Validation Study Research Articles

Heterogeneous treatment effect of dose-dense paclitaxel plus carboplatin therapy for advanced ovarian cancer.

A Japanese clinical trial (JGOG3016) showed that dose-dense weekly paclitaxel in combination with carboplatin extensively prolonged overall survival (OS) in patients with advanced ovarian cancer. However, in other clinical trials, dose-dense paclitaxel regimens were not superior to triweekly paclitaxel regimens. In this study, causal tree analysis was applied to explore subpopulations with different treatment effects of dose-dense paclitaxel in a data-driven approach. The 587 participants with stage II-IV ovarian cancer in the JGOG3016 trial were used for model development. The primary endpoint was treatment effect in terms of 3-year OS in patients receiving dose-dense vs. conventional paclitaxel therapies. In patients <50 years, the 3-year OS was similar in both groups; however, it was higher in the dose-dense group in patients ≥50 years. Dose-dense paclitaxel showed strong positive treatment effects in patients ≥50 years with stage II/III disease, BMI <23 kg/m2, non-CC/MC, and residual tumor ≥1 cm. In contrast, although there was no significant difference in OS; the 3-year OS rate was 23% lower in dose-dense paclitaxel than conventional paclitaxel in patients ≥60 years with stage IV cancer. Patients in this group had a particularly lower performance status than other groups. Our causal tree analysis suggested that poor prognosis groups represented by residual tumor tissue ≥1 cm benefit from dose-dense paclitaxel, whereas elderly patients with advanced disease and low-performance status are negatively impacted by dose-dense paclitaxel. These subpopulations will be of interest to future validation studies. Personalized treatments based on clinical features are expected to improve advanced ovarian cancer prognosis.

A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) offers a principled approach to assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. Additionally, we demonstrate how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prospective validation of the Global Leadership Initiative on Malnutrition criteria for identifying malnutrition in hospitals: A protocol and feasibility pilot study.

The aim of this study was to pilot a protocol for prospective validation of the Global Leadership Initiative on Malnutrition (GLIM) criteria in hospital patients and evaluate its feasibility and patient acceptability. The validation protocol follows the GLIM consortium's rigorous methodological guidance. Protocol feasibility was assessed against criteria on recruitment (≥50%) and data collection completion (≥80%); protocol acceptability was assessed via patient satisfaction surveys and interviews. Adult inpatients in a tertiary hospital underwent four nutrition assessments (each by a different assessor); two Subjective Global Assessments (SGAs) and two GLIM assessments. All five GLIM criteria were assessed with bioelectrical impedance analysis used for muscle mass. Interrater reliability, criterion validity, and predictive validity were reported to detect trends. All primary feasibility criteria were met (consent rate 76%; data for GLIM criterion validity collected on 83% participants). Of predictive outcome data, 100% of hospital-related data, 82% of 6-month mortality data, and 39% of 6-month health-related quality of life data were collected. The mean (SD) age of participants was 61.0 ± 16.2 years, and 51.5% were male. The median (interquartile range) length of stay and body mass index were 7 (4-15) days and 25.6 (24.2-33.0) kg/m2, respectively. GLIM criteria diagnosed 70% of the patients as malnourished vs 55% with SGA. Most patients found the data collection acceptable with minimal burden. The methods outlined in this rigorous GLIM validation protocol are feasible to undertake in hospitals and acceptable to patients. This paper provides practical methodological guidance for future prospective GLIM validation studies.

The additive value of complementing diagnostic idiopathic intracranial hypertension criteria by MRI – an external validation study

BackgroundRecently, diagnostic criteria including a standardized MRI criterion were presented to identify patients suffering from idiopathic intracranial hypertension (IIH) proposing that IIH might be defined by two out of three objective findings (papilledema, ≥ 25 cm cerebrospinal fluid opening pressure (CSF-OP) and ≥ 3/4 neuroimaging signs).MethodsTo provide independent external validation, we retrospectively applied the proposed diagnostic criteria to our cohort of patients with clinical suspicion of IIH from the Vienna IIH database. Neuroimaging was reevaluated for IIH signs according to standardized definitions by a blinded expert neuroradiologist. We determined isolated diagnostic accuracy of the neuroimaging criterion (≥ 3/4 signs) as well as overall accuracy of the new proposed criteria.ResultsWe included patients with IIH (n = 102) and patients without IIH (no-IIH, n = 23). Baseline characteristics were balanced between IIH and no-IIH groups, but papilledema and CSF-OP were significantly higher in IIH. For the presence of ≥ 3/4 MRI signs, sensitivity was 39.2% and specificity was 91.3% with positive predictive value (PPV) of 95.2% and negative predictive value (NPV) 25.3%. Reclassifying our cohort according to the 2/3 IIH definition correctly identified 100% of patients without IIH, with definite IIH and suggested to have IIH without papilledema by Friedman criteria, respectively.ConclusionThe standardized neuroimaging criteria are easily applicable in clinical routine and provide moderate sensitivity and excellent specificity to identify patients with IIH. Defining IIH by 2/3 criteria significantly simplifies diagnosis without compromising accuracy.

Adaptation and validation of the EPIC-Norfolk food frequency questionnaire for assessing dietary intake in Ukrainian adults

IntroductionCurrently, there are no validated food frequency questionnaires (FFQs) for evaluating nutrient intake in Ukrainian adults. This study aimed to adapt and validate the European Prospective Investigation into Cancer Food...

Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study.

To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment. We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively. In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively). Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Agnostic identification of plasma biomarkers for postpartum hemorrhage risk.

Postpartum hemorrhage (PPH) is difficult to predict, is associated with significant maternal morbidity, and is the leading cause of maternal mortality worldwide. The identification of maternal biomarkers that can predict increased PPH risk would enhance clinical care and may uncover mechanisms that lead to PPH. This retrospective case-control study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and PPH cases. Maternal plasma samples were procured from a cohort of >60,000 participants in a single institution's perinatal repository. PPH was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours of delivery. PPH cases (N=30) were matched by maternal age and delivery mode (vaginal or cesarean) with controls (N=56). Mass spectrometry was used to identify differentially abundant proteins using integrated peptide peak areas. Statistically significant differences between groups were defined by a p-value of <0.05 after controlling for multiple comparisons. By study design, cases and controls did not differ in race, ethnicity, gestational age at delivery, blood type, or pre-delivery platelet count. Cases had slightly, but significantly lower pre- and post-delivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 77 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<0.05). Of these differentially abundant plasma proteins, most had likely liver or placental origins. Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Significantly differentially abundant proteins with the highest fold change (prostaglandin D2 synthase, periostin, and several serine protease inhibitors) did not correlate with pre-delivery hematocrit or hemoglobin, but identified PPH cases with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (AUROC 0.802-0.874). Incorporating pre-delivery hemoglobin with these candidate proteins further improved identification of PPH cases. Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and PPH cases. Several of these proteins are known to participate in biologically plausible pathways for PPH risk and have potential value for predicting PPH. These findings identify candidate protein biomarkers for future validation and mechanistic studies.

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Potential mechanisms of traditional Chinese medicine in treating insomnia: A network pharmacology, GEO validation, and molecular-docking study.

The purpose of this study is to investigate the potential mechanisms of Chinese herbs for the treatment of insomnia using a combination of data mining, network pharmacology, and molecular-docking validation. All the prescriptions for insomnia treated by the academician Qi Wang from 2020 to 2022 were collected. The Ancient and Modern Medical Case Cloud Platform v2.3 was used to identify high-frequency Chinese medicinal herbs and the core prescription. The Traditional Chinese Medicine Systems Pharmacology and UniProt databases were utilized to predict the effective active components and targets of the core herbs. Insomnia-related targets were collected from 4 databases. The intersecting targets were utilized to build a protein-protein interaction network and conduct gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis using the STRING database, Cytoscape software, and clusterProfiler package. Gene chip data (GSE208668) were obtained from the Gene Expression Omnibus database. The limma package was applied to identify differentially expressed genes (DEGs) between insomnia patients and healthy controls. To create a "transcription factor (TF)-miRNA-mRNA" network, the differentially expressed miRNAs were entered into the TransmiR, FunRich, Targetscan, and miRDB databases. Subsequently, the overlapping targets were validated using the DEGs, and further validations were conducted through molecular docking and molecular dynamics simulations. Among the 117 prescriptions, 65 herbs and a core prescription were identified. Network pharmacology and bioinformatics analysis revealed that active components such as β-sitosterol, stigmasterol, and canadine acted on hub targets, including interleukin-6, caspase-3, and hypoxia-inducible factor-1α. In GSE208668, 6417 DEGs and 7 differentially expressed miRNAs were identified. A "TF-miRNA-mRNA" network was constructed by 4 "TF-miRNA" interaction pairs and 66 "miRNA-mRNA" interaction pairs. Downstream mRNAs exert therapeutic effects on insomnia by regulating circadian rhythm. Molecular-docking analyses demonstrated good docking between core components and hub targets. Molecular dynamics simulation displayed the strong stability of the complex formed by small molecule and target. The core prescription by the academician Qi Wang for treating insomnia, which involves multiple components, targets, and pathways, showed the potential to improve sleep, providing a basis for clinical treatment of insomnia.

