Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Ou, Yangming; Weinstein, Susan P.; Conant, Emily F.; Englander, Sarah; Xiao, Da; Gaonkar, Bilwaj; Hsieh, Meng Kang; Rosen, Mark; DeMichele, Angela; Davatzikos, Christos; Kontos, Despina

doi:10.1002/mrm.25368

Cited by 36 publications

(46 citation statements)

References 101 publications

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“…Because of these characteristics and its public availability 3 , DRAMMS has found applications in numerous translational studies including neuro-degenerative studies [17,41,72,90], neuro-developmental ones [21,33,60,70] as well as oncology studies [6,59]. These applications underline the versatility of combining attribute-based similarity criteria with graph-based formulations.…”

Section: Experimental Validationmentioning

confidence: 96%

Graph-based Deformable Image Registration

Sotiras

Paragios

et al. 2015

Handbook of Biomedical Imaging

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Deformable image registration is a field that has received considerable attention in the medical image analysis community. As a consequence, there is an important body of works that aims to tackle deformable registration. In this chapter we review one class of these techniques that use discrete optimization, and more specifically Markov Random Field models. We begin the chapter by explaining how one can formulate the deformable registration problem as a minimal cost graph problem where the nodes of the graph corresponds to the deformation grid, the graph connectivity encodes regularization constraints, and the labels correspond to 3D displacements. We then explain the use of discrete models in intensitybased volumetric registration. In the third section, we detail the use of Gabor-based attribute vectors in the context of discrete deformable registration, demonstrating the versatility of the graph-based models. In the last section of the chapter, the case of landmark-based registration is discussed. We first explain the discrete graphical model behind establishing landmark correspondences, and then continue to show how one can integrate it with the intensity-based model towards creating enhanced models that combine the best of both worlds.

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Section: Experimental Validationmentioning

confidence: 96%

Graph-based Deformable Image Registration

Sotiras

Paragios

et al. 2015

Handbook of Biomedical Imaging

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“…Each deformable registration algorithm is typically defined by three essential components: 1) voxel characterization, 2) similarity metric and 3) transformation model. The details for the selected six algorithms of three packages are listed in Table 1, and the detailed parameter setting for the selected algorithms can be referred in [21]. Note, in order to carry out a fair comparison, the parameters for all the studied software were optimally adjusted according to the given imaging set (see more details in [21]).…”

Section: Deformable Registration Algorithmsmentioning

confidence: 99%

“…Previously, our group has performed a pioneering study to compare our in-house algorithm DRAMMS [20] with five other deformable registration algorithms for longitudinal breast image registration, in general with DRAMMS giving the highest registration accuracy [21]. In this study, we intend to further evaluate and validate this open source registration The main contributions of this paper are two-fold: 1) we have proposed new ways to assess and more deeply understand the performance of DRAMMS within the imaging subregions (note the whole breast region are labeled with various registration challenge levels based on inter-rater disagreement and 2) comprehensively tune the parameters of DRAMMS to optimize the configuration for longitudinal breast DCE-MR imaging registration application, which will guide future large population studies.…”

Section: Introductionmentioning

confidence: 99%

Quantification of tumor changes during neoadjuvant chemotherapy with longitudinal breast DCE-MRI registration

Weinstein

et al. 2015

SPIE Proceedings

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Imaging plays a central role in the evaluation of breast tumor response to neoadjuvant chemotherapy. Image-based assessment of tumor change via deformable registration is a powerful, quantitative method potentially to explore novel information of tumor heterogeneity, structure, function, and treatment response. In this study, we continued a previous pilot study to further validate the feasibility of an open source deformable registration algorithm DRAMMS developed within our group as a means to analyze spatio-temporal tumor changes for a set of 14 patients with DCE-MR imaging. Two experienced breast imaging radiologists marked landmarks according to their anatomical meaning on image sets acquired before and during chemotherapy. Yet, chemotherapy remarkably changed the anatomical structure of both tumor and normal breast tissue, leading to significant discrepancies between both raters for landmarks in certain areas. Therefore, we proposed a novel method to grade the manually denoted landmarks into different challenge levels based on the inter-rater agreement, where a high level indicates significant discrepancies and considerable amounts of anatomical structure changes, which would indeed impose giant problem for the following registration algorithm. It is interesting to observe that DRAMMS performed in a similar manner as the human raters: landmark errors increased as inter-rater differences rose. Among all selected six deformable registration algorithms, DRAMMS achieves the highest overall accuracy, which is around 5.5 mm, while the average difference between human raters is 3 mm. Moreover, DRAMMS performed consistently well within both tumor and normal tissue regions. Lastly, we comprehensively tuned the fundamental parameters of DRAMMS to better understand DRAMMS to guide similar works in the future. Overall, we further validated that DRAMMS is a powerful registration tool to accurately quantify tumor changes and potentially predict early tumor response to chemotherapy. Therefore, future studies that aim at examining if DRAMMS can generate valuable biomarkers for tumor response prediction during chemotherapy become feasible.

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“…DBM evaluates displacement of the objects while TBM exploits the gradient of the DVF, i.e. the Jacobian matrix, to track the morphometric change (Rey et al , 2002; Sakamoto et al , 2014; Dennis et al , 2016; Ou et al , 2015). The Jacobian map obtained from the Jacobian matrix describes local volumetric shrinkage/expansion.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using 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. Jacobian map (J) was computed as the determinant of the gradient of the Deformation Vector Field. 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±1.09mm (LR:1.63mm AP:1.59mm SI:3.05mm) indicating registration error was within two voxels and close to 4mm slice thickness. Visually, 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, 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 Median Jacobian and Minimum Jacobian achieved high accuracy in predicting pathologic tumor response. Jacobian map showed great potential for longitudinal evaluation of tumor response.

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Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy

Cited by 36 publications

References 101 publications

Graph-based Deformable Image Registration

Graph-based Deformable Image Registration

Quantification of tumor changes during neoadjuvant chemotherapy with longitudinal breast DCE-MRI registration

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

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