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Prediction of Pathologic Complete Response to Neoadjuvant Chemoradiation in the Treatment of Esophageal Cancer Using Machine Learning
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Volume 96 Number 2S Supplement 2016 Physics; University Hospitals. F. Jesseph: Employee; University Hospitals. D. Mattson: Employee; University Hospitals. T. Biswas: Employee; University Hospitals. D.B. Mansur: Employee; University Hospitals.
3714 Prediction of Pathologic Complete Response to Neoadjuvant Chemoradiation in the Treatment of Esophageal Cancer Using Machine Learning M.W. Macomber,1 A. Samareh,2 W.A. Chaovalitwongse,2 S.R. Bowen,3 S.A. Patel,4 J. Zeng,4 and M. Nyflot5; 1Department of Radiation Oncology, University of Washington, Seattle, WA, 2University of Washington Department of Industrial and Systems Engineering, Seattle, WA, 3 University of Washington Radiation Oncology and Radiology, Seattle, WA, 4University of Washington, Seattle, WA, 5University of Washington Radiation Oncology, Seattle, WA Purpose/Objective(s): PET/CT has a key role in the management of esophageal cancer; however, improved prediction models would be useful for clinical decision making. In particular, ability to predict pathologic complete response (pCR) to chemoradiation would help spare patients from an esophagectomy, a high-risk procedure with significant morbidity. Machine learning is a promising approach to develop models that can predict unseen data, but the efficacy of these models in predicting the onset of esophageal cancer has not been established. We assessed the classification accuracy of prediction models for pathologic complete response based on commonly used machine learning techniques of quantitative pretreatment PET/CT features. Materials/Methods: We identified 39 patients with esophageal cancer who completed neoadjuvant chemoradiation followed by surgery (trimodality therapy) and had pretreatment PET/CT at our institution from 2007-2015. PET/CT images were rigidly fused with radiation planning CT scans based on spine anatomy at the level of gross disease. The following quantitative PET/CT features were evaluated in the clinical target volume (CTV): mean uptake, max uptake, total lesion glycolysis (TLG), and volume. Based on these 4 features, 5 commonly used machine learning techniques (i.e., k nearest neighbors, decision tree, support vector machines, Naive Bayes, decision analysis) were employed to predict the pathologic response (binary classification). Using a leave-one-out cross-validation, the resulting prediction accuracies of these techniques were compared. Results: There were 29 male (74%) and 10 female patients included in this study (median age 61 years, 85% adenocarcinoma, 15% squamous cell carcinoma, all patients stage IIB-IIIC). Median follow-up was 17 months. Pathologic complete response was achieved in 10 patients (26%). Most disease recurrences were distant (13 patients), with 1 local recurrence. Median overall survival was 17 months. Across the 5 machine learning techniques, the average classification accuracy of predicting the pathological response was 0.60 (range, 0.47-0.74) with k nearest neighbors having greatest accuracy at 0.74. Conclusion: Prediction models with fair-to-good prediction accuracy can be developed using simple quantitative pretreatment PET/CT features for patients undergoing trimodality therapy for esophageal cancer. K nearest neighbors may be an especially appropriate method due to robustness to nonlinear data. These data suggest promise in applying machine learning algorithms on quantitative PET data to aid clinical decision making in larger patient populations. Further prospective studies are needed to validate this dataset. Author Disclosure: M.W. Macomber: None. A. Samareh: Graduate Research Assistant; University of Washington Medical Center. W.A. Chaovalitwongse: None. S.R. Bowen: Employee; Providence Health and Services. Board of Directors; Boyer Children’s Clinic. S.A. Patel: None. J. Zeng: None. M. Nyflot: None.
3715 Automatic Skull Stripping in Computed Tomographic Images Based on K-Means Statistical Classifier for Radiation Therapy Planning J. Villafruela,1,2 B.K. Menon,2,3 and N.D. Forkert1,2; 1University of Calgary, Department of Radiology, Calgary, AB, Canada, 2University of
Calgary, Hotchkiss Brain Institute, Calgary, AB, Canada, 3University of Calgary, Department of Clinical Neurosciences, Calgary, AB, Canada Purpose/Objective(s): Exact brain segmentation is fundamental for radiation treatment planning and many advanced image analyses. Manual delineation is time consuming and prone to errors. Many skull stripping methods have been developed for T1-weighted MRI brain images but cannot be applied directly to noncontrast CT (NCCT) datasets. The aim of this work was to develop and evaluate a fast and automatic skull stripping method for NCCT datasets. Materials/Methods: In the first step, noise artefacts were reduced using an edge-preserving curvature-driven flow algorithm. After this, voxels with a Hounsfield unit below 15 and above 60 were masked out to exclude air, bone, and metal structures. To account for intensity variations between different scans, the image intensities were normalized to zero mean and unit variance. After this, 4 (kZ4) tissue classes corresponding to extracranial tissues, cerebrospinal fluid, white matter, and gray matter were identified in the datasets using a k-means algorithm. The k-means clustering led to multiple misclassified voxels. This problem was solved by first dilating the identified extracranial structures by one voxel to correct for partial volume effects at the border of the brain. After this, the corrected white and gray matter labels were merged and a morphological opening by 2 voxels was applied to remove vessels and nerves connected to the brain. Finally, a largest connected component analysis was applied to extract the final brain segmentation. Results: The proposed method was evaluated using 6 NCCT datasets, which were manually segmented by 1 observer. The automatic segmentations were quantitatively compared to the manual segmentations using the Dice metric. Overall, the proposed method achieved a mean Dice value of 0.97 (ranging from 0.92 to 0.99). Differences between the segmentations were mostly found in regions with unclear borders like cortical sulci and fissures and small regions like the pituitary gland and the olfactory tract. Conclusion: The proposed skull stripping method leads to robust brain segmentations with high agreement to manual segmentations. The presented method could help accelerate radiation treatment planning and enabling advanced image analysis of NCCT datasets. Author Disclosure: J. Villafruela: None. B.K. Menon: None. N.D. Forkert: None.