Contribution of Virtual Reality to the Reproduction of Social Interaction characteristics in Neuropsychology: A Systematic Review of the Literature

Abstract Sociocognitive difficulties are observed in many pathologies associated with brain lesions or atypical brain functioning. The development of virtual reality offers an opportunity to assess this dimension from an interactionist perspective and to increase the ecological validity of the material used. However, this approach can be operationalized and conceptualized in a variety of ways. To understand these aspects, we conducted a systematic review of articles found in the PubMed, PubMed Central and ScienceDirect databases, following PRISMA-P guidelines. A corpus of 48 articles was identified and analyzed in terms of the experimental devices used, to gauge the extent to which they reproduced the characteristics of real-world social interactions, as well as the associated conceptions. Results suggested that interactionist research lies on a continuum between ecological validity (studies exploring interactions via general functions) and a high level of experimental control (exploration of interactions with respect to specific processes). They are discussed in terms of the operationalization and objectification of interactionism.

The development and cross-cultural validation of a Student Online Learning Experience Scale

ABSTRACT A growing body of research has explored the effects of Covid-19-induced online learning on students’ experiences. However, there is a lack of validated tools that measure the perceived online learning experiences of domestic and international students in various national and cultural contexts. To address this gap, this article aims to develop the Student Online Learning Experience Scale (SOLE-S). We conceptualised four potential dimensions of students’ perceptions of their online learning experiences: online interactions with students, teachers, university, and technology (i.e. students’ capacity to participate in online learning). Confirmatory factor analysis determined three distinct factors: online interactions with students, the university (including teachers), and technology. Based on multiple validation studies reported in this article, the 10-item SOLE-S demonstrated sound internal consistency, discriminant validity, and convergent validity. Multigroup confirmatory factor analysis on data from domestic and international students transitioning from conventional classroom learning to online learning during the COVID-19 pandemic in Italy, France, Poland, and the UK showed a stable factor structure across different cultural contexts. Bivariate and point-biserial correlation analyses revealed that the SOLE-S is associated with students’ adjustment, performance, satisfaction, and loyalty to the university. This tool will assist higher education institutions in monitoring students’ online learning experiences and developing relevant support structures to enhance their academic experience and well-being during the pandemic and post-pandemic online learning.

