Digital Algorithm Research Articles

Digital neutron-gamma discrimination algorithm using adaptive noise filter

Pulse shape discrimination of a liquid scintillator-based detector (BC501A) has been studied using a digital approach. The objective of this work is to develop a new neutron-gamma discrimination algorithm to improve the discrimination efficiency. The new approach is a combination of two parts. The first part uses an adaptive Sallen-Key filter to reduce noise in the pulse followed by traditional approaches of either charge comparison or time over threshold. The approach of adaptive Sallen-Key combined with time over threshold was found to have a much better capability to discriminate neutron events from that of gamma. The pulse shape discrimination performance obtained for a 5 inches by 5 inches (length by diameter) liquid scintillator detector using the combination of the adaptive Sallen-Key filter with time over threshold is found to be significantly higher compared to that obtained using traditional methods. Moreover, this combination has the potential for real-time hardware implementation.

Image Analysis Techniques Applied in the Drilling of a Carbon Fibre Reinforced Polymer and Aluminium Multi-Material to Assess the Delamination Damage

Due to the high abrasiveness and anisotropic nature of composites, along with the need to machine different materials at the same time, drilling multi-materials is a difficult task, and usually results in material damage, such as uncut fibres and delamination, hindering hole functionality and reliability. Image processing and analysis algorithms can be developed to effectively assess such damage, allowing for the calculation of delamination factors essential to the quality control of hole inspection in composite materials. In this study, a digital image processing and analysis algorithm was developed in Python to perform the delamination evaluation of drilled holes on a carbon fibre reinforced polymer (CFRP) and aluminium (Al) multi-material. This algorithm was designed to overcome several limitations often found in other algorithms developed with similar purposes, which frequently lead to user mistakes and incorrect results. The new algorithm is easy to use and, without requiring manual pre-editing of the input images, is fully automatic, provides more complete and reliable results (such as the delamination factor), and is a free-of-charge software. For example, the delamination factors of two drilled holes were calculated using the new algorithm and one previously developed in Matlab. Using the previous Matlab algorithm, the delamination factors of the two holes were 1.380 and 2.563, respectively, and using the new Python algorithm, the results were equal to 3.957 and 3.383, respectively. The Python results were more trustworthy, as the first hole had a higher delamination area, so its factor should be higher than that of the second one.

A dual watermarking algorithm for trajectory data based on robust watermarking and fragile watermarking

Digital watermarking technology plays a crucial role in securing trajectory data. However, as trajectory data usage scenarios continue to expand, the security requirements for it have changed from a single copyright protection to one that takes into account data integrity. Existing digital watermarking algorithms for trajectory data can only choose between implementing copyright protection or ensuring integrity, unable to simultaneously achieve both functionalities. This limitation impedes the sharing and utilization of trajectory data. A dual watermarking algorithm that combines robust and fragile watermarking was innovatively proposed to solve this problem based on the geometric domain. Firstly, a set of feature points is extracted from the trajectory, and the farthest point pair of the minimum convex hull of the feature points is set as fixed points. The robust watermark is then embedded in the angles constructed by the feature points and the fixed points using quantization index modulation. Meanwhile, the trajectory points are grouped based on the angle and distance ratio constructed from the trajectory points to the fixed points. In each group, the spatiotemporal attributes of the trajectory points are mapped to the fragile watermark, which is then embedded into the distance ratios constructed by the trajectory points. Experimental results show that the proposed algorithm achieves both copyright protection and integrity verification for trajectory data and exhibits stronger robustness and tampering localization ability. This research can provide security and privacy protection for trajectory data and contribute positively to the application of trajectory data.

