Veterans Affairs Radiation Oncology Quality Surveillance Program and American Society for Radiation Oncology Quality Measures Initiative.

PURPOSE: Ensuring high quality, evidence-based radiation therapy for patients is of the upmost importance. As a part of the largest integrated health system in America, the Department of Veterans Affairs National Radiation Oncology Program (VA-NROP) established a quality surveillance initiative to address the challenge and necessity of providing the highest quality of care for veterans treated for cancer. METHODS AND MATERIALS: As part of this initiative, the VA-NROP contracted with the American Society for Radiation Oncology to commission 5 Blue Ribbon Panels for lung, prostate, rectal, breast, and head and neck cancers experts. This group worked collaboratively with the VA-NROP to develop consensus quality measures. In addition to the site-specific measures, an additional Blue Ribbon Panel comprised of the chairs and other members of the disease sites was formed to create 18 harmonized quality measures for all 5 sites (13 quality, 4 surveillance, and 1 aspirational). CONCLUSIONS: The VA-NROP and American Society for Radiation Oncology collaboration have created quality measures spanning 5 disease sites to help improve patient outcomes. These will be used for the ongoing quality surveillance of veterans receiving radiation therapy through the VA and its community partners.

The Radiosensitivity Index Gene Signature Identifies Distinct Tumor Immune Microenvironment Characteristics Associated With Susceptibility to Radiation Therapy.

PURPOSE: Radiation therapy (RT) is a mainstay of cancer care, and accumulating evidence suggests the potential for synergism with components of the immune response. However, few data describe the tumor immune contexture in relation to RT sensitivity. To address this challenge, we used the radiation sensitivity index (RSI) gene signature to estimate the RT sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI. METHODS AND MATERIALS: We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes combined with single sample gene set enrichment analysis. Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n = 2368) of tumors underwent DNA sequencing for mutational frequency characterization. RESULTS: We identified a wide range of RSI values within and across various tumor types, with several demonstrating nonunimodal distributions (eg, colon, renal, lung, prostate, esophagus, pancreas, and PAM50 breast subtypes; P < .05). Across all tumor types, stratifying RSI at a tumor type-specific median identified 7148 differentially expressed genes, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1ß transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (eg, CD8+ T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules. CONCLUSIONS: This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.

Dynamics-Adapted Radiotherapy Dose (DARD) for Head and Neck Cancer Radiotherapy Dose Personalization.

Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66-70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics-a dynamics-adapted radiotherapy dose (DDARD). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66-70 Gy RT in 2-2.12 Gy weekday fractions. This trial was repeated constraining DDARD between (54, 82) Gy to test more moderate dose adjustment. DDARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by >10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates.

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability.

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Genomic identification of sarcoma radiosensitivity and the clinical implications for radiation dose personalization.

BACKGROUND: Soft-tissue sarcomas (STS) are heterogeneous with variable response to radiation therapy (RT). Utilizing the radiosensitivity index (RSI) we estimated the radiobiologic ratio of lethal to sublethal damage (α/ß), genomic-adjusted radiation dose(GARD), and in-turn a biological effective radiation dose (BED). METHODS: Two independent cohorts of patients with soft-tissue sarcoma were identified. The first cohort included 217 genomically-profiled samples from our institutional prospective tissue collection protocol; RSI was calculated for these samples, which were then used to dichotomize the population as either highly radioresistant (HRR) or conventionally radioresistant (CRR). In addition, RSI was used to calculate α/ß ratio and GARD, providing ideal dosing based on sarcoma genomic radiosensitivity. A second cohort comprising 399 non-metastatic-STS patients treated with neoadjuvant RT and surgery was used to validate our findings. RESULTS: Based on the RSI of the sample cohort, 84% would historically be considered radioresistant. We identified a HRR subset that had a significant difference in the RSI, and clinically a lower tumor response to radiation (2.4% vs. 19.4%), 5-year locoregional-control (76.5% vs. 90.8%), and lower estimated α/ß (3.29 vs. 5.98), when compared to CRR sarcoma. Using GARD, the dose required to optimize outcome in the HRR subset is a BEDα/ß=3.29 of 97 Gy. CONCLUSIONS: We demonstrate that on a genomic scale, that although STS is radioresistant overall, they are heterogeneous in terms of radiosensitivity. We validated this clinically and estimated an α/ß ratio and dosing that would optimize outcome, personalizing dose.

American Brachytherapy Society (ABS) consensus statement for soft-tissue sarcoma brachytherapy.

PURPOSE: Growing data supports the role of radiation therapy in the treatment of soft tissue sarcoma (STS). Brachytherapy has been used for decades in the management of STS and can be utilized as monotherapy or as a boost to external beam radiation. We present updated guidelines from the American Brachytherapy Society regarding the utilization of brachytherapy in the management of STS. METHODS AND MATERIALS: Members of the American Brachytherapy Society with expertise in STS and STS brachytherapy created an updated clinical practice guideline including step-by-step details for performing STS brachytherapy based on a literature review and clinical experience. RESULTS: Brachytherapy monotherapy should be considered for lower-recurrence risk patients or after a local recurrence following previous external beam radiation; a brachytherapy boost can be considered in higher-risk patents meeting implant criteria. Multiple dose/fractionation regimens are available, with determination based on tumor location and treatment intent. Techniques to limit wound complications are based on the type of wound closure; wound complication can be mitigated with a delay in the start of brachytherapy with immediate wound closure or by utilizing a staged reconstruction technique, which allows an earlier treatment start with a delayed wound closure. CONCLUSIONS: These updated guidelines provide clinicians with data on indications for STS brachytherapy as well as guidelines on how to perform and deliver high quality STS brachytherapy safely with minimal toxicity.

Forecasting Individual Patient Response to Radiation Therapy in Head and Neck Cancer With a Dynamic Carrying Capacity Model.

PURPOSE: To model and predict individual patient responses to radiation therapy. METHODS AND MATERIALS: We modeled tumor dynamics as logistic growth and the effect of radiation as a reduction in the tumor carrying capacity, motivated by the effect of radiation on the tumor microenvironment. The model was assessed on weekly tumor volume data collected for 2 independent cohorts of patients with head and neck cancer from the H. Lee Moffitt Cancer Center (MCC) and the MD Anderson Cancer Center (MDACC) who received 66 to 70 Gy in standard daily fractions or with accelerated fractionation. To predict response to radiation therapy for individual patients, we developed a new forecasting framework that combined the learned tumor growth rate and carrying capacity reduction fraction (δ) distribution with weekly measurements of tumor volume reduction for a given test patient to estimate δ, which was used to predict patient-specific outcomes. RESULTS: The model fit data from MCC with high accuracy with patient-specific δ and a fixed tumor growth rate across all patients. The model fit data from an independent cohort from MDACC with comparable accuracy using the tumor growth rate learned from the MCC cohort, showing transferability of the growth rate. The forecasting framework predicted patient-specific outcomes with 76% sensitivity and 83% specificity for locoregional control and 68% sensitivity and 85% specificity for disease-free survival with the inclusion of 4 on-treatment tumor volume measurements. CONCLUSIONS: These results demonstrate that our simple mathematical model can describe a variety of tumor volume dynamics. Furthermore, combining historically observed patient responses with a few patient-specific tumor volume measurements allowed for the accurate prediction of patient outcomes, which may inform treatment adaptation and personalization.

Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy.

BACKGROUND: The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T). MATERIALS AND METHODS: An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Three separate predictive models were built for composite features from CT + PET. RESULTS: The patterns of pathological response were TRG 0 (n = 13; 19.7%), 1 (n = 34; 51.5%), 2 (n = 16; 24.2%), and 3 (n = 3; 4.5%). There were 20 (30.3%) patients with low, 22 (33.3%) with intermediate and 24 (36.4%) with high NAR scores. Three separate predictive models were built for composite features from CT + PET and analyzed separately for clinical endpoints. Composite features with α = 0.2 resulted in the best predictive power using logistic regression. For pathological response prediction, the signature resulted in 88.1% accuracy in predicting TRG 0 vs. TRG 1-3; 91% accuracy in predicting TRG 0-1 vs. TRG 2-3. For the surrogate of DFS and OS, it resulted in 67.7% accuracy in predicting low vs. intermediate vs. high NAR scores. CONCLUSION: The pre-treatment composite radiomics signatures were highly predictive of pathological response in rectal cancer treated with NACR T. A larger cohort is warranted for further validation.

Status of Cancer Care at Network Sites of the Nation's Academic Cancer Centers.

BACKGROUND: Cancer care coordination across major academic medical centers and their networks is evolving rapidly, but the spectrum of organizational efforts has not been described. We conducted a mixed-methods survey of leading cancer centers and their networks to document care coordination and identify opportunities to improve geographically dispersed care. METHODS: A mixed-methods survey was sent to 91 cancer centers in the United States and Canada. We analyzed the number and locations of network sites; access to electronic medical records (EMRs); clinical research support and participation at networks; use of patient navigators, care paths, and quality measures; and physician workforce. Responses were collected via Qualtrics software between September 2017 and December 2018. RESULTS: Of the 69 responding cancer centers, 74% were NCI-designated. Eighty-seven percent of respondents were part of a matrix health system, and 13% were freestanding. Fifty-six reported having network sites. Forty-three respondents use navigators for disease-specific populations, and 24 use them for all patients. Thirty-five respondents use ≥1 types of care path. Fifty-seven percent of networks had complete, integrated access to their main center's EMRs. Thirty-nine respondents said the main center provides funding for clinical research at networks, with 22 reporting the main center provides all funding. Thirty-five said the main center provided pharmacy support at the networks, with 15 indicating the main center provides 100% pharmacy support. Certification program participation varied extensively across networks. CONCLUSIONS: The data show academic cancer centers have extensive involvement in network cancer care, often extending into rural communities. Coordinating care through improved clinical trial access and greater use of patient navigation, care paths, coordinated EMRs, and quality measures is likely to improve patient outcomes. Although it is premature to draw firm conclusions, the survey results are appropriate for mapping next steps and data queries.

Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer.

INTRODUCTION: To develop a radiomic-based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer. METHODS: We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in-house data-chjmirocterization algorithm was used to extract 3D-radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, Fc  = α * FPET  + (1 - α) * FCT , 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco-regional control (LRC), recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) were estimated by Kaplan-Meier analysis, and compared using log-rank test. RESULTS: Median follow-up was 59 months. pCR was achieved in 34 (50%) patients. Five-year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0-1 indicative of complete response or minimal residual disease was significantly associated with improved 5-year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04-0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5-year LRC favouring low-score group (91.1% vs. 80%, 95% CI 0.09-1.77, P = 0.2). CONCLUSION: The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.

Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLC.

INTRODUCTION: Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. METHODS: We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. RESULTS: Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit. CONCLUSIONS: Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Harnessing COVID-Driven Technical Innovations for Improved Multi-Disciplinary Cancer Care in the Post-COVID Era: The Virtual Patient Room.

Emergence of the COVID-19 crisis has catalyzed rapid paradigm shifts throughout medicine. Even after the initial wave of the virus subsides, a wholesale return to the prior status quo is not prudent. As a specialty that values the proper application of new technology, radiation oncology should strive to be at the forefront of harnessing telehealth as an important tool to further optimize patient care. We remain cognizant that telehealth cannot and should not be a comprehensive replacement for in-person patient visits because it is not a one for one replacement, dependent on the intention of the visit and patient preference. However, we envision the opportunity for the virtual patient "room" where multidisciplinary care may take place from every specialty. How we adapt is not an inevitability, but instead, an opportunity to shape the ideal image of our new normal through the choices that we make. We have made great strides toward genuine multidisciplinary patient-centered care, but the continued use of telehealth and virtual visits can bring us closer to optimally arranging the spokes of the provider team members around the central hub of the patient as we progress down the road through treatment.

Prognostic potential of mid-treatment nodal response in oropharyngeal squamous cell carcinoma.

