Mean Sensitivity Research Articles

Evaluation of the peripheral visual performance of DIMS spectacle lenses versus single vision lenses

PurposeThis study evaluates differences in the visual field performance when wearing the Defocus Incorporated Multiple Segments (DIMS) spectacle lens compared to wearing a conventional single vision (SV) spectacle lens.MethodsTwenty-one children aged 9–14 years with spherical equivalent refraction (SER) between −1.13D to −4.75D were recruited. Mid-peripheral near visual acuity (NVA) under room lighting condition (500 lux ±10%) was measured using DIMS and SV lenses, respectively. Automated static perimetry (Zeiss, Humphrey Visual Field HFA 750i) with SITA Fast 30–2 protocol was used to investigate the visual field sensitivity. During the test, the study lens (Plano DIMS or SV lens) were inserted into the lens holder in front of the trial lenses with each child’s compensated prescription.ResultsThree children were not able to complete the reliable visual tests due to fixation losses (>20%) or high false positive rate (>15%) while 18 children successfully completed the test. The mean visual field sensitivity was 29.2 ± 3.7 decibels (dB) and 29.3 ± 3.5 dB when wearing DIMS and SV lens, respectively. The mean sensitivity differences between DIMS and SV lens among 76 locations ranged from −2.4 ± 3.9 dB to 1.6 ± 3.9 dB. No statistically significant difference in sensitivity was observed across 76 locations within the central 30o between DIMS and SV lens (Wilcoxon signed rank test with bonferroni correction for multiple comparisons, p > 0.00065). Compared to SV lens, 0.05 logarithm of minimal angle of resolution (logMAR) reduction in mid-peripheral NVA in all 4 quadrants (Superior, Temporal, Inferior and nasal, p < 0.05) was noted with the DIMS lens (N = 18). However, no statistically significant correlation was found between the mid-peripheral NVA and visual sensitivity at the specific locations.ConclusionAlthough the mid-peripheral NVA was slightly reduced using DIMS lens, wearing DIMS lens did not change the children’s visual sensitivity to detect the static stimulus within 30o visual field when compared to wearing SV lens.

Development of an automated tool for the estimation of histological remission in ulcerative colitis using single wavelength endoscopy technology.

Ulcerative colitis (UC) management employs a strategy targeting histological and endoscopic remission. Correlation of white-light endoscopy (WLE) scores with histological activity is limited. Single-wavelength endoscopy (SWE) addressing microvascular changes reflecting histological disease activity, may better assess histological remission. Our goal was to assess the accuracy of a computer-aided diagnosis (CAD) system for histological activity estimation in UC, based on either WLE or SWE. We collected 6926 sets of corresponding WLE and SWE frames in 112 patients with UC, using a prototype endoscopic system enabling both imaging methods (FUJIFILM, Tokyo, Japan). Histological remission (Geboes score ≤2B.0) assessed at the location of imaging was annotated for all frames and separate WLE-CAD and SWE-CAD models were trained using deep learning for automated detection of histological remission with either imaging modality. Initial training of both models on the same subset of 42 patients, resulted in SWE-CAD outperforming WLE-CAD with a mean sensitivity of 88.0% vs 73.9% (p < 0.001), a mean specificity of 71.7% vs 65.6% (p=0.45), and a diagnostic accuracy of 83.3% vs 67.5% (p<0.005), respectively. Further training of the SWE-CAD model on the entire dataset of 112 patients resulted in SWE-CAD achieving a 95.2% accuracy, 96.4% sensitivity, and 92.9% specificity on a section level. By utilizing automated CAD based on non-magnifying SWE for enhanced capillary visibility versus WLE, histological remission was detected with 95.2% diagnostic accuracy in patients with UC, offering stable objectivity and helping to exclude inter-reader variability.

Long-term co-circulation of multiple arboviruses in southeast Australia revealed by xeno-monitoring and viral whole genome sequencing

Abstract Arbovirus surveillance of wild-caught mosquitoes is an affordable and sensitive means of monitoring virus transmission dynamics at various spatial-temporal scales, and emergence and re-emergence during epidemic and interepidemic periods. A variety of molecular diagnostics for arbovirus screening of mosquitoes (known as xeno-monitoring) are available, but most provide limited information about virus diversity. PCR-based screening coupled with RNA sequencing is an increasingly affordable and sensitive pipeline for integrating complete viral genome sequencing into surveillance programs. This enables large-scale, high-throughput arbovirus screening from diverse samples. We collected mosquitoes in CO2-baited light traps from five urban parks in Brisbane from March 2021 to May 2022. Mosquito pools of ≤200 specimens were screened for alphaviruses and flaviviruses using virus genus-specific primers and reverse transcription quantitative PCR (qRT-PCR). A subset of virus-positive samples was then processed using a mosquito-specific ribosomal RNA depletion method and then sequenced on the Illumina NextSeq. Overall, 54,670 mosquitoes, representing 26 species were screened in 382 pools. Thirty detections of arboviruses were made in 28 pools. Twenty of these positive pools were further characterised using RNA sequencing generating 18 full-length genomes. These full-length sequences belonged to four medically relevant arboviruses: Barmah Forest, Ross River, Sindbis-like and Stratford viruses. Phylogenetic and evolutionary analyses revealed the evolutionary progression of arbovirus lineages over the last 100 years, demonstrating that different epidemiological, immunological, and evolutionary processes may actively shape the evolution of Australian arboviruses. These results underscore the need for more genomic surveillance data to explore the complex evolutionary pressures acting on arboviruses. Overall, our findings highlight the effectiveness of our methodology, which can be applied broadly to enhance arbovirus surveillance in various ecological contexts and improve understanding of transmission dynamics.

Test-retest variability of mesopic microperimetry-associated parameters in patients with retinitis pigmentosa: REPEAT Study Report No. 2.

Understanding test-retest variability (TRV) of mesopic microperimetry is critical for defining meaningful treatment effects in retinitis pigmentosa (RP) trials. This study uniquely evaluates intra- and intervisit TRV and coefficients of repeatability (CoRs) for microperimetry parameters in RP patients with varying best-corrected visual acuity (BCVA) levels. In this single-centre prospective cohort study, RP patients were assessed on two visits, 14.0 days apart. Patients were grouped by BCVA: low (≤20/50 Snellen; ≥0.4 logMAR) or moderate (>20/50 Snellen; <0.4 logMAR). Using Bland-Altman analyses, the CoRs for intra- and intervisit variability were determined for pointwise (dB), mean (dB), and volume sensitivity (dB*deg2) on mesopic microperimetry. Intravisit CoRs for mean, volume, and pointwise sensitivity were 1.7 dB, 353.2 dB*deg2, and 8.6 dB, respectively, in the low-BCVA group (n = 32), and 0.9 dB, 254.5 dB*deg2, and 7.3 dB in the moderate-BCVA group (n = 15). Intervisit CoRs for mean, volume, and pointwise sensitivity were 2.4 dB, 355.2 dB*deg2, and 10.2 dB in the low-BCVA group (n = 31). The moderate-BCVA group (n = 16) showed smaller CoRs of 1.6 dB, 386.8 dB*deg2, and 7.7 dB for mean, volume, and pointwise sensitivity. BCVA and mean sensitivity, but not fixation stability, are predictors of TRV for volume sensitivity. Due to significant TRV, pointwise sensitivity is an unreliable endpoint for RP patients, irrespective of BCVA. Mean sensitivity is suitable as an endpoint when BCVA is relatively preserved. Volume sensitivity provides additional spatial information, and shows promise as a clinical endpoint for assessing macular sensitivity changes on mesopic microperimetry in patients with RP.

MalKinID: A Classification Model for Identifying Malaria Parasite Genealogical Relationships Using Identity-by-Descent.

Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent (IBD) is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, IBD can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for quantitative-trait-locus experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide IBD proportions and IBD segment distributions. MalKinID was calibrated to the genomic data from three laboratory-based genetic crosses (yielding 440 parent-child [PC] and 9060 full-sibling [FS] comparisons). MalKinID identified lab generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for PC and FS and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multi-sample comparisons. Genealogical inference is most powered when 1) outcrossing is the norm or 2) multi-sample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using IBD to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.

Generalizability of lesion detection and segmentation when ScaleNAS is trained on a large multi-organ dataset and validated in the liver.

Tumor assessment through imaging is crucial for diagnosing and treating cancer. Lesions in the liver, a common site for metastatic disease, are particularly challenging to accurately detect and segment. This labor-intensive task is subject to individual variation, which drives interest in automation using artificial intelligence (AI). Evaluate AI for lesion detection and lesion segmentation using CT in the context of human performance on the same task. Use internal testing to determine how an AI-developed model (ScaleNAS) trained on lesions in multiple organs performs when tested specifically on liver lesions in a dataset integrating real-world and clinical trial data. Use external testing to evaluate whether ScaleNAS's performance generalizes to publicly available colorectal liver metastases (CRLM) from The Cancer Imaging Archive (TCIA). The CUPA study dataset included patients whose CT scan of chest, abdomen, or pelvis at Columbia University between 2010-2020 indicated solid tumors (CUIMC, n=5011) and from two clinical trials in metastatic colorectal cancer, PRIME (n=1183) and Amgen (n=463). Inclusion required ≥1 measurable lesion; exclusion criteria eliminated 1566 patients. Data were divided at the patient level into training (n=3996), validation (n=570), and testing (n=1529) sets. To create the reference standard for training and validation, each case was annotated by one of six radiologists, randomly assigned, who marked the CUPA lesions without access to any previous annotations. For internal testing we refined the CUPA test set to contain only patients who had liver lesions (n=525) and formed an enhanced reference standard through expert consensus reviewing prior annotations. For external testing, TCIA-CRLM (n=197) formed the test set. The reference standard for TCIA-CRLM was formed by consensus review of the original annotation and contours by two new radiologists. Metrics for lesion detection were sensitivity and false positives. Lesion segmentation was assessed with median Dice coefficient, under-segmentation ratio (USR), and over-segmentation ratio (OSR). Subgroup analysis examined the influence of lesion size ≥ 10mm (measurable by RECIST1.1) versus all lesions (important for early identification of disease progression). ScaleNAS trained on all lesions achieved sensitivity of 71.4% and Dice of 70.2% for liver lesions in the CUPA internal test set (3,495 lesions) and sensitivity of 68.2% and Dice 64.2% in the TCIA-CRLM external test set (638 lesions). Human radiologists had mean sensitivity of 53.5% and Dice of 73.9% in CUPA and sensitivity of 84.1% and Dice of 88.4% in TCIA-CRLM. Performance improved for ScaleNAS and radiologists in the subgroup of lesions that excluded sub-centimeter lesions. Our study presents the first evaluation of ScaleNAS in medical imaging, demonstrating its liver lesion detection and segmentation performance across diverse datasets. Using consensus reference standards from multiple radiologists, we addressed inter-observer variability and contributed to consistency in lesion annotation. While ScaleNAS does not surpass radiologists in performance, it offers fast and reliable results with potential utility in providing initial contours for radiologists. Future work will extend this model to lung and lymph node lesions, ultimately aiming to enhance clinical applications by generalizing detection and segmentation across tissue types.

Predicting intraoperative 5-ALA-induced tumor fluorescence via MRI and deep learning in gliomas with radiographic lower-grade characteristics.

Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely sampled to avoid undergrading. We aimed to analyze whether a deep learning model could predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI). We evaluated a cohort of 163 glioma patients categorized intraoperatively as fluorescent (n = 83) or non-fluorescent (n = 80). The preoperative MR images of gliomas lacking high-grade characteristics (e.g., necrosis or irregular ring contrast-enhancement) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a tenfold cross-validation procedure. Our proposed approach's performance was assessed using mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively. Our findings highlight the potential of a U-Net model, coupled with a Random Forest classifier, for pre-operative prediction of intraoperative fluorescence. We achieved high accuracy using the features extracted by the U-Net model pre-trained for brain tumor segmentation. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.

Retinal vascular alterations are associated with cognitive function and neuroimaging in white matter hyperintensities

AimTo reveal alterations in retinal structure, vessels, and function, and their association with cognitive function and neuroimaging in white matter hyperintensities (WMH). MethodsThis study enlisted WMH and age-matched healthy controls (HC). All participants underwent six different tests: magnetic resonance imaging (MRI) of the brain, the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), fundus photography, optical coherence tomography (OCT), and visual field testing. Visual field can reflect the function of optic nerve and retina. The peripapillary retinal nerve fiber layer (p-RNFL) was analyzed using OCT. Image J software was employed to measure retinal vascular caliber in fundus photographs and to compute the central retinal artery equivalent (CRAE), central retinal venous equivalent (CRVE) and arteriole-to-venule ratio (AVR). ResultsA total of 90 WMH patients and 93 HC participants. In comparison with the HC, the WMH group exhibited reduced cognitive function scores (MoCA: P < 0.001; MMSE: P < 0.001), narrower retinal arteries (P < 0.001), smaller AVR (P < 0.001) and thinner p-RNFL thickness (total: P = 0.026; temporal: P = 0.006). About visual field, both univariate and multivariate analysis showed that mean sensitivity decreased, and mean defect increased in WMH group (P < 0.05). Additionally, correlation analysis indicated a positive correlation between CRAE and AVR with MMSE and MoCA score (r = 0.424–0.57, P < 0.001) and a negative correlation with Fazekas score (CRAE: r = −0.515, P < 0.001; AVR: r = −0.554, P < 0.001), and p-RNFL was negatively correlated with Fazekas score (total p-RNFL: r = −0.192, P = 0.009; temporal p-RNFL: r = −0.217, P = 0.003). Notably, no significant correlation was found between cognitive function and p-RNFL. ConclusionWMH group exhibit narrower retinal arteries, smaller arteriole-to-venule ratio, damaged p-RNFL and visual function. These alterations in retinal vessels are associate with both neuroimaging and cognitive function. Our results suggest that retinal imaging could serve as a valuable instrument for evaluating WMH and provides some new approaches to study the characteristic markers of WMH.

A graphene quantum dots based dual-modal fluorometric and visualized detection of copper ions

In this study, we present a dual-modal fluorometric and visualized detection method for Cu2+ ion, leveraging the synergistic properties of graphene quantum dots (GQDs) and Cu2+ ion catalyzed Fenton-like reaction. The Fenton-like reaction of Cu2+ ions and ascorbate generates highly reactive hydroxyl radical (·OH), which effectively disrupt the structure of GQDs, leading to fluorescence quenching. Under optimized conditions, the fluorescence quenching degree exhibited a linear correlation with Cu2+ concentration within the range of 40 to 2000 nM, enabling the detection of Cu2+ ions as low as 40 nM. Furthermore, we demonstrated the feasibility of semi-quantitative visual detection of Cu2+ ion concentrations in water using a portable ultraviolet instrument. The method achieved a minimum detectable concentration of Cu2+ ion as low as 10 μM, surpassing the maximum contaminant level goals of 20.47 μM set by the EPA and the guideline value of 31.47 μM recommended by the WHO. As such, this approach holds promise as a point-of-care testing (POCT) method for Cu2+ ion detection during copper pollution emergency events in water. Additionally, this method can be adapted for the detection of ascorbic acid. Our findings showcase the potential of this dual-modal detection approach, offering a sensitive, rapid, and efficient means for detecting Cu2+ ion, thereby contributing to environmental monitoring and public health applications.

Accuracy of transabdominal ultrasound to diagnose functional constipation and fecal impaction in children: a systematic review and meta-analysis.

Functional constipation is common in children and accurate diagnostic methods are essential for early diagnosis and effective management. The diagnostic accuracy of transabdominal ultrasound to diagnose functional constipation is unclear. To evaluate the diagnostic accuracy of transverse rectal diameter measurement via transabdominal ultrasound in diagnosing children with functional constipation and in identifying fecal impaction. Electronic databases were searched from inception to March 2023. Original studies investigating the diagnostic accuracy of measuring transverse rectal diameter via transabdominal ultrasound, including children with and without functional constipation, or with and without fecal impaction were included. Data extraction and quality assessment were performed independently by two reviewers. Sixteen studies were included (n = 1,801 children, 0-17years). Thirteen studies investigated the diagnostic accuracy for functional constipation, and five for fecal impaction. High risk of bias was found across the majority of studies mainly due to un-blinded case-control designs. Cut-off transverse rectal diameter values to diagnose functional constipation ranged from 2.4cm to 3.8cm. Meta-analysis (seven studies, n = 509 children) estimated mean sensitivity and specificity to diagnose functional constipation were 0.68 (95% confidence interval (CI) 0.55-0.78) and 0.81 (95% CI 0.71-0.88), respectively. Meta-analysis of diagnostic accuracy of identifying fecal impaction was not feasible. Studies reported a sensitivity and specificity ranging between 68-100% and 83-100%, respectively. Transabdominal ultrasound may be a valuable non-invasive diagnostic tool to diagnose functional constipation by measuring transverse rectal diameter and identifying fecal impaction in children. Heterogeneous study methods and lack of age-dependent normal values impair current clinical recommendations. Future research should focus on separating age groups and developing a standardized protocol.

Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Retinal blood vessel segmentation is a crucial process in medical image analysis. However, it is often challenging due to the variation in color, shape, intensity, size, and contrast of blood vessels. Most of the relevant studies concentrate on algorithms based on supervised learning and few on deep learning. However, due to the several challenges in retinal image acquisition, these algorithms cannot deliver the highest possible level of accuracy. Therefore, this paper implements retinal blood vessel segmentation and classification (RBVSC) methods using density-based fuzzy C-means clustering and vessel neighborhood-connected components, hereafter denoted as DBFCM-VNCC. Initially, the given retinal images are preprocessed using the contrast enhancement method that involves Histogram Equalization with Variable Enhancement Degree (HEVED), selecting the appropriate color channel, optic disc elimination, and using a Gaussian filter, which removes the noise or artifacts from the retinal images. Then, a fully fuzzy C-means clustering is used for coarse segmentation of vessel lesions, which can detect the affected blood vessel features quite efficiently. Finally, structure-based algorithms based on vessel neighborhood-connected components based on mathematical dilation operators and local thicknesses are used to obtain accurate skeletonization and segmentation of the retinal vessels. The algorithm was assessed using three open-access retinal image databases: DRIVE, CHASE_DB1, and HRF, where it achieved mean sensitivity, specificity, accuracy, area overlap measure, and error rate scores of 98.16%, 98.74%, 97.68%, and 4.54%; 98.25%, 98.81%, 97.68%, 97.86%, and 2.14%; 98.22%, 98.78%, 97.56%, 97.40%, and 2.60% for segmenting the retinal vessel. This demonstrates the effectiveness of the proposed DBFCM-VNCC techniques.

MAIA and Humphrey Perimetry Differ in Their Estimation of Homonymous Visual Field Defects.

To assess whether the Humphrey Visual Field Analyzer (HFA) and the Macular Integrity Assessment instrument (MAIA) provide equivalent estimates of visual deficit size, severity, and progression in cortically blinded participants. Reliable, monocular 10-2 HFA and MAIA fields were collected at baseline, and after a blind-field training intervention (n = 54) or no intervention (n = 6) in adult participants with occipital strokes. Binocular HFA and MAIA mean sensitivities (MS) were first computed, before creating binocular maps of visual sensitivity for each perimetry system to calculate deficit areas, using a unitary, published, less than 10 dB criterion to define blindness. We contrasted HFA/MAIA MS and deficit area at individual study visits, together with change in these measures between visits. At individual visits, MS and deficit areas were well-correlated, but there were systematic differences between machines, greater for the intact than impaired hemifields, with the MAIA overestimating areas of visual deficit relative to the HFA. Between visits, the two perimeters' assessment of change in MS was correlated, but change in the deficit area was not, despite good test reliability. Reliable HFA and MAIA tests produce well-correlated but systematically offset estimates of visual sensitivity and deficit area in patients with homonymous field defects, which can impact assessment of progression. Cortically blinded patients exhibited comparable changes in sensitivity (spontaneous and training induced) when assessed using Humphrey and fundus-controlled MAIA perimetry. However, the photopic vs. mesopic nature of these tests impacted participant performance and suggests that custom, machine-specific criteria are needed to comparably define blindness on both systems.

Abstract B012: M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies

Abstract Background: DNA methylation-based classification has been transformative in the molecular diagnostics of pediatric central nervous system (CNS) tumors. Current diagnostic pipelines integrate histopathology and molecular tools in accordance with the WHO classification of CNS tumors which rely on the availability of tissue specimens. However, some tumors are not amenable to neurosurgical resection due to their high-risk locations and tissue collection is not routinely performed at relapse. In addition, molecular biomarkers are largely lacking for longitudinal disease monitoring in pediatric neuro-oncology. Liquid biopsies (LBs) have emerged as a minimally invasive, longitudinal source of tumor-derived cell-free DNA (cfDNA) ideally suited for detecting minimal residual disease (MRD). In addition, LBs provide a sensitive means of studying tumor evolution at high temporal resolution. Success rates of LB analyses in neuro-oncology have varied drastically between studies, warranting the deployment of more robust and reproducible assays. Methods: We applied a novel cfDNA methylation-based workflow to a large cohort of cerebrospinal fluid (CSF) samples collected from pediatric brain tumor patients (n&amp;gt;200 patients). Enzymatic methylation sequencing (EM-seq) was adapted for CSF-derived cfDNA inputs in the picogram range, enabling the acquisition of genome-wide cfDNA methylation profiles across the cohort. Utilizing non-oncological cfDNA profiles and CNS tumor DNA methylation datasets as a reference, we developed an innovative computational pipeline that incorporates DNA methylation imputation and deep learning of a neural network. In addition, deconvolution of cfDNA profiles facilitated classification of low tumor burden samples, including those with balanced genomes that would have been missed using previous generation assays. Results: Our CSF-based classifier, M-PACT (Methylation-based Predictive Algorithm for CNS Tumors), yielded accurate tumor detection and entity prediction (AUC=0.9) in our benchmarking cohort (n&amp;gt;100 CSF samples). Proof-of-concept studies showed the potential of this methodology to track tumor evolution in serial CSF samples at both the genetic and epigenetic level, illuminating potential mechanisms driving treatment resistance and recurrence. Conclusions: Collectively, this study provides the framework for robust methylation- based tumor detection and classification of CSF LBs. M-PACT can be applied to aid in tumor diagnostics and to detect MRD during follow-up, warranting prospective validation of its broad applicability and clinical utility in future trials. Citation Format: Tom T. Fischer, Kyle S. Smith, Katie Han, Anna Kostecka, Hong Lin, Daniel Senfter, Tatjana Wedig, Nathalie Schwarz, Natalia Stepien, Sibylle Madlener, Christine Haberler, Esther Hulleman, Sandeep K. Dhanda, Stefan M. Pfister, Santhosh Upadhyaya, Amar Gajjar, Giles W. Robinson, Joonas Haapasalo, Hannu Haapasalo, Kristiina Nordfors, Johannes Gojo, Kendra K. Maass, Kristian W. Pajtler, Paul A. Northcott. M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B012.

Using non-continuous accelerometry to identify cryptic nesting events of Galapagos giant tortoises

BackgroundTriaxial accelerometers have revolutionized wildlife research by providing an unprecedented understanding of the behavior of free-living animals. Machine learning is often applied to acceleration data to classify diverse animal behaviors across taxa. However, the high frequency, continuous data collection typically favored for behavioral classification studies often generates very large data sets, which may inhibit remote data acquisition and make data storage challenging. Coarse-frequency sampling or non-continuous bursts of acceleration data reduce these problems. To analyze such data, a suite of variables that summarize key features of the behavior of interest can be generated. These variables can then be used in numerous classification approaches, accommodating variation in data collection methods or sampling regimes. We demonstrate the potential for non-continuous accelerometer data to identify long-duration behavior and employ machine learning to classify the nesting behaviors of the critically endangered eastern Santa Cruz giant tortoise (Chelonoidis donfaustoi).ResultsWe field validated 112 nesting events from 21 giant tortoises. We then derived summary statistics based on accelerometry (e.g., overall dynamic body acceleration, metrics comparing acceleration before and after the probable event) and used them as inputs for Random Forest and Boosted Regression Tree classification algorithms. Our models produced a harmonic mean of precision and sensitivity (F1-score) of 0.91. We tested the generality of our model and found that the model performs well when applied to both novel individuals and years. The most important variable in accurately classifying data sequences was the proportion of acceleration data bursts above an activity threshold followed by the average overall dynamic body acceleration value of the bursts.ConclusionsThese results demonstrate the feasibility and efficacy of using non-continuous accelerometer data to identify prolonged, biologically relevant behaviors in free-living wildlife. By using summary variables that do not require continuous sampling, this approach facilitates long-term monitoring of animal behavior. Similar methodology has potential to inform priority questions in ecology and conservation, such as predicting wildlife responses to climate change and identifying critical habitats, with applications across diverse species and behaviors.

Multimodal neuroimaging-based prediction of Parkinson’s disease with mild cognitive impairment using machine learning technique

This study aimed to identify potential markers that can predict Parkinson’s disease with mild cognitive impairment (PDMCI). We retrospectively collected general demographic data, clinically relevant scales, plasma samples, and neuroimaging data (T1-weighted magnetic resonance imaging (MRI) data as well as resting-state functional MRI [Rs-fMRI] data) from 173 individuals. Subsequently, based on the aforementioned multimodal indices, a support vector machine was employed to investigate the machine learning (ML) classification of PD patients with normal cognition (PDNC) and PDMCI. The performance of 29 classifiers was assessed based on various combinations of indicators. Results demonstrated that the optimal classifier in the validation set was composed by clinical + Rs-fMRI+ neurofilament light chain, exhibiting a mean Accuracy of 0.762, a mean area under curve of 0.840, a mean sensitivity of 0.745, along with a mean specificity of 0.783. The ML algorithm based on multimodal data demonstrated enhanced discriminative ability between PDNC and PDMCI patients.

Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study

BackgroundPredicting the progression of hip osteoarthritis (OA) remains challenging, and no reliable predictive method has been established. This study aimed to develop an artificial intelligence (AI) model to predict hip OA progression via plain radiographs and patient data and to determine its accuracy.MethodsThis retrospective study utilized anteroposterior pelvic radiographs of consecutive patients with hip OA who underwent primary unilateral total hip arthroplasty. Radiographs diagnosed with Kellgren–Lawrence (KL) grade 0–2 were extracted from 361 patients and 1697 images. This AI model was developed to predict whether OA would progress from KL grade 0–2 to KL grade ≥ 3 within n years (n = 3, 4, 5). A gradient-boosting decision tree approach was utilized according to feature extractions obtained by a convolutional neural network from radiographs and patient data (height, body weight, sex, age, and KL grade given by an orthopedic surgeon) with five-fold cross-validation. The model performance was assessed using accuracy, specificity, sensitivity, and the area under the receiver operating characteristic curve (AUC).ResultsThe mean accuracy, specificity, sensitivity, and AUC of our prediction model were, respectively, 81.8%, 88.0%, 66.7%, and 0.836 for 3 years; 79.8%, 85.0%, 71.6%, and 0.836 for 4 years; and 78.5%, 80.4%, 76.9%, and 0.846 for 5 years.ConclusionsThe proposed AI model performed adequately in predicting hip OA progression and may be clinically applicable with additional datasets and validation.

Analysis of the Predictability of Postoperative Meningioma Resection Status Based on Clinical Features.

Background: Our aim was to investigate the predictability of postoperative meningioma resection status based on clinical features. Methods: We examined 23 clinical features to assess their effectiveness in distinguishing gross total resections (GTR) from subtotal resections (STR). We analyzed whether GTR/STR cases are better predictable if the classification is based on the Simpson grading or the postoperative operative tumor volume (POTV). Results: Using a study cohort comprising a total of 157 patients, multivariate models for the preoperative prediction of GTR/STR outcome in relation to Simpson grading and POTV were developed and subsequently compared. Including only two clinical features, our models showed a notable discriminatory power in predicting postoperative resection status. Our final model, a straightforward decision tree applicable in daily clinical practice, achieved a mean AUC of 0.885, a mean accuracy of 0.866, a mean sensitivity of 0.889, and a mean specificity of 0.772 based on independent test data. Conclusions: Such models can be a valuable tool both for surgical planning and for early planning of postoperative treatment, e.g., for additional radiotherapy/radiosurgery, potentially required in case of subtotal resections.

Smaller is better? Compact vs. Conventional gamma camera for sentinel lymph node localization in patients with breast cancer.

Sentinel lymph node biopsy (SLNB) has been recognized as "the gold standard" for axillary staging in early breast cancer patients with clinically negative lymph nodes, resulting in significant morbidity decrease and quality of life improvement. This study aims to validate the performance of a newly developed handheld portable gamma camera (PGC) produced by Imagensys (Italy), in detecting and locating sentinel lymph nodes (SLNs) during the preoperative and intraoperative phases in breast cancer patients compared to conventional lymphoscintigraphy. Adult female patients with histologically confirmed breast cancer, candidates for surgery and SLNB, were prospectively enrolled in this open-label, pre-marketing clinical trial. All patients underwent pre- operative assessment using both the PGC and conventional lymphoscintigraphy. The performance of the two devices was compared using the Poisson regression model for incidence rate ratios (IRRs). The intrinsic sensitivity of the devices was compared using the Wilcoxon Ranked Sign Test. The utility of PGC during intra-operative procedures was also evaluated. The manoeuvrability of the devices was evaluated using operator-satisfaction questioner. Sixty-eight patients (median age 50 years, BMI 21.4) were enrolled, including two patients with bilateral breast cancer, who underwent SLNB on both axillae. The PGC demonstrated superior preoperative lymph node detection rate (IRR 8.01, 95% CI 6.11-10.50; p < 0.0001) and intrinsic device sensitivity (mean counts per second 409 ± 286 vs. 255 ± 1173 for conventional device, p = 0.0003) compared to the conventional gamma camera. Intra-operative assessment with PGC was performed in 62 patients and no additional lymph nodes were visualised. However, the conventional gamma camera demonstrated superior manoeuvrability (p < 0.0001). The PGC handheld gamma camera showed promising results for preoperative SLN assessment in patients with breast cancer. The limited manoeuvrability may be related to the operator's experience leading to higher inter-operator variability. Appropriate training and frequent use of nuclear medicine and surgical equipment could overcome this limitation.

Molecular Imaging Biomarkers for Early Cancer Detection: A Systematic Review of Emerging Technologies and Clinical Applications.

Early cancer detection is crucial for improving patient outcomes. Molecular imaging biomarkers offer the potential for non-invasive, early-stage cancer diagnosis. To evaluate the effectiveness and accuracy of molecular imaging biomarkers for early cancer detection across various imaging modalities and cancer types. A comprehensive search of PubMed/MEDLINE, Embase, Web of Science, Cochrane Library, and Scopus was performed, covering the period from January 2010 to December 2023. Eligibility criteria included original research articles published in English on molecular imaging biomarkers for early cancer detection in humans. The risk of bias for included studies was evaluated using the QUADAS-2 tool. The findings were synthesized through narrative synthesis, with quantitative analysis conducted where applicable. In total, 50 studies were included. Positron emission tomography (PET)-based biomarkers showed the highest sensitivity (mean: 89.5%, range: 82-96%) and specificity (mean: 91.2%, range: 85-100%). Novel tracers such as [68Ga]-PSMA for prostate cancer and [18F]-FES for breast cancer demonstrated promising outcomes. Optical imaging techniques showed high specificity in intraoperative settings. Molecular imaging biomarkers show significant potential for improving early cancer detection. Integration into clinical practice could lead to earlier interventions and improved outcomes. Further research is needed to address standardization and cost-effectiveness.

Screening ability of dental students to detect osteoporosis on dental panoramic radiographs

ObjectivesPostmenopausal women with osteoporosis are frequently underdiagnosed. In Japan, general dental practitioners have begun using the cortical shape of the mandible on dental panoramic radiographs (PRs) to identify and refer women at risk of osteoporosis to medical professionals. It remains unclear whether dental students, after relevant education, possess the ability to identify these at-risk individuals. This study evaluated the ability of dental students to screen for osteoporosis on PRs. MethodsA cohort of 113 fifth-year dental students participated in a lecture on osteoporosis screening using PRs. The students then categorized the mandibular inferior cortex on PRs from 30 postmenopausal women (11 with osteoporosis) as normal, mildly to moderately eroded, or severely eroded. Interobserver agreement between the students and an expert oral radiologist using two cortical groups (normal to moderately eroded and severely eroded) was assessed through kappa statistics. Screening ability for osteoporosis detection by identifying severely eroded cortices was calculated and compared with that of the Osteoporosis Self-assessment Tool for Asians (OSTA). ResultsTwenty-one (18.6%) students demonstrated moderate to substantial agreement with the expert. The mean sensitivity, specificity, and positive and negative predictive values for these students in identifying osteoporosis were 60.2%, 88.0%, 76.3%, and 80.0%, respectively. For the OSTA, sensitivity, specificity, and positive and negative predictive values were 27.3%, 68.4%, 33.3%, and 61.9%, respectively. ConclusionsEven with minimal education, approximately one-fourth of dental students may accurately identify postmenopausal women with osteoporosis on PRs, outperforming questionnaire-based screening tools.