scholarly journals P01.03 VOC pattern recognition of lung cancer: a comparative evaluation of different dog- and eNose-based strategies using different sampling materials

2020 ◽  
Vol 8 (Suppl 2) ◽  
pp. A9.1-A9
Author(s):  
W Biehl ◽  
H Schmetzer ◽  
R Koczulla ◽  
A Hattesohl ◽  
R Jörres ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Methodological Approach ◽  
Breath Sample ◽  
Dog Training ◽  
Breath Sampling ◽  
Carrier Materials ◽  
Glass Tubes ◽  
Working Dogs ◽  
Reference Measurements

BackgroundIt has been reported that canine scent tests offer the possibility to screen for cancer. Assuming that breath samples can be collected with carrier materials, we tested the practicability of different carrier materials to be presented to dogs, and validated and compared results with an eNose. Moreover, we hypothesised that cancer detection ability of dogs differs according to their working experience.Materials and MethodsIn a methodological approach two dog teams participated, one using experienced working dogs and the other ordinary household dogs to find discover which dogs were better qualified and the best training method. To find best carrier material for breath sampling we compared charcoal containing glass tubes with fleece masks. In a second validating part, experienced working dogs were trained with improved training strategies. For breath sampling two different, previously successfully tested fleece-based carrier materials were used: one was used with the dog team and both materials were compared with eNose.ResultsIn the first part of the study it was shown overall that experienced working dogs performed better to family dogs and the dogs achieved a sensitivity of 45–59% and a specificity of 45–69%. Charcoal based breath sample carrier materials did not qualify for detection of VOC by dogs. In the second part of the study, the dogs achieved a specificity of 83% and a sensitivity of 56%, but with considerable differences between individual dogs. The eNose provided a specificity of 97% for both fleece based carrier materials and a sensitivity of 89% for fleece filled glass tubes and 100% for earloop masks. Measurements of breath samples collected directly in respiratory bags as reference measurements achieved a sensitivity and specificity of 100%.ConclusionsOur data confirmed that diagnostic accuracy of dogs depended on the type of dog training and on the carrier materials. A comparison of breath samples analysis with an eNose achieved better results for both, sensitivity and specificity, than for dogs. The use of fleece masks or fleeces in glass tubes as a sampling material can be recommended as successful VOC carriers, encouraging their use for clinical screenings.Disclosure InformationW. Biehl: None. H. Schmetzer: None. R. Koczulla: None. A. Hattesohl: None. R. Jörres: None. T. Duell: None. U. Althöhn: None.

An Information-theoretical Model for Breast Cancer Detection

2008 ◽  
Vol 47 (04) ◽  
pp. 322-327 ◽  
Author(s):  
D. Blokh ◽  
N. Zurgil ◽  
I. Stambler ◽  
E. Afrimzon ◽  
Y. Shafran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Hamming Distance ◽  
Formal Model ◽  
Methodological Approach ◽  
Breast Cancer Detection ◽  
Receptor Expression ◽  
Diagnostic Model ◽  
Model Parameters ◽  
Two Stages

Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

scholarly journals Electronic Nose for Bladder Cancer Detection

2021 ◽  
Vol 5 (1) ◽  
pp. 22
Author(s):  
Heena Tyagi ◽  
Emma Daulton ◽  
Ayman S. Bannaga ◽  
Ramesh P. Arasaradnam ◽  
James A. Covington
Keyword(s):  
Bladder Cancer ◽  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Electronic Nose ◽  
Random Forest Classifier ◽  
Urine Samples ◽  
Sparse Logistic Regression ◽  
High Separation

This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.

Can a Broad Molecular Screen Based on Circulating Tumor DNA Aid in Early Cancer Detection?

2020 ◽  
Vol 5 (6) ◽  
pp. 1372-1377
Author(s):  
Clare Fiala ◽  
Eleftherios P Diamandis
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Early Cancer ◽  
Circulating Tumor Dna ◽  
Genetic Alterations ◽  
Population Screening ◽  
Early Cancer Detection ◽  
Potential Applicability ◽  
American Society ◽  
Tumor Dna

Abstract Early detection of cancer has been a major research focus for almost a century. Current methods for early cancer detection suffer from suboptimal sensitivity and specificity, especially when used for population screening. For most major cancers, including breast, prostate, lung, ovarian, and pancreatic cancer, population screening is still controversial or is not recommended by expert bodies. Circulating tumor DNA (ctDNA) is an exciting new cancer biomarker with potential applicability to all cancer types. Recent investigations have shown that genetic alterations or epigenetic modifications in ctDNA could be used for cancer detection with a liquid biopsy (i.e., a tube of blood). Tests based on ctDNA have attracted considerable attention for various applications, such as patient management, prognosis, early diagnosis, and population screening. Recently, new biotechnology companies were founded, with the goal of revolutionizing early cancer detection by using ctDNA. We previously examined this technology, as published by various academic laboratories and of one leading company, Grail, and drew attention to potential obstacles. After 3 years of intense development, this technology seems to have made some progress. Here, we will analyze the latest clinical data presented by Grail in October 2019, during the inaugural American Society of Clinical Oncology (ASCO) 2019 Breakthrough Conference. Despite considerable technical improvements, it seems that the sensitivity and specificity of the Grail test as a pan-cancer screening tool are still too low for clinical use. The prospects that this test could be further improved are also discussed.

Potential Use of Biomarkers to Augment Clinical Decisions for the Early Detection of Breast Cancer

2014 ◽  
Vol 10 (02) ◽  
pp. 103 ◽  
Author(s):  
Alan B Hollingsworth ◽  
David E Reese ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Survival Rates ◽  
High Sensitivity ◽  
Standard Of Care ◽  
Serum Biomarkers ◽  
Breast Cancer Detection ◽  
Breast Cancers

Breast cancer remains a significant worldwide health problem, despite the fact that early detection is associated with excellent survival rates. Currently, a substantial proportion of breast cancers are not detected using routine screening. Therefore, there is a need to identify a technology that can improve the precision and accuracy of early breast cancer detection. Biomarkers are attractive in that they can potentially detect early cancers with high sensitivity, while distinguishing between benign disease and invasive cancers. Many commonly used serum biomarkers have limited use in screening assays for breast cancer as single agents due to the heterogeneous nature of breast cancer. However, the use of protein panels that detect multiple serum biomarkers offer the potential for enhanced sensitivity and specificity in a clinical setting. Recently, a serum biomarker test comprising five serum biomarkers for breast cancer was clinically validated and showed high sensitivity and specificity. Additional panels have been developed that combine serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAb) to further enhance the clinical utility of the assay. Serum biomarkers are currently not the standard of care and are not recommended in any detection guidelines. However, tumor biomarkers are used in the breast cancer setting to determine the course of care. The purpose of this article is to review recent advances in SPB, TAb, and biomarkers used in breast cancer detection to provide a perspective on how these technologies may offer benefit when combined with current imaging modalities.

scholarly journals Accuracy of high b-value diffusion-weighted MRI for prostate cancer detection: a meta-analysis

2017 ◽  
Vol 59 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Keith Craig Godley ◽  
Tom Joseph Syer ◽  
Andoni Paul Toms ◽  
Toby Oliver Smith ◽  
Glyn Johnson ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Visual Assessment ◽  
B Value ◽  
B Values ◽  
Prostate Cancer Detection ◽  
High B Value

Background The diagnostic accuracy of diffusion-weighted imaging (DWI) to detect prostate cancer is well-established. DWI provides visual as well as quantitative means of detecting tumor, the apparent diffusion coefficient (ADC). Recently higher b-values have been used to improve DWI’s diagnostic performance. Purpose To determine the diagnostic performance of high b-value DWI at detecting prostate cancer and whether quantifying ADC improves accuracy. Material and Methods A comprehensive literature search of published and unpublished databases was performed. Eligible studies had histopathologically proven prostate cancer, DWI sequences using b-values ≥ 1000 s/mm2, less than ten patients, and data for creating a 2 × 2 table. Study quality was assessed with QUADAS-2 (Quality Assessment of diagnostic Accuracy Studies). Sensitivity and specificity were calculated and tests for statistical heterogeneity and threshold effect performed. Results were plotted on a summary receiver operating characteristic curve (sROC) and the area under the curve (AUC) determined the diagnostic performance of high b-value DWI. Results Ten studies met eligibility criteria with 13 subsets of data available for analysis, including 522 patients. Pooled sensitivity and specificity were 0.59 (95% confidence interval [CI], 0.57–0.61) and 0.92 (95% CI, 0.91–0.92), respectively, and the sROC AUC was 0.92. Subgroup analysis showed a statistically significant ( P = 0.03) improvement in accuracy when using tumor visual assessment rather than ADC. Conclusion High b-value DWI gives good diagnostic performance for prostate cancer detection and visual assessment of tumor diffusion is significantly more accurate than ROI measurements of ADC.

A novel biophysical based marker with multilevel, multiparameter expression for early stage cancer detection.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20673-e20673
Author(s):  
Junjie Wu ◽  
Gengxi Jiang ◽  
Jiansong Ji ◽  
Xuedong Du ◽  
Yue Lin ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Early Stage ◽  
Physical Property ◽  
Stage I ◽  
Potential Candidate ◽  
Healthy Individuals ◽  
Blind Test ◽  
Early Stage Cancer ◽  
Multi Level

e20673 Background: Early stage cancer detection remains to be elusive despite of many years of efforts. In this work, a bio-physical based marker (named Cancer Differentiation Analysis (CDA)) with multi-level and multi-parameter expression features has been developed which has shown a number of clear advantages over the traditional approaches such as bio-chemistry based marker, circulating tumor cell (CTC) and circuiting DNA (ct-DNA). In stage I non-small cell lung cancer (NSCLC), sensitivity and specificity reached a record high of 85.2% and 93.0%, respectively. Methods: In this study, 832 NSCLC cancer samples with pathological information and 642 samples from healthy individuals were measured in a single blind test. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. The CDA data were first processed using an algorithm built from data base and subsequently analyzed using SPSS. The results were shown in the table. Results: The results indicated that CDA technology has a very good sensitivity and specificity even at stage I (85% and 93%, respectively), which is much better than those previously reported results by other methods. Conclusions: Initial results showed that CDA technology could effectively screen NSCLC patients from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA technology could be a potential candidate for early stage cancer screening. [Table: see text]

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