BACKGROUND: We examine the prognostic implications of mid-course nodal response in oropharyngeal cancer (OPX) to radiation therapy. METHODS: In 44 patients with node-positive OPX undergoing concurrent chemoradiation, nodal volumes were measured on cone beam CTs from days 1, 10, 20, and 35. Nodal decrease (ND) was based on percent shrinkage from day 1. RESULTS: At a median follow-up of 17 months, the 2-year disease-free survival (DFS), locoregional control (LRC), distant metastasis-free survival (DMFS), and overall survival (OS) were 87%, 92%, 89%, and 92%, respectively. Patients with ND ≥43% at D20 had improved LRC (100% vs 78.4%, P = .03) compared to D20 ND <43%. On multivariate analysis, D20 ≥43% was independently prognostic for LRC (HR 1.17, P = .05). CONCLUSION: Patients with low-risk oropharynx cancer with ND of ≥43% by treatment day 20 had significantly improved LRC. The prognostic benefit of ND may assist in identifying candidates for treatment de-escalation.

CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study.

INTRODUCTION: Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT-based radiomic imaging biomarker to predict pathological response. METHODS: We used two independent cohorts of rectal cancer patients to develop and validate a CT-based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre-treatment non-contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort. RESULTS: The patterns of pathological response in training and validation groups were TRG 0 (n = 14, 23.3%; n = 6, 19.4%), 1 (n = 31, 51.7%; n = 15, 48.4%), 2 (n = 12, 20.0%; n = 7, 22.6%) and 3 (n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1-3 in validation. CONCLUSION: The pre-treatment CT-based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non-operative management.

Integrating Mathematical Modeling into the Roadmap for Personalized Adaptive Radiation Therapy.

In current radiation oncology practice, treatment protocols are prescribed based on the average outcomes of large clinical trials, with limited personalization and without adaptations of dose or dose fractionation to individual patients based on their individual clinical responses. Predicting tumor responses to radiation and comparing predictions against observed responses offers an opportunity for novel treatment evaluation. These analyses can lead to protocol adaptation aimed at the improvement of patient outcomes with better therapeutic ratios. We foresee the integration of mathematical models into radiation oncology to simulate individual patient tumor growth and predict treatment response as dynamic biomarkers for personalized adaptive radiation therapy (RT).

Immunologic Consequences of Sequencing Cancer Radiotherapy and Surgery.

PURPOSE: Early-stage cancers are routinely treated with surgery followed by radiotherapy (SR). Radiotherapy before surgery (RS) has been widely ignored for some cancers. We evaluate overall survival (OS) and disease-free survival (DFS) with SR and RS for different cancer types and simulate the plausibility of RS- and SR-induced antitumor immunity contributing to outcomes. MATERIALS AND METHODS: We analyzed a SEER data set of early-stage cancers treated with SR or RS. OS and DFS were calculated for cancers with sufficient numbers for statistical power (cancers of lung and bronchus, esophagus, rectum, cervix uteri, corpus uteri, and breast). We simulated the immunologic consequences of SR, RS, and radiotherapy alone in a mathematical model of tumor-immune interactions. RESULTS: RS improved OS for cancers with low 20-year survival rates (lung: hazard ratio [HR], 0.88; P = .046) and improved DFS for cancers with higher survival (breast: HR = 0.64; P < .001). For rectal cancer, with intermediate 20-year survival, RS improved both OS (HR = 0.89; P = .006) and DFS (HR = 0.86; P = .04). Model simulations suggested that RS could increase OS by eliminating cancer for a broader range of model parameters and radiotherapy-induced antitumor immunity compared with SR for selected parameter combinations. This could create an immune memory that may explain increased DFS after RS for certain cancers. CONCLUSION: Study results suggest plausibility that radiation to the bulk of the tumor could induce a more robust immune response and better harness the synergy of radiotherapy and antitumor immunity than postsurgical radiation to the tumor bed. This exploratory study provides motivation for prospective evaluation of immune activation of RS versus SR in controlled clinical studies.

Novel Genomic-Based Strategies to Personalize Lymph Node Radiation Therapy.

Current standard radiotherapy doses have been derived from empiric methods rather than a scientific framework. Subclinical nodal dosing remains relatively uniform across most disease sites, despite heterogeneity in patient and tumor biology. It is now clear that there are subsets of patients who will benefit from genomically-informed radiotherapy planning, and there are increasing efforts toward prescribing radiation dose to match the radiosensitivity of the tumor. By using novel genomic biomarkers to personalize delivery of radiotherapy, there is an opportunity to improve loco-regional control and cure rates. We survey the current landscape of personalized radiation oncology across commonly treated disease sites.