Citations

INTRODUCTION: The risk of progression to esophageal adenocarcinoma in Barrett's esophagus (BE) increases with advancing degrees of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver variability and overcalls of dysplasia during manual community pathologist reads. We aimed to externally validate a previously cross-validated BE dysplasia diagnosis deep learning model (BEDDLM) that predicts dysplasia grade on whole slide images (WSIs). METHODS: We digitized nondysplastic BE (NDBE), low-grade (LGD), and high-grade dysplasia (HGD) histology slides from 3 external academic centers. A consensus read by 2 expert study pathologists was used as the criterion standard. Slide stain characteristics were normalized using cycle-generative adversarial networks. WSIs were assessed by BEDDLM using an ensemble approach, combining a “You Only Look Once” model followed by a ResNet101 classifier model. RESULTS: We included 489 WSIs. Consensus histopathology revealed 232 NDBE, 117 LGD, and 140 HGD WSIs. The mean age (SD) was 66.9 (11.4) years; 413 (84.7%) were men. Using the BEDDLM ensemble model, sensitivity and specificity for NDBE were 73.3% (95% confidence interval [CI]: 67.09%–78.85%) and 93.4% (95% CI: 89.62%–96.10%); for LGD, 84.6% (95% CI: 76.78%–90.62%) and 80.6% (95% CI: 76.26%–84.54%); and for HGD, 80.7% (95% CI: 73.19%–86.89%) and 94.8% (95% CI: 91.97%–96.91%), respectively. The F1 score was 0.81, 0 0.69, and 0.83 for NDBE, LGD, and HGD, respectively. DISCUSSION: Our externally validated deep learning model demonstrates substantial accuracy for the diagnosis of BE dysplasia grade on WSIs.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3583.

Abstract Introduction: Monitoring minimal residual disease (MRD) is critical in multiple myeloma (MM) to predict outcomes and guide therapy. Traditional bone marrow (BM) aspirates for MRD detection are invasive and limited by sample quality. We therefore explored cell-free DNA (cfDNA) fragmentation as a less invasive alternative for MRD detection. Methods: We performed 30-40X whole-genome sequencing (WGS) on peripheral blood cfDNA from 45 MM patients, collecting baseline (n = 45) and follow-up samples (n = 98) from eight Canadian sites (M4 and IMMAGINE studies) and one U.S. site (SPORE study), plus 25 healthy controls. Samples were collected post-induction therapy (n = 13), 100 days post-autologous stem cell transplantation (ACST, n = 36), and during maintenance therapy (n = 49). Multiparameter flow cytometry (MFC) MRD testing was conducted on 77 samples. We evaluated insert size metrics and MM-specific chromatin accessibility (from Ordoñez et al., 2020) using Griffin (described in Doebley et al., 2022). Results: Baseline samples had a higher proportion of short fragments (20-150 bp) than follow-up samples (p = 0.0037) and healthy controls (p = 0.019). A fragment score (FS) based on weighted fragment distribution (per Vessies et al., 2022) was highest at baseline (mean = -0.384), followed by MRD-positive (mean = -0.495) and MRD-negative (mean = -0.550) samples. MRD-negative FS was lower than baseline samples (p < 0.001) but higher than healthy controls (mean = -0.639; p = 0.016). Baseline samples had lower coverage at MM-specific chromatin regions than MRD timepoints (p < 0.05; baseline mean = 0.982; MRD-positive = 0.989; MRD-negative = 0.994; healthy = 0.993), suggesting higher transcriptional activity pre-treatment. MRD-negative samples had higher coverage than MRD-positive cases (p < 0.01). Using logistic regression, we assessed the predictive performance of FS and coverage at MM-specific sites. FS alone achieved an AUC of 0.632 (sensitivity [SN] 39.4%, specificity [SP] 92.1%, accuracy [AC] 67.6%). Coverage yielded an AUC of 0.681 (SN 75.8%, SP 60.5%, AC 67.6%). Combining FS and coverage at MM sites improved performance (AUC = 0.734; SN 48.5%, SP 92.1%, AC 71.8%). We next evaluated whether adjusting the BM tumor cell percentage cutoff by MFC could improve performance. At 0.017% (1 in 5, 789 cells) for MRD positivity, the combined model’s accuracy reached 83% with an AUC of 0.757 (SN 17%, SP 97%). Conclusions: cfDNA fragmentomic analysis achieved high specificity (92-97%) in detecting MRD, providing a less invasive alternative to facilitate serial monitoring. While sensitivity remains an area for improvement, the high specificity of cfDNA makes it valuable for confirming MRD negativity and reducing invasive procedures. Future efforts aim to enhance sensitivity by integrating additional fragmentomic features. Citation Format: Dor D. Abelman, Jenna Eagles, Aimee Wong, Saumil Shah, Stephanie Pedersen, Stephenie Prokopec, David S. Scott, Sarah Bridges, Darrell White, Irwindeep Sandhu, Kevin Song, Esteban Braggio, Alli Murugesan, Anthony Reiman, Suzanne Trudel, Trevor J. Pugh. Tumor-independent monitoring of minimal residual disease in multiple myeloma using cfDNA fragmentomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3250.

