Detecting and Evaluating Therapy Induced Changes in Radiomics Features Measured from Non-Small Cell Lung Cancer to Predict Patient Outcomes

The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers (NSCLC) change during treatment and if those changes are prognostic for patient outcomes or dependent on treatment modality. Radiomics features are quantitative metrics designed to evaluate tumor heterogeneity from routine medical imaging. Features that are prognostic for patient outcome could be used to monitor tumor response and identify high-risk patients for adaptive treatment. This would be especially valuable for NSCLC due to the high prevalence and mortality of this disease. A novel process was designed to select feature-specific image preprocessing and remove features that were not robust to differences in CT model or tumor volumes. These features were then measured from weekly 4DCT images. These features were evaluated to determine at which point in treatment they first begin changing if those changes were different for patients treated with protons versus photons. A subset of features demonstrated significant changes by the second or third week of treatment, however changes were never significantly different between patient groups. Delta-radiomics features were defined as relative net changes, linear regression slopes, and end of treatment feature values. Features were then evaluated in univariate and multivariate models for overall survival, distant metastases, and local-regional recurrence. In general, the delta-radiomics features were not more prognostic than models built using clinical factors or features at pre-treatment. However one shape descriptor measured at pre-treatment significantly improved model fit and performance for overall survival and distant metastases. Additionally for local-regional recurrence, the only significant covariate was texture strength measured at the end of treatment. A separate study characterized radiomics feature variability in cone-beam CT images to increased scatter, increased motion, and different scanners. Features were affected by all three parameters and specifically by motion amplitudes greater than 1 cm. This study resulted in strong evidence that a set of robust radiomics features change significantly during treatment. While these changes were not prognostic or dependent on treatment modality, future studies may benefit from the methodologies described here to explore delta-radiomics in alternative tumor sites or imaging modalities

Upright CBCT: A novel imaging technique

Purpose: We present a method for acquiring and correcting upright images using the on board CBCT imager. An upright imaging technique would allow for the introduction of upright radiation therapy treatments, which would benefit a variety of patients including those with thoracic cancers whose lung volumes are increased in an upright position and those who experience substantial discomfort during supine treatment positions.Methods: To acquire upright CBCT images, the linac head was positioned at 0 degrees, the KV imager and detector arms extended to their lateral positions, and the couch placed at 270 degrees. The KV imager was programmed to begin taking continuous fluoroscopic projections as the couch rotated from 270 to 90 degrees. The FOV was extended by performing this procedure twice, once with the detector shifted 14.5 cm towards the gantry and once with it shifted 14.5 cm away from the gantry. The two resulting sets of images were stitched together prior to reconstruction. The imaging parameters were chosen to deliver the some dose as that delivered during a simulation CT. A simulation CT was deformably registered to an upright CBCT reconstruction in order to evaluate the possibility of correcting the HU values via mapping.Results: Both spatial linearity and high contrast resolution were maintained in upright CBCT when compared to a simulation CT. Low contrast resolution and HU linearity decreased. Streaking artifacts were caused by the limited 180 degree arc angle and a sharp point artifact in the center of the axial slices resulted at the site of the stitching. A method for correcting the HUs was shown to be robust against these artifacts.Conclusion: Upright CBCT could be of great benefit to many patients. This study demonstrates its feasibility and presents solutions to some of its first hurdles before clinical implementation.--------------------------Cite this article as:Fave X, Yang J, Balter P, Court L. Upright CBCT: A novel imaging technique. Int J Cancer Ther Oncol 2014; 2(2):020221. DOI: 10.14319/ijcto.0202.2

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

Upright CBCT: A novel imaging technique

Purpose: We present a method for acquiring and correcting upright images using the on board CBCT imager. An upright imaging technique would allow for the introduction of upright radiation therapy treatments, which would benefit a variety of patients including those with thoracic cancers whose lung volumes are increased in an upright position and those who experience substantial discomfort during supine treatment positions.Methods: To acquire upright CBCT images, the linac head was positioned at 0 degrees, the KV imager and detector arms extended to their lateral positions, and the couch placed at 270 degrees. The KV imager was programmed to begin taking continuous fluoroscopic projections as the couch rotated from 270 to 90 degrees. The FOV was extended by performing this procedure twice, once with the detector shifted 14.5 cm towards the gantry and once with it shifted 14.5 cm away from the gantry. The two resulting sets of images were stitched together prior to reconstruction. The imaging parameters were chosen to deliver the some dose as that delivered during a simulation CT. A simulation CT was deformably registered to an upright CBCT reconstruction in order to evaluate the possibility of correcting the HU values via mapping.Results: Both spatial linearity and high contrast resolution were maintained in upright CBCT when compared to a simulation CT. Low contrast resolution and HU linearity decreased. Streaking artifacts were caused by the limited 180 degree arc angle and a sharp point artifact in the center of the axial slices resulted at the site of the stitching. A method for correcting the HUs was shown to be robust against these artifacts.Conclusion: Upright CBCT could be of great benefit to many patients. This study demonstrates its feasibility and presents solutions to some of its first hurdles before clinical implementation.--------------------------Cite this article as:Fave X, Yang J, Balter P, Court L. Upright CBCT: A novel imaging technique. Int J Cancer Ther Oncol 2014; 2(2):020221. DOI: 10.14319/ijcto.0202.21</p

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

{Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants’ feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n=5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n=2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets towards clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings