Lung deformations and radiation-induced regional lung collapse in patients treated with stereotactic body radiation therapy

Evaluation of tissue shrinkage after CT-guided microwave ablation in patients with liver malignancies using Jacobian determinant

PURPOSE

To assess short-term tissue shrinkage in patients with liver malignancies undergoing computed tomography (CT)-guided microwave ablation (MWA) using Jacobian determinant (JD).

MATERIALS AND METHODS

Twenty-nine patients with 29 hepatic malignancies (primary n = 24; metastases n = 5; median tumor diameter 18 mm) referred to CT-guided MWA (single position; 10 min, 100 W) were included in this retrospective IRB-approved study, after exclusion of five patients. Following segmentation of livers and tumors on pre-interventional images, segmentations were registered on post-interventional images. JD mapping was applied to quantify voxelwise tissue volume changes after MWA. Percentual volume changes were evaluated in the ablated tumor, a 5-cm tumor perimeter and in the whole liver and compared in different clinical conditions (tumor entity: primary vs. secondary; tumor location: subcapsular vs. non-subcapsular; tumor volume: >/<6 ml: cirrhosis: yes vs. no; prior chemotherapy: yes vs. no using Shapiro-Wilk, χ² and Wilcoxon rank sum tests, respectively (with p < 0.05 deemed significant).

RESULTS

Tissue volume change was 0.6% in the ablated tumor, 1.6% in the 5-cm perimeter and 0.3% in the whole liver. Shrinkage in the ablated tumor was pronounced in non-subcapsular located tumors, whereas tissue expansion was noted in subcapsular tumors (median -3.5 vs. 1.1%; p = 0.0195). Shrinkage in the whole liver was higher in tumor volumes >6ml, compared with smaller tumors, in which tissue expansion was noted (median -1.0 vs. 2.5%; p = 0.002). Other clinical conditions had no significant influence on the extent of tissue shrinkage (p > 0.05).

CONCLUSION

3D Jacobian analysis shows that hepatic tissue deformation following MWA is most pronounced in a 5-cm area surrounding the treated tumor. Tumor location and tumor volume may have an impact on the extent of tissue shrinkage which may affect estimation of the safety margin.

Identification of modes of tumor regression in non-small cell lung cancer patients during radiotherapy

PURPOSE

Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression.

METHODS

In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies.

RESULTS

Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies.

CONCLUSION

Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.

Deformable Registration for Dose Accumulation

As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.

Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer

We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using a Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline deformable registration was performed to register the follow-up (post-CRT) CT to the baseline CT image. The Jacobian map (J) was computed as the determinant of the gradient of the deformation vector field. The Jacobian map measured the ratio of local tumor volume change where J  <  1 indicated tumor shrinkage and J  >  1 denoted expansion. The tumor was manually delineated and corresponding anatomical landmarks were generated on the baseline and follow-up images. Intensity, texture and geometry features were then extracted from the Jacobian map of the tumor to quantify tumor morphological changes. The importance of each Jacobian feature in predicting pathologic tumor response was evaluated by both univariate and multivariate analysis. We constructed a multivariate prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO) for feature selection. The SVM-LASSO model was evaluated using ten-times repeated 10-fold cross-validation (10  ×  10-fold CV). After registration, the average target registration error was 4.30  ±  1.09 mm (LR:1.63 mm AP:1.59 mm SI:3.05 mm) indicating registration error was within two voxels and close to 4 mm slice thickness. Visually, the Jacobian map showed smoothly-varying local shrinkage and expansion regions in a tumor. Quantitatively, the average median Jacobian was 0.80  ±  0.10 and 1.05  ±  0.15 for responder and non-responder tumors, respectively. These indicated that on average responder tumors had 20% median volume shrinkage while non-responder tumors had 5% median volume expansion. In univariate analysis, the minimum Jacobian (p  =  0.009, AUC  =  0.98) and median Jacobian (p  =  0.004, AUC  =  0.95) were the most significant predictors. The SVM-LASSO model achieved the highest accuracy when these two features were selected (sensitivity  =  94.4%, specificity  =  91.8%, AUC  =  0.94). Novel features extracted from the Jacobian map quantified local tumor morphological changes using only baseline tumor contour without post-treatment tumor segmentation. The SVM-LASSO model using the median Jacobian and minimum Jacobian achieved high accuracy in predicting pathologic tumor response. The Jacobian map showed great potential for longitudinal evaluation of tumor response.