scholarly journals The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Guruduth Banavar ◽  
Oyetunji Ogundijo ◽  
Ryan Toma ◽  
Sathyapriya Rajagopal ◽  
Yen Kai Lim ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Average Risk ◽  
Operating Characteristics ◽  
Early Diagnostics ◽  
New Era ◽  
Oral Cancers ◽  
Non Invasive ◽  
Roc Area ◽  
Stage 1 ◽  
Invasive Diagnostics

AbstractDespite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

scholarly journals The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer

2020 ◽  
Author(s):  
Guruduth Banavar ◽  
Oyetunji Ogundijo ◽  
Ryan Toma ◽  
Sathyapriya Rajagopal ◽  
Yenkai Lim ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Average Risk ◽  
Operating Characteristics ◽  
Early Diagnostics ◽  
New Era ◽  
Oral Cancers ◽  
Novel Taxa ◽  
Roc Area ◽  
Stage 1 ◽  
Invasive Diagnostics

Abstract Despite advances in cancer treatment, the five-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n=433) collected from oral premalignant disorders (OPMD), OC patients (n=71) and normal controls (n=171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of previously known and novel taxa and functional pathways associated with OC. For the first time, we demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

scholarly journals Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

2019 ◽  
Vol 11 (17) ◽  
pp. 2049 ◽  
Author(s):  
Moeini Rad ◽  
Abkar ◽  
Mojaradi
Keyword(s):  
Target Detection ◽  
Area Under The Curve ◽  
Image Clustering ◽  
False Alarms ◽  
Detection Accuracy ◽  
Operating Characteristics ◽  
Autocorrelation Matrix ◽  
Target Energy ◽  
Roc Area ◽  
The Impact

Feature/band selection (FS/BS) for target detection (TD) attempts to select features/bands that increase the discrimination between the target and the image background. Moreover, TD usually suffers from background interference. Therefore, bands that help detectors to effectively suppress the background and magnify the target signal are considered to be more useful. In this regard, three supervised distance-based filter FS methods are proposed in this paper. The first method is based on the TD concept. It uses the image autocorrelation matrix and the target signature in the detection space (DS) for FS. Features that increase the first-norm distance between the target energy and the mean energy of the background in DS are selected as optimal. The other two methods use background modeling via image clustering. The cluster mean spectra, along with the target spectrum, are then transferred into DS. Orthogonal subspace projection distance (OSPD) and first-norm distance (FND) are used as two FS criteria to select optimal features. Two datasets, HyMap RIT and SIM.GA, are used for the experiments. Several measures, i.e., true positives (TPs), false alarms (FAs), target detection accuracy (TDA), total negative score (TNS), and the receiver operating characteristics (ROC) area under the curve (AUC) are employed to evaluate the proposed methods and to investigate the impact of FS on the TD performance. The experimental results show that our proposed FS methods, as compared with five existing FS methods, have improving impacts on common target detectors and help them to yield better results.

scholarly journals Circulating miRNA as Biomarkers for Colorectal Cancer Diagnosis and Liver Metastasis

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 341
Author(s):  
Farah J. Nassar ◽  
Zahraa S. Msheik ◽  
Maha M. Itani ◽  
Remie El Helou ◽  
Ruba Hadla ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Diagnostic Accuracy ◽  
Area Under The Curve ◽  
Stage Iv ◽  
Screening Tests ◽  
Operating Characteristics ◽  
Diagnostic Biomarkers ◽  
Circulating Mirna ◽  
Non Invasive

Colorectal cancer (CRC) is the second leading cause of cancer deaths worldwide. Stage IV CRC patients have poor prognosis with a five-year survival rate of 14%. Liver metastasis is the main cause of mortality in CRC patients. Since current screening tests have several drawbacks, effective stable non-invasive biomarkers such as microRNA (miRNA) are needed. We aim to investigate the expression of miRNA (miR-21, miR-19a, miR-23a, miR-29a, miR-145, miR-203, miR-155, miR-210, miR-31, and miR-345) in the plasma of 62 Lebanese Stage IV CRC patients and 44 healthy subjects using RT-qPCR, as well as to evaluate their potential for diagnosis of advanced CRC and its liver metastasis using the Receiver Operating Characteristics (ROC) curve. miR-21, miR-145, miR-203, miR-155, miR-210, miR-31, and miR-345 were significantly upregulated in the plasma of surgery naïve CRC patients when compared to healthy individuals. We identified two panels of miRNA that could be used for diagnosis of Stage IV CRC (miR-21 and miR-210) with an area under the curve (AUC) of 0.731 and diagnostic accuracy of 69% and liver metastasis (miR-210 and miR-203) with an AUC = 0.833 and diagnostic accuracy of 72%. Panels of specific circulating miRNA, which require further validation, could be potential non-invasive diagnostic biomarkers for CRC and liver metastasis.

scholarly journals Simple and non-invasive screening method for diabetes based on myoinositol levels in urine samples collected at home

2020 ◽  
Vol 8 (1) ◽  
pp. e000984
Author(s):  
Misaki Takakado ◽  
Yasunori Takata ◽  
Fumio Yamagata ◽  
Michiko Yaguchi ◽  
Go Hiasa ◽  
...  
Keyword(s):  
Test Meal ◽  
Screening Method ◽  
Area Under The Curve ◽  
Tolerance Test ◽  
Urine Samples ◽  
Oral Glucose ◽  
Operating Characteristics ◽  
Non Invasive ◽  
Before And After ◽  
At Home

ObjectiveTo establish a simple screening method for diabetes based on myoinositol (MI) in urine samples collected at home.Research design and methodsInitially, we evaluated the stability of urinary MI (UMI) at room temperature (RT; 25°C) and 37°C in 10 outpatients with type 2 diabetes. We then enrolled 115 volunteers without a current or history of diabetes. In all subjects, glucose intolerance was diagnosed by 75 g oral glucose tolerance test (75gOGTT). To assess the association between UMI or urine glucose (UG) and plasma glucose (PG), urine samples were also collected at 0 and 2 hours during 75gOGTT. All the subjects collected urine samples at home before and 2 hours after consuming the commercially available test meal. UMI levels at wake-up time (UMIwake-up), before (UMIpremeal) and 2 hours after the test meal (UMI2h-postprandial) were measured using an enzymatic method. ΔUMI was defined as UMI2h-postprandial minus UMIpremeal.ResultsDiffering from UG, UMI was stable at RT and 37°C. UMI was increased linearly along with an increase in PG, and no threshold for UMI was observed. UMI was closely associated with blood glucose parameters obtained from a 75gOGTT and hemoglobin A1c (HbA1c) at hospital after adjustment for age, sex, body mass index and serum creatinine. UMIwake-up, UMIpremeal, UMI2h-postprandial and ΔUMI at home were higher in diabetic subjects than non-diabetic subjects even after the above adjustment. Receiver operating characteristics curve (ROC) analyses revealed that for the screening of diabetes, the area under the curve for ROC for UMI2h-postprandial and ΔUMI (0.83 and 0.82, respectively) were not inferior to that for HbA1c ≥48 mmol/mol, which is the American Diabetes Association (ADA) criteria for diabetes.ConclusionsMI measurement in urine samples collected at home before and after the meal would be a simple, non-invasive and valuable screening method for diabetes.

scholarly journals Non-invasive diagnostics in fossils - Magnetic Resonance Imaging of pathological belemnites

2005 ◽  
Vol 2 (2) ◽  
pp. 133-140 ◽  
Author(s):  
D. Mietchen ◽  
H. Keupp ◽  
B. Manz ◽  
F. Volke
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Soft Tissue ◽  
Clinical Diagnostics ◽  
Resonance Imaging ◽  
Non Invasive ◽  
Physiological Processes ◽  
Magnetic Resonance Imaging Mri ◽  
Invasive Diagnostics