Artificial intelligence in digital marketing

Development of a comprehensive approach to the integration of artificiale intelligence (AI) into a companyʼs marketing strategy in the digital environment is important to directly ensure the loyalty of customers and achieve an economically primed level of sales conversion period. In this article it was focused on the direct integration of digital marketing algorithms of artificiale inteligence to optimize interaction between companies and target audiences. During the research, the use of fundamental scientific methods of analysis and synthesis was carried out to characterize the AI main algorithms that are used in digital marketing; empirical methods, graphical images, and sys­tem structural analysis. The AI role in digital marke­ting has been emphasized and the impor­tance of using current algorithms to ensure effective communications between com­pa­nies and vendors has been proved. A compr­ehensive analysis of the main digital marketing tools and the features of their use to ensure the competitive position of companies on the Internet was carried out. The scheme of obtain­ning informa­tion from users and optimizing the use of AI tools based on the applying of effective mathematical algorithms is given. The key trends in the development of the market for generative AI in the field of digital marketing have been identi­fied. A subdivision of content created by gene­rative AI has been presented. It has been determined that piece intelligence is a priority in the marketing strategies of companies in the digital environment, which allows companies to ensure personalization of the approach to each client.

Disentangling the role of algorithm awareness and knowledge in digital inequalities: an empirical validation of an explanatory model

ABSTRACT Algorithms have become almost ubiquitous on the internet. They shape the way internet users engage with internet services across diverse online domains and what outcomes they obtain from their internet uses. While scholars have extensively studied the societal implications of algorithms in the digital world, only a few studies investigated the relationship between digital inequalities and algorithm awareness and knowledge. Little is known about how ubiquity of internet access and disparities in skills affect algorithm awareness and knowledge of internet users, and how they affect their internet uses and outcomes. To fill this important gap, this study presents and tests an explanatory model to assert how algorithm awareness and knowledge are related to the three levels of digital inequalities. The data were collected with a face-to-face survey (response rate = 54%) on a representative sample of internet users (N = 802) in Slovenia in 2022. Results of path analysis confirmed the sequential paths between the three levels of digital inequalities, suggesting that ubiquity of internet access strongly determines internet skills and internet uses, which in turn affect tangible internet outcomes. While ubiquity of internet access affected only algorithm awareness, internet skills predicted both algorithm awareness and knowledge. Importantly, algorithm knowledge was a significant determinant of internet uses. Age, education, and income moderated some of the relationships in the model. Overall, the study demonstrates that research on digital inequalities and related interventions need to address algorithm awareness and knowledge, and also consider how social inequalities among internet users shape them.

Asymmetric fingerprinting protocol for vector map based on homomorphic encryption

ABSTRACT Most of the existing digital fingerprint algorithms for vector maps are symmetric fingerprint algorithms, and merchants may frame a legitimate buyer. An asymmetric fingerprinting protocol utilizing homomorphic encryption is proposed. First, the buyer encrypts his fingerprint. Then, this encrypted fingerprint and the merchant’s fingerprint are embedded into an encrypted vector map by the merchant. Finally, the encrypted data with the fingerprints of both parties is distributed to buyers. The buyer can use his private key to decrypt the encrypted data and get the Non-encrypted data with the fingerprints. In this way, merchants can track illegal users without the buyer’s fingerprint. Experimental results show that after various attacks, fingerprints can still be detected correctly.

The Safe Catch: AI Protects Your Health from Formalin-Laced Fish

In Bangladesh, where fish is a staple food, ensuring its safety from formalin contamination poses a critical challenge due to its perishable nature. This study introduces an intelligent application employing digital image processing for the rapid and non-intrusive detection of formalin in fish. Leveraging image analysis of fish eyes, the system distinguishes between formalin and non-formalin treated fish. The proposed architecture, utilizing EfficientNet-B3 and VGG-16 models, achieved a 98.05% and 98% accuracy rate in training and validation on the dataset. This method offers a swift and accurate means of examination without damaging sample preparation, particularly beneficial in large-scale operations where manual inspection is impractical. Unlike human senses, digital image processing algorithms remain impartial, overcoming human biases and subjective judgments. Challenges persist, such as the diverse appearance of fish and external factors like varying illumination, which may impact the reliability and effectiveness of image processing programs for formalin detection. Nonetheless, this technology holds promise in addressing the pressing need for dependable and automated formalin detection in the fish supply chain, ensuring food safety and public health.

