References

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