Biofilm of petroleum-based and bio-based microplastics in seawater in response to Zn(II): Biofilm formation, community structure, and microbial function.

Microplastic biofilms are novel vectors for the transport and spread of pathogenic and drug-resistant bacteria. With the increasing use of bio-based plastics, there is an urgent need to investigate the microbial colonization characteristics of these materials in seawater, particularly in comparison with conventional petroleum-based plastics. Furthermore, the effect of co-occurring contaminants, such as heavy metals, on the formation of microplastic biofilms and bacterial communities remains unclear. In this study, we compared the biofilm bacterial community structure of petroleum-based polyethylene (PE) and bio-based polylactic acid (PLA) in seawater under the influence of zinc ions (Zn2+). Our findings indicate that the biofilm on PLA microplastics in the late stage was impeded by the formation of a mildly acidic microenvironment resulting from the hydrolysis of the ester group on PLA. The PE surface had higher bacterial abundance and diversity, with a more intricate symbiotic pattern. The bacterial structures on the two types of microplastics were different; PE was more conducive to the colonization of anaerobic bacteria, whereas PLA was more favorable for the colonization of aerobic and acid-tolerant species. Furthermore, Zn increased the proportion of the dominant genera that could utilize microplastics as a carbon source, such as Alcanivorax and Nitratireductor. PLA had a greater propensity to harbor and disseminate pathogenic and drug-resistant bacteria, and Zn promoted the enrichment and spread of harmful bacteria such as, Pseudomonas and Clostridioides. Therefore, further research is essential to fully understand the potential environmental effects of bio-based microplastics and the role of heavy metals in the dynamics of bacterial colonization.

The clinical manifestations and molecular pathogenesis of radiation fibrosis.

Advances in radiation techniques have enabled the precise delivery of higher doses of radiotherapy to tumours, while sparing surrounding healthy tissues. Consequently, the incidence of radiation toxicities has declined, and will likely continue to improve as radiotherapy further evolves. Nonetheless, ionizing radiation elicits tissue-specific toxicities that gradually develop into radiation-induced fibrosis, a common long-term side-effect of radiotherapy. Radiation fibrosis is characterized by an aberrant wound repair process, which promotes the deposition of extensive scar tissue, clinically manifesting as a loss of elasticity, tissue thickening, and organ-specific functional consequences. In addition to improving the existing technologies and guidelines directing the administration of radiotherapy, understanding the pathogenesis underlying radiation fibrosis is essential for the success of cancer treatments. This review integrates the principles for radiotherapy dosimetry to minimize off-target effects, the tissue-specific clinical manifestations, the key cellular and molecular drivers of radiation fibrosis, and emerging therapeutic opportunities for both prevention and treatment.

Progress in site-specific cancer mortality in Canada over the last 70 years.

In Canada, the absolute number of cancer deaths has been steadily increasing, however, age-standardized cancer mortality rates peaked decades ago for most cancers. The objective of this study was to estimate the reduction in deaths for each cancer type under the scenario where peak mortality rates had remained stable in Canada. Data for this study were obtained the Global Cancer Observatory and Statistics Canada. We estimated age-standardized mortality rates (ASMR, per 100,000) from 1950 to 2022, standardized to the 2011 Canadian standard population. We identified peak mortality rates and applied the age-specific mortality rates from the peak year to the age-specific Canadian population estimates for subsequent years (up to 2022) to estimate the number of expected deaths. Avoided cancer deaths were the difference between the observed and expected number of cancer deaths. There have been major reductions in deaths among cancers related to tobacco consumption and other modifiable lifestyle habits (417,561 stomach; 218,244 colorectal; 186,553 lung; 66,281 cervix; 32,732 head and neck; 27,713 bladder; 22,464 leukemia; 20,428 pancreas; 8863 kidney; 3876 esophagus; 290 liver). There have been 201,979 deaths avoided for female-specific cancers (breast, cervix, ovary, uterus). Overall, there has been a 34% reduction in mortality for lung cancer among males and a 9% reduction among females. There has been a significant reduction in cancer mortality in Canada since site-specific cancer mortality rates peaked decades ago for many cancers. This shows the exceptional progress made in cancer control in Canada due to substantial improvements in prevention, screening, and treatment. This study highlights priority areas where more attention and investment are needed to achieve progress.

CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level.

Detecting cyanobacteria in environments is an important concern due to their crucial roles in ecosystems, and they can form blooms with the potential to harm humans and nonhuman entities. However, the most widely used methods for high-throughput detection of environmental cyanobacteria, such as 16S rRNA sequencing, typically provide above-species-level resolution, thereby disregarding intraspecific variation. To address this, we developed a novel DNA microarray tool, termed the CyanoStrainChip, that enables strain-level comprehensive profiling of environmental cyanobacteria. The CyanoStrainChip was designed to target 1277 strains; nearly all major groups of cyanobacteria are included by implementing 43,666 genome-wide, strain-specific probes. It demonstrated strong specificity by in vitro mock community experiments. The high correlation (Pearson's R > 0.97) between probe fluorescence intensities and the corresponding DNA amounts (ranging from 1-100 ng) indicated excellent quantitative capability. Consistent cyanobacterial profiles of field samples were observed by both the CyanoStrainChip and next-generation sequencing methods. Furthermore, CyanoStrainChip analysis of surface water samples in Lake Chaohu uncovered a high intraspecific variation of abundance change within the genus Microcystis between different severity levels of cyanobacterial blooms, highlighting two toxic Microcystis strains that are of critical concern for Lake Chaohu harmful blooms suppression. Overall, these results suggest a potential for CyanoStrainChip as a valuable tool for cyanobacterial ecological research and harmful bloom monitoring to supplement existing techniques.

Controllable nonreciprocal phonon laser in a hybrid photonic molecule based on directional quantum squeezing.

Here, a scheme for a controllable nonreciprocal phonon laser is proposed in a hybrid photonic molecule system consisting of a whispering-gallery mode (WGM) optomechanical resonator and a χ(2)-nonlinear WGM resonator, by directionally quantum squeezing one of two coupled resonator modes. The directional quantum squeezing results in a chiral photon interaction between the resonators and a frequency shift of the squeezed resonator mode with respect to the unsqueezed bare mode. We show that the directional quantum squeezing can modify the effective optomechanical coupling in the optomechanical resonator, and analyze the impacts of driving direction and squeezing extent on the phonon laser action in detail. Our analytical and numerical results indicate that the controllable nonreciprocal phonon laser action can be effectively realized in this system. The proposed scheme uses an all-optical and chip-compatible approach without spinning resonators, which may be more beneficial for integrating and packaging of the system on a chip. Our proposal may provide a new route to realize integratable phonon devices for on-chip nonreciprocal phonon manipulations, which may be used in chiral quantum acoustics, topological phononics, and acoustical information processing.

The effects of obstructive sleep apnea on blood pressure variability and load in patients with hypertension.

BACKGROUND: Hypertension frequently coexists with obstructive sleep apnea (OSA), and their interplay substantially impacts the prognosis of affected individuals. Investigating the influence of OSA on blood pressure variability (BPV) and blood pressure load (BPL) in hypertensive patients has become a focal point of clinical research. METHODS: This cross-sectional study recruited hypertensive patients (n = 265) without discrimination and classified them into four groups based on their apnea-hypopnea index (AHI): control group (n = 40), AHI < 5; mild group (n = 74), 5 ≤ AHI ≤ 15; moderate group (n = 68), 15 < AHI ≤ 30; severe group (n = 83), AHI > 30. All participants underwent comprehensive assessments, including polysomnography (PSG) monitoring, 24-h ambulatory blood pressure (ABP) monitoring, cardiac Doppler ultrasound, and additional examinations when indicated. RESULTS: BPV and BPL exhibited significant elevations in the moderate and severe OSA groups compared to the control and mild OSA groups (P < 0.05). Moreover, interventricular septum thickness and left ventricular end-diastolic volume (LVEDV) demonstrated higher values in the moderate and severe OSA groups (P < 0.05). Multiple stepwise regression analysis identified noteworthy risk factors for elevated BPV in hypertensive patients with OSA, including AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea. CONCLUSION: Hypertensive patients with moderate to severe OSA exhibited substantially increased BPV and BPL. Moreover, BPV was correlated with AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea in hypertensive patients with OSA.

AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer.

The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.

Three new species of Cortinarius section Delibuti (Cortinariaceae, Agaricales) from China.

Three new species of CortinariussectionDelibuti, namely C.fibrillososalor, C.pseudosalor, and C.subtropicus are described as new to science based on morphological and phylogenetic evidences. Cortinariuspseudosalor is extremely morphologically similar to C.salor, but it differs from the latter by smaller coarsely verrucose basidiospores. Cortinariusfibrillososalor can be easily differentiated by its fibrillose pileus. The pileus of C.subtropicus becomes brown without lilac tint at maturity comparing with other members of section Delibuti. A combined dataset of ITS and LSU sequences was used for phylogenetic analysis. The phylogenetic reconstruction of section Delibuti revealed that these three new species clustered and formed independent lineages with full support respectively. A key to the three new species and related species of section Delibuti is provided in this work.

Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer.

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

A Systematic Literature Review of Health-Related Quality of Life Outcomes and Associated Utility Values in Relapsed and/or Refractory Large B Cell Lymphoma.

BACKGROUND: In this ever-expanding treatment landscape, there is a lack of consolidated health-related quality of life (HRQOL) outcomes and utility reports in relapsed or refractory (R/R) large B cell lymphoma (LBCL) to inform health care policy and decision-maker assessments for both old and new products. These assessments can have a direct effect on what treatment options are available to patients and physicians. OBJECTIVE: A systematic literature review (SLR) was performed to understand the HRQOL evidence for treatments in R/R LBCL and identify associated health utility values. METHODS: The SLR searched and screened literature published from 1 January 2003 to 2 May 2022. Studies were screened based on Population, Intervention, Comparator, Outcome, Study design criteria established a priori and were assessed by two independent reviewers; quality assessments of the evidence were performed in accordance with health technology assessment recommendations from the National Institute for Health and Care Excellence. Several types of therapies were included, such as chimeric antigen receptor (CAR) T cell products (lisocabtagene maraleucel, axicabtagene ciloleucel, tisagenlecleucel), novel therapies (selinexor, nivolumab, polatuzumab vedotin, and bendamustine), salvage therapies, and rituximab. RESULTS: The review identified 33 unique studies reporting HRQOL, including 15 economic studies that reported health state utility values, 9 clinical trials, 7 health technology assessment reports, and 1 each of a vignette-based study and a point-in-time survey. Improvements in general and/or lymphoma-specific HRQOL measures were observed with CAR T cell therapy in both the second-line and third-line or later settings. On-treatment utility values for CAR T cell therapies ranged from 0.50 to 0.74. Values for remission/progression-free survival (0.70-0.90) and for disease progression (0.39-0.59) were similar across studies. For novel therapies, utility values were 0.83 for progression-free survival and ranged from 0.39 to 0.71 for disease progression. On-treatment utility values for salvage chemotherapy ranged from 0.63 to 0.67. CONCLUSIONS: Overall, the evidence synthesized in this SLR provides a comprehensive understanding of the HRQOL evidence in R/R LBCL. This article identified several sources for utility values in the published literature showing variation in the HRQOL outcomes for patients across a variety of therapeutics. Treatment of R/R LBCL with CAR T cell therapies was associated with improvement in health utility values. Mixed results were found for novel therapies and salvage therapies. More data are needed as new therapies are used in this patient population to inform treatment decision-making.

Lisocabtagene maraleucel for second-line relapsed or refractory large B-cell lymphoma: patient-reported outcomes from the PILOT study.

