Citations

Abstract Molecular characterization of high-grade B-cell lymphoma, not otherwise specified (HGBCL-NOS), is hindered by its rarity, evolving definition, and poor diagnostic reproducibility. To address this challenge, we analyzed 92 HGBCL-NOS tumors collected across Lymphoma/Leukemia Molecular Profiling Project sites. Leveraging comparison cohorts of diffuse large B-cell lymphoma, NOS (DLBCL-NOS) and Burkitt lymphoma (BL), and molecular frameworks described in these entities, our analysis revealed a heterogenous molecular landscape, reminiscent of DLBCL-NOS but with an enrichment of BL features. By cell-of-origin classification, 59% were germinal center B-cell–like (GCB), and 25% were activated B-cell–like (ABC). LymphGen, a genetic classifier for DLBCL-NOS, assigned a genetic subtype to 34% of HGBCL-NOS. Although classification rate was lower than in DLBCL-NOS (66%), assigned subtypes spanned the spectrum of LymphGen classes, including 31% of ABCs classified as MCD. Features differentiating HGBCL-NOS from DLBCL-NOS included MYC rearrangement (47% vs 6%); dark zone signature (DZsig) expression (45% vs 7%); and more frequent mutation of ID3, MYC, CCND3, and TP53, all common to BL. A genetic classifier that differentiates DLBCL-NOS from BL classified 53% of DZsig+ tumors as BL-like, and those classified as DLBCL-like were frequently BCL2-rearranged. Among DZsig− GCB tumors, 95% were DLBCL-like. Centralized pathology review reclassified almost half of tumors as DLBCL-NOS but did not identify a more homogenous HGBCL-NOS population, with no difference in features between confirmed and reclassified tumors. In conclusion, molecular testing enables a subset of HGBCL-NOS to be assigned to established categories. Based on rarity and diagnostic challenges, broader inclusion of HGBCL-NOS should be considered in biomarker-driven DLBCL trials.

Abstract Background Preexisting diabetes (PDM) increases the risk of maternal and perinatal mortality and morbidity. Reduction of maternal hyperglycemia prior to and during pregnancy can reduce these risks. Despite compelling evidence that preconception care (PCC), which includes achieving strict glycemic goals, reduces the risk of congenital malformations and other adverse pregnancy outcomes, only a minority of individuals receive PCC. Suboptimal pregnancy outcomes demonstrated in real-world data highlight the need to further optimize prenatal glycemia. New evolving technology shows promise in helping to achieve that goal. Dysglycemia is not the only driver of poor pregnancy outcomes in PDM. The increasing impact of obesity on pregnancy outcomes underscores the importance of optimal nutrition and management of insulin sensitizing medications during prenatal care for PDM. Objective To provide recommendations for the care of individuals with PDM that lead to a reduction in maternal and neonatal adverse outcomes. Methods The Guideline Development Panel (GDP) composed of a multidisciplinary panel of clinical experts, along with experts in guideline methodology and systematic literature review, identified and prioritized 10 clinically relevant questions related to the care of individuals with diabetes before, during and after pregnancy. The GDP prioritized randomized controlled trials (RCTs) evaluating the effects of different interventions (eg, PCC, nutrition, treatment options, delivery) during the reproductive life cycle of individuals with diabetes, including type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Systematic reviews queried electronic databases for publications related to these 10 clinical questions. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology was used to assess the certainty of evidence and develop recommendations. The approach incorporated perspectives from 2 patient representatives and considered patient values, costs and resources required, acceptability and feasibility, and impact on health equity of the proposed recommendations. Results In individuals with diabetes mellitus who have the possibility of becoming pregnant, we suggest asking a screening question about pregnancy intention at every reproductive, diabetes, and primary care visit. Screening for pregnancy intent is also suggested at urgent care/emergency room visits when clinically appropriate (2 | ⊕OOO). This was suggested based on indirect evidence demonstrating a strong association between PCC and both reduced glycated hemoglobin (HbA1c) at the first prenatal visit and congenital malformations. In individuals with diabetes mellitus who have the possibility of becoming pregnant, we suggest use of contraception when pregnancy is not desired (2 | ⊕⊕OO). This was suggested based on indirect evidence in women with diabetes, where PCC—including contraception as a key component—showed a clinically significant association with improvements in first-trimester HbA1c and the rate of congenital malformations, together with indirect evidence from the general population regarding the reduction of unplanned pregnancies and pregnancy terminations with the use of contraception. In individuals with T2DM, we suggest discontinuation of glucagon-like peptide-1 receptor agonist (GLP-1RA) before conception rather than discontinuation between the start of pregnancy and the end of the first trimester (2 | ⊕OOO). This was suggested based on limited data on risk of exposure to GLP-1RA receptor agonists during pregnancy. In pregnant individuals with T2DM already on insulin, we suggest against routine addition of metformin (2 | ⊕OOO). This was suggested based on the GDP judgment that the benefit of adding metformin to insulin to achieve decrease in rates of large for gestational age infants did not outweigh the potential harm of increasing the risk of small for gestational age infants or adverse childhood outcomes related to changes in body composition. In individuals with PDM, we suggest either a carbohydrate-restricted diet (<175 g/day) or usual diet (>175 g/day) during pregnancy (2 | ⊕OOO). This was suggested based on the GDP judgment that the available evidence was limited and very indirect, resulting in significant uncertainty about the net benefits or harms. As such, the evidence was insufficient to support a recommendation either for or against a carbohydrate intake cutoff of 175 g/day. In pregnant individuals with T2DM, we suggest either the use of a continuous glucose monitor (CGM) or self-monitoring of blood glucose (SMBG) (2 | ⊕OOO). There is lack of direct evidence supporting superiority of CGM use over SMBG for T2DM during pregnancy. There is indirect evidence supporting improved glucometrics with the use of CGM for individuals with T2DM outside of pregnancy, substantial improvements in neonatal outcomes for individuals with T1DM using CGM during pregnancy and the potential for decreasing adverse pregnancy outcomes with improved glucometrics in individuals with T2DM. In individuals with PDM using a CGM, we suggest against the use of a single 24-hour CGM target <140 mg/dL (7.8 mmol/L) in place of standard-of-care pregnancy glucose targets of fasting <95 mg/dL (5.3 mmol/L), 1-hour postprandial <140 mg/dL (7.8 mmol/L), and 2-hour postprandial < 120 mg/dL (6.7 mmol/L) (2 | ⊕OOO). This was suggested based on indirect evidence that associated adverse pregnancy outcomes with a fasting glucose > 126 mg/dL (7 mmol/L). In individuals with T1DM who are pregnant, we suggest the use of a hybrid closed-loop pump (pump adjusting automatically based on CGM) rather than an insulin pump with CGM (without an algorithm) or multiple daily insulin injections with CGM (2 | ⊕OOO). This was suggested based on a meta-analysis of RCTs which demonstrated improvement in glucometrics with increased time in range (MD +3.81%; CI −4.24 to 11.86) and reduced time below range (MD −0.85%; CI −1.98 to 0.28) with the use of hybrid closed-loop pump technology. In individuals with PDM, we suggest early delivery based on risk assessment rather than expectant management (2 | ⊕OOO). This was suggested based on indirect evidence that risks may outweigh benefits of expectant management beyond 38 weeks gestation and that risk assessment criteria may be useful to inform ideal delivery timing. In individuals with PDM (including those with pregnancy loss or termination), we suggest postpartum endocrine care (diabetes management), in addition to usual obstetric care (2 | ⊕OOO). As the postpartum period frequently overlaps with preconception, this was suggested based on indirect evidence demonstrating a strong association between PCC and both reduced HbA1c at the first prenatal visit and congenital malformations. Conclusion The data supporting these recommendations were of very low to low certainty, highlighting the urgent need for research designed to provide high certainty evidence to support the care of individuals with diabetes before, during, and after pregnancy. Investment in implementation science for PCC is crucial to prevent significant mortality and morbidity for individuals with PDM and their children. RCTs to further define glycemic targets in pregnancy and refinement of emerging technology to achieve those targets can lead to significant reduction of harm and in the burden of diabetes care. Data on optimal nutrition and obesity management in pregnancy are lacking. More research on timing of delivery in women with PDM is also needed.

The head and neck region is a marvel of anatomic complexity and it encompasses a broad array of subsites, each with its own architectural nuances. This histologic richness begets a commensurate diversity of disease. Advances in our understanding of the genetic and epigenetic alterations that underlie many of these tumors have not only refined their classification, but also reshaped our approaches to diagnosis, prognosis, and therapy. This Fascicle reflects these scientific and diagnostic evolutions. Departing from prior editions this volume is organized by anatomic subsite. Each chapter delves into the neoplastic conditions and key differential diagnoses germane to a specific location. This anatomic framework is intended to provide readers with a more practical and intuitive means of navigating the field. This Fascicle serves as a useful companion—equal parts atlas, reference, and diagnostic confidant—for pathologists encountering the intricate, fascinating, and occasionally exasperating world of head and neck pathology.

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

TPS3634 Background: For patients with oligometastatic colorectal cancer (CRC), aggressive local therapy of isolated metastases, particularly in the liver, has been associated with long-term progression-free and overall survival (OS) primarily based on retrospective evidence. However, in patients with limited metastatic CRC that is deemed inoperable or those with additional disease outside of the liver or lungs, the role of local ablative therapies, including microwave ablation (MWA) and stereotactic body radiation therapy (SBRT), to render patients disease free is less clear. Despite the long history of treating oligometastatic CRC with local therapy, which is largely provider biased, questions remain regarding the benefit of extending the paradigm of metastatic directed therapy to patients with more extensive disease. This trial seeks to use a pragmatic multimodality approach that mirrors the current clinical dilemma. This study is designed to evaluate the safety and efficacy of adding total ablative therapy (TAT) of all sites of disease to standard of care systemic treatment in those with limited metastatic CRC. Methods: A022101/NRG-GI009 is a National Clinical Trials Network randomized phase III study planned to enroll 364 patients with newly diagnosed metastatic CRC (BRAF wild-type, microsatellite stable) without peritoneal metastasis or liver-only disease on baseline imaging. Patients receive first-line systemic therapy for 12-39 weeks. Patients with ≤4 sites of metastatic disease following initial systemic therapy that are amenable to any combination of surgical resection, MWA, and/or SBRT are then randomized 1:1, stratified by number of metastatic organ sites (1-2 vs. 3-4), timing of metastatic disease diagnosis (de novo vs. secondary), and presence of metastatic disease outside the liver/lungs in at least 1 site. Patients in Arm 1 will receive TAT consisting of treatment of all metastatic sites with SBRT, MWA, and/or surgical resection followed by standard of care systemic therapy. Patients in Arm 2 will continue with standard of care systemic therapy alone. The primary endpoint is OS. Secondary endpoints include event-free survival, treatment-related toxicities, and local recurrence with exploratory biomarker analyses. The study needs 346 evaluable patients combined in the 2 arms to demonstrate an improvement in OS with a hazard ratio of 0.7 to provide 80% power with a one-sided alpha of 5%. The trial utilizes a group sequential design with two interim analyses (25% and 50% of events) for futility. The trial activated in January 2023 and recruitment is ongoing. Support: U10CA180821, U10CA180882; https://acknowledgments.alliancefound.org. U10CA180820 (ECOG-ACRIN); U10CA180868 (NRG); U10CA180888 (SWOG); Clinical trial information: NCT05673148 .

e17155 Background: Localized PCa is classified into six NCCN risk categories, each with specific treatment recommendations. However, treatment concordance across these categories and its variation by race in clinical practice remain unexplored. Therefore, we aim to describe patterns of guideline-concordant treatment receipt for localized PCa across risk groups and race. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2018–2020) was queried to identify patients with localized PCa from 17 registries. Data regarding clinical T stage, Gleason patterns, PSA level, biopsy cores and initial treatment received (radiation, radical prostatectomy, systemic therapy) was obtained. Each patient was then stratified into one of six NCCN-based risk categories using a rule-based algorithm. Using the stratified risk category of each patient, treatment recommendations were obtained from the NCCN guidelines. Concordance was assessed by comparing the received treatment and guideline-recommended treatment. A focused bi-variate logistic regression analysis was performed adjusting for risk groups, and race. Odds ratio (OR) with 95% confidence intervals (CI) were computed to quantify the magnitude of guideline-discordant treatment receipt. Results: This analysis included 69,584 patients with localized PCa (White: 53,875 [77.4%]; Black: 11,265 [16.2%]; Asian/Pacific Islander: 4,133 [5.9%]; American Indian/Alaska Native: 311 0.5%]). In terms of risk groups, patients in higher risk categories had increased odds of discordance compared to the lower risk groups (Table). In terms of race, Black men had significantly higher odds of receiving guideline-discordant therapy (OR:1.67, 95% CI:1.58–1.76), while no statistically significant difference was observed with American Indian/Alaska Native men (OR:1.21, 95% CI: 0.92–1.61) and Asian/Pacific Islander men (OR:1.05, 95% CI:0.97–1.14) compared to White men. Conclusions: Population level data, based on limited sample size in some groups, suggests that treatment concordance with NCCN guidelines varies by risk group and race in localized PCa. Black patients, and those with higher risk groups were more likely to receive guideline-discordant therapy. These findings highlight the need for strategies and prospective studies at individual patient level to evaluate and enhance guideline adherence to promote personalized care for these patients. Variable Discordant Treatment (%) OR (95% CI) White 22168 (41.1) REF Black 5653 (47.5) 1.67 (1.58-1.76) * Asian/Pacific Islander 1822 (44.1) 1.05 (0.97-1.14) American Indian/Alaska Native 191 (61.4) 1.22 (0.92- 1.61) Very Low Risk 770 (9) REF Low Risk 146 (2) 0.16 (0.13-0.19) * Favorable Risk 35 (0.4) 0.03 (0.02-0.04) * Unfavorable Risk 15125 (83.2) 36.30 (33.72-39.25) * High/Very High Risk 13441 (50) 7.32 (6.83-7.86) * * p <0.05.

5515 Background: Clear cell ovarian carcinoma (CCOC) is a chemoresistant subtype of ovarian cancer. Immune checkpoint inhibitors have been reported to have clinical activity in CCOC. Additionally, CCOC harbors molecular alterations suggesting a role for anti-angiogenic agents. We therefore conducted a single-arm two-stage phase 2 trial to investigate the clinical activity of the combination of the PD-1 inhibitor pembrolizumab with the anti-angiogenic tyrosine kinase inhibitor lenvatinib in patients (pts) with CCOC. Methods: Pts with CCOC and measurable disease received pembrolizumab 200 mg IV every 3 weeks and lenvatinib 20 mg daily. Pts could have received an unlimited number of prior therapies; prior bevacizumab and immune checkpoint inhibitors were allowed, but prior lenvatinib was exclusionary. Malignant bowel involvement was not allowed. Co-primary endpoints were objective response rate (H 0 5%; H a 25%) and rate of PFS at 6 months (mo) per RECIST v1.1 (H 0 10%; H a 30%), restricting the probabilities of type I and type II errors to 10% and 10%, respectively. Two pts with objective responses or 3 pts progression-free and alive at 6 mos were needed to proceed from stage 1 (n=18) to stage 2 (n=13); 5 pts with objective responses or 6 pts progression-free and alive at 6 mos were needed to declare the combination worthy of further study. Results: Data cut-off occurred 22-Oct-2024. Of 30 enrolled pts, 83.3% were white; the mean age among all pts was 54.1 years. 30% of pts (9/30) experienced a confirmed response (2 CR, 7 PR); an additional 3 pts (10%) experienced unconfirmed PRs and 4 pts (13.3%) had SD ≥6 mo. As of data cut-off, 3 pts (10%) had not yet reached their first radiographic assessments, and 17 pts were still receiving study therapy. With a median of 9.72 mo of follow up, 16 pts were alive and progression-free at 6 months. The estimated 6-month PFS was 75.96% (95% CI 53.82-88.51%). Median PFS was 10.9 mo. The estimated 12-month PFS was 48.86% (95% CI 23.67-70.04%). The most common any-grade TRAEs were hypertension (71%), hypothyroidism (66%), and fatigue (60%). There were no unanticipated TRAEs. Conclusions: The combination of pembrolizumab/lenvatinib demonstrates encouraging evidence of clinical activity in CCOC, with 9 pts experiencing a confirmed response and 16 pts alive and progression-free at 6 months. As both co-primary endpoints of the study were met, enrollment closed with 30 pts. Updated data for all pts will be reported. There were no new safety signals. Clinical trial information: NCT05296512 .