Validating a predictive atlas of tumor shrinkage for adaptive radiotherapy of locally advanced lung cancer

Publication date: Available online 2 June 2018
Source:International Journal of Radiation Oncology*Biology*Physics
Author(s): Pengpeng Zhang, Ellen Yorke, Gig Mageras, Andreas Rimner, Jan-Jakob Sonke, Joseph O. Deasy
PurposeTo cross-validate and expand a predictive atlas that can estimate geometric patterns of lung tumor shrinkage during radiotherapy using data from two independent institutions, and model its integration into adaptive radiotherapy (ART) for enhanced dose escalation.MethodsData from 22 patients at a collaborating institution were obtained to cross-validate an atlas originally created with 12 patients for predicting patterns of tumor shrinkage during radiotherapy. Subsequently, the atlas was expanded by integrating all 34 patients. Each study patient was selected via a leave-one-out scheme and matched with a subgroup in the remaining 33 patients based on similarity measures of tumor volume and surroundings. The spatial distribution of residual tumor was estimated by thresholding the superimposed shrinkage patterns in the subgroup. A Bayesian method was also developed to recalibrate the prediction using the tumor observed on the mid-course images. Finally, in a retrospective predictive treatment planning (PTP) study, at the initial planning stage, the predicted residual tumors were escalated to the highest achievable dose, while maintaining the original prescription dose to the remainder of the tumor. The PTP approach was compared isotoxically to ART that replans with mid-course imaging, and to PTP-ART with the recalibrated prediction.ResultsPredictive accuracy (true positive plus true negative ratios based on predicted and actual residual tumor) were comparable across institutions, 0.71 vs 0.73, and improved to 0.74 with an expanded atlas including two institutions. Recalibration further improved accuracy to 0.76. PTP increased the mean dose to the actual residual tumor by an averaged 6.3Gy compared to ART.ConclusionA predictive atlas found to perform well across institutions, and benefit from more diversified shrinkage patterns and tumor locations. Elevating tumoricidal dose to the predicted residual tumor throughout the entire treatment course, could potentially improve the efficacy and efficiency compared to ART with mid-course replanning.

Teaser

We generated a predictive atlas that can estimate the spatial distribution of residual tumor in response to radiotherapy, and validated the atlas across independent institutions. Treatment planning guided by the prediction as well as recalibration of the prediction based on imaging surveillance can provide relevant dose escalation to the actual residual tumor, and improve the efficacy compared to the adaptive mid-course replanning approach.


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