Transferable adversarial attack on image tampering localization

It is significant to evaluate the security of existing digital image tampering localization algorithms in real-world applications. In this paper, we propose an adversarial attack scheme to reveal the reliability of such deep learning-based tampering localizers, which would be fooled and fail to predict altered regions correctly. Specifically, two practical adversarial example methods are presented in a unified attack framework. In the optimization-based adversarial attack, the victim image forgery is treated as the parameter to be optimized via Adam optimizer. In the gradient-based adversarial attack, the invisible perturbation yielded by Fast Gradient Sign Method (FGSM) is added to the tampered image along gradient ascent direction. The black-box attack is achieved by relying on the transferability of such adversarial examples to different localizers. Extensive experiments verify that our attacks can sharply reduce the tampering localization accuracy while preserving high visual quality for attacked images. Source code is available at https://github.com/multimediaFor/AttackITL.

A digital pathology AI model to predict immune-oncology biomarker status and pembrolizumab response in a real-world cohort of patients with colorectal and breast cancer.

e13617 Background: Deep learning (DL) algorithms have garnered significant attention for prediction of biomarker status in digitized tissue slides. Here, we trained and validated a digital pathology algorithm to predict microsatellite instability (MSI)/mismatch repair deficiency (MMRd) in colorectal cancer (CRC) and PD-L1 status in breast cancer (BC) using a large real-world patient cohort. Methods: H&E and immunohistochemistry (IHC) slides of CRC and BC from Caris Life Sciences (Phoenix, AZ) with known biomarker status were digitally scanned (CRC: N=27,494, 6.6% MSI-high by next-generation sequencing [NGS]; N=25,788, 6.4% MMRd by IHC; BC: N=16,069, 15.8% PD-L1+ by IHC). A transformer-based DL model (Wagner, Sophia J., et al., 2023) was trained to predict biomarker status, with 5-fold cross-validation. Accuracy, F1 score, area under the curve (AUC), and other metrics were calculated comparing the model to NGS/IHC “truth”. Risk stratification was performed on holdout datasets of pembrolizumab-treated CRC (N=422-454) and triple negative breast cancer (TNBC; N=513) pts. Post-treatment overall survival (OS) was calculated from start of pembrolizumab to last contact, per insurance claims data. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by Cox-Proportional hazards and compared between model and “truth”. Results: When applied to holdout datasets for biomarker prediction, the validated model achieved AUC of 0.78-0.95. In CRC and BC, performance was better among primary specimens than metastatic. For MSI-H/MMRd risk stratification in pembrolizumab-treated CRC, HR for OS were comparable between model and “truth” for primary and metastatic cohorts. When applied to the TNBC holdout dataset, the model performed better than “truth” for PD-L1 risk stratification in pembrolizumab-treated pts. Conclusions: A digital pathology DL model predicts biomarker status with high accuracy and is non-inferior to NGS/IHC for risk stratification of pembrolizumab-treated CRC and TNBC pts. These results support further investigation of AI algorithms developed from large datasets as possible rapid screening tools for biomarker detection in molecular testing programs. [Table: see text]

Correlation of TILs indices generated by visual review and digital computational algorithm with outcomes from preoperative chemotherapy for TNBC in TBCRC 030.

555 Background: Predictive biomarkers for response to neoadjuvant chemotherapy (NAC) in early-stage triple negative breast cancer (TNBC) are needed. Stromal tumor infiltrating lymphocytes (TILs) are prognostic and predictive in TNBC, typically scored by visual examination (VE) of H&E stained slides. Evaluation of image-based surrogate signatures for TILs and other immune profiles by computational assessment (CA) of slides may offer greater precision and more comprehensive assessment of the immune microenvironment. This study was designed to evaluate TILs by VE and CA methods and investigate through blinded assessment the predictive capacity of immune activation. Methods: The phase II study TBCRC 030 randomized patients (pts) with BRCA1/2-proficient stage I-III TNBC to cisplatin (C) or paclitaxel (T) NAC, with primary endpoint response at surgery. TILs were visually scored on a continuous scale by a single experienced pathologist on digitized H&E images of pre-treatment core needle biopsies (CNB). CA used the 4D QPOR platform’s statistical physics and tumor biology based algorithm to quantify cell cycle aberrations and microenvironment dynamics including TILs, immune response (IHI), and combined immune response and cell cycle signature (CmbI) as continuous biomarkers from digitized CNB images. Evaluation of TILs by VE and CA were each correlated with response (RCB 0/1) or non-response (RCB 2/3) to NAC. Receiver operator characteristic (ROC) curves with area under the curve (AUC) measures and odds ratios (OR) with 95% confidence intervals were used to assess the predictive performance of each biomarker. Results: Of 139 evaluable pts, 121 had usable data for TILs by both VE and CA (59 received C, 62 received T). Descriptive statistics for biomarkers by response are shown in the Table. Notably TILs score by VE was significantly predictive of response, as was CA CmbI. TILs by VE had OR 1.86 (95% CI 1.24, 2.87, p=0.031) and AUC 0.69 (95% CI 0.57, 0.66), whereas CA CmbI had OR 1.54 (95% CI 1.00, 2.42, p=0.053) and AUC 0.62 (95% CI 0.51, 0.73). CA TILs and IHI were not predictive of response. TILs by VE predicted response to T (OR 2.91 95% CI 1.56, 6.16, p=0.002) but not C (OR 1.22 95% CI 0.67, 2.18 p=0.49). Conclusions: In this analysis of TBCRC 030, VE of baseline TILs and CA CmbI were predictive of response to NAC for TNBC, however CA did not outperform VE. TILs predicted response to specific chemotherapy. Automated computational evaluation of TILs or the broader immune microenvironment is an emerging field which may allow accurate and reproducible assessment of these important biomarkers. Clinical trial information: NCT01982448 . [Table: see text]

PD-L1 immunohistochemistry in gastric cancer: Comparison of combined positive score and tumor area positivity across 28-8, 22C3, and SP263 assays.

2625 Background: The clinical application of programmed death-ligand 1 (PD-L1) immunohistochemistry is complicated by multiple available assays and different testing platforms, scoring algorithms, and cut-offs applied. This study assessed the analytical comparability of three commercially available PD-L1 assays (28-8, 22C3, and SP263) and two scoring algorithms used in gastric cancer (GC). Methods: Serial sections of 100 commercially procured GC samples, selected by the 28-8 assay to represent the dynamic range of PD-L1 expression, were stained with the three in vitrodiagnostic (IVD)-grade PD-L1 assays. Stained slides were blindly and independently evaluated by three trained pathologists for intra- and inter-reader assessment. Scoring was performed using the combined positive score (CPS) and tumor area positivity (TAP) methods, followed by statistical analysis. Digital image analysis (DIA) was used to objectively assess the technical performance of each assay by simulating CPS and TAP scoring methods using the HALO platform. Results: Comparable, specific, staining patterns were observed with the three PD-L1 assays. Pathologist assessment of PD-L1 positivity was reproducible in GC sample cohorts despite discernible variability in the observed staining intensity. When the same PD-L1 cut-offs were applied, inter- and intra-assay assessments of all three assays, using either CPS or TAP scoring methods, demonstrated moderate to almost-perfect (inter-assay Cohen’s kappa [κ] ranged from 0.47 to 0.83) and substantial to almost-perfect (intra-assay κ ranged from 0.77 to 1.00) agreement, respectively. Moreover, inter-pathologist evaluation showed a significant level of reproducibility (intraclass correlation coefficient (ICC) ≥0.92). DIA confirmed no difference in technical performance when specific digital algorithms were applied. Conclusions: This GC study highlights analytical concordance in PD-L1 testing among three major PD-L1 assays when TAP and CPS scoring algorithms are prospectively applied. Independently, DIA further supports the comparability of the technical performance of the assays. These observations support flexibility in cross-application of the different PD-L1 assays and scoring algorithms currently used to characterize PD-L1–positive GC samples.

The impact of digital platform pricing algorithms on market equilibrium

The impact of digital platform pricing algorithms on market equilibrium

Spatio-Temporal Detection of Land Use/Land Cover Changes in Kokrajhar District of Assam

The use of multi-temporal satellite images in digital change detection algorithms aids in the comprehension of landscape dynamics. The present study illustrates the spatio temporal dynamics of land use/land cover of Kokrajhar district of Assam, India. Landsat Satellite imageries of four different time periods. i.e., Landsat Thematic Mapper (TM) of 1991, 2001, 2011 and 2021 were acquired from Google Earth Explorer site and quantify the changes of Kokrajhar district from 1991 to 2021 over a period of 30 years. Supervised classification methodology has been employed using maximum likelihood technique in ArcMap 10.8 Software. The images of the study area were categorised into four different classes namely vegetation, agriculture, built up and water body. The results indicate that during the last three decades, built up have been increased by 3.8% (658.75 km2) while agriculture, vegetation and water body have been decreased by 0.74 (708.9 km2) %, 0.56(1494.46 km2) % and 2.46 (273.5 km2) % respectively.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Influence of Front-End Electronics on Metrological Performance of QCM Systems.

Quartz Crystal Microbalances (QCMs) are versatile sensors employed in various fields, from environmental monitoring to biomedical applications, owing mainly to their very high sensitivity. However, the assessment of their metrological performance, including the impact of conditioning circuits, digital processing algorithms, and working conditions, is a complex and novel area of study. The purpose of this work is to investigate and understand the measurement errors associated with different QCM measurement techniques, specifically focusing on the influence of conditioning electronic circuits. Through a tailored and novel experimental setup, two measurement architectures-a Quartz Crystal Microbalance with dissipation monitoring (QCM-D) system and an oscillator-based QCM-R system-were compared under the same mechanical load conditions. Through rigorous experimentation and signal processing techniques, the study elucidated the complexities of accurately assessing QCM parameters, especially in liquid environments and under large mechanical loads. The comparison between the two different techniques allows for highlighting the critical aspects of the measurement techniques. The experimental results were discussed and interpreted based on models allowing for a deep understanding of the measurement problems encountered with QCM-based measurement systems. The performance of the different techniques was derived, showing that while the QCM-D technique exhibited higher accuracy, the QCM-R technique offered greater precision with a simpler design. This research advances our understanding of QCM-based measurements, providing insights for designing robust measurement systems adaptable to diverse conditions, thus enhancing their effectiveness in various applications.

Adaptive fusion transfer learning-based digital multitwin-assised intelligent fault diagnosis

With the development of digital twin technology, mechanical failures can be more comprehensively described through the interaction between sensors and twin models, which thereby demonstrate significant potential in fault diagnosis. However, due to the limitations of modeling methods, digital twin model construction poses challenges and exhibits poor generalization, making it difficult to apply across domains. To address these issues, this paper proposes a digital multitwin fusion diagnostic algorithm based on transfer learning. The digital multitwin comprise the source domain mechanistic twin, the target domain distributional feature 1D Auxiliary Classifier GAN (1D-ACGAN) twin, and the target domain time-series feature shared weight stacking LSTM (SWSLSTM) twin. First, based on the fault features collected by multiple sensors in the mechanism twin, a multichannel, multiscale mid-fusion diagnostic network is constructed. Second, the network is hierarchically transferred to the target domain network using a frozen transfer technique, automatically finding the optimal network parameters. Furthermore, to enhance the data quality, a data twin composed of the SWSLSTM twin and 1D-ACGAN twin is proposed to capture the dependency relationships of target domain data features. Finally, the migrated network adaptively adjusts the generated samples of the data twin based on the designed indicators, achieving high-precision cross-domain application of the multitwin diagnostic model. The proposed algorithm has been validated utilising data from triple-piston pumps and openly accessible bearing datasets, achieving diagnostic accuracies of 91.6% and 95.28%, respectively. These results substantiate the efficacy of the algorithm, demonstrating its superior generalisation capability and robustness in comparison to extant classical methodologies.

Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications.

Digital Division Algorithms for Efficient Execution on Integrated Circuits

In this paper, we analyse division algorithms for use on chips and propose the implementation of an optimal divider for these chips. By “optimal”, we refer to an algorithm that meets the following criteria: space efficiency – which involves minimizing resource utilization on the IC’s die area; speed efficiency – the algorithm's processing time (measured in n clock cycles); power efficiency – power consumption of the divider; implementation time – time for implementation of the algorithm using HDL. The chosen algorithm should strike a balance between space efficiency and processing speed, ensuring the efficient use of hardware resources while delivering swift computational results. The ultimate goal is to create a division module that aligns seamlessly with the integrated circuit's architecture, catering to computational efficiency and resource constraints.

Abstract PO5-04-04: Development of a method to detect very low levels of HER2 expression in Circulating Tumor Cells (CTCs) by liquid biopsy in patients with metastatic breast cancer

Abstract Introduction Blood-based liquid biopsies are a non-invasive diagnostic approach for detecting circulating tumor cells (CTCs) or circulating tumor DNA (ctDNA) that may provide clinically actionable information for treatment decisions for metastatic breast cancer (MBC) patients when a conventional tissue biopsy is not feasible. Recently, the novel anti-HER2 antibody-drug conjugate, Trastuzumab Deruxtecan (T-DXd), has shown effectiveness against MBC with low HER2 expression detected by tissue biopsy characterized as HER2-low (immunohistochemically 1+ or 2+ and ERBB2 amplification-negative via in situ hybridization) and is being explored in an ultra-low population. Here we report on the application of a liquid biopsy platform designed to sensitively detect low levels of HER2 protein expression in CTCs from MBC patients. Methods Blood samples from 704 metastatic breast cancer patients as well as control cells lines were collected for cell-based and cell-free DNA analysis. After plasma isolation, nucleated cells deposited on glass slides went through immunofluorescent staining and imaging. CTCs were identified using Epic Sciences’ digital imaging and machine learning algorithms, and a subset of CTCs were isolated and sequenced for genomic quantification of large scale-state transitions (LSTs) as well as copy number variants (CNVs). The HER2 status was based upon on ASCO-CAP guidelines and the previous tissue biopsy results from the ordering healthcare provider, as documented on the DefineMBC™ test requisition form. Results Within a DefineMBC early-access program (EAP), 93% of patients with MBC had detectable CTCs. Among ERBB2-non-amp patients tested with the DefineMBC assay, 25% of patients with MBC had findings that were reported as consistent with HER2-low in the CAP-CLIA setting. To explore the lower boundaries of HER2 protein expression, we created and validated an MCF-7 HER2-negative ERBB2 knock-out cell line and utilized its 95% confidence level threshold (271 MFI) as a negative fluorescence cutoff for very low HER2 expression. Based upon publicly available CCLE datasets, MDA-MB-453, MCF7, and MCF7 ERBB2 KO breast cancer cell lines were selected for their high and low HER2 RNA expression levels and were corroborated via observed HER2 immunofluorescence median levels of 15740, 381, and 188 MFI respectively. In an exploratory analysis, among ERBB2-non-amp patients, we retrospectively applied the 271 MFI HER2 cutoff to the EAP cohort and identified 38% of patients with very low HER2 expression. We examined the impact of the HER2 ultra-low cutoff in patients not eligible for T-DXd treatment. Among patients reported as IHC=0 or with unknown IHC status from the most recent tissue biopsy, 44% and 42%, respectively, may qualify for treatment with T-DXd. Conclusion Using our higher sensitivity assay that combines CTC immunofluorescence and single-cell genomic analysis, we examined the lower boundary of HER2 protein expression. Feasibility data support further characterization of a very low HER2 expression phenotype, for the eventual inclusion into our comprehensive cancer profiling solution. To date, studies with biomarker expression have been limited to tissue biopsy, which may not always yield contemporaneous sampling in the metastatic setting. These results offer a liquid biopsy test that might identify patients potentially responsive to HER2-directed therapies. Citation Format: Giuseppe Di Caro, David Bourdon, Andrew Kunihiro, Alessandra Cunsolo, Ernest Lam, Megan Slade, Martin Blankfard, Lee Schwartzberg. Development of a method to detect very low levels of HER2 expression in Circulating Tumor Cells (CTCs) by liquid biopsy in patients with metastatic 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 PO5-04-04.

An algorithm to identify patients aged 0–3 with rare genetic disorders

BackgroundWith over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0–3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders.ResultsThrough expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy.ConclusionsThe PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.