Serum microRNA signatures for the detection of pancreatobiliary cancer.

e15718 Background: The diagnostic value of serum microRNAs (miRNA) in a highly sensitive microarray for pancreatobiliary cancer (PBca) has been demonstrated. This study attempted to build and validate a signature comprised of multiple serum miRNA markers for discriminating PBca from healthy controls. Methods: A multicenter prospective study on the diagnostic performance of serum miRNAs was conducted. The patients (pts) with treatment-naïve PBca and healthy participants aged ≥60 years were enrolled. Clinical data and sera were collected. Target population was randomly divided to training or validation cohort with an allocation ratio of 2:1. Twenty-nine serum miRNA markers on the microarray data were analyzed. Using any combinations of the markers, a Fisher’s linear discriminant analysis was performed, and the resulting sensitivity, specificity and AUC of ROC curve to discriminate PBca from healthy controls were calculated for each combination. Marker combinations with a sensitivity/specificity (SN/SP) of ≥80%/90% and high AUC in comparison with AUC of CA19-9 were defined as the diagnostic miRNA signature, which were selected in the training cohort. Next, the signatures were screened out which showed a good reproducibility in the validation cohort. As an independent external cohort, PBca pts and healthy with pooled frozen sera were enrolled and the identified miRNA signatures were further validated. Results: Total of 546 participants (80 healthy and 223 PBca in training set, 40 healthy and 104 PBca in validation set, 49 healthy and 50 PBca in external validation set) were analyzed in this study. Four serum miRNA combinations were identified as the diagnostic miRNA signature. In the training set, four miRNA signatures, consisted of 10 miRNAs, were developed. For the best-performed miRNA signature, the SN/SP and AUC in the validation and external validation cohorts were 84/90% and 0.95 (CA19-9: 73/95% and 0.88) and 84/90% and 0.93 (CA19-9: 80/94% and 0.87), respectively. Conclusions: The diagnostic serum miRNA signatures for PBca were identified in this study.

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Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

Methodologies and Applications for Chemoinformatics and Chemical Engineering ◽

10.4018/978-1-4666-4010-8.ch021 ◽

2013 ◽

pp. 322-427

Keyword(s):

External Validation ◽

Correlation Coefficients ◽

Classification Models ◽

Training Set ◽

Linear Discriminant ◽

Oecd Principles ◽

Qsar Models ◽

Validation Set ◽

Global Accuracy

In this paper, the authors present an effort to increase the applicability domain (AD) by means of retraining models using a database of 701 great dissimilar molecules presenting anti-tyrosinase activity and 728 drugs with other uses. Atom-based linear indices and best subset linear discriminant analysis (LDA) were used to develop individual classification models. Eighteen individual classification-based QSAR models for the tyrosinase inhibitory activity were obtained with global accuracy varying from 88.15-91.60% in the training set and values of Matthews correlation coefficients (C) varying from 0.76-0.82. The external validation set shows globally classifications above 85.99% and 0.72 for C. All individual models were validated and fulfilled by OECD principles. A brief analysis of AD for the training set of 478 compounds and the new active compounds included in the re-training was carried out. Various assembled multiclassifier systems contained eighteen models using different selection criterions were obtained, which provide possibility of select the best strategy for particular problem. The various assembled multiclassifier systems also estimated the potency of active identified compounds. Eighteen validated potency models by OECD principles were used.

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Retrained Classification of Tyrosinase Inhibitors and “In Silico” Potency Estimation by Using Atom-Type Linear Indices

International Journal of Chemoinformatics and Chemical Engineering ◽

10.4018/ijcce.2012070104 ◽

2012 ◽

Vol 2 (2) ◽

pp. 42-144

Author(s):

Gerardo M. Casañola-Martín ◽

Mahmud Tareq Hassan Khan ◽

Huong Le-Thi-Thu ◽

Yovani Marrero-Ponce ◽

Ramón García-Domenech ◽

...

Keyword(s):

External Validation ◽

Correlation Coefficients ◽

Training Set ◽

Atom Type ◽

Linear Discriminant ◽

Oecd Principles ◽

Qsar Models ◽

Validation Set ◽

Global Accuracy

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A prediction model using 2-propanol and 2-butanone in urine distinguishes breast cancer

Scientific Reports ◽

10.1038/s41598-021-99396-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shoko Kure ◽

Sera Satoi ◽

Toshihiko Kitayama ◽

Yuta Nagase ◽

Nobuo Nakano ◽

...

Keyword(s):

Breast Cancer ◽

Volatile Organic Compounds ◽

Cancer Patients ◽

Organic Compounds ◽

External Validation ◽

Healthy Controls ◽

Breast Cancer Patients ◽

Training Set ◽

Volatile Organic ◽

Validation Set

AbstractSafe and noninvasive methods for breast cancer screening with improved accuracy are urgently needed. Volatile organic compounds (VOCs) in biological samples such as breath and blood have been investigated as noninvasive novel markers of cancer. We investigated volatile organic compounds in urine to assess their potential for the detection of breast cancer. One hundred and ten women with biopsy-proven breast cancer and 177 healthy volunteers were enrolled. The subjects were divided into two groups: a training set and an external validation set. Urine samples were collected and analyzed by gas chromatography and mass spectrometry. A predictive model was constructed by multivariate analysis, and the sensitivity and specificity of the model were confirmed using both a training set and an external set with reproducibility tests. The training set included 60 breast cancer patients (age 34–88 years, mean 60.3) and 60 healthy controls (age 34–81 years, mean 58.7). The external validation set included 50 breast cancer patients (age 35–85 years, mean 58.8) and 117 healthy controls (age 18–84 years, mean 51.2). One hundred and ninety-one compounds detected in at least 80% of the samples from the training set were used for further analysis. The predictive model that best-detected breast cancer at various clinical stages was constructed using a combination of two of the compounds, 2-propanol and 2-butanone. The sensitivity and specificity in the training set were 93.3% and 83.3%, respectively. Triplicated reproducibility tests were performed by randomly choosing ten samples from each group, and the results showed a matching rate of 100% for the breast cancer patient group and 90% for the healthy control group. Our prediction model using two VOCs is a useful complement to the current diagnostic tools. Further studies inclusive of benign tumors and non-breast malignancies are warranted.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features

Journal of Personalized Medicine ◽

10.3390/jpm11010008 ◽

2020 ◽

Vol 11 (1) ◽

pp. 8

Author(s):

Claudia-Gabriela Moldovanu ◽

Bianca Boca ◽

Andrei Lebovici ◽

Attila Tamas-Szora ◽

Diana Sorina Feier ◽

...

Keyword(s):

Computed Tomography ◽

Renal Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

Predictive Ability ◽

Nuclear Grade ◽

Training Set ◽

Cell Renal Cell Carcinoma ◽

Validation Set ◽

Sensitivity Specificity

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

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The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

Hematology Reports ◽

10.4081/hr.2018.7605 ◽

2018 ◽

Vol 10 (3) ◽

Cited By ~ 3

Author(s):

Pokpong Piriyakhuntorn ◽

Adisak Tantiworawit ◽

Thanawat Rattanathammethee ◽

Chatree Chai-Adisaksopha ◽

Ekarat Rattarittamrong ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Area Under The Curve ◽

Microcytic Anemia ◽

Deficiency Anemia ◽

Operating Characteristics ◽

Training Set ◽

Distribution Width ◽

Validation Set ◽

Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

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Use of serum biomarkers in staging of canine hepatic fibrosis

Journal of Veterinary Diagnostic Investigation ◽

10.1177/1040638719866881 ◽

2019 ◽

Vol 31 (5) ◽

pp. 665-673 ◽

Cited By ~ 1

Author(s):

Maud Menard ◽

Alexis Lecoindre ◽

Jean-Luc Cadoré ◽

Michèle Chevallier ◽

Aurélie Pagnon ◽

...

Keyword(s):

Liver Biopsy ◽

Hepatic Fibrosis ◽

External Validation ◽

Model Performance ◽

Area Under The Curve ◽

Gamma Glutamyl Transferase ◽

Training Set ◽

Internal Validation ◽

Validation Set ◽

Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

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Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

Molecules ◽

10.3390/molecules24102006 ◽

2019 ◽

Vol 24 (10) ◽

pp. 2006 ◽

Cited By ~ 1

Author(s):

Liadys Mora Lagares ◽

Nikola Minovski ◽

Marjana Novič

Keyword(s):

In Silico ◽

Transmembrane Protein ◽

External Validation ◽

Assessment Process ◽

Classification Model ◽

Training Set ◽

Test Set ◽

Active Compounds ◽

P Glycoprotein ◽

Validation Set

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

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Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

10.21203/rs.3.rs-123528/v1 ◽

2020 ◽

Author(s):

Ruyi Zhang ◽

Mei Xu ◽

Xiangxiang Liu ◽

Miao Wang ◽

Qiang Jia ◽

...

Keyword(s):

Thyroid Carcinoma ◽

External Validation ◽

Cancer Staging ◽

Curve Analysis ◽

Calibration Plot ◽

Net Benefit ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set ◽

Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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BIOM-49. SERUM microRNA IS A PROGNOSTIC AND POST-OPERATIVE MONITORING BIOMARKER IN GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noaa215.046 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii12-ii12

Author(s):

Jordan Jones ◽

Andrew Morokoff ◽

Kate Drummond

Keyword(s):

Tumor Volume ◽

Digital Pcr ◽

Healthy Controls ◽

Lasso Regression ◽

Mirna Signature ◽

Biological Behaviour ◽

Sequencing Platform ◽

Serum Mirna ◽

Candidate Mirnas

Abstract Although magnetic resonance imaging (MRI) provides accurate anatomical and spatial details regarding gliomas, it is not reliable at predicting biological behaviour and is confounded by problems such as pseudo-progression. A circulating biomarker has potential to improve predictions of glioma outcomes and identify tumor progression post treatment, however no such biomarker is currently available. We aimed to discover a microRNA (miRNA) serum biomarker for longitudinal monitoring of glioma patients as well as identify miRNAs that are predictive of survival outcomes. To investigate this a prospectively collected cohort of 91 gliomas and 17 healthy controls underwent pre- and post-operative serum miRNA profiling using the next-generation sequencing platform Nanostring®. Differentially expressed miRNAs were discovered using a machine learning random forest analysis. Candidate miRNAs were then assessed by droplet digital PCR in 11 patients with multiple follow-up samples and compared to tumor volume based on MRI. A lasso-regression model was used to identify candidate miRNAs from pre-operative serum that were associated with both progression-free and overall survival. We identified a 9-gene miRNA signature that could distinguish between glioma and healthy controls with 99.8% accuracy. From this signature, we identified two miRNAs that demonstrated dynamic changes which correlated closely with tumor volume in both LGG and GBM. Importantly, miRNA levels did not increase in two cases of pseudo-progression, indicating the potential utility of this test in guiding treatment decisions. Additionally, altered expressions of 6 miRNAs were found to be associated with overall and progression-free survival and were incorporated in a predictive model with already known survival factors. In conclusion, we have identified a highly accurate 9-miRNA signature associated with glioma serum and observed specific miRNAs that both correlated with tumor volume over long-term follow up and were predictive of survival. These results support a large prospective validation study of serum miRNA biomarkers in glioma.

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