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Title: SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy

Abstract

Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linearmore » counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Authors:
; ; ; ; ;  [1]
  1. University of Maryland School of Medicine, Baltimore, MD (United States)
Publication Date:
OSTI Identifier:
22624355
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; ERRORS; ESOPHAGUS; HISTOLOGY; IMAGES; NEOPLASMS; PATIENTS; POSITRON COMPUTED TOMOGRAPHY; SIMULATION; SPATIAL RESOLUTION; VALIDATION

Citation Formats

Riyahi, S, Choi, W, Bhooshan, N, Tan, S, Zhang, H, and Lu, W. SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy. United States: N. p., 2016. Web. doi:10.1118/1.4955602.
Riyahi, S, Choi, W, Bhooshan, N, Tan, S, Zhang, H, & Lu, W. SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy. United States. doi:10.1118/1.4955602.
Riyahi, S, Choi, W, Bhooshan, N, Tan, S, Zhang, H, and Lu, W. 2016. "SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy". United States. doi:10.1118/1.4955602.
@article{osti_22624355,
title = {SU-C-207B-06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy},
author = {Riyahi, S and Choi, W and Bhooshan, N and Tan, S and Zhang, H and Lu, W},
abstractNote = {Purpose: To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer. Methods: Linear and multi-resolution BSpline deformable registration were performed on Pre-Post-CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi-modality. Similarity of Warped-CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial-temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10-fold cross-validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC). Results: Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation. Conclusion: Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration. This work was supported in part by the National Cancer Institute Grants R01CA172638.},
doi = {10.1118/1.4955602},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changesmore » resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.« less
  • Purpose: To extract and study comprehensive spatial-temporal {sup 18}F-labeled fluorodeoxyglucose ([{sup 18}F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer. Methods and Materials: Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [{sup 18}F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, andmore » associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value. Results: The best traditional response measure was decline in maximum SUV (SUV{sub max}; AUC, 0.76). Two new intensity features, decline in mean SUV (SUV{sub mean}) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUV{sub mean} decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [{sup 18}F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone. Conclusion: Spatial-temporal [{sup 18}F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.« less
  • Purpose: Pilot study developing a CT-texture based model for early assessment of treatment response during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Daily CT data acquired for 24 pancreatic head cancer patients using CT-on-rails, during the routine CT-guided CRT delivery with a radiation dose of 50.4 Gy in 28 fractions, were analyzed. The pancreas head was contoured on each daily CT. Texture analysis was performed within the pancreas head contour using a research tool (IBEX). Over 1300 texture metrics including: grey level co-occurrence, run-length, histogram, neighborhood intensity difference, and geometrical shape features were calculated for each dailymore » CT. Metric-trend information was established by finding the best fit of either a linear, quadratic, or exponential function for each metric value verses accumulated dose. Thus all the daily CT texture information was consolidated into a best-fit trend type for a given patient and texture metric. Linear correlation was performed between the patient histological response vector (good, medium, poor) and all combinations of 23 patient subgroups (statistical jackknife) determining which metrics were most correlated to response and repeatedly reliable across most patients. Control correlations against CT scanner, reconstruction kernel, and gated/nongated CT images were also calculated. Euclidean distance measure was used to group/sort patient vectors based on the data of these trend-response metrics. Results: We found four specific trend-metrics (Gray Level Coocurence Matrix311-1InverseDiffMomentNorm, Gray Level Coocurence Matrix311-1InverseDiffNorm, Gray Level Coocurence Matrix311-1 Homogeneity2, and Intensity Direct Local StdMean) that were highly correlated with patient response and repeatedly reliable. Our four trend-metric model successfully ordered our pilot response dataset (p=0.00070). We found no significant correlation to our control parameters: gating (p=0.7717), scanner (p=0.9741), and kernel (p=0.8586). Conclusion: We have successfully created a CT-texture based early treatment response prediction model using the CTs acquired during the delivery of chemoradiation therapy for pancreatic cancer. Future testing is required to validate the model with more patient data.« less
  • Purpose: To quantitatively evaluate the esophageal cancer response to chemoradiation therapy (CRT) by measuring the esophageal wall thickness in CT. Method: Two datasets were used in this study. The first dataset is composed of CT scans of 15 esophageal cancer patients and 15 normal controls. The second dataset is composed of 20 esophageal cancer patients who underwent PET/CT scans before (Pre-CRT) and after CRT (Post-CRT). We first segmented the esophagus using a multi-atlas-based algorithm. The esophageal wall thickness was then computed, on each slice, as the equivalent circle radius of the segmented esophagus excluding the lumen. To evaluate the changesmore » of wall thickness, we computed the standard deviation (SD), coefficient of variation (COV, SD/Mean), and flatness [(Max–Min)/Mean] of wall thickness along the entire esophagus. Results: For the first dataset, the mean wall thickness of cancer patients and normal controls were 6.35 mm and 6.03 mm, respectively. The mean SD, COV, and flatness of the wall thickness were 2.59, 0.21, and 1.27 for the cancer patients and 1.99, 0.16, and 1.13 for normal controls. Statistically significant differences (p < 0.05) were identified in SD and flatness. For the second dataset, the mean wall thickness of pre-CRT and post-CRT patients was 7.13 mm and 6.84 mm, respectively. The mean SD, COV, and flatness were 1.81, 0.26, and 1.06 for pre-CRT and 1.69, 0.26, and 1.06 for post-CRT. Statistically significant difference was not identified for these measurements. Current results are based on the entire esophagus. We believe significant differences between pre- and post-CRT scans could be obtained, if we conduct the measurements at tumor sites. Conclusion: Results show thicker wall thickness in pre-CRT scans and differences in wall thickness changes between normal and abnormal esophagus. This demonstrated the potential of esophageal wall thickness as a marker in the tumor CRT response evaluation. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less
  • Purpose: To investigate whether symptom burden before and during preoperative chemoradiation therapy (CRT) for rectal cancer predicts for pathologic tumor response. Methods and Materials: Fifty-four patients with T3/T4/N+ rectal cancers were treated on a Phase II trial using preoperative capecitabine and concomitant boost radiotherapy. Symptom burden was prospectively assessed before (baseline) and weekly during CRT by patient self-reported questionnaires, the MD Anderson Symptom Inventory (MDASI), and Brief Fatigue Inventory (BFI). Survival probabilities were estimated using the Kaplan-Meier method. Symptom scores according to tumor downstaging (TDS) were compared using Student's t tests. Logistic regression was used to determine whether symptom burdenmore » levels predicted for TDS. Lowess curves were plotted for symptom burden across time. Results: Among 51 patients evaluated for pathologic response, 26 patients (51%) had TDS. Fatigue, pain, and drowsiness were the most common symptoms. All symptoms increased progressively during treatment. Patients with TDS had lower MDASI fatigue scores at baseline and at completion (Week 5) of CRT (p = 0.03 for both) and lower levels of BFI 'usual fatigue' at baseline. Conclusion: Lower levels of fatigue at baseline and completion of CRT were significant predictors of pathologic tumor response gauged by TDS, suggesting that symptom burden may be a surrogate for tumor burden. The relationship between symptom burden and circulating cytokines merits evaluation to characterize the molecular basis of this phenomenon.« less