Insurance Denial of Care for Randomized Controlled Trial-Eligible Patients: Incidence and Success Rate of Peer-To-Peer Authorization in Allowing Patients to Remain Trial-Eligible.

INTRODUCTION: Insurance denials for clinical trials serve as a pertinent barrier for patients to remain trial-eligible, thus hindering the development of therapies and the overall advancement of health care. We present results from an ongoing oncology randomized clinical trial regarding insurance denials and peer-to-peer authorization (P2PA) success rate in allowing patients to remain trial-eligible. METHODS: The ongoing Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms Phase II trial randomizes spine cancer patients to treatment with spine radiosurgery/stereotactic body radiation therapy (SBRT) versus conventional external beam radiation therapy (EBRT). Trial-eligible patients during the first 3 months of enrollment are examined to determine whether the option of SBRT was denied by their insurance. Advocacy for overcoming SBRT denial in P2PA centered on SBRT being recommended as a preferred treatment modality in the National Comprehensive Cancer Network guidelines, and the recent level I evidence demonstrating the advantages of SBRT over EBRT for symptomatic spine cancer. RESULTS: Of 15 trial-eligible patients, 3 (20%) experienced insurance denials for SBRT. P2PA resulted in the reversal of denials in all 3 patients, allowing each to remain trial-eligible for randomization between SBRT and cEBRT. CONCLUSIONS: Despite a clinical oncologic treatment modality for which recent Level 1 evidence is available, the insurance denial rate was 20%. A vigilant P2PA strategy focusing on highlighting National Comprehensive Cancer Network guidelines and the supporting Level 1 evidence resulted in a very high rate of reversing initial denial.

Open RT Structures: A Solution for TG-263 Accessibility.

PURPOSE: Consistency of nomenclature within radiation oncology is increasingly important as big data efforts and data sharing become more feasible. Automation of radiation oncology workflows depends on standardized contour nomenclature that enables toxicity and outcomes research, while also reducing medical errors and facilitating quality improvement activities. Recommendations for standardized nomenclature have been published in the American Association of Physicists in Medicine (AAPM) report from Task Group 263 (TG-263). Transitioning to TG-263 requires creation and management of structure template libraries and retraining of staff, which can be a considerable burden on clinical resources. Our aim is to develop a program that allows users to create TG-263-compliant structure templates in English, Spanish, or French to facilitate data sharing. METHODS AND MATERIALS: Fifty-three premade structure templates were arranged by treated organ based on an American Society for Radiation Oncology (ASTRO) consensus paper. Templates were further customized with common target structures, relevant organs at risk (OARs) (eg, spleen for anatomically relevant sites such as the gastroesophageal junction or stomach), subsite- specific templates (eg, partial breast, whole breast, intact prostate, postoperative prostate, etc) and brachytherapy templates. An informal consensus on OAR and target coloration was also achieved, although color selections are fully customizable within the program. RESULTS: The resulting program is usable on any Windows system and generates template files in practice-specific Digital Imaging and Communications In Medicine (DICOM) or XML formats, extracting standardized structure nomenclature from an online database maintained by members of the TG-263U1, which ensures continuous access to up-to-date templates. CONCLUSIONS: We have developed a tool to easily create and name DICOM radiation therapy (DICOM-RT) structures sets that are TG-263-compliant for all planning systems using the DICOM standard. The program and source code are publicly available via GitHub to encourage feedback from community users for improvement and guide further development.

Artificial intelligence applications in prostate cancer.

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

BACKGROUND: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Use of the Decipher genomic classifier among men with prostate cancer in the United States.

BACKGROUND: Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer. METHODS: Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy. RESULTS: A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84). CONCLUSIONS: There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

Contemporary trends in management of uveal melanoma.

Purpose: In the management of uveal melanoma, eye plaque brachytherapy (EPBT) has replaced enucleation as the standard of care for small size tumors that require treatment, and for medium size tumors. In the modern era, EPBT is being utilized more frequently for certain large tumors as well. While there is prospective randomized evidence to support utilization of EPBT for tumors of appropriate dimensions, it is unclear what the actual practice patterns are across the United States. The purpose of this publication was to look at contemporary trends in the management of uveal melanoma across the United States to determine whether practices are appropriately adopting EPBT, and to investigate demographic and socio-economic factors that might be associated with deviations from this standard of care. Material and methods: The National Cancer Database was queried (2004-2015) for patients with uveal melanoma. Data regarding tumor characteristics and treatment were collected. Two-sided Pearson χ2 test was used to compare categorical frequencies between patients who received globe preserving treatments vs. those who received enucleation. Multivariable logistic regression modeling was used to determine characteristics predictive for receiving enucleation. Results: The enucleation rate for small/medium tumors (≤ 10 mm apical height and ≤ 16 mm basal diameter) decreased from 20% in 2004 to 10% in 2015. The EPBT rate for large tumors increased from 30% in 2004 to 45% in 2015. Numerous demographic and socio-economic factors were found to be associated with higher rates of enucleation. Conclusions: The overall trend across the nation is a decreased enucleation rate for small/medium tumors, and an increased EPBT rate for large tumors. A fraction of patients who should be candidates for EPBT are instead receiving enucleation, and in this study, we have shown that certain adverse demographic factors are associated with this.

Prognostic impact of radiation therapy in tubular carcinoma of the breast.

PURPOSE: Tubular carcinoma (TC) is an invasive breast cancer with favorable prognosis. While pathology-specific guidelines exist for TC regarding adjuvant chemotherapy and endocrine therapy, no recommendations exist regarding locoregional treatment based on tumor histology. Prognostic impact of radiotherapy for patients with TC remains unclear. MATERIALS AND METHODS: The National Cancer Database was queried (2004-2015) for patients with pN0M0 TC who underwent lumpectomy. Chi-square testing compared categorized variables between those who did and did not receive radiotherapy. Kaplan-Meier analysis evaluated overall survival (OS). Cox proportional hazard analysis identified variables prognostic for OS. Patients were divided into age cohorts ≤60 years and >60 years. Propensity score matching (PSM) was utilized to create similar cohorts. RESULTS: 9705 patients met selection criteria; 6182 (75.1%) received radiotherapy while 2045 (24.9%) did not. After PSM, radiotherapy (HR 0.582; 95% CI 0.494-0.686) and endocrine therapy (HR 0.737; 95% CI 0.623-0.872) were favorable prognostic factors on multivariate Cox regression analysis while age > 60 years (HR 5.131; 95% CI 3.753-7.016), Black race (HR 1.445; 95% CI 1.016-2.055), and Charlson-Deyo comorbidity score > 0 (HR 1.708; 95% CI 1.403-2.079) were unfavorable prognostic factors. After PSM, 5-year OS was 91.7% for those who received radiotherapy and 84.5% for those who did not; 10-year OS was 76.1% and 64.1%, respectively (p < 0.001). CONCLUSION: This is the largest study to date on TC and the prognostic impact of adjuvant radiotherapy. Postoperative radiotherapy is a favorable prognostic factor for OS in patients with pN0M0 TC, suggesting adjuvant radiotherapy should remain standard of care in these patients.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach.

BACKGROUND: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels.

PURPOSE: Our goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole. METHODS: We performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC's clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology. RESULTS: Across 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10-6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific. CONCLUSION: This article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

Eye plaque brachytherapy versus enucleation for ocular melanoma: an analysis from the National Cancer Database.

PURPOSE: There is no current randomized data comparing the efficacy of brachytherapy and enucleation for patients with larger sized tumors. The purpose of the present study was to use a large, contemporary database to determine current practice patterns and compare survival outcomes between different management options for patients with choroidal melanoma of various sizes. MATERIAL AND METHODS: The National Cancer Database was queried (2004-2014) for histologically-confirmed choroidal melanoma for patients treated with brachytherapy versus enucleation. Chi-square test was used to compare categorized demographic and clinical variables in both arms. Kaplan-Meier analysis evaluated overall survival (OS). Cox proportional hazards assessment determined variables associated with OS. Patients were divided into cohorts representing small, medium, and large tumors. Propensity scores matching (PSM) was utilized to compare more similar cohorts. RESULTS: A total of 7,096 patients met the selection criteria; 5,501 (78%) patients received brachytherapy and 1,595 (22%) patients were treated with enucleation. After PSM, 5-yr OS for small tumors was 87% vs. 64%, for medium tumors was 77% vs. 57%, and for large tumors was 68% vs. 46% for brachytherapy and enucleation, respectively (p < 0.001). Following PSM, multivariate Cox regression found older age (hazard ratio [HR] = 1.76, 95% confidence interval [CI] = 1.51-2.06), more comorbidities (HR = 1.46, 95% CI = 1.25-1.70), extraocular extension (EOE) (HR = 1.25, 95% CI = 1.06-1.48), ciliary body invasion (CBI) (HR = 1.20, 95% CI = 1.02-1.40), and larger size (HR = 1.52, 95% CI = 1.40-1.66) were negative prognosticators of survival. Brachytherapy was a positive prognosticator of survival (HR = 0.45, 95% CI = 0.40-0.51). CONCLUSIONS: Patients selected for brachytherapy had improved survival compared to enucleation in all size cohorts. EOE and CBI are significantly higher in the enucleation cohort and are important negative prognosticators for survival selected against patients having brachytherapy. Brachytherapy is a reasonable treatment option for certain patients with large size tumors.