3716 Challenges Associated With Pencil Beam Scanning Proton Therapy for Spinal Tumors Following Surgical Stabilization: A Robustness Evaluation of Carbon Fiber Reinforced Polyetheretherketone (Carbon-PEEK) Versus Titanium J.W. Snider III,1,2 L. Mikroutsikos,3 F. Albertini,1 A. Bolsi,1 M. Walser,1 D.C. Weber,4 and R.A. Schneider1; 1Paul Scherrer Institute, Villigen, Switzerland, 2University of Maryland Medical Center, Baltimore, MD, 3 Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland, 4 Department of Radiation Oncology, Inselspital Bern University Hospital and University of Bern, Bern, Switzerland Purpose/Objective(s): The presence of metal implant surgical stabilization (SS) within the target or beam path during delivery of high-dose particle therapy has correlated with substantially worse outcomes in clinical series. Titanium SS causes extensive imaging artifacts on CT/MRI that hinder radiation therapy planning and clinical follow-up. Alternative SS such as carbon fiberereinforced polyetheretherketone (Carbon-PEEK) have demonstrated considerably reduced image corruption. Carbon-PEEK has the added benefit for proton therapy of a stopping power much more akin to normal tissue. We hypothesized that pencil beam scanning proton therapy (PBSPT) plans with delivery in proximity to and through SS would prove more robust to daily setup uncertainties with Carbon-PEEK fixation as compared to titanium. Materials/Methods: A representative patient with entirely Carbon-PEEK fixation was planned to 74 Gy(RBE) for a chordoma of the C3 vertebral body. SS included a cervical cage and an anterior cervical plate. The nominal plan (NP) included no HU-correction for the SS. Two additional plans were generated with the SS manually overridden to appropriate HU
Volume 96 Number 2S Supplement 2016 Physics; University Hospitals. F. Jesseph: Employee; University Hospitals. D. Mattson: Employee; University Hospitals. T. Biswas: Employee; University Hospitals. D.B. Mansur: Employee; University Hospitals.
3714 Prediction of Pathologic Complete Response to Neoadjuvant Chemoradiation in the Treatment of Esophageal Cancer Using Machine Learning M.W. Macomber,1 A. Samareh,2 W.A. Chaovalitwongse,2 S.R. Bowen,3 S.A. Patel,4 J. Zeng,4 and M. Nyflot5; 1Department of Radiation Oncology, University of Washington, Seattle, WA, 2University of Washington Department of Industrial and Systems Engineering, Seattle, WA, 3 University of Washington Radiation Oncology and Radiology, Seattle, WA, 4University of Washington, Seattle, WA, 5University of Washington Radiation Oncology, Seattle, WA Purpose/Objective(s): PET/CT has a key role in the management of esophageal cancer; however, improved prediction models would be useful for clinical decision making. In particular, ability to predict pathologic complete response (pCR) to chemoradiation would help spare patients from an esophagectomy, a high-risk procedure with significant morbidity. Machine learning is a promising approach to develop models that can predict unseen data, but the efficacy of these models in predicting the onset of esophageal cancer has not been established. We assessed the classification accuracy of prediction models for pathologic complete response based on commonly used machine learning techniques of quantitative pretreatment PET/CT features. Materials/Methods: We identified 39 patients with esophageal cancer who completed neoadjuvant chemoradiation followed by surgery (trimodality therapy) and had pretreatment PET/CT at our institution from 2007-2015. PET/CT images were rigidly fused with radiation planning CT scans based on spine anatomy at the level of gross disease. The following quantitative PET/CT features were evaluated in the clinical target volume (CTV): mean uptake, max uptake, total lesion glycolysis (TLG), and volume. Based on these 4 features, 5 commonly used machine learning techniques (i.e., k nearest neighbors, decision tree, support vector machines, Naive Bayes, decision analysis) were employed to predict the pathologic response (binary classification). Using a leave-one-out cross-validation, the resulting prediction accuracies of these techniques were compared. Results: There were 29 male (74%) and 10 female patients included in this study (median age 61 years, 85% adenocarcinoma, 15% squamous cell carcinoma, all patients stage IIB-IIIC). Median follow-up was 17 months. Pathologic complete response was achieved in 10 patients (26%). Most disease recurrences were distant (13 patients), with 1 local recurrence. Median overall survival was 17 months. Across the 5 machine learning techniques, the average classification accuracy of predicting the pathological response was 0.60 (range, 0.47-0.74) with k nearest neighbors having greatest accuracy at 0.74. Conclusion: Prediction models with fair-to-good prediction accuracy can be developed using simple quantitative pretreatment PET/CT features for patients undergoing trimodality therapy for esophageal cancer. K nearest neighbors may be an especially appropriate method due to robustness to nonlinear data. These data suggest promise in applying machine learning algorithms on quantitative PET data to aid clinical decision making in larger patient populations. Further prospective studies are needed to validate this dataset. Author Disclosure: M.W. Macomber: None. A. Samareh: Graduate Research Assistant; University of Washington Medical Center. W.A. Chaovalitwongse: None. S.R. Bowen: Employee; Providence Health and Services. Board of Directors; Boyer Children’s Clinic. S.A. Patel: None. J. Zeng: None. M. Nyflot: None.
