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

Abstract

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.

Supervised Machine Learning in Oncology: A Clinician's Guide

“…To this end, some early research has been conducted to predict treatment response from tumor traits and clinical imaging. 21,[24][25][26] Most importantly, clinical applicability needs to be brought to the forefront of machine learning research in cancer. Further work must be done to compare the performance of machine learning models to general clinician performance to tangibly demonstrate the value of these methods in patient care.…”

“…20 Another study was able to accomplish the similar task of predicting tumor response to chemoradiotherapy in esophageal cancer using readily available imaging radiomics features, which, in turn, improved the relative translational utility of the model. 21 As genomic analysis of tumors is incorporated into clinical practice, the translational potential of these studies will grow in tandem. However, the present-day clinical applicability of a proposed model should be considered.…”

Structure guided deformable image registration for treatment planning CT and post stereotactic body radiation therapy (SBRT) Primovist® (Gd‐EOB‐DTPA) enhanced MRI

“…Using the above approach, the Jacobian is calculated for every voxel thus creating a Jacobian map. J i greater than 1 corresponds to the increase in volume, J i less than 1 implies a decrease in volume, J i = 1 corresponds to no volume change, and J i < 0 is indicative of unrealistic and nonphysiological voxel motion such as DVF tearing and folding . The percentage of liver volume with negative Jacobian values was quantified for DIR, and two types of SG‐DIR.…”

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

“…Weekly local volumetric esophagus expansion was quantified using Jacobian maps (J) calculated from DIR between the wMRI images. Each MRI at current week was registered to its previous week MRI: J was calculated at each voxel as the determinant of the gradient of the DVF that measured the ratio of local volume change where J > 1 indicates local volume expansion, J < 1 shrinkage and J = 1 no change [24]. The Jacobian integral defined as [(Mean J -1) × baseline volume] measured the net local volume change [20,24].…”