Abstract PO4-27-10: A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer

Abstract Background: Evaluation of axillary lymph nodes status in cN0 axilla is performed by sentinel lymph node biopsy (SLNB) utilizing a combination of radioactive isotope and blue dye. There is a paucity of radioactive tracer in many countries of transitional economies. Hence, alternatives to isotope have been explored, viz. indocyanine green (ICG), superparamagnetic iron oxide nanoparticles and Fluorescein Sodium (FS). This systematic review and meta-analysis, evaluates the diagnostic performance of Fluorescein Sodium guided SLNB. Objectives: The objective was to evaluate the diagnostic performance of Fluorescein Sodium for sentinel lymph node biopsy in early breast cancer. Methods: PRISMA 2020 guidelines were followed for this meta-analysis. Eligibility criteria: Studies where SLNB was performed using Fluorescein Sodium for breast carcinoma. Information sources: PubMed, EMBASE, Cochrane library and online clinical trial registers Risk of bias: Articles were assessed for risk of bias using the QUADAS-2 tool. Synthesis of results: The main summary measures were pooled detection rate and pooled false negative rate using random effects model. In order to describe heterogeneity, we constructed summary receiver operating characteristic (SROC) curves with 95% prediction regions, estimated using bivariate meta-analysis with a test level random effect only, and forest plots. Calculations were performed using the Meta-Disc v1.4 program (available from https://metadisc.software.informer.com/1.4/). Results: Out of 45 studies found on initial systematic search, 7 studies were found eligible and were included in the meta-analysis based on data on 332 patients. The pooled detection rate was 93.2% (95% confidence interval [CI] 87% to 97%).). The heterogeneity (I2 value) of the pooled detection rate was 71%. Five validation studies were included for pooling the false negative rate (FNR) and included a total of 211 patients. The pooled FNR was 5.6% (95% CI; 2.9 to 9.07). Five validation studies were included for sensitivity, specificity, diagnostic odds ratio, LR+ and LR- meta-analysis. With regards to the identification of SLN the pooled sensitivity was 80% [(95% CI, 68% to 89%); I2=79.8]; specificity was 98% [(95% CI, 95% to 100%); I2=0.0]. The likelihood ratio for a positive test LR+ was 23.2 [(95% CI, 5.91 to 95.18); I2=0.0] and likelihood ratio for a negative test a LR- of 0.41 [(95% CI, 0.16 to 1.05); I2=88.1]. The diagnostic Odds ratio OR was 76.94 (95% CI, 15.88 to 372.79). Diagnostic accuracy as measured by SROC was 99.08%. Q index was 0.96. Publication bias for pooled detection rate as per Egger’s test was -2.2 (p= 0.66) and for FNR was 1.85 (p=0.29). Visual inspection of summary ROC curve and of forest plots did not show significant heterogeneity. Conclusion: Fluorescein guided SLNB is an affordable option for detection of lymph node metastases in cN0 patients with early breast cancer. It achieves a high detection rate of 93% with a false negative rate of 5.6% for the detection of axillary lymph node metastasis. We recommend larger multinational trials to confirm its high detection rate with low false negativity. Citation Format: Sanjay Kumar Yadav, Bharath S, Sarada Khadka, Dhananjaya Sharma, Chandan Kumar Jha, Gaurav Agarwal, Manju Singh, Anurag Srivastava, Amit Goyal. A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-27-10.

Validation of the Spanish version of the Pediatric Asthma Severity Score (PASS) in a population of Hispanic children.

The Pediatric Asthma Severity Score (PASS)is one of the most-used clinical scoring systems for assessing the severity of asthma exacerbations in children. The aim of the present study was to validate a Spanish version of the PASS in a population of Hispanic children with asthma exacerbations living in urban Bogota, Colombia. In a prospective cohort and a validation study, parents/caregivers of children between 2 and 18 years old attended in the emergency department (ED)with asthma exacerbations who were admitted to the inpatient unit were invited to participate in the study. During the hospitalization period, we gathered the necessary data for assessing the criterion validity (comparing its score with the Pediatric Respiratory Assessment Measure [PRAM]), construct validity, interrater reliability, responsiveness, and internal consistency of the Col-PASS,the Colombian version of the PASS. At baseline, the scores of the Col-PASScorrelated positively with the scores of the PRAMscore (ρ = 0.588, p < .001). The baseline Col-PASSscores in patients who required admission to a more complex service were significantly higher than those in patients who presented clinical improvement (1.0 (0.0-2.0) vs. 0.0 (0.0-0.0), p < .001). The interrater reliability was found to be κ = 0.897, 95% CI 0.699-1.000, p < .001. Cronbach's α was .701 for the questionnaire as a whole. The Col-PASShas excellent construct validity, adequate criterion validity, interrater reliability, responsiveness; and acceptable internal consistency when used in children between 2 and 18 years old with asthma exacerbations.

