Quantifying risk of tech-driven disparities.

PURPOSE: New technologies are continuously emerging in radiation oncology. Inherent technological limitations can result in healthcare disparities in vulnerable patient populations. These limitations must be considered for existing and new technologies in the clinic to provide equitable care. MATERIALS AND METHODS: We created a health disparity risk assessment metric inspired by failure mode and effects analysis (FMEA). We provide sample patient populations and their potential associated disparities, guidelines for clinics and vendors, and example applications of the methodology. RESULTS: A disparity risk priority number (dRPN) can be calculated from the product of three quantifiable metrics: the percent of patients impacted (P), the severity of the impact of dosimetric uncertainty or quality of the radiation plan (S), and the clinical dependence on the evaluated technology (C). The dRPN can be used to rank the risk of sub-optimal care due to technical limitations when comparing technologies and to plan interventions when technology is shown to have inequitable performance in the patient population of a clinic. CONCLUSION: The proposed methodology may simplify the evaluation of how new technology impacts vulnerable populations, help clinics quantify the limitations of their technological resources, and plan appropriate interventions to improve equity in radiation treatments.

Prostate-Specific Antigen Stratification for Predicting Advanced Prostate Cancer Events in Men Approaching Age Limits for Recommended Screening.

PURPOSE: Our goal was to quantify the ability of various PSA values in predicting the likelihood of developing metastatic or fatal prostate cancer in older men. MATERIALS AND METHODS: We used a random sample of patients in the US Veterans Health Administration to identify 80,706 men who had received PSA testing between ages 70 to 75. Our primary end point was time to development of either metastatic prostate cancer or death from prostate cancer. We used cumulative/dynamic modeling to account for competing events (death from non-prostate cancer causes) in studying both the discriminative ability of PSA as well as for positive predictive value and negative predictive value at 3 time points. RESULTS: PSA demonstrated time-dependent predictive discrimination, with receiver operating characteristic AUC at 5, 10, and 14 years decreasing from 0.83 to 0.77 to 0.73, respectively, but without statistically significant difference when stratified by race. At PSA thresholds between 1 and 8 ng/mL, the positive predictive value of developing advanced prostate cancer was significantly greater in Black than White patients. For instance, at a PSA > 3, at 5, 10, and 14 years, White patients had 2.4%, 2.9%, and 3.7% risk of an event, whereas Black patients had 4.3%, 6.5%, and 8.3% risk. CONCLUSIONS: In men aged 70 to 75 deciding whether to cease PSA testing with borderline-elevated PSA values, the risk of developing metastatic or fatal prostate cancer is quantifiable and relatively low. Risk assessment in this setting must account for the higher incidence of prostate cancer in Black men.

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances.

PURPOSE: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. METHODS: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. RESULTS: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. CONCLUSIONS: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

Impact of lung cancer screening on stage migration and mortality among the national Veterans Health Administration population with lung cancer.

BACKGROUND: Despite randomized trials demonstrating a mortality benefit to low-dose computed tomography screening to detect lung cancer, uptake of lung cancer screening (LCS) has been slow, and the benefits of screening remain unclear in clinical practice. METHODS: This study aimed to assess the impact of screening among patients in the Veterans Health Administration (VA) health care system diagnosed with lung cancer between 2011 and 2018. Lung cancer stage at diagnosis, lung cancer-specific survival, and overall survival between patients with cancer who did and did not receive screening before diagnosis were evaluated. We used Cox regression modeling and inverse propensity weighting analyses with lead time bias adjustment to correlate LCS exposure with patient outcomes. RESULTS: Of 57,919 individuals diagnosed with lung cancer in the VA system between 2011 and 2018, 2167 (3.9%) underwent screening before diagnosis. Patients with screening had higher rates of stage I diagnoses (52% vs. 27%; p ≤ .0001) compared to those who had no screening. Screened patients had improved 5-year overall survival rates (50.2% vs. 27.9%) and 5-year lung cancer-specific survival (59.0% vs. 29.7%) compared to unscreened patients. Among screening-eligible patients who underwent National Comprehensive Cancer Network guideline-concordant treatment, screening resulted in substantial reductions in all-cause mortality (adjusted hazard ratio [aHR], 0.79; 95% confidence interval [CI], 0.67-0.92; p = .003) and lung-specific mortality (aHR, 0.61; 95% CI, 0.50-0.74; p < .001). CONCLUSIONS: While LCS uptake remains limited, screening was associated with earlier stage diagnoses and improved survival. This large national study corroborates the value of LCS in clinical practice; efforts to widely adopt this vital intervention are needed.

Projected environmental and public health benefits of extended-interval dosing: an analysis of pembrolizumab use in a US national health system.

BACKGROUND: Health care is a major source of greenhouse gas emissions, leading to climate change and public health harms. Changes are needed to improve the environmental sustainability of health-care practices, but such changes should not sacrifice patient outcomes or financial sustainability. Alternative dosing strategies that reduce the frequency with which specialty drugs are administered, without sacrificing patient outcomes, are an attractive possibility for improving environmental sustainability. We sought to inform environmentally sustainable cancer care by estimating and comparing the environmental and financial effects of alternative, clinically equivalent strategies for pembrolizumab administration. METHODS: We conducted a retrospective analysis using a cohort of patients from the Veterans Health Administration (VHA) in the USA who received one or more pembrolizumab doses between May 1, 2020, and Sept 30, 2022. Using baseline, real-world administration of pembrolizumab, we generated simulated pembrolizumab use data under three near-equivalent counterfactual pembrolizumab administration strategies defined by combinations of weight-based dosing, pharmacy-level vial sharing and dose rounding, and extended-interval dosing (ie, every 6 weeks). For each counterfactual dosing strategy, we estimated greenhouse gas emissions related to pembrolizumab use across the VHA cohort using a deterministic environmental impact model that estimated greenhouse gas emissions due to patient travel, drug manufacture, and medical waste as the primary outcome measure. FINDINGS: We identified 7813 veterans who received at least one dose of pembrolizumab-containing therapy in the VHA during the study period. 59 140 pembrolizumab administrations occurred in the study period, of which 46 255 (78·2%) were dosed at 200 mg every 3 weeks, 12 885 (21·8%) at 400 mg every 6 weeks, and 14 955 (25·3%) were coadministered with infusional chemotherapies. Adoption of weight-based, extended-interval pembrolizumab dosing (4 mg/kg every 6 weeks) and pharmacy-level stewardship strategies (ie, dose rounding and vial sharing) for all pembrolizumab infusions would have resulted in 24·7% fewer administration events than baseline dosing (44 533 events vs 59 140 events) and an estimated 200 metric tons less CO2 emitted per year as a result of pembrolizumab use within the VHA (650 tons vs 850 tons of CO2, a relative reduction of 24%), largely due to reductions in distance travelled by patients to receive treatment. Similar results were observed when weight-based and extended-interval dosing were applied only to pembrolizumab monotherapy and pembrolizumab in combination with oral therapies. INTERPRETATION: Alternative pembrolizumab administration strategies might have environmental advantages over the current dosing and compounding paradigms. Specialty medication dosing can be optimised for health-care spending and environmental sustainability without sacrificing clinical outcomes. FUNDING: None.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Adrenal-Permissive Germline HSD3B1 Allele and Prostate Cancer Outcomes.

Importance: The adrenal androgen-metabolizing 3ß-hydroxysteroid dehydrogenase-1 enzyme, encoded by the HSD3B1 gene, catalyzes the rate-limiting step necessary for synthesizing nontesticular testosterone and dihydrotestosterone production. The common adrenal-permissive HSD3B1(1245C) allele is responsible for encoding the 3ß-HSD1 protein with decreased susceptibility to degradation resulting in higher extragonadal androgen synthesis. Retrospective studies have suggested an association of the HSD3B1 adrenal-permissive homozygous genotype with androgen deprivation therapy resistance in prostate cancer. Objective: To evaluate differences in mortality outcomes by HSD3B1 genetic status among men with prostate cancer. Design, Setting, and Participants: This cohort study of patients with prostate cancer who were enrolled in the Million Veteran Program within the Veterans Health Administration (VHA) system between 2011 and 2023 collected genotyping and phenotyping information. Exposure: HSD3B1 genotype status was categorized as AA (homozygous adrenal-restrictive), AC (heterozygous adrenal-restrictive), or CC (homozygous adrenal-permissive). Main Outcomes and Measures: The primary outcome of this study was prostate cancer-specific mortality (PCSM), defined as the time from diagnosis to death from prostate cancer, censored at the date of last VHA follow-up. Secondary outcomes included incidence of metastases and PCSM in predefined subgroups. Results: Of the 5287 participants (median [IQR] age, 69 [64-74] years), 402 (7.6%) had the CC genotype, 1970 (37.3%) had the AC genotype, and 2915 (55.1%) had the AA genotype. Overall, the primary cause of death for 91 patients (1.7%) was prostate cancer. Cumulative incidence of PCSM at 5 years after prostate cancer diagnosis was higher among men with the CC genotype (4.0%; 95% CI, 1.7%-6.2%) compared with the AC genotype (2.1%; 95% CI, 1.3%-2.8%) and AA genotype (1.9%; 95% CI, 1.3%-2.4%) (P = .02). In the 619 patients who developed metastatic disease at any time, the cumulative incidence of PCSM at 5 years was higher among patients with the CC genotype (36.0%; 95% CI, 16.7%-50.8%) compared with the AC genotype (17.9%; 95% CI, 10.5%-24.7%) and AA genotype (18.5%; 95% CI, 12.0%-24.6%) (P = .01). Conclusions and Relevance: In this cohort study of US veterans undergoing treatment for prostate cancer at the VHA, the HSD3B1 CC genotype was associated with inferior outcomes. The HSD3B1 biomarker may help identify patients who may benefit from therapeutic targeting of 3ß-hydroxysteroid dehydrogenase-1 and the androgen-signaling axis.

Lung Cancer Survival Trends in the Veterans Health Administration.

INTRODUCTION: Lung cancer survival is improving in the United States. We investigated whether there was a similar trend within the Veterans Health Administration (VHA), the largest integrated healthcare system in the United States. MATERIALS AND METHODS: Data from the Veterans Affairs Central Cancer Registry were analyzed for temporal survival trends using Kaplan-Meier estimates and linear regression. RESULTS: A total number of 54,922 Veterans were identified with lung cancer diagnosed from 2010 to 2017. Histologies were classified as non-small-cell lung cancer (NSCLC) (64.2%), small cell lung cancer (SCLC) (12.9%), and 'other' (22.9%). The proportion with stage I increased from 18.1% to 30.4%, while stage IV decreased from 38.9% to 34.6% (both P < .001). The 3-year overall survival (OS) improved for stage I (58.6% to 68.4%, P < .001), stage II (35.5% to 48.4%, P < .001), stage III (18.7% to 29.4%, P < .001), and stage IV (3.4% to 7.8%, P < .001). For NSCLC, the median OS increased from 12 to 21 months (P < .001), and the 3-year OS increased from 24.1% to 38.3% (P < .001). For SCLC, the median OS remained unchanged (8 to 9 months, P = .10), while the 3-year OS increased from 9.1% to 12.3% (P = .014). Compared to White Veterans, Black Veterans with NSCLC had similar OS (P = .81), and those with SCLC had higher OS (P = .003). CONCLUSION: Lung cancer survival is improving within the VHA. Compared to White Veterans, Black Veterans had similar or higher survival rates. The observed racial equity in outcomes within a geographically and socioeconomically diverse population warrants further investigation to better understand and replicate this achievement in other healthcare systems.

An NLP Framework for the Extraction of Concept Measurements from Radiology and Pathology Notes.

Natural language processing (NLP) tools can automate the identification of cancer patients eligible for specific pathways. We developed and validated a cancer agnostic, rules-based NLP framework to extract the dimensions and measurements of several concepts from pathology and radiology reports. This framework was then efficiently and cost-effectively deployed to identify patients eligible for breast, lung, and prostate cancers clinical pathways.

Genetic risk and likelihood of prostate cancer detection on first biopsy by ancestry.

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male veterans using 2-sided tests. Our analysis included 36 717 veterans (10 297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; P < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (odds ratio = 2.18, 95% confidence interval = 1.93 to 2.47), but the effect was stronger among men of European descent (odds ratio = 3.89, 95% confidence interval = 3.62 to 4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

Association between prediagnostic prostate-specific antigen and prostate cancer probability in Black and non-Hispanic White men.

BACKGROUND: Although Black men are more likely than non-Hispanic White men to develop and die from prostate cancer, limited data exist to guide prostate-specific antigen (PSA) screening protocols in Black men. This study investigated whether the risk for prostate cancer was higher than expected among self-identified Black than White veterans based on prebiopsy PSA level. METHODS: Multivariable logistic regression models were estimated to predict the likelihood of prostate cancer diagnosis on first biopsy for 75,295 Black and 207,658 White male veterans. Self-identified race, age at first PSA test, prebiopsy PSA, age at first biopsy, smoking status, statin use, and socioeconomic factors were used as predictors. The adjusted predicted probabilities of cancer detection on first prostate biopsy from the logistic models at different PSA levels were calculated. RESULTS: After controlling for PSA and other covariates, Black veterans were 50% more likely to receive a prostate cancer diagnosis on their first prostate biopsy than White veterans (odds ratio [OR], 1.50; 95% CI, 1.47-1.53; p < .001). At a PSA level of 4.0 ng/mL, the probability of prostate cancer for a Black man was 49% compared with 39% for a White man. This model indicated that Black veterans with a PSA of 4.0 ng/mL have an equivalent risk of prostate cancer as White veterans with a PSA of 13.4 ng/mL. CONCLUSIONS: The findings indicate that, at any given PSA level, Black men are more likely to harbor prostate cancer than White men. Prospective studies are needed to better evaluate risks and benefits of PSA screening in Black men and other high-risk populations.

Pneumonitis After Chemoradiotherapy and Adjuvant Durvalumab in Stage III Non-Small Cell Lung Cancer.

PURPOSE: Adjuvant durvalumab after definitive chemoradiotherapy (CRT) for unresectable stage III non-small cell lung cancer (NSCLC) is well-tolerated in clinical trials. However, pneumonitis rates outside of clinical trials remain poorly defined with CRT followed by durvalumab. We aimed to describe the influence of durvalumab on pneumonitis rates among a large cohort of patients with stage III NSCLC. METHODS AND MATERIALS: We studied patients with stage III NSCLC in the national Veterans Health Administration from 2015 to 2021 who received concurrent CRT alone or with adjuvant durvalumab. We defined pneumonitis as worsening respiratory symptoms with radiographic changes within 2 years of CRT and graded events according to National Cancer Institute Common Terminology Criteria for Adverse Events version 4.03. We used Cox regression to analyze risk factors for pneumonitis and the effect of postbaseline pneumonitis on overall survival. RESULTS: Among 1994 patients (989 CRT alone, 1005 CRT followed by adjuvant durvalumab), the 2-year incidence of grade 2 or higher pneumonitis was 13.9% for CRT alone versus 22.1% for CRT plus durvalumab (unadjusted P < .001). On multivariable analysis, durvalumab was associated with higher risk of grade 2 pneumonitis (hazard ratio, 1.45; 95% CI, 1.09-1.93; P = .012) but not grade 3 to 5 pneumonitis (P = .2). Grade 3 pneumonitis conferred worse overall survival (hazard ratio, 2.51; 95% CI, 2.06-3.05; P < .001) but grade 2 pneumonitis did not (P = .4). CONCLUSIONS: Adjuvant durvalumab use was associated with increased risk of low-grade but not higher-grade pneumonitis. Reassuringly, low-grade pneumonitis did not increase mortality risk. We observed increased rates of high-grade pneumonitis relative to clinical trials; the reasons for this require further study.

Adopting Weight-Based Dosing With Pharmacy-Level Stewardship Strategies Could Reduce Cancer Drug Spending By Millions.

Immune checkpoint inhibitors, a class of drugs used in approximately forty unique cancer indications, are a sizable component of the economic burden of cancer care in the US. Instead of personalized weight-based dosing, immune checkpoint inhibitors are most commonly administered at "one-size-fits-all" flat doses that are higher than necessary for the vast majority of patients. We hypothesized that personalized weight-based dosing along with common stewardship efforts at the pharmacy level, such as dose rounding and vial sharing, would lead to reductions in immune checkpoint inhibitor use and lower spending. Using data from the Veterans Health Administration (VHA) and Medicare drug prices, we estimated reductions in immune checkpoint inhibitor use and spending that would be associated with pharmacy-level stewardship strategies, in a case-control simulation study of individual patient-level immune checkpoint inhibitor administration events. We identified baseline annual VHA spending for these drugs of approximately $537 million. Combining weight-based dosing, dose rounding, and pharmacy-level vial sharing would generate expected annual VHA health system savings of $74 million (13.7 percent). We conclude that adoption of pharmacologically justified immune checkpoint inhibitor stewardship measures would generate sizable reductions in spending for these drugs. Combining these operational innovations with value-based drug price negotiation enabled by recent policy changes may improve the long-term financial viability of cancer care in the US.

Prospective Evaluation of Limited-Stage Small Cell Lung Cancer Radiotherapy Fractionation Regimen Usage and Acute Toxicity in a Large Statewide Quality Collaborative.

PURPOSE: National guidelines on limited-stage small cell lung cancer (LS-SCLC) treatment give preference to a hyperfractionated regimen of 45 Gy in 30 fractions delivered twice daily; however, use of this regimen is uncommon compared with once-daily regimens. The purpose of this study was to characterize the LS-SCLC fractionation regimens used throughout a statewide collaborative, analyze patient and treatment factors associated with these regimens, and describe real-world acute toxicity profiles of once- and twice-daily radiation therapy (RT) regimens. METHODS AND MATERIALS: Demographic, clinical, and treatment data along with physician-assessed toxicity and patient-reported outcomes were prospectively collected by 29 institutions within the Michigan Radiation Oncology Quality Consortium between 2012 and 2021 for patients with LS-SCLC. We modeled the influence of RT fractionation and other patient-level variables clustered by treatment site on the odds of a treatment break specifically due to toxicity with multilevel logistic regression. National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0, incident grade 2 or worse toxicity was longitudinally compared between regimens. RESULTS: There were 78 patients (15.6% overall) treated with twice-daily RT and 421 patients treated with once-daily RT. Patients receiving twice-daily RT were more likely to be married or living with someone (65% vs 51%; P = .019) and to have no major comorbidities (24% vs 10%; P = .017). Once-daily RT fractionation toxicity peaked during RT, and twice-daily toxicity peaked within 1 month after RT. After stratifying by treatment site and adjusting for patient-level variables, once-daily treated patients had 4.11 (95% confidence interval, 1.31-12.87) higher odds of treatment break specifically due to toxicity than twice-daily treated patients. CONCLUSIONS: Hyperfractionation for LS-SCLC remains infrequently prescribed despite the lack of evidence demonstrating superior efficacy or lower toxicity of once-daily RT. With peak acute toxicity after RT and lower likelihood of a treatment break with twice-daily fractionation in real-word practice, providers may start using hyperfractionated RT more frequently.

Impact of COVID-19 on the incidence of localized and metastatic prostate cancer among White and Black Veterans.

The COVID-19 pandemic disrupted prostate-specific antigen (PSA) screening and prostate biopsy procedures. We sought to determine whether delayed screening and diagnostic workup of prostate cancer (PCa) was associated with increased subsequent rates of incident PCa and advanced PCa and whether the rates differed by race. We analyzed data from the Veterans Health Administration to assess the changes in the rates of PSA screening, prostate biopsy procedure, incident PCa, PCa with high-grade Gleason score, and incident metastatic prostate cancer (mPCa) before and after January 2020. While the late pandemic mPCa rate among White Veterans was comparable to the pre-pandemic rate (5.4 pre-pandemic vs 5.2 late-pandemic, p = 0.67), we observed a significant increase in incident mPCa cases among Black Veterans in the late pandemic period (8.1 pre-pandemic vs 11.3 late-pandemic, p < 0.001). Further investigation is warranted to fully understand the impact of the COVID-19 pandemic on the diagnosis of advanced prostate cancer.

Cannabis Use in Patients Seen in an Academic Radiation Oncology Department.

PURPOSE: Cannabis use rates are increasing in the United States. Patients with cancer use cannabis for many reasons, even without high-quality supporting data. This study sought to characterize cannabis use among patients seen in radiation oncology in a state that has legalized adult nonmedical use cannabis and to identify key cannabis-related educational topics. METHODS AND MATERIALS: Cannabis history was documented by providers using a structured template at patient visits in an academic radiation oncology practice October 2020 to November 2021. Cannabis use data, including recency/frequency of use, reason, and mode of administration, were summarized, and logistic regression was used to explore associations between patient and disease characteristics and recent cannabis use. A multivariable model employed stepwise variable selection using the Akaike Information Criterion. RESULTS: Of 3143 patients total, 91 (2.9%) declined to answer cannabis use questions, and 343 (10.9%) endorsed recent use (≤1 month ago), 235 (7.5%) noted nonrecent use (>1 month ago), and 2474 (78.7%) denied history of cannabis use. In multivariable analyses, those ≥50 years old (odds ratio [OR], 0.409; 95% confidence interval [CI], 0.294-0.568; P < .001) or with history of prior courses of radiation (OR, 0.748; 95% CI, 0.572-0.979; P = .034) were less likely, and those with a mental health diagnosis not related to substance use (OR, 1.533; 95% CI, 1.171-2.005; P = .002) or who smoked tobacco (OR, 3.003; 95% CI, 2.098-4.299; P < .001) were more likely to endorse recent cannabis use. Patients reported pain, insomnia, and anxiety as the most common reasons for use. Smoking was the most common mode of administration. CONCLUSIONS: Patients are willing to discuss cannabis use with providers and reported recent cannabis use for a variety of reasons. Younger patients new to oncologic care and those with a history of mental illness or tobacco smoking may benefit most from discussions about cannabis given higher rates of cannabis use in these groups.

Association of Prostate-Specific Antigen Screening Rates With Subsequent Metastatic Prostate Cancer Incidence at US Veterans Health Administration Facilities.

Importance: There is controversy about the benefit of prostate-specific antigen (PSA) screening. Prostate-specific antigen screening rates have decreased since 2008 in the US, and the incidence of metastatic prostate cancer has increased. However, there is no direct epidemiologic evidence of a correlation between population PSA screening rates and subsequent metastatic prostate cancer rates. Objective: To assess whether facility-level variation in PSA screening rates is associated with subsequent facility-level metastatic prostate cancer incidence. Design, Setting, and Participants: This retrospective cohort used data for all men aged 40 years or older with an encounter at 128 facilities in the US Veterans Health Administration (VHA) from January 1, 2005, to December 31, 2019. Exposures: Yearly facility-level PSA screening rates, defined as the proportion of men aged 40 years or older with a PSA test in each year, and long-term nonscreening rates, defined as the proportion of men aged 40 years or older without a PSA test in the prior 3 years, from January 1, 2005, to December 31, 2014. Main Outcomes and Measures: The main outcomes were facility-level yearly counts of incident metastatic prostate cancer diagnoses and age-adjusted yearly metastatic prostate cancer incidence rates (per 100 000 men) 5 years after each PSA screening exposure year. Results: The cohort included 4 678 412 men in 2005 and 5 371 701 men in 2019. Prostate-specific antigen screening rates decreased from 47.2% in 2005 to 37.0% in 2019, and metastatic prostate cancer incidence increased from 5.2 per 100 000 men in 2005 to 7.9 per 100 000 men in 2019. Higher facility-level PSA screening rates were associated with lower metastatic prostate cancer incidence 5 years later (incidence rate ratio [IRR], 0.91 per 10% increase in PSA screening rate; 95% CI, 0.87-0.96; P < .001). Higher long-term nonscreening rates were associated with higher metastatic prostate cancer incidence 5 years later (IRR, 1.11 per 10% increase in long-term nonscreening rate; 95% CI, 1.03-1.19; P = .01). Conclusions and Relevance: From 2005 to 2019, PSA screening rates decreased in the national VHA system. Facilities with higher PSA screening rates had lower subsequent rates of metastatic prostate cancer. These data may be used to inform shared decision-making about the potential benefits of PSA screening among men who wish to reduce their risk of metastatic prostate cancer.

De-escalating adjuvant durvalumab treatment duration in stage III non-small cell lung cancer.

BACKGROUND: One year of adjuvant durvalumab following concurrent chemoradiotherapy significantly improves progression-free survival (PFS) and overall survival (OS) for patients with stage III non-small cell lung cancer (NSCLC). However, the optimal length of adjuvant therapy has not been determined. METHODS: We identified patients with stage III NSCLC treated with definitive chemoradiation and adjuvant durvalumab from November 2017 to April 2021 from the United States Veterans Affairs system. Predictors of early durvalumab discontinuation were evaluated with Cox proportional hazards regression. The effect of differing durations of durvalumab treatment (up to 6, 9, and 12 months) on PFS and OS were compared with a marginal structural model and time-dependent Cox modelling. RESULTS: We included 1006 patients with stage III non-small cell lung cancer who received concurrent chemoradiotherapy and at least one dose of adjuvant durvalumab. The median duration of durvalumab treatment was 7 months (interquartile range 2.8-11.5) and 31% completed the intended durvalumab course. The most common reasons for early discontinuation were tumour progression (22%), immune-related adverse events (15%), and non-immune-related toxicity (6.0%), Marginal structural models suggested similar PFS for 9 months versus 12 months of durvalumab treatment and inferior PFS for 6 months versus 12 months. CONCLUSIONS: A substantial proportion of patients undergoing adjuvant durvalumab discontinue therapy early due to toxicity, and shorter durvalumab treatment durations may provide similar disease control to 12 months of therapy. Prospective randomised controlled studies are needed to characterise the optimal durvalumab treatment duration in locally advanced NSCLC patients.

