A geometric atlas to predict lung tumor shrinkage for radiotherapy treatment planning

To develop a geometric atlas that can predict tumor shrinkage and guide treatment planning for non-small-cell lung cancer. To evaluate the impact of the shrinkage atlas on the ability of tumor dose escalation.The creation of a geometric atlas included twelve patients with lung cancer who underwent both planning CT and weekly CBCT for radiotherapy planning and delivery. The shrinkage pattern from the original pretreatment to the residual posttreatment tumor was modeled using a principal component analysis, and used for predicting the spatial distribution of the residual tumor. A predictive map was generated by unifying predictions from each individual patient in the atlas, followed by correction for the tumor's surrounding tissue distribution. Sensitivity, specificity, and accuracy of the predictive model for classifying voxels inside the original gross tumor volume were evaluated. In addition, a retrospective study of predictive treatment planning (PTP) escalated dose to the predicted residual tumor while maintaining the same level of predicted complication rates for a clinical plan delivering uniform dose to the entire tumor. The effect of uncertainty on the predictive model's ability to escalate dose was also evaluated.The sensitivity, specificity and accuracy of the predictive model were 0.73, 0.76, and 0.74, respectively. The area under the receiver operating characteristic curve for voxel classification was 0.87. The Dice coefficient and mean surface distance between the predicted and actual residual tumor averaged 0.75, and 1.6 mm, respectively. The PTP approach allowed elevation of PTV D95 and mean dose to the actual residual tumor by 6.5 Gy and 10.4 Gy, respectively, relative to the clinical uniform dose approach.A geometric atlas can provide useful information on the distribution of resistant tumors and effectively guide dose escalation to the tumor without compromising the organs at risk complications. The atlas can be further refined by using more patient data sets.