B.S. Chera

The systematic application of quality measures and process control in clinical radiation oncology.

There has been growing emphasis on quality measures and process analysis techniques that may be implemented in the daily practice of radiation oncology to improve the overall quality of patient care. In this work, quality measures are a form of monitoring that should be actionable and specific to guide process improvement efforts. They are most effective when used to determine the level of execution proficiency against a standard. Control charts are an effective way to separate a change in the process from process noise such that the user can focus on issues that are more likely to improve quality. The field of radiotherapy would benefit from a common dashboard of quality measures for the different processes in radiation oncology clinics, and some suggestions are provided. The dashboard would be used to provide continuous feedback on a clinics capability to maintain or exceed standards.

Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data

Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1–3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4). Our existing clinical practice generates discrete, quantitative, and structured patient-specific data (eg, images, doses, and volumes) that position us well to exploit and participate in big data initiatives. The well-established electronic infrastructure within radiation oncology should facilitate the retrieval and aggregation of much of the needed data. With additional efforts to integrate structured data collection of patient outcomes and assessments into the clinical workflow, the field of radiation oncology has a tremendous opportunity to generate large, comprehensive patient-specific data sets (5). However, there are major challenges to realizing this goal. For example, existing data are presently housed across different platforms at multiple institutions and are often not stored in a standardized manner or with common terminologies to enable pooling of data. In addition, many important data elements are not routinely discretely captured in clinical practice. There are cultural, structural, and logistical challenges (eg, computer compatibility and workflow demands) that will make the dream of big data research difficult. The big data research workshop provided a forum for leaders in cancer registries, incident report quality-assurance systems, radiogenomics, ontology of oncology, and a wide range of ongoing big data and cloud computing development projects to interact with peers in radiation oncology to develop strategies to harness data for research, quality assessment, and clinical care. The workshop provided a platform to discuss items such as data capture, data infrastructure, and protection of patient confidentiality and to improve awareness of the wide-ranging opportunities in radiation oncology, as well as to enhance the potential for research and collaboration opportunities with NIH on big data initiatives. The goals of the workshop were as follows: To discuss current and future sources of big data for use in radiation oncology research, To identify ways to improve our current data collection methods by adopting new strategies used in fields outside of radiation oncology, and To consider what new knowledge and solutions big data research can provide for clinical decision support for personalized medicine.

Truth or myth: Definitive chemoradiotherapy doesn't work for HPV/p16 negative oropharyngeal squamous cell carcinoma?

Article history: Received 1 November 2016 Received in revised form 23 November 2016 Accepted 3 December 2016 Available online xxxx

SU-D-204-04: Correlations Between Dosimetric Indices and Follow-Up Data for Salivary Glands Six Months After Radiation Therapy for Head and Neck Cancer

PURPOSE To investigate the correlation between different dosimetric indices of salivary glands (as separate or combined structures) to patient-reported dry mouth 6 months post radiotherapy using the novel patient reported outcome version of the CTCAE (PRO-CTCAE). METHODS Forty-three patients with oropharyngeal squamous cell carcinoma were treated on a prospective multi-institutional study. All patients received de-intensified 60 Gy intensity modulated radiotherapy. Dosimetric constraints were used for the salivary glands (e.g. mean dose to the contralateral-parotid < 26 Gy). We investigated correlations of individual patient dosimetric data of the parotid and submandibular glands (as separate or combined structures) to their self-reported 6 month post-treatment dry mouth responses. Moderate dry mouth responses were most prevalent and were used as the clinical endpoint indicating response. The correlation of Dmean, Dmax and a range of dosevolume (VD) points were assessed through the area under the Receiver Operating Characteristic curve (ROC) and Odds Ratios (OR). RESULTS Patients reporting non/mild dry mouth response (N=22) had average Dmean = 19.6 ± 6.2Gy to the contralateral-parotid compared to an average Dmean = 28.0 ± 8.3Gy and an AUC = 0.758 for the patients reporting moderate/severe/very severe dry mouth (N=21). Analysis of the range of VDs for patients who had reported dry mouth showed that for the contralateral-parotid the indices V18 through V22 had the highest area under the curves (AUC) (0.762 - 0.772) compared to a more traditional dosimetric index V30, which had an AUC = 0.732. The highest AUC was observed for the combination of contralateral parotid and contralateral submandibular glands, for which V16 through V28 had AUC = 0.801 - 0.834. CONCLUSION Patients who report moderate/severe/very severe dry mouth 6 months post radiotherapy had on average higher Dmean. The V16-V28 of the combination of the contralateral glands showed the highest correlation with the clinical endpoint.

