Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma.

OBJECTIVES: Lung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT). METHODS: The study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method. RESULTS: The study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation. CONCLUSION: Most ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.

Safety and efficacy of a new high-dose regimen of panobinostat, gemcitabine, busulfan, and melphalan for 1st or 2nd salvage ASCT for refractory/relapsed or high-risk myeloma: Matched-pair comparisons with concurrent control cohorts.

Improvement of autologous stem-cell transplantation (ASCT) for myeloma is needed. Building on our prior work, we prospectively evaluated panobinostat and gemcitabine/busulfan/melphalan (GemBuMel) with ASCT in this population. Patients aged 18-65 years with relapsed/refractory or high-risk myeloma and adequate end-organ function were eligible. Treatment included panobinostat (20 mg/day, days -9 to -2) and GemBuMel (days -8 to -2). Patients were enrolled in 1st (ASCT-1) or 2nd ASCT (ASCT-2) cohorts. We compared their outcomes with all our other concurrent ASCT patients who met eligibility criteria but received melphalan or BuMel off study, matched for age, prior therapy lines, high-risk cytogenetics, and response at ASCT. We enrolled 80 patients, 48 and 32 in the ASCT-1 and ASCT-2 cohorts, respectively; in these two cohorts, high-risk cytogenetics were noted in 33 and 15 patients, respectively; unresponsive disease in 12 and 11 patients, respectively, after a median of 2 and 3 therapy lines, respectively. Transplant-related mortality (TRM) occurred in two ASCT-2 patients. One-year PFS rates were 69% (ASCT-1) and 72% (ASCT-2); 1-year OS rates were 79% (ASCT-1) and 84% (ASCT-2). Minimal residual disease negativity improved after ASCT-1 (8.5%-23%, p < .0001) and ASCT-2 (34%-55%, p = .02), which correlated with improved outcomes. Trial patients and controls (N = 371) had similar TRM and post-ASCT maintenance. Trial patients had better PFS after either a 1st (p = .02) or a 2nd ASCT (p = .04) than matched-paired control patients. In conclusion, panobinostat/GemBuMel is effective for relapsed/refractory or high-risk myeloma patients, with better PFS than concurrent matched controls receiving melphalan or BuMel.

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation.

BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS: Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS: After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean ± standard deviation: -0.97 ± 0.20), sarcoma (-1.04 ± 0.54), H&N (-1.00 ± 0.24), and GI (-2.95 ± 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.

High-Dose Chemotherapy and Autologous Stem Cell Transplantation for Relapsed or Refractory Primary Mediastinal Large B-Cell Lymphoma.

Primary mediastinal large B-cell lymphoma (PMBCL) is an uncommon, aggressive type of non-Hodgkin lymphoma. Rituximab-containing chemoimmunotherapy with or without radiation therapy (RT) is standard first-line treatment. Relapsed or refractory (R/R) disease has long been treated with salvage chemotherapy followed by high-dose chemotherapy (HDC), with autologous stem cell transplantation (ASCT) in appropriate patients. We retrospectively analyzed all patients with R/R PMBCL treated with HDC/ASCT at our center between January 2000 and August 2022. The 60 study patients received either rituximab-BEAM (n = 37) or rituximab-gemcitabine/busulfan/melphalan (R-GemBuMel) with or without vorinostat (n = 23), followed by ASCT. Forty-six patients received mediastinal RT, either as prior consolidation of frontline therapy or following ASCT. At median follow-up of 6 years (range, .3 to 21 years), the 5-year progression-free survival (PFS) and overall survival (OS) rates of the whole group were 58% and 77%, respectively, for the entire cohort, 51% and 65% for the R-BEAM recipients, and 69% and 82% for R-vorinostat/GemBuMel recipients. Multivariable analyses showed that a negative positron emission tomography scan at ASCT (hazard ratio [HR], .28) and involvement of only 1 organ (HR, .33) were independently associated with improved PFS. In addition, receipt of R-vorinostat/GemBuMel (HR, .23) was an independent favorable predictor of OS. Our data indicate that HDC/ASCT is effective in R/R PMBCL, with improved outcomes in patients receiving R-vorinostat/GemBuMel.

Incidence of resting-state functional connectivity to secondary language areas and contralateral language areas in patients with brain tumors.

PURPOSE: Secondary language areas, including the pre-supplementary motor area (pre-SMA), dorsolateral prefrontal cortex (DLPFC), and the visual word form area (VWFA) play important roles in speech, but have been under-evaluated in the realm of resting-state (rs)-fMRI. The purpose of this study is to determine the incidence that secondary language areas and contralateral language areas can be localized using seed-based correlation (SBC) rs-fMRI. METHODS: We retrospectively reviewed 40 rs-fMRIs for functional connectivity (FC) to secondary language areas in cases where FC to Broca's or Wernicke's area near tumor in the left hemisphere were successfully generated using SBC analysis. Logistical regression was used for statistical analysis. RESULTS: SBC rs-fMRI with a seed in the left Broca's or Wernicke's area ipsilateral to the tumor was performed in the 40 patients. 72.5% of cases showed FC to the left DLPFC, 67.5% to left pre-SMA, and 52.5% of cases had FC to right Broca's area. In addition to other correlations, we found older patients have a lower incidence of FC to the right Wernicke's area when seeded from both left Broca's and left Wernicke's area (p-value = .016, odds ratio = 0.94). CONCLUSION: SBC rs-fMRI can detect left hemispheric secondary language areas as well as right hemispheric primary and secondary language areas. The left DLPFC showed the highest incidence of FC, followed by the left pre-SMA when seeded from both left Broca's and Wernicke's area. Logistics regression also showed in some instances, differences in the incidence of FC to language areas was dependent on age, seed location, and gender.

Comparative Analyses of the Clinicopathologic Features of Short-Term and Long-Term Survivors of Patients with Pancreatic Ductal Adenocarcinoma Who Received Neoadjuvant Therapy and Pancreatoduodenectomy.

Neoadjuvant therapy (NAT) is increasingly used to treat patients with pancreatic ductal adenocarcinoma (PDAC). Patients with PDAC often show heterogenous responses to NAT with variable clinical outcomes, and the clinicopathologic parameters associated with these variable outcomes remain unclear. In this study, we systematically examined the clinicopathologic characteristics of 60 short-term survivors (overall survival < 15 months) and 149 long-term survivors (overall survival > 60 months) and compared them to 352 intermediate-term survivors (overall survival: 15-60 months) of PDAC who received NAT and pancreatoduodenectomy. We found that the short-term survivor group was associated with male gender (p = 0.03), tumor resectability prior to NAT (p = 0.04), poorly differentiated tumor histology (p = 0.006), more positive lymph nodes (p = 0.04), higher ypN stage (p = 0.002), and higher positive lymph node ratio (p = 0.03). The long-term survivor group had smaller tumor size (p = 0.001), lower ypT stage (p = 0.001), fewer positive lymph nodes (p < 0.001), lower ypN stage (p < 0.001), lower positive lymph node ratio (p < 0.001), lower rate of lymphovascular invasion (p = 0.001) and perineural invasion (p < 0.001), better tumor response grading (p < 0.001), and less frequent recurrence/metastasis (p < 0.001). The ypN stage is an independent predictor of both short-term and long-term survivors by multivariate logistic regression analyses. In addition, tumor differentiation was also an independent predictor for short-term survivors, and tumor response grading and perineural invasion were independent predictors for long-term survivors. Our results may help to plan and select post-operative adjuvant therapy for patients with PDAC who received NAT and pancreatoduodenectomy based on the pathologic data.

B Cells Are Required to Generate Optimal Anti-Melanoma Immunity in Response to Checkpoint Blockade.

Immunotherapies such as checkpoint blockade therapies are known to enhance anti-melanoma CD8+ T cell immunity, but only a fraction of patients treated with these therapies achieve durable immune response and disease control. It may be that CD8+ T cells need help from other immune cells to generate effective and long-lasting anti-tumor immunity or that CD8+ T cells alone are insufficient for complete tumor regression and cure. Melanoma contains significant numbers of B cells; however, the role of B cells in anti-melanoma immunity is controversial. In this study, B16 melanoma mouse models were used to determine the role of B cells in anti-melanoma immunity. C57BL/6 mice, B cell knockout (KO) C57BL/6 mice, anti-CD19, and anti-CXCL13 antibody-treated C57BL/6 mice were used to determine treatment efficacy and generation of tumor-specific CD8+ T cells in response to PD-L1 blockade alone or combination with TLR-7/8 activation. Whole transcriptome analysis was performed on the tumors from B cell depleted and WT mice, untreated or treated with anti-PD-L1. Both CD40-positive and CD40-negative B cells were isolated from tumors of TLR-7/8 agonist-treated wild-type mice and adoptively transferred into tumor-bearing B cell KO mice, which were treated with anti-PD-L1 and TLR-7/8 agonist. Therapeutic efficacy was determined in the presence of activated or inactivated B cells. Microarray analysis was performed on TLR-7/8-treated tumors to look for the B cell signatures. We found B cells were required to enhance the therapeutic efficacy of monotherapy with anti-PD-L1 antibody and combination therapy with anti-PD-L1 antibody plus TLR-7/8 agonist. However, B cells were not essential for anti-CTLA-4 antibody activity. Interestingly, CD40-positive but not CD40-negative B cells contributed to anti-melanoma immunity. In addition, melanoma patients' TCGA data showed that the presence of B cell chemokine CXCL13 and B cells together with CD8+ T cells in tumors were strongly associated with improved overall survival. Our transcriptome data suggest that the absence of B cells enhances immune checkpoints expression in the tumors microenvironment. These results revealed the importance of B cells in the generation of effective anti-melanoma immunity in response to PD-1-PD-L1 blockade immunotherapy. Our findings may facilitate the design of more effective anti-melanoma immunotherapy.

Cerebrospinal fluid CXCL10 is associated with the presence of low level CSF HIV during suppressive antiretroviral therapy.

Surrogate markers of HIV central nervous system (CNS) persistence are needed because direct HIV measurements from the CNS require specialized protocols and are not always detectable or quantifiable. We analyzed paired plasma and CSF samples from people with HIV (PWH) on suppressive therapy (ART) with a validated HIV single copy RNA assay. Two potential markers of CNS persistence were measured (CXCL10 and sCD30). We then examined associations with CSF HIV RNA positivity in univariable and multivariable analyses. Among 66 individuals, 18.2% had detectable CSF HIV. Individuals who had detectable HIV in CSF had higher CSF CXCL10 concentrations (median 514 pg/ml versus median 317 pg/ml, p = 0.019), but did not have significantly different CSF sCD30 concentrations (median 7.5 ng/ml versus median 7.6 ng/ml, p = 0.78). In the multiple logistic analysis, both higher CSF CXCL10 (p = 0.038) and plasma HIV detectability (p = 0.035) were significantly associated with detectable CSF HIV. Both sCD30 and CXCL10 correlated positively with NfL and NSE, two neuronal markers. This study demonstrates that CSF CXCL10 concentrations reflect low level HIV CNS persistence despite virologic suppression on ART. Given that it is readily detectable and quantifiable, this chemokine may be a promising biomarker to evaluate HIV eradication therapies that target the CNS.

Predictive Value of First Posttreatment Imaging Using Standardized Reporting in Head and Neck Cancer.

OBJECTIVE: The Neck Imaging Reporting and Data System (NI-RADS) is a standardized numerical reporting template for surveillance of head and neck squamous cell carcinoma (HNSCC). Our aim was to analyze the accuracy of NI-RADS on the first posttreatment fluorodeoxyglucose positron emission tomography/contrast-enhanced computed tomography (PET/CECT). STUDY DESIGN: Retrospective cohort study. SETTING: Academic tertiary hospital. SUBJECT AND METHODS: Patients with HNSCC with a 12-week posttreatment PET/CECT interpreted using the NI-RADS template and 9 months of clinical and radiologic follow-up starting from treatment completion between June 2014 and July 2016 were included. Treatment failure was defined as positive tumor confirmed by biopsy or Response Evaluation Criteria in Solid Tumors criteria. Cox proportional hazards models were performed. RESULTS: This study comprised 199 patients followed for a median of 15.5 months after treatment completion (25% quartile, 11.8 months; 75% quartile, 20.2 months). The rates of treatment failure increased with each incremental increase in NI-RADS category from 1 to 3 (4.3%, 9.1%, and 42.1%, respectively). A Cox proportional hazards model demonstrated a strong association between NI-RADS categories and treatment failure at both primary and neck sites (hazard ratio [HR], 2.60 and 5.22, respectively; P < .001). In the smaller treatment subgroup analysis, increasing NI-RADS category at the primary site in surgically treated patients and treatment failure did not achieve statistically significant association (HR, 0.88; P = .82). CONCLUSION: Increasing NI-RADS category at the baseline posttreatment PET/CECT is strongly associated with increased risk of treatment failure in patients with HNSCC.

Efficient Bayesian Regularization for Graphical Model Selection.

There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for large dimensional settings where the dimension increases with the sample size, by decoupling model fitting and covariance selection. First, a full model based on a complete graph is fit under a novel class of mixtures of inverse-Wishart priors, which induce shrinkage on the precision matrix under an equivalence with Cholesky-based regularization, while enabling conjugate updates. Subsequently, a post-fitting model selection step uses penalized joint credible regions to perform model selection. This allows our methods to be computationally feasible for large dimensional settings using a combination of straightforward Gibbs samplers and efficient post-fitting inferences. Theoretical guarantees in terms of selection consistency are also established. Simulations show that the proposed approach compares favorably with competing methods, both in terms of accuracy metrics and computation times. We apply this approach to a cancer genomics data example.

Bayesian variable selection with graphical structure learning: Applications in integrative genomics.

Significant advances in biotechnology have allowed for simultaneous measurement of molecular data across multiple genomic, epigenomic and transcriptomic levels from a single tumor/patient sample. This has motivated systematic data-driven approaches to integrate multi-dimensional structured datasets, since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. We propose a novel multi-scale Bayesian approach that combines integrative graphical structure learning from multiple sources of data with a variable selection framework-to determine the key genomic drivers of cancer progression. The integrative structure learning is first accomplished through novel joint graphical models for heterogeneous (mixed scale) data, allowing for flexible and interpretable incorporation of prior existing knowledge. This subsequently informs a variable selection step to identify groups of co-ordinated molecular features within and across platforms associated with clinical outcomes of cancer progression, while according appropriate adjustments for multicollinearity and multiplicities. We evaluate our methods through rigorous simulations to establish superiority over existing methods that do not take the network and/or prior information into account. Our methods are motivated by and applied to a glioblastoma multiforme (GBM) dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, copy number and methylation data. We find a high concordance between our selected prognostic gene network modules with known associations with GBM. In addition, our model discovers several novel cross-platform network interactions (both cis and trans acting) between gene expression, copy number variation associated gene dosing and epigenetic regulation through promoter methylation, some with known implications in the etiology of GBM. Our framework provides a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers.

Scalable Bayesian variable selection for structured high-dimensional data.

Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. However, most of the existing methods may not be scalable to high-dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies. We propose an adaptive Bayesian shrinkage approach which incorporates prior network information by smoothing the shrinkage parameters for connected variables in the graph, so that the corresponding coefficients have a similar degree of shrinkage. We fit our model via a computationally efficient expectation maximization algorithm which scalable to high-dimensional settings ( p ∼ 100 , 000 ). Theoretical properties for fixed as well as increasing dimensions are established, even when the number of variables increases faster than the sample size. We demonstrate the advantages of our approach in terms of variable selection, prediction, and computational scalability via a simulation study, and apply the method to a cancer genomics study.

Number needed to treat for time-to-event data with competing risks.

The number needed to treat is a tool often used in clinical settings to illustrate the effect of a treatment. It has been widely adopted in the communication of risks to both clinicians and non-clinicians, such as patients, who are better able to understand this measure than absolute risk or rate reductions. The concept was introduced by Laupacis, Sackett, and Roberts in 1988 for binary data, and extended to time-to-event data by Altman and Andersen in 1999. However, up to the present, there is no definition of the number needed to treat for time-to-event data with competing risks. This paper introduces such a definition using the cumulative incidence function and suggests non-parametric and semi-parametric inferential methods for right-censored time-to-event data in the presence of competing risks. The procedures are illustrated using the data from a breast cancer clinical trial.