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Title: SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

Abstract

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informativemore » than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

Authors:
; ;  [1]
  1. University of Maryland School of Medicine, Baltimore, MD (United States)
Publication Date:
OSTI Identifier:
22545135
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 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; ACCURACY; ALGORITHMS; COMPUTERIZED TOMOGRAPHY; IMAGE PROCESSING; IMAGES; NEOPLASMS; POSITRON COMPUTED TOMOGRAPHY; REVIEWS; THERAPY

Citation Formats

Lu, W, Wang, J, and Zhang, H. SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy. United States: N. p., 2015. Web. doi:10.1118/1.4924361.
Lu, W, Wang, J, & Zhang, H. SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy. United States. doi:10.1118/1.4924361.
Lu, W, Wang, J, and Zhang, H. Mon . "SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy". United States. doi:10.1118/1.4924361.
@article{osti_22545135,
title = {SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy},
author = {Lu, W and Wang, J and Zhang, H},
abstractNote = {Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.},
doi = {10.1118/1.4924361},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: To review the literature in using quantitative PET and CT image features for the evaluation of tumor response. Methods: We reviewed and summarized more than fifty papers that use advanced, quantitative PET/CT image features for the evaluation of tumor response. We also discussed future works on extracting disease-specific features, combining multiple and complementary features in response modeling, delineating tumor in multimodality images, and exploring biological explanations of these advanced features. Results: Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features (characterizing spatial distribution of FDG uptake) havemore » been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Conclusions: Advanced, quantitative FDG PET/CT image features have been shown promising for the evaluation of tumor response. With the emerging multi-modality imaging performed at multiple time points for each patient, it becomes more important to analyze the serial images quantitatively, select and combine both complementary and contradictory information from various sources, for accurate and personalized evaluation of tumor response to therapy.« less
  • Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver onmore » pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less
  • Purpose: The purpose of this study was to compare a radiation therapy treatment planning that would spare active bone marrow and whole pelvic bone marrow using 18F FLT PET/CT image. Methods: We have developed an IMRT planning methodology to incorporate functional PET imaging using 18F FLT/CT scans. Plans were generated for two cervical cancer patients, where pelvicactive bone marrow region was incorporated as avoidance regions based on the range: SUV>2., another region was whole pelvic bone marrow. Dose objectives were set to reduce the volume of active bone marrow and whole bone marraw. The volumes of received 10 (V10) andmore » 20 (V20) Gy for active bone marrow were evaluated. Results: Active bone marrow regions identified by 18F FLT with an SUV>2 represented an average of 48.0% of the total osseous pelvis for the two cases studied. Improved dose volume histograms for identified bone marrow SUV volumes and decreases in V10(average 18%), and V20(average 14%) were achieved without clinically significant changes to PTV or OAR doses. Conclusion: Incorporation of 18F FLT/CT PET in IMRT planning provides a methodology to reduce radiation dose to active bone marrow without compromising PTV or OAR dose objectives in cervical cancer.« less
  • Purpose: Positron-emitting isotope distributions can be used for the image fusion of the carbon ion planning CT and online target verification PETCT, after radiation in the same decay period,the relationship between the same target volume and the SUV value of different every single fraction dose can be found,then the range of SUV for the radiation target could be decided.So this online range also can provide reference for the correlation and consistency in planning target dose verification and evaluation for the clinical trial. Methods: The Rando head phantom can be used as real body,the 10cc cube volume target contouring is done,beammore » ISO Center depth is 7.6cm and the 90 degree fixed carbon ion beams should be delivered in single fraction effective dose of 2.5GyE,5GyE and 8GyE.After irradiation,390 seconds later the 30 minutes PET-CT scanning is performed,parameters are set to 50Kg virtual weight,0.05mCi activity.MIM Maestro is used for the image processing and fusion,five 16mm diameter SUV spheres have been chosen in the different direction in the target.The average SUV in target for different fraction dose can be found by software. Results: For 10cc volume target,390 seconds decay period,the Single fraction effective dose equal to 2.5Gy,Ethe SUV mean value is 3.42,the relative range is 1.72 to 6.83;Equal to 5GyE,SUV mean value is 9.946,the relative range is 7.016 to 12.54;Equal or above to 8GyE,SUV mean value is 20.496,the relative range is 11.16 to 34.73. Conclusion: Making an evaluation for accuracy of the dose distribution using the SUV range which is from the planning CT with after treatment online PET-CT fusion for the normal single fraction carbon ion treatment is available.Even to the plan which single fraction dose is above 2GyE,in the condition of other parameters all the same,the SUV range is linearly dependent with single fraction dose,so this method also can be used in the hyper-fraction treatment plan.« less
  • Purpose: The aim of our proposed system is to confirm the feasibility of extraction of two types of images from one positron emission tomography (PET) module with an insertable collimator for brain tumor treatment during the BNCT. Methods: Data from the PET module, neutron source, and collimator was entered in the Monte Carlo n-particle extended (MCNPX) source code. The coincidence events were first compiled on the PET detector, and then, the events of the prompt gamma ray were collected after neutron emission by using a single photon emission computed tomography (SPECT) collimator on the PET. The obtaining of full widthmore » at half maximum (FWHM) values from the energy spectrum was performed to collect effective events for reconstructed image. In order to evaluate the images easily, five boron regions in a brain phantom were used. The image profiles were extracted from the region of interest (ROI) of a phantom. The image was reconstructed using the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The image profiles and the receiver operating characteristic (ROC) curve were compiled for quantitative analysis from the two kinds of reconstructed image. Results: The prompt gamma ray energy peak of 478 keV appeared in the energy spectrum with a FWHM of 41 keV (6.4%). On the basis of the ROC curve in Region A to Region E, the differences in the area under the curve (AUC) of the PET and SPECT images were found to be 10.2%, 11.7%, 8.2% (center, Region C), 12.6%, and 10.5%, respectively. Conclusion: We attempted to acquire the PET and SPECT images simultaneously using only PET without an additional isotope. Single photon images were acquired using an insertable collimator on a PET detector. This research was supported by the Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technologies (ICT) and Future Planning (MSIP)(Grant No.2009 00420) and the Radiation Technology R and D program (Grant No.2013M2A2A7043498), Republic of Korea.« less