SU-D-204-05: Fitting Four NTCP Models to Treatment Outcome Data of Salivary Glands Recorded Six Months After Radiation Therapy for Head and Neck Tumors

PURPOSE To estimate the radiobiological parameters of four popular NTCP models that describe the dose-response relations of salivary glands to the severity of patient reported dry mouth 6 months post chemo-radiotherapy. To identify the glands, which best correlate with the manifestation of those clinical endpoints. Finally, to evaluate the goodness-of-fit of the NTCP models. METHODS Forty-three patients were treated on a prospective multiinstitutional phase II study for oropharyngeal squamous cell carcinoma. All the patients received 60 Gy IMRT and they reported symptoms using the novel patient reported outcome version of the CTCAE. We derived the individual patient dosimetric data of the parotid and submandibular glands (SMG) as separate structures as well as combinations. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS), Logit and Relative Logit (RL) NTCP models were used to fit the patients data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC) and the Odds Ratio methods. RESULTS The AUC values were highest for the contralateral parotid for Grade ≥ 2 (0.762 for the LKB, RS, Logit and 0.753 for the RL). For the salivary glands the AUC values were: 0.725 for the LKB, RS, Logit and 0.721 for the RL. For the contralateral SMG the AUC values were: 0.721 for LKB, 0.714 for Logit and 0.712 for RS and RL. The Odds Ratio for the contralateral parotid was 5.8 (1.3-25.5) for all the four NTCP models for the radiobiological dose threshold of 21Gy. CONCLUSION It was shown that all the examined NTCP models could fit the clinical data well with very similar accuracy. The contralateral parotid gland appears to correlated best with the clinical endpoints of severe/very severe dry mouth. An EQD2Gy dose of 21Gy appears to be a safe threshold to be used as a constraint in treatment planning.

SU-F-T-104: Determining the NTCP Parameters of Pharyngeal Constrictors and Proximal Esophagus for Radiation Induced Swallowing Problems Recorded Six Months After Radiation Therapy for Head and Neck Tumors.

PURPOSE To estimate the radiobiological parameters of four NTCP models that describe the dose-response relations of pharyngeal constrictors and proximal esophagus regarding the severity of patient reported swallowing problems 6 months post chemo-radiotherapy. To identify the section/structure that best correlates with the manifestation of the clinical endpoints. Finally, to compare the goodness-of-fit of those models. METHODS Forty-three patients were treated on a prospective multi-institutional phase II study for oropharyngeal squamous cell carcinoma. All the patients received 60 Gy IMRT and they reported symptoms using the novel patient reported outcome version of the CTCAE. We derived the individual patient dosimetric data of superior, medium and inferior sections of pharyngeal constrictors (SPC, MPC and IPC), superior and inferior sections of esophagus (SES and IES) as separate structures as well as combinations. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS), Logit and Relative Logit (RL) NTCP models were used to fit the patient data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC) and the Odds Ratio methods. RESULTS The AUC values were highest for the SPC for Grade ≥ 2 (0.719 for the RS and RL models, and 0.716 for LKB and Logit). For Grade ≥ 1, the respective values were 0.699 for RS, LKB and Logit and 0.676 for RL. For MPC the AUC values varied between 0.463-0.477, for IPC between 0.396-0.458, for SES between 0.556-0.613 and for IES between 0.410-0.519. The Odds Ratio for the SPC was 15.6 (1.7-146.4) for RS, LKB and Logit for NTCP of 55%. CONCLUSION All the examined NTCP models could fit the clinical data with similar accuracy. The SPC appear to correlate best with the clinical endpoints of swallowing problems. A prospective study could establish the use of NTCP values of SPC as a constraint in treatment planning.