Numerical investigations on heat transfer of supercritical RP-3 flowing in circular tube

This study aims to develop a reliable numerical model for predicting the supercritical heat transfer of aviation kerosene RP-3 in a tube under heating conditions, thereby providing a reference for revealing the mechanism behind the experimental phenomena. Based on validation studies between seven turbulence models and experiments, a numerical method using the Yang-Shih turbulence model is proposed. A detailed prediction of the turbulent flow process is obtained, and the heat transfer characteristics of RP-3 are analyzed. The evolution of parameters and properties in axial and radial directions is demonstrated, followed by investigations of the effects of system pressure, fuel inlet temperature, and mass flow rate. The drastic change in the specific heat of the fuel when its temperature is close to the pseudocritical value and the temperature difference between the area near the wall and the center of the tube are the main causes of the enhancement and deterioration of the heat exchange. A higher inlet temperature increases the heat transfer coefficient, but due to its different effects on decreasing the density and the viscosity, it increases the pressure drop. In addition, larger mass flow rates can promote turbulence intensity and heat transfer, but cause a higher pressure drop across the tube.

Abstract PO2-07-03: Retrospective Validation Study of an Artificial Neural Network-Based Preoperative Decision-Support Tool for Noninvasive Lymph Node Staging (NILS) in Women with Primary Breast Cancer (ISRCTN14341750)

Abstract Background Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling [1,2]. Methods This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator (Table 1), and the probability of benign lymph nodes was predicted. Vascular invasion, the tenth feature of the NILS model, was difficult to determine preoperatively. Therefore, a separate ANN model was developed to impute this feature, using the other nine features of the NILS model as predictors. A user-friendly web implementation of the NILS model was tested in this study. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC). The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology. Results The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers (Table 2). Approximately three-fourths of the patients had no metastases in SLNB (N0 74%). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n=151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node negativity from the development cohort (Table 3). Conclusion The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750.

Abstract PO5-07-01: A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study

Abstract Introduction: Breast cancer (BC) in women below the age of 40 accounts for approximately 7% of all BC cases. According to studies, BC in younger women is more likely to have negative tumor characteristics and outcomes compared to older women. The rate of early-onset breast cancer in the United Arab Emirates (UAE) is higher than in the Western population (24%, previously reported). Young breast cancer patients in the UAE have worse adverse features, including a higher grade, a larger tumor size, and lymph node involvement, as we reported previously. Therefore, combining several clinical indicators to predict recurrence risk in our unique, heterogeneous young breast cancer group is needed. Aim:The aim of this study is to develop a statistical machine learning model that can predict survival and the risk of recurrence in a young breast cancer population seen and treated in a large-volume tertiary cancer center in the United Arab Emirates (Tawam Hospital). Methods: Early-onset breast cancer patients from a retrospective observational cohort study were included in this study (N = 904). A broad range of clinical data was collected. A multivariate ridge Cox regression model was performed using age, histology, molecular type (ER,PR,HER-2 status), T-stage, N-Stage, M-stage, grade, and BMI, and the interaction term age*BMI at the time of diagnosis. The primary outcome was time-to-death. A novel breast cancer risk score (BCRS) was developed as the linear predictor of the fitted-ridge Cox regression model. Breast cancer patients were categorized into low, medium, and high risk using the 33th and 66th centiles of the novel BCRS. A univariate Cox regression model was performed with a categorized BCRS as a predictor of time-to-death. Hazard ratios (HR) with a 95% confidence interval (95% CI) were estimated. Kaplan-Meier (KM) curves were plotted and compared using the log-rank test. Time-dependent sensitivity and specificity were computed at five- and ten-year follow-ups, respectively. Statistical analyses were performed in R version 4.2.3. Results: The median age was 36 years (IQR: 32–38), and 64% were Arabic. Fifteen percent had an event with a median duration of follow-up of 15.4 years (95% CI: 13.6–17.1). The estimated HR of the categorized novel BCRS in a univariate Cox regression model was 1.968 (95% CI: 1.075- 3.601) and 8.040 (95% CI: 4.695–13.769) for the intermediate and high-risk groups as compared to the low-risk group, respectively. KM curves show a clear separation in terms of survival between the three risk groups, p&amp;lt; 0.0001 (Figure 1). A cutoff value of -1.733 gives a sensitivity of 93.8 and 90.6 with corresponding specificities of 31.42 and 31.31 at five- and ten-year follow-up, respectively. Conclusion: This is the first study in the region that uses clinical data that combines several clinical characteristics and aims to design a statistical machine learning model to predict the risk of death from breast cancer. Our novel model can be used in daily clinical practice to identify high risk breast cancer patients and aid clinical decision-making in patients with breast cancer. However, further validation studies are needed. Figure 1:KM curves of breast-cancer survival for patients with low-risk (BCRS in [-2.32, -1.63], black curve), intermediate-risk (BCRS in [-1.64, -1.25], red curve), and high-risk BCRS in [-1.26, 1.41], green curve). Citation Format: Aydah Al-Awadhi, Mohammed Hourani, Mawada Hussein, Fatima Alkindi, Lina Wahba, Abla AlAgha, Alaa Shoqeir, Mouza AlShebli, Atlal Abusanad, Amar Ahmad. A novel clinical risk score that can accurately predict the survival of young breast cancer patients: A UAE-based cohort study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-01.

Abstract PO1-10-01: Racial/Ethnic Disparities in Rates of Pathological Complete Response and Survival in Patients with Inflammatory Breast Cancer

Abstract Radiation Therapy Toxicities and Survival Outcomes in Monoallelic ATM Variant Carriers with Non-Metastatic Breast Cancer: A Retrospective Analysis Rayan Bensenane1 MD, Arnaud Beddok1,2,3 MD, Nadine Andrieu4 PhD, Fabienne Lesueur4 PhD, Eve Cavaciuti 4 MSc, Dorothee Le Gal4 MSc, Eon-Marchais Severine4 PhD, Dominique Stoppa Lyonnet 5MD PhD, Youlia Kirova1 MD 1. Institut Curie, PSL Research University, Radiation Oncology Department, Paris/Saint-Cloud/Orsay, France. 2. Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, 125 Nashua St., Boston, MA, 02114, USA 3. Institut Curie, PSL Research University, University Paris Saclay, Inserm LITO U1288 Orsay, France 4. Inserm, U900, Institut Curie, PSL Research University, Mines ParisTech, Paris, France 5.Department of Genetics, Institut Curie; Inserm U830, Institut Curie; Paris-Cité University Abstract (characters: 2952; max 3400 characters, not include spaces) Background: The Ataxia-Telangiectasia Mutated (ATM) gene, involved in the repair of DNA double-strand breaks, can contribute to radiosensitivity when a bi-allelic variant is present and lead to Ataxia-Telangiectasia syndrome. Moreover, monoallelic ATM pathologic variant (PV) carriers, especially women, has an estimated occurrence rate of 0.5-1% globally and face a 2 to 3-fold increased risk of developing breast cancer. Despite evidence of in vitro radiosensitivity in cells derived from monoallelic variant carriers, there is a dearth of patient studies examining the risk of radiation-induced toxicity. This study aims to explore radiation therapy (RT) toxicities in non-metastatic breast cancer women carrying a germline monoallelic ATM variant, compared to non-carriers. Methods: A retrospective study was conducted on patients treated at Institut Curie, Paris from 1999 to 2014 and participating to CoF-AT (a French national study) and GENESIS database. ATM variant screenings encompassed both PV and non-PV, with toxicities evaluated using CTCAE v.5. Variants were classified as pathogenic, variant of unknown significance (VUS), or benign. Follow-up started from age/date at breast cancer to acute, late toxicities, disease recurrence or last news. Survival and toxicity comparisons were made using Kaplan-Meier survival analysis and Chi-square tests, respectively, with a significance level of α set at 0.05. Results: Among 50 patients, nine were ATM variant carriers (3 PV/5 VUS/1 benign), and 41 were non-carriers. Most patients had no smoking history (68%), and invasive ductal carcinoma was the predominant diagnosis (82%). The majority underwent breast-conservative surgery (80%), and the dominant RT techniques were 3D-Conformational Radiation Therapy (70%) and Isocentric Lateral Decubitus (30%). The median RT dose was 50 Gy over an average period of 36.5 days. With a median follow-up of 12 years post-diagnosis, no significant difference in acute dermatitis, esophagitis, lymphedema, cutaneous fibrosis, telangiectasia, or heart disease was observed between the groups. Analysis of overall survival (OS) showed a 5-year OS of 98%, decreasing to 89% at 10 years. For ATM variant carriers, the OS at 5, 10, and 15 years was 100%, 89%, and 89%, respectively, similar to non-carriers. Kaplan-Meier analysis revealed no significant differences in 5, 10, and 15-year overall survival, progression-free survival, local failure-specific survival, and contralateral breast cancer rates between the groups. Conclusion: In non-metastatic breast cancer patients, monoallelic ATM variant carrier status does not significantly influence acute or late RT toxicities and survival outcomes. These findings, derived from a small cohort, highlight the need for prospective studies for further validation. Table: Acute and Late Toxicities Post-Radiation Therapy in Monoallelic ATM Variant Carriers vs Non-Carriers Citation Format: Rebecca Zasloff, Samantha Thomas, Kendra Parrish, Astrid Botty van de Bruele, Gayle DiLalla, Maggie DiNome, Laura Rosenberger, Hannah Woriax, E Shelley Hwang, Jennifer Plichta, Akiko Chiba. Racial/Ethnic Disparities in Rates of Pathological Complete Response and Survival in Patients with Inflammatory Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-01.

Abstract PO1-22-05: Radiation Therapy Toxicities and Survival Outcomes in Monoallelic ATM Variant Carriers with Non-Metastatic Breast Cancer: A Retrospective Analysis

Abstract Radiation Therapy Toxicities and Survival Outcomes in Monoallelic ATM Variant Carriers with Non-Metastatic Breast Cancer: A Retrospective Analysis Rayan Bensenane1 MD, Arnaud Beddok1,2,3 MD, Nadine Andrieu4 PhD, Fabienne Lesueur4 PhD, Eve Cavaciuti 4 MSc, Dorothee Le Gal4 MSc, Eon-Marchais Severine4 PhD, Dominique Stoppa Lyonnet 5MD PhD, Youlia Kirova1 MD 1. Institut Curie, PSL Research University, Radiation Oncology Department, Paris/Saint-Cloud/Orsay, France. 2. Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, 125 Nashua St., Boston, MA, 02114, USA 3. Institut Curie, PSL Research University, University Paris Saclay, Inserm LITO U1288 Orsay, France 4. Inserm, U900, Institut Curie, PSL Research University, Mines ParisTech, Paris, France Abstract (characters: 2952; max 3400 characters, not include spaces) Background: The Ataxia-Telangiectasia Mutated (ATM) gene, involved in the repair of DNA double-strand breaks, can contribute to radiosensitivity when a bi-allelic variant is present and lead to Ataxia-Telangiectasia syndrome. Moreover, monoallelic ATM pathologic variant (PV) carriers, especially women, has an estimated occurrence rate of 0.5-1% globally and face a 2 to 3-fold increased risk of developing breast cancer. Despite evidence of in vitro radiosensitivity in cells derived from monoallelic variant carriers, there is a dearth of patient studies examining the risk of radiation-induced toxicity. This study aims to explore radiation therapy (RT) toxicities in non-metastatic breast cancer women carrying a germline monoallelic ATM variant, compared to non-carriers. Methods: A retrospective study was conducted on patients treated at Institut Curie, Paris from 1999 to 2014 and participating to CoF-AT (a French national study) and GENESIS database. ATM variant screenings encompassed both PV and non-PV, with toxicities evaluated using CTCAE v.5. Variants were classified as pathogenic, variant of unknown significance (VUS), or benign. Follow-up started from age/date at breast cancer to acute, late toxicities, disease recurrence or last news. Survival and toxicity comparisons were made using Kaplan-Meier survival analysis and Chi-square tests, respectively, with a significance level of α set at 0.05. Results: Among 50 patients, nine were ATM variant carriers (3 PV/5 VUS/1 benign), and 41 were non-carriers. Most patients had no smoking history (68%), and invasive ductal carcinoma was the predominant diagnosis (82%). The majority underwent breast-conservative surgery (80%), and the dominant RT techniques were 3D-Conformational Radiation Therapy (70%) and Isocentric Lateral Decubitus (30%). The median RT dose was 50 Gy over an average period of 36.5 days. With a median follow-up of 12 years post-diagnosis, no significant difference in acute dermatitis, esophagitis, lymphedema, cutaneous fibrosis, telangiectasia, or heart disease was observed between the groups. Analysis of overall survival (OS) showed a 5-year OS of 98%, decreasing to 89% at 10 years. For ATM variant carriers, the OS at 5, 10, and 15 years was 100%, 89%, and 89%, respectively, similar to non-carriers. Kaplan-Meier analysis revealed no significant differences in 5, 10, and 15-year overall survival, progression-free survival, local failure-specific survival, and contralateral breast cancer rates between the groups. Conclusion: In non-metastatic breast cancer patients, monoallelic ATM variant carrier status does not significantly influence acute or late RT toxicities and survival outcomes. These findings, derived from a small cohort, highlight the need for prospective studies for further validation. Table: Acute and Late Toxicities Post-Radiation Therapy in Monoallelic ATM Variant Carriers vs Non-Carriers * Pearson/Chi2 test ; NS: non significant at α=0.05; ATM +/+: wild type for ATM; ATM +/- : ATM monoallelic germline variant carrier Citation Format: Rayan BENSENANE, Youlia Kirova, Arnaud BEDDOK, Andine ANDRIEU, Fabienne Lesueur, Eve CAVACIUTI, Dorothée Le GAL, Severine EON-MARCHAIS, Dominique STOPPA LYONNET. Radiation Therapy Toxicities and Survival Outcomes in Monoallelic ATM Variant Carriers with Non-Metastatic Breast Cancer: A Retrospective Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-22-05.

Clinical validation of automated depth camera-based measurement of the Fugl-Meyer assessment for upper extremity.

Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. Validation and feasibility study of a measurement instrument based on single cross-sectional data. Rehabilitation unit in a general hospital. Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021-2023). Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment.

Abstract PO2-28-09: Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model

Abstract Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation in Black women of a mammography-driven AI breast cancer risk model (Mirai) originally developed on screening cohorts primarily consisting of White women. In this institutional review board-approved, Health Insurance Portability and Protection Act (HIPAA)-compliant study under a waiver of consent, we retrospectively analyzed a case–cohort sample nested within the core academic breast cancer screening practice of BJC Healthcare, the hospital partner of Washington University in St. Louis. For the purposes of this validation study, relying on 2D digital mammography (DM) images, we focused on Black women presenting for annual DM screening (Selenia or Selenia Dimensions; Hologic) between 2008 and 2018. Eligible breast cancer cases were derived from all women with a breast cancer diagnosis (with associated biopsy-confirmed tumor pathology via institutional cancer registry) after negative (BI-RADS 1 or 2) DM screening 1 to 5 years prior to cancer diagnosis. We also identified a random sample of controls, defined as women who had negative (BI-RADS 1 or 2) DM screening, with 1 to 5 years of screening follow-up without a cancer diagnosis. Risk scores for all DM exams were calculated via the Mirai model. Performance was evaluated using concordance-index (C) analyses and associated 95% confidence intervals (CIs) for the entire cohort, as well as for study subgroups of invasive versus in-situ cancer and cancer molecular subtypes. We analyzed 1368 DM screening exams, including 672 DM exams from 391 women diagnosed with breast cancer (mean age, 58 years; standard deviation, 10 years) and 696 DM exams from 406 controls (mean age, 55 years; standard deviation, 10 years). The overall C-index was 0.62 [95% CIs 0.60–0.64] for all Black women, which was lower compared to previously reported validation results for Mirai in studies of similar design on predominantly White and racially diverse screening cohorts (C-index = 0.67-0.78). There was no evidence of a significant difference between invasive and in-situ cancer (C-index = 0.64 [95% CIs 0.61–0.66] vs. 0.64 [95% CIs 0.61–0.67]). Compared to other cancer molecular subtypes, performance was significantly higher among triple-negative (C-index = 0.67 [95% CIs 0.62–0.71]) and estrogen receptor (ER) negative cancer (C-index = 0.66 [95% CIs 0.61–0.70]). A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, particularly for triple-negative and ER- cancer types. However, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts. Our results suggest that further refinements are needed towards more accurate breast cancer risk assessment in Black women. Long-term breast cancer risk prediction performance (C-indices) of Mirai in the full cohort of Black women and in study subgroups. Citation Format: Juanita Hernandez Lopez, Zhixin Sun, Shirin Shoushtari, Ulugbek Kamilov, Debbie Bennett, Aimilia Gastounioti. Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-09.