3715 Automatic Skull Stripping in Computed Tomographic Images Based on K-Means Statistical Classifier for Radiation Therapy Planning J. Villafruela,1,2 B.K. Menon,2,3 and N.D. Forkert1,2; 1University of Calgary, Department of Radiology, Calgary, AB, Canada, 2University of
Calgary, Hotchkiss Brain Institute, Calgary, AB, Canada, 3University of Calgary, Department of Clinical Neurosciences, Calgary, AB, Canada Purpose/Objective(s): Exact brain segmentation is fundamental for radiation treatment planning and many advanced image analyses. Manual delineation is time consuming and prone to errors. Many skull stripping methods have been developed for T1-weighted MRI brain images but cannot be applied directly to noncontrast CT (NCCT) datasets. The aim of this work was to develop and evaluate a fast and automatic skull stripping method for NCCT datasets. Materials/Methods: In the first step, noise artefacts were reduced using an edge-preserving curvature-driven flow algorithm. After this, voxels with a Hounsfield unit below 15 and above 60 were masked out to exclude air, bone, and metal structures. To account for intensity variations between different scans, the image intensities were normalized to zero mean and unit variance. After this, 4 (kZ4) tissue classes corresponding to extracranial tissues, cerebrospinal fluid, white matter, and gray matter were identified in the datasets using a k-means algorithm. The k-means clustering led to multiple misclassified voxels. This problem was solved by first dilating the identified extracranial structures by one voxel to correct for partial volume effects at the border of the brain. After this, the corrected white and gray matter labels were merged and a morphological opening by 2 voxels was applied to remove vessels and nerves connected to the brain. Finally, a largest connected component analysis was applied to extract the final brain segmentation. Results: The proposed method was evaluated using 6 NCCT datasets, which were manually segmented by 1 observer. The automatic segmentations were quantitatively compared to the manual segmentations using the Dice metric. Overall, the proposed method achieved a mean Dice value of 0.97 (ranging from 0.92 to 0.99). Differences between the segmentations were mostly found in regions with unclear borders like cortical sulci and fissures and small regions like the pituitary gland and the olfactory tract. Conclusion: The proposed skull stripping method leads to robust brain segmentations with high agreement to manual segmentations. The presented method could help accelerate radiation treatment planning and enabling advanced image analysis of NCCT datasets. Author Disclosure: J. Villafruela: None. B.K. Menon: None. N.D. Forkert: None.
3716 Challenges Associated With Pencil Beam Scanning Proton Therapy for Spinal Tumors Following Surgical Stabilization: A Robustness Evaluation of Carbon Fiber Reinforced Polyetheretherketone (Carbon-PEEK) Versus Titanium J.W. Snider III,1,2 L. Mikroutsikos,3 F. Albertini,1 A. Bolsi,1 M. Walser,1 D.C. Weber,4 and R.A. Schneider1; 1Paul Scherrer Institute, Villigen, Switzerland, 2University of Maryland Medical Center, Baltimore, MD, 3 Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland, 4 Department of Radiation Oncology, Inselspital Bern University Hospital and University of Bern, Bern, Switzerland Purpose/Objective(s): The presence of metal implant surgical stabilization (SS) within the target or beam path during delivery of high-dose particle therapy has correlated with substantially worse outcomes in clinical series. Titanium SS causes extensive imaging artifacts on CT/MRI that hinder radiation therapy planning and clinical follow-up. Alternative SS such as carbon fiberereinforced polyetheretherketone (Carbon-PEEK) have demonstrated considerably reduced image corruption. Carbon-PEEK has the added benefit for proton therapy of a stopping power much more akin to normal tissue. We hypothesized that pencil beam scanning proton therapy (PBSPT) plans with delivery in proximity to and through SS would prove more robust to daily setup uncertainties with Carbon-PEEK fixation as compared to titanium. Materials/Methods: A representative patient with entirely Carbon-PEEK fixation was planned to 74 Gy(RBE) for a chordoma of the C3 vertebral body. SS included a cervical cage and an anterior cervical plate. The nominal plan (NP) included no HU-correction for the SS. Two additional plans were generated with the SS manually overridden to appropriate HU