SU-F-T-107: Correlations Between Dosimetric Indices of Pharyngeal Constrictors and Proximal Esophagus with Associated Patient-Reported Outcomes Six Months After Radiation Therapy for Head and Neck Cancer

PURPOSE To compare the correlations between different dosimetric indices derived from the pharyngeal constrictor muscles and proximal esophagus with patient-reported difficulty in swallowing 6 months post radiotherapy using a novel patient reported outcome version of CTCAE (PRO-CTCAE). METHODS Forty-three patients with oropharyngeal squamous cell carcinoma were treated on a prospective multi-institutional study. All patients received de-intensified 60 Gy intensity modulated radiotherapy. We investigated correlations of individual patient dosimetric data of the superior (SPC), middle (MPC), inferior (IPC) pharyngeal constrictor muscles, the superior esophagus (SES), and the inferior esophagus (IES) to their self-reported 6 month post-treatment swallowing difficulty responses. Mild (≥ Grade 1) swallowing difficulty responses were used as the clinical endpoint indicating response. The predictive efficacy of Dmean and dose-volume (VD) points were assessed through the area under the Receiver Operating Characteristic curve (ROC) and Odds Ratio (OR). RESULTS The SES and SPC had more favorable area under the curves (AUC) for the Dmean (0.62 and 0.70) while the Dmean to the IPC, MPC, and IES produced suboptimal AUCs (0.42, 0.48, and 0.52). Additionally, over the range of VD, the V54 and V55 for the SES and SPC demonstrated the highest AUCs: AUC(SES) = 0.76-0.73 and AUC(SPC) = 0.72-0.69, respectively. The IES, IPC, and MPC had worse AUC results over the range of VD. An optimal OR can be found when V54 = 96% for the SPC, where OR = 3.96 (1.07-14.62). CONCLUSION The V45 and V55 of the SES and SPC had the highest correlation to the clinical endpoint compared to the commonly used dosimetric index, Dmean for both the esophagus and constrictor muscles. The reported dosimetric data demonstrates that new dosimetric indices may need to be considered in the setting of dose de-escalation and self-reported outcomes.

SU-F-T-523: Radiobiological Comparison of Helical Tomotherapy and VMAT in the Treatment of Head and Neck Tumors

PURPOSE This study aims at comparing the efficacy of Helical Tomotherapy (TOMO) and Volumetric Modulated Arc Therapy (VMAT) in producing highly conformal dose distributions in challenging head & neck cancer patients. Furthermore, to interpret the dosimetric findings into expected tissue response rates in order to estimate their clinical impact. METHODS Seven patients were studied and for each patient two treatment plans (one TOMO and one VMAT) were created. Structures used for optimization were: high risk PTV, standard risk PTV, brainstem, spinal cord, parotid gland, larynx, mandible and surrounding tissue. The prescription varied between 60 - 74.4Gy to the HR. The same dosimetric constraints were used for both pairs of plans. Additionally, the TCP and NTCP values of the different targets and organs at risk (OAR) were calculated and compared together with the P+, which is the probability of achieving tumor control without normal tissue complications. RESULTS The proposed plan evaluation shows that the VMAT gives better results than TOMO in terms of expected clinical outcome. Specifically, the average difference in P+ = 3.3±1.8%, TCP = 3.3±1.8%, NTCP = 0.0±0.0%. The difference observed mainly stems from the lower TCP with TOMO (average difference of 2.9% for the high risk PTV and 3.1% for the standard risk PTV). The NTCP differences between the two modalities are very small even though their average differences in BEUD can be large (e.g. -21.9 Gy in brainstem, 27.2 Gy in larynx). CONCLUSION The findings of the analysis indicate that VMAT achieves better coverage to the targets with lower doses to the OARs compared to TOMO for the patients tested. However, the dosimetric differences appear to have a measurable impact only in the expected TCP rates of the targets. Radiobiological evaluation of treatment plans should provide a closer association of the delivered treatment with the clinical outcome.