Background: Mandible contour significantly influences facial appearance, framing the lower facial silhouette. Redefining mandibular contour is key for facial and neck rejuvenation. Yet, there is limited facial aging research across different lifespans and sexes. Here, we utilize artificial intelligence and advanced 3-dimensional (3D) analysis to elucidate mandibular aging patterns in male and female subjects. Methods: A retrospective analysis of facial computed tomography scans in White patients was conducted, categorizing subjects into 3 age groups (20–79 y) and stratifying them by sex. Artificial intelligence–assisted segmentation into 3D mandibles was done in Mimics v.25, and statistical shape modeling was used to create an average mandible for each group. Volume and linear measurements were assessed via 3D overlays. Results: Analysis of 280 mandibles demonstrated statistically significant aging changes in both sexes. Ramus height showed a marked decrease with age, by approximately 5.3 mm in women and 4.2 mm in men (P < 0.001). Interrami and intercondylar widths increased by a mean of 4–5 mm (P < 0.01). Women exhibited an increase in mandibular angle (P < 0.01), and bony resorption over the chin compared to men, who exhibited concentrated bone resorption at the gonion projection. Conclusions: Mandibular aging, independent of tooth loss, exhibits specific bone remodeling patterns by sex. Posteriorly, mandibular widths increase in both sexes, whereas ramus height decreases. Women experience more resorption at the anterior alveolar surface and chin than men. Statistical shape modeling effectively visualizes these patterns on a population level, bridging the gap between traditional aging research and current understanding.

Abstract Background Recently, a model to predict 5-year recurrence-free survival (RFS) and melanoma-specific survival (MSS) after sentinel lymph node biopsy (SLNB) was published. The aim of this study was to validate that model in a large independent international cohort. Methods The database of the Sentinel Lymph Node Working Group (SLNWG) was analysed for patients with malignant melanoma who underwent SLNB. Patients with clinical stage III melanoma, a history of other malignancies, or receiving concomitant systemic therapies during follow-up were excluded. The model’s predictive performance was evaluated using discrimination and calibration metrics in the eligible cohort. Decision curve analysis was performed to assess the clinical value of the model. Results The external validation cohort consisted of 6174 patients of the SLNWG from the USA, Europe, and Israel. A positive sentinel node was found in 788 patients (12.8%). The area under the time-dependent receiver operating characteristic (ROC) curve of the external validation was 0.76 (95% c.i. 0.74 to 0.77) for RFS and 0.79 (95% c.i. 0.76 to 0.81) for MSS. The model was well calibrated, as the observed 5-year survival rates aligned closely with the predicted survival rates (calibration slope of 0.98 for RFS and calibration slope of 0.99 for MSS). The model provided a net benefit versus the ‘treat all’ and ‘treat none’ strategies at the predetermined probability threshold for recurrence of 45%. Conclusion The model demonstrated good performance in a large heterogeneous independent cohort, emphasizing its robustness. Decision curve analysis revealed a clear net benefit of the model over a treat all strategy, highlighting its potential for clinical use.

Abstract Background: Prior research on the associations of p16, COX-2, and Ki67 immunopositivity in ductal carcinoma in situ (DCIS) tissue with the risk of subsequent ipsilateral invasive breast cancer (IBC) is limited. Methods: In a case–control study nested in a cohort of women diagnosed with DCIS, immunostaining for p16, COX-2, and Ki67 was performed on DCIS tissue from those who developed subsequent ipsilateral IBC (cases; n = 146) and on matched subjects who did not develop IBC (controls; n = 273). Conditional logistic regression was used to estimate ORs and 95% confidence intervals for the associations between immunopositivity for p16, COX-2, and Ki67 and the risk of subsequent ipsilateral IBC. Results: There was no association between p16, COX-2, and Ki67 immunopositivity, examined either individually or in combination, and a risk of ipsilateral IBC. Compared with all other groups, the multivariable OR (95% confidence interval) for women who were triple positive for the three markers was 1.16 (0.38–3.54). Conclusions: p16, COX-2, and Ki67 immunopositivity was not associated with altered risk of ipsilateral IBC in women with DCIS. Impact: p16, COX-2, and Ki67 may not be prognostic for ipsilateral IBC in women with DCIS.

We aimed to describe outcomes, including shoulder motion, deltoid strength and Disabilities of Arm, Shoulder and Hand scores, in 19 patients with terrible triad shoulder injuries (anterior shoulder dislocation, rotator cuff tear or greater tuberosity fracture, and axillary nerve injury) who required axillary nerve reconstruction. The type of nerve surgery (grafting vs. nerve transfer), demographic factors and injury characteristics were not significantly associated with outcomes. The mean postoperative abduction and Disabilities of Arm, Shoulder and Hand score were 95° and 28, respectively. Patients with rotator cuff tears had a higher Disabilities of Arm, Shoulder and Hand score (more disability) than those with isolated greater tuberosity fractures. We conclude that spontaneous recovery of the axillary nerve may not occur in patients with a terrible triad injury. Functional outcomes after axillary nerve repair are poor with respect to motion, strength and patient-reported outcomes compared with reported results for axillary nerve reconstruction in the absence of rotator cuff injury. Level of evidence: IV