In the single-arm, open-label, multicenter, phase II PILOT study, second-line treatment with the chimeric antigen receptor (CAR) T-cell therapy lisocabtagene maraleucel (liso-cel) in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) for whom hematopoietic stem cell transplantation (HSCT) was not intended resulted in high response rates, durable responses, and a safety profile consistent with previous reports. Here, we analyzed changes in health-related quality of life (HRQOL) in patients who received liso-cel in PILOT. Patients received liso-cel, an autologous, CD19-directed, 4-1BB CAR T-cell product administered at equal target doses of CD8+ and CD4+ CAR+ T cells, for a total target dose of 100×106 CAR+ T cells. HRQOL, a secondary endpoint of PILOT, was assessed as prespecified using three patient-reported outcome instruments (EORTC QLQ-C30; FACT-LymS; EQ-5D-5L). Evaluable datasets for the EORTC QLQ-C30, FACT-LymS, and EQ-5D-5L health utility index, and visual analog scale (EQ-VAS) included 56 (92%), 49 (80%), 55 (90%), and 54 (89%) patients, respectively. Clinically meaningful improvement was achieved across most post-treatment visits for EORTC QLQ-C30 fatigue and FACT-LymS. Overall mean changes from baseline through day 545 showed significant improvements in EORTC QLQ-C30 fatigue, pain, and appetite loss, FACT-LymS, and EQ VAS. In within-patient analyses, clinically meaningful improvements or maintenance in scores were observed in most patients at days 90, 180, 270, and 365. HRQOL was maintained or improved in patients who received liso-cel as second-line therapy in PILOT. These findings support liso-cel as a preferred second-line treatment in patients with R/R LBCL not intended for HSCT (clinicaltrials gov. Identifier: NCT03483103).

Progress in cancer control leads to a substantial number of cancer deaths avoided in Canada.

It is currently not known how many more cancer deaths would have occurred among Canadians if cancer mortality rates were unchanged following various modern human interventions. The objective of this study was to estimate the number of cancer deaths that have been avoided in Canada since the age-standardized overall cancer mortality rate peaked in 1988. We applied the age-specific overall cancer mortality rates from 1988 to the Canadian population for all subsequent years to estimate the number of expected deaths. Avoided cancer deaths were estimated as the difference between the observed and expected number of cancer deaths for each year. Since 1988, there have been 372 584 (standardized mortality ratio = 0.77) and 120 045 (standardized mortality ratio = 0.90) avoided cancer deaths in males and females, respectively (492 629 total). Nearly half a million cancer deaths have been avoided in Canada since the overall cancer mortality rate peaked, which demonstrates the exceptional progress made in modern cancer control in Canada.

Omitting Radiotherapy after Breast-Conserving Surgery in Luminal A Breast Cancer.

BACKGROUND: Adjuvant radiotherapy is prescribed after breast-conserving surgery to reduce the risk of local recurrence. However, radiotherapy is inconvenient, costly, and associated with both short-term and long-term side effects. Clinicopathologic factors alone are of limited use in the identification of women at low risk for local recurrence in whom radiotherapy can be omitted. Molecularly defined intrinsic subtypes of breast cancer can provide additional prognostic information. METHODS: We performed a prospective cohort study involving women who were at least 55 years of age, had undergone breast-conserving surgery for T1N0 (tumor size <2 cm and node negative), grade 1 or 2, luminal A-subtype breast cancer (defined as estrogen receptor positivity of ≥1%, progesterone receptor positivity of >20%, negative human epidermal growth factor receptor 2, and Ki67 index of ≤13.25%), and had received adjuvant endocrine therapy. Patients who met the clinical eligibility criteria were registered, and Ki67 immunohistochemical analysis was performed centrally. Patients with a Ki67 index of 13.25% or less were enrolled and did not receive radiotherapy. The primary outcome was local recurrence in the ipsilateral breast. In consultation with radiation oncologists and patients with breast cancer, we determined that if the upper boundary of the two-sided 90% confidence interval for the cumulative incidence at 5 years was less than 5%, this would represent an acceptable risk of local recurrence at 5 years. RESULTS: Of 740 registered patients, 500 eligible patients were enrolled. At 5 years after enrollment, recurrence was reported in 2.3% of the patients (90% confidence interval [CI], 1.3 to 3.8; 95% CI, 1.2 to 4.1), a result that met the prespecified boundary. Breast cancer occurred in the contralateral breast in 1.9% of the patients (90% CI, 1.1 to 3.2), and recurrence of any type was observed in 2.7% (90% CI, 1.6 to 4.1). CONCLUSIONS: Among women who were at least 55 years of age and had T1N0, grade 1 or 2, luminal A breast cancer that were treated with breast-conserving surgery and endocrine therapy alone, the incidence of local recurrence at 5 years was low with the omission of radiotherapy. (Funded by the Canadian Cancer Society and the Canadian Breast Cancer Foundation; LUMINA ClinicalTrials.gov number, NCT01791829.).