SU-C-210-02: Impact of Intrafractional Motion On TomoTherapy Stereotactic Body Radiotherapy (SBRT) 4D Dosimetry

Purpose: TomoTherapy treatment has unique challenges in handling intrafractional motion compared to conventional LINAC. This study is aimed to gain a realistic and quantitative understanding of motion impact on TomoTherapy SBRT treatment of lung and prostate cancer patients. Methods: A 4D dose engine utilizing GPUs and including motion during treatment was developed for the efficient simulation of TomoTherapy delivered dosimetry. Two clinical CyberKnife lung cases with respiratory motion tracking and two prostate cases with a slower non-periodical organ motion treated by LINAC plus Calypso tracking were used in the study. For each disease site, one selected case has an average motion (6mm); the other has a large motion (10mm for lung and 15mm for prostate). SBRT of lung and prostate cases were re-planned on TomoTherapy with 12 Gyx4 fractions and 7Gyx5 fractions, respectively, all with 95% PTV coverage. Each case was planned with 4 jaw settings: 1) conventional 1cm static, 2) 2.5cm static, 3) 2.5cm dynamic, and 4) 5cm dynamic. The intrafractional rigid motion of the target was applied in the dose calculation of individual fractions of each plan and total dose was accumulated from multiple fractions. Results: For 1cm static jaw plans with motions applied, PTV coverage is related to motion type and amplitude. For SBRT patients with average motion (6mm), the PTV coverage remains > 95% for lung case and 74% for prostate case. For cases with large motion, PTV coverage drops to 61% for lung SBRT and 49% for prostate SBRT. Plans with other jaws improve uniformity of moving target, but still suffer from poor PTV coverage (< 70%). Conclusion: TomoTherapy lung SBRT is less motion-impacted when average amplitude of respiratory-induced intrafractional motion is present (6mm). When motion is large and/or non-periodic (prostate), all studied plans lead to significantly decreased target coverage in actual delivered dosimetry.

SU-E-T-657: Quantitative Assessment of Plan Robustness for Helical Tomotherapy for Head and Neck Cancer Radiotherapy

Introduction: Geometric uncertainties in daily patient setup can lead to variations in the planned dose, especially when using highly conformal techniques such as helical Tomotherapy. To account for the potential effect of geometric uncertainty, our clinical practice is to expand critical structures by 3mm expansion into planning risk volumes (PRV). The PRV concept assumes the spatial dose cloud is insensitive to patient positioning. However, no tools currently exist to determine if a Tomotherapy plan is robust to the effects of daily setup variation. We objectively quantified the impact of geometric uncertainties on the 3D doses to critical normal tissues during helical Tomotherapy. Methods: Using a Matlab-based program created and validated by Accuray (Madison, WI), the planned Tomotherapy delivery sinogram recalculated dose on shifted CT datasets. Ten head and neck patients were selected for analysis. To simulate setup uncertainty, the patient anatomy was shifted ±3mm in the longitudinal, lateral and vertical axes. For each potential shift, the recalculated doses to various critical normal tissues were compared to the doses delivered to the PRV in the original plan Results: 18 shifted scenarios created from Tomotherapy plans for three patients with head and neck cancers were analyzed. For all simulated setup errors, the maximum doses to the brainstem, spinal cord, parotids and cochlea were no greater than 0.6Gy of the respective original PRV maximum. Despite 3mm setup shifts, the minimum dose delivered to 95% of the CTVs and PTVs were always within 0.4Gy of the original plan. Conclusions: For head and neck sites treated with Tomotherapy, the use of a 3mm PRV expansion provide a reasonable estimate of the dosimetric effects of 3mm setup uncertainties. Similarly, target coverage appears minimally effected by a 3mm setup uncertainty. Data from a larger number of patients will be presented. Future work will include other anatomical sites.