Presenting Clinical Outcome Modeling of 3D MLC-Based SFRT Treatments for Large and Unresectable Tumors
Abstract
Purpose
This study models clinical outcomes of SFRT treatments delivered using the 3D MLC-based Crossfire technique.
Methods
A total of 131 SFRT patients (median tumor volume = 261.7 cc) with follow-up data (median interval 4 months) consisting of either tumor control, pain relief, toxicity, and overall survival (OS). Median SFRT prescription was 15Gy/1fx. The linear-quadratic model was used to calculate biological effective dose (BED) metrics (D10%, D50%, D90%, D95%) and equivalent dose in 2Gy-per-fraction (EQD2) from the combined effective dose of SFRT plus follow-up treatments using an α/β of 10 and 3 Gy for tumors and normal tissues, respectively. Follow-up treatments were either palliative (median: 30Gy/10fx) or curative (median: 70Gy/35fx) depending on histopathology. Multivariable logistic regression was used to model tumor control probability (TCP) and pain relief using BED metrics. OS was modeled with an univariable Cox hazard model using BED metrics. Toxicity was modeled with univariable logistic regression using maximum EQD2.
Results
The TCP model (75/98 tumors reported clinical benefit) yielded an AUC of 0.667 with no dose metrics independently associated with local control (p>0.05). The pain relief model (59/82 reported benefit) had an AUC of 0.567, again with no statistically significant metrics. In the Cox model, increases in all dose metrics were associated with improved OS (p<0.05). Toxicities were reported in 28/95 patients, including seven grade 3+ events. The skin’s max EQD2 was associated with skin toxicity (p=0.014), and the parotid’s max EQD2 was associated with xerostomia (p=0.002).
Conclusion
We present predictive models using the outcomes from a single institution’s experience with SFRT planned with a 3D MLC-Crossfire technique. These models provide a framework for identifying key metrics that may improve SFRT planning and delivery. Results should be interpreted cautiously given the limited number of adverse events (toxicity and tumor growth) and the heterogeneity of the patient cohort.