Abstract. For more than a decade, Magnetic Resonance Imaging (MRI) has been routinely employed in clinical diagnostics because it allows non-invasive studies of anatomical structures and physiological processes in vivo and to differentiate between healthy and pathological states, particularly of soft tissue. Here, we demonstrate that MRI can likewise be applied to fossilized biological samples and help in elucidating paleopathological and paleoecological questions: Five anomalous guards of Jurassic and Cretaceous belemnites are presented along with putative paleopathological diagnoses directly derived from 3D MR images with microscopic resolution. Syn vivo deformities of both the mineralized internal rostrum and the surrounding former soft tissue can be traced back in part to traumatic events of predator-prey-interactions, and partly to parasitism. Besides, evidence is presented that the frequently observed anomalous apical collar might be indicative of an inflammatory disease. These findings highlight the potential of Magnetic Resonance techniques for further paleontological applications.

scholarly journals Variability of serum IgG sialylation and galactosylation degree in women with advanced endometriosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katarzyna Sołkiewicz ◽  
Hubert Krotkiewski ◽  
Marcin Jędryka ◽  
Ewa M. Kratz
Keyword(s):  
Cluster Analysis ◽  
Sialic Acid ◽  
Control Group ◽  
Clinical Tool ◽  
Non Invasive ◽  
Healthy Women ◽  
Serum Igg ◽  
And Cluster Analysis ◽  
Invasive Diagnostics ◽  
And Control

AbstractEndometriosis is an inflammatory disease which diagnostics is difficult and often invasive, therefore non-invasive diagnostics methods and parameters are needed for endometriosis detection. The aim of our study was to analyse the glycosylation of native serum IgG and IgG isolated from sera of women classified as: with endometriosis, without endometriosis but with some benign ginecological disease, and control group of healthy women, in context of its utility for differentiation of advanced endometriosis from the group of healthy women. IgG sialylation and galactosylation/agalactosylation degree was determined using specific lectins: MAA and SNA detecting sialic acid α2,3- and α2,6-linked, respectively, RCA-I and GSL-II specific to terminal Gal and terminal GlcNAc, respectively. The results of ROC and cluster analysis showed that the serum IgG MAA-reactivity, sialylation and agalactosylation factor may be used as supplementary parameters for endometriosis diagnostics and could be taken into account as a useful clinical tool to elucidate women with high risk of endometriosis development. Additionally, we have shown that the analysis of native serum IgG glycosylation, without the prior time-consuming and expensive isolation of the protein, is sufficient to differentiation endometriosis from a group of healthy women.

scholarly journals Integrated Devices for Non‐Invasive Diagnostics

2021 ◽  
pp. 2010388
Author(s):  
Hatice Ceren Ates ◽  
Anna Brunauer ◽  
Felix Stetten ◽  
Gerald A. Urban ◽  
Firat Güder ◽  
...  
Keyword(s):  
Non Invasive ◽  
Integrated Devices ◽  
Invasive Diagnostics

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 870
Author(s):  
Alessandro Bevilacqua ◽  
Diletta Calabrò ◽  
Silvia Malavasi ◽  
Claudio Ricci ◽  
Riccardo Casadei ◽  
...  
Keyword(s):  
Neuroendocrine Tumour ◽  
Area Under The Curve ◽  
Tumour Volume ◽  
Assessment Method ◽  
Second Order ◽  
Tumour Grade ◽  
Non Invasive ◽  
Pet Ct ◽  
Median Test ◽  
Fdg Pet Ct

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

scholarly journals Multi-scale characterizations of colon polyps via computed tomographic colonography

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Weiguo Cao ◽  
Marc J. Pomeroy ◽  
Yongfeng Gao ◽  
Matthew A. Barish ◽  
Almas F. Abbasi ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Texture Features ◽  
Learning Model ◽  
Colon Polyps ◽  
Texture Descriptor ◽  
Operating Characteristics ◽  
Computed Tomographic ◽  
Multi Scale ◽  
Definition Of ◽  
Occurrence Matrix

AbstractTexture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

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