Impact of age on treatment response in men with prostate cancer treated with radiotherapy.

Objective: To analyse the effect of age at diagnosis on clinical outcomes of localized prostate cancer (PCa) treated with radiation therapy. Subjects and methods: We identified 12 784 patients with intermediate- or high-risk localized PCa treated with radiation therapy (RT) and neoadjuvant androgen deprivation therapy (ADT) between 2000 and 2015 from nationwide Veterans Affairs data. Patients were grouped into three age categories (≤59, 60-69, and ≥70 years old). Outcomes included immediate PSA response (3-month post-RT PSA and 2-year PSA nadir, grouped into <0.10 ng/ml, 0.10-0.49 ng/ml, and ≥0.50 ng/ml), biochemical recurrence, and PCa-specific mortality. Multivariable regression models included ordinal logistic regression for short-term PSA outcomes, Cox regression for biochemical recurrence, and Fine-Gray competing risks regression for PCa-specific mortality. Results: A total of 2136 patients (17%) were ≤59 years old at diagnosis, 6107 (48%) were 60-69 years old, and 4541 (36%) were ≥70 years old. Median follow-up was 6.3 years. Younger age was associated with greater odds of higher 3-month PSA group (≤59 vs. ≥70: adjusted odds ratio [aOR] 1.90, 95% CI 1.64-2.20; p < 0.001) and higher 2-year PSA nadir group (≤59 vs. ≥70: aOR 1.89, 95% CI 1.62-2.19, p < 0.001). Younger age was associated with greater risk of biochemical recurrence (≤59 vs. ≥70: adjusted hazard ratio 1.45, 95% CI 1.26-1.67, p < 0.001) but not PCa-specific mortality (p = 0.16). Conclusion: In a large nationwide sample of US veterans treated with ADT and RT for localized PCa, younger age was associated with inferior short-term PSA response and higher risk of biochemical recurrence.

Prognostic and Predictive Role of PD-L1 Expression in Stage III Non-small Cell Lung Cancer Treated With Definitive Chemoradiation and Adjuvant Durvalumab.

PURPOSE: It is unclear whether programmed death ligand 1 (PD-L1) expression is prognostic or predictive of immunotherapy benefit among patients with stage III non-small cell lung cancer (NSCLC) treated with definitive chemoradiation and adjuvant durvalumab. METHODS AND MATERIALS: We determined pretreatment tumor PD-L1 expression for 312 patients with stage III NSCLC treated with definitive chemoradiation and at least 1 dose of adjuvant durvalumab between November 2017 and April 2021 across the national Veterans Health Administration. Progression-free survival (PFS) and overall survival (OS) in PD-L1 expression subgroups (<1%, 1%-49%, and 50%-100%) were compared with 994 patients with stage III NSCLC treated without adjuvant durvalumab from 2015 to 2016. RESULTS: PD-L1 expression was <1%, 1% to 49%, and 50% to 100% in 109 (34.9%), 96 (30.7%), and 107 (34.3%) patients, respectively. Increasing PD-L1 expression was associated with longer PFS (adjusted hazard ratio [aHR], 0.84 per 25% absolute increase in expression; 95% confidence interval [CI], 0.75-0.94; P = .003) and OS (aHR, 0.86 per 25% absolute increase in expression; 95% CI, 0.74-0.99; P = .036). Compared with the no-durvalumab group, PFS was longer for PD-L1 50% to 100% (aHR, 0.44; 95% CI, 0.32-0.60; P < .001) and PD-L1 1% to 49% (aHR, 0.64; 95% CI, 0.47-0.86; P = .003) but not PD-L1 <1% (aHR, 0.84; 95% CI, 0.64-1.10; P = .19). Similar results were found for OS, with no significant difference between the no-durvalumab group and PD-L1 <1% (aHR, 0.81; 95% CI, 0.58-1.13; P = .22). CONCLUSIONS: Increasing tumor PD-L1 expression is prognostic for PFS and OS among patients with stage III NSCLC treated with adjuvant durvalumab, and patients with PD-L1 expression <1% may have limited benefit from adjuvant durvalumab.