Validating a predictive atlas of tumor shrinkage for adaptive radiotherapy of locally advanced lung cancer Journal Article
| Authors: | Zhang, P.; Yorke, E.; Mageras, G.; Rimner, A.; Sonke, J. J.; Deasy, J. O. |
| Article Title: | Validating a predictive atlas of tumor shrinkage for adaptive radiotherapy of locally advanced lung cancer |
| Abstract: | Purpose: To cross-validate and expand a predictive atlas that can estimate geometric patterns of lung tumor shrinkage during radiation therapy using data from 2 independent institutions and to model its integration into adaptive radiation therapy (ART) for enhanced dose escalation. Methods and Materials: Data 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 radiation therapy. Subsequently, the atlas was expanded by integrating all 34 patients. Each study patient was selected via a leave-one-out scheme and was matched with a subgroup of 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 midcourse 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 midcourse imaging and to PTP-ART with the recalibrated prediction. Results: Predictive accuracy (true positive plus true negative ratios based on predicted and actual residual tumor) were comparable across institutions, 0.71 versus 0.73, and improved to 0.74 with an expanded atlas including 2 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. Conclusion: A 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 improve the efficacy and efficiency of treatment compared to ART with midcourse replanning. © 2018 Elsevier Inc. |
| Keywords: | treatment planning; radiotherapy; tumors; forecasting; biological organs; similarity measure; shrinkage; adaptive radiotherapy; predictive accuracy; adaptive radiation therapies; methods and materials; bayesian networks; geometric patterns; predicted residuals |
| Journal Title: | International Journal of Radiation Oncology, Biology, Physics |
| Volume: | 102 |
| Issue: | 4 |
| ISSN: | 0360-3016 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2018-11-15 |
| Start Page: | 978 |
| End Page: | 986 |
| Language: | English |
| DOI: | 10.1016/j.ijrobp.2018.05.056 |
| PROVIDER: | scopus |
| PUBMED: | 30061006 |
| PMCID: | PMC6643285 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 November 2018 -- Source: Scopus |
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