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma.

BACKGROUND: Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations. OBJECTIVES: We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL. METHODS: OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL. RESULTS: For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL. CONCLUSIONS: Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.

Artificial intelligence (AI) and machine learning (ML) are becoming critical in developing and deploying personalized medicine and targeted clinical trials. Recent advances in ML have enabled the integration of wider ranges of data including both medical records and imaging (radiomics). However, the development of prognostic models is complex as no modeling strategy is universally superior to others and validation of developed models requires large and diverse datasets to demonstrate that prognostic models developed (regardless of method) from one dataset are applicable to other datasets both internally and externally. Using a retrospective dataset of 2,552 patients from a single institution and a strict evaluation framework that included external validation on three external patient cohorts (873 patients), we crowdsourced the development of ML models to predict overall survival in head and neck cancer (HNC) using electronic medical records (EMR) and pretreatment radiological images. To assess the relative contributions of radiomics in predicting HNC prognosis, we compared 12 different models using imaging and/or EMR data. The model with the highest accuracy used multitask learning on clinical data and tumor volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction, outperforming models relying on clinical data only, engineered radiomics, or complex deep neural network architecture. However, when we attempted to extend the best performing models from this large training dataset to other institutions, we observed significant reductions in the performance of the model in those datasets, highlighting the importance of detailed population-based reporting for AI/ML model utility and stronger validation frameworks. We have developed highly prognostic models for overall survival in HNC using EMRs and pretreatment radiological images based on a large, retrospective dataset of 2,552 patients from our institution.Diverse ML approaches were used by independent investigators. The model with the highest accuracy used multitask learning on clinical data and tumor volume.External validation of the top three performing models on three datasets (873 patients) with significant differences in the distributions of clinical and demographic variables demonstrated significant decreases in model performance. Significance: ML combined with simple prognostic factors outperformed multiple advanced CT radiomics and deep learning methods. ML models provided diverse solutions for prognosis of patients with HNC but their prognostic value is affected by differences in patient populations and require extensive validation.

An effective QUATRE algorithm based on reorganized mechanism and its application for parameter estimation in improved photovoltaic module.

The traditional parameter estimation methods for photovoltaic (PV) module are strictly limited by the reference standards. On the basis of the double diode model (DDM), this paper proposes a modified PV module that is independent of the reference conditions and can be used for the transformation and reconfiguration of PV module. With respect to the issue of the slow convergence precision and the tendency to trap in the local extremum of the QUATRE algorithm, this research incorporates the QUATRE algorithm with recombination mechanism (RQUATRE) to tackle the problem of parameter estimation for the improved PV modules described above. Simulation data show that the RQUATRE wins 29, 29, 21, 17 and 15 times with the FMO, PIO, QUATRE, PSO and GWO algorithms on the CEC2017 test suite. In addition, in a modified PV module for the parameter extraction problem, the final experimental results achieved a value of 2.99 × 10-3 at RMSE, all better than the accuracy values of the compared algorithms. In the fitting process of IAE, the final values are also all less than 10%, which can satisfy the fitting needs.

Assessment of Differences in Academic Rank and Compensation by Gender and Race/Ethnicity Among Academic Radiation Oncologists in the United States.

Purpose: Advancing equity, diversity, and inclusion in the physician workforce is essential to providing high-quality and culturally responsive patient care and has been shown to improve patient outcomes. To better characterize equity in the field of radiation oncology, we sought to describe the current academic radiation oncology workforce, including any contemporary differences in compensation and rank by gender and race/ethnicity. Methods and Materials: We conducted a retrospective cohort study using data from the Society of Chairs of Academic Radiation Oncology Programs (SCAROP) 2018 Financial Survey. Multivariable logistic regression models were used to identify factors associated with associate or full professor rank. Compensation was compared by gender and race/ethnicity overall and stratified by rank and was further analyzed using multivariable linear regression models. Results: Of the 858 academic radiation oncologists from 63 departments in the United States in the sample, 33.2% were female, 65.2% were White, 27.2% were Asian, and 7.6% were underrepresented in medicine (URiM). There were 44.0% assistant professors, 32.0% associate professors, and 22.8% full professors. Multivariable logistic regression analysis for factors associated with associate or full professor rank did not reveal statistically significant associations between gender or race/ethnicity with academic rank (odds ratio [OR], 0.86; 95% confidence interval [CI], 0.56-1.32; P = .48 for gender; OR, 0.81; 95% CI, 0.5-1.30; P = .37 for Asian vs White; and OR, 0.69; 95% CI, 0.31-1.55; P = .37 for URiM vs White), but CIs were wide due to sample size, and point estimates were <1. Similarly, multivariable linear regression analysis modeling the log relative total compensation did not detect statistically significant differences between radiation oncologists by gender (-1.7%; 95% CI, -6.8% to 3.4%; P = .51 for female vs male) or race/ethnicity (-1.6%; 95% CI, -7.3% to 4.0%; P = .57 for Asian vs White and -3.0%; 95% CI, -12.1% to 6.0%; P = .51 for URiM vs White). Conclusions: The low numbers of women and faculty with URiM race/ethnicity in this radiation oncology faculty sample limits the ability to compare career trajectory and compensation by those characteristics. Given that point estimates were <1, our findings do not contradict larger multispecialty studies that suggest an ongoing need to monitor equity.

Cost-Effectiveness of Lisocabtagene Maraleucel Versus Axicabtagene Ciloleucel and Tisagenlecleucel in the Third-Line or Later Treatment Setting for Relapsed or Refractory Large B-cell Lymphoma in the United States.

INTRODUCTION: The objective of this study was to evaluate the cost-effectiveness of lisocabtagene maraleucel (liso-cel) versus other available chimeric antigen receptor T-cell therapies, including axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel), in patients who had received at least two prior therapies from a United States (US) commercial third-party payer perspective. METHODS: To capture this heterogeneity in survival outcomes, we used mixture cure models to extrapolate progression-free survival (PFS) and overall survival (OS). Patient-level data from TRANSCEND NHL 001 for liso-cel and reconstructed patient-level data from ZUMA-1 for axi-cel, JULIET for tisa-cel, and SCHOLAR-1 for salvage chemotherapy, derived using the Guyot method, were used for OS and PFS. The model included adverse events associated with liso-cel, axi-cel, and tisa-cel. RESULTS: Liso-cel was less costly (incremental cost of - $74,980) and marginally more effective (0.002 incremental quality-adjusted life-years [QALY]) than axi-cel and had an incremental cost of $67,925 and 2.02 incremental QALYs over tisa-cel in the base case. Results remained consistent in sensitivity analyses, with the liso-cel OS cure fraction being the main driver of cost-effectiveness compared with both axi-cel and tisa-cel. CONCLUSION: This analysis estimated that liso-cel is cost-effective compared with tisa-cel and axi-cel from a commercial US payer perspective.