scholarly journals Development and Validation of a Genomic Tool to Predict Seminal Vesicle Invasion in Adenocarcinoma of the Prostate

2020 ◽  
pp. 1228-1238
Author(s):  
William A. Hall ◽  
Nick Fishbane ◽  
Yang Liu ◽  
Melody J. Xu ◽  
Elai Davicioni ◽  
...  
Keyword(s):  
Seminal Vesicle ◽  
Area Under The Curve ◽  
Clinical Pathology ◽  
Cancer Center ◽  
Model Parameters ◽  
Clinical Recurrence ◽  
Seminal Vesicle Invasion ◽  
Genomic Tool ◽  
Mskcc Nomogram ◽  
Validation Set

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

scholarly journals Head-to-Head Comparison of Two Nomograms Predicting Probability of Lymph Node Invasion in Prostate Cancer and the Therapeutic Impact of Higher Nomogram Threshold

2021 ◽  
Vol 10 (5) ◽  
pp. 999
Author(s):  
Zilvinas Venclovas ◽  
Tim Muilwijk ◽  
Aivaras J. Matjosaitis ◽  
Mindaugas Jievaltas ◽  
Steven Joniau ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lymph Node ◽  
Area Under The Curve ◽  
Pelvic Lymph Node ◽  
Cancer Center ◽  
Study Cohort ◽  
Net Benefit ◽  
Characteristic Analysis ◽  
Therapeutic Impact ◽  
Mskcc Nomogram

Introduction: The aim of the study was to compare the performance of the 2012 Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms as a predictor for pelvic lymph node invasion (LNI) in men who underwent radical prostatectomy (RP) with pelvic lymph node dissection (PLND), to examine their performance and to analyse the therapeutic impact of using 7% nomogram cut-off. Materials and Methods: The study cohort consisted of 807 men with clinically localised prostate cancer (PCa) who underwent open RP with PLND between 2001 and 2019. The area under the curve (AUC) of the receiver operator characteristic analysis was used to quantify the accuracy of the 2012 Briganti and MSKCC nomograms to predict LNI. Calibration plots were used to visualise over or underestimation by the models and a decision curve analysis (DCA) was performed to evaluate the net benefit associated with the used nomograms. Results: A total of 97 of 807 patients had LNI (12%). The AUC of 2012 Briganti and MSKCC nomogram was 80.6 and 79.2, respectively. For the Briganti nomogram using the cut-off value of 7% would lead to reduce PLND in 47% (379/807), while missing 3.96% (15/379) cases with LNI. For the MSKCC nomogram using the cut-off value of 7% a PLND would be omitted in 44.5% (359/807), while missing 3.62% (13/359) of cases with LNI. Conclusions: Both analysed nomograms demonstrated high accuracy for prediction of LNI. Using a 7% nomogram cut-off would allow the avoidance up to 47% of PLNDs, while missing less than 4% of patients with LNI.

scholarly journals A novel combination of serum microRNAs for the detection of early gastric cancer

2021 ◽  
Author(s):  
Seiichiro Abe ◽  
Juntaro Matsuzaki ◽  
Kazuki Sudo ◽  
Ichiro Oda ◽  
Hitoshi Katai ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Expression Profiles ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Cancer Center ◽  
Serum Samples ◽  
Retrospective Case ◽  
Serum Mirnas ◽  
Validation Set

Abstract Background The aim of this study was to identify serum miRNAs that discriminate early gastric cancer (EGC) samples from non-cancer controls using a large cohort. Methods This retrospective case–control study included 1417 serum samples from patients with EGC (seen at the National Cancer Center Hospital in Tokyo between 2008 and 2012) and 1417 age- and gender-matched non-cancer controls. The samples were randomly assigned to discovery and validation sets and the miRNA expression profiles of whole serum samples were comprehensively evaluated using a highly sensitive DNA chip (3D-Gene®) designed to detect 2565 miRNA sequences. Diagnostic models were constructed using the levels of several miRNAs in the discovery set, and the diagnostic performance of the model was evaluated in the validation set. Results The discovery set consisted of 708 samples from EGC patients and 709 samples from non-cancer controls, and the validation set consisted of 709 samples from EGC patients and 708 samples from non-cancer controls. The diagnostic EGC index was constructed using four miRNAs (miR-4257, miR-6785-5p, miR-187-5p, and miR-5739). In the discovery set, a receiver operating characteristic curve analysis of the EGC index revealed that the area under the curve (AUC) was 0.996 with a sensitivity of 0.983 and a specificity of 0.977. In the validation set, the AUC for the EGC index was 0.998 with a sensitivity of 0.996 and a specificity of 0.953. Conclusions A novel combination of four serum miRNAs could be a useful non-invasive diagnostic biomarker to detect EGC with high accuracy. A multicenter prospective study is ongoing to confirm the present observations.

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

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.

scholarly journals A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 913
Author(s):  
Johannes Fahrmann ◽  
Ehsan Irajizad ◽  
Makoto Kobayashi ◽  
Jody Vykoukal ◽  
Jennifer Dennison ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Polyamine Metabolism ◽  
Healthy Controls ◽  
Test Set ◽  
Exact Test ◽  
Oncogenic Driver ◽  
Characteristic Area ◽  
Validation Set

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74–0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

Change in Spontaneous Swallowing Frequency in HNC Patients Undergoing C/RT

2021 ◽  
pp. 019459982110207
Author(s):  
Giselle D. Carnaby ◽  
Aarthi Madhavan ◽  
Ali Barikroo ◽  
Michael Crary
Keyword(s):  
Repeated Measures ◽  
Prospective Cohort ◽  
Area Under The Curve ◽  
Objective Measure ◽  
Oral Intake ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Swallowing Function ◽  
Function Change ◽  
Functional Oral Intake Scale

Objective This study sought to evaluate the role and trajectory of spontaneous swallowing frequency (SFA) in patients with head and neck cancer (HNC) undergoing chemoradiotherapy (C/RT). Study Design. Prospective cohort. Setting University comprehensive cancer center. Methods A prospective cohort of 80 patients with HNC was followed from baseline to 3 months post-C/RT. Subjects were evaluated for performance on swallowing function, functional diet consumed, weight, swallowing frequency rate, perceived xerostomia, perceived pain, and mucositis. Relationships were evaluated using univariate correlations, t tests, and repeated-measures analysis of variance. The diagnostic accuracy of SFA to express dysphagia was calculated by area under the curve (AUROC) and displayed using receiver operator characteristic curves. Results In general, patients with HNC demonstrated a parabolic decline in most measures over the C/RT trajectory. SFA and perceived xerostomia did not show improved recovery by 3 months. SFA was related to swallow function, xerostomia, and functional diet consumed posttreatment and pain at 3 months. The ability of SFA to correctly identify clinical dysphagia (Mann Assessment of Swallowing–Cancer version [MASA-C]) and reduced oral intake (Functional Oral Intake Scale [FOIS]) at posttreatment was strong (AUROC MASA-C: 0.824 [95% CI, 0.63-1.00], P < .0018; AUROC FOIS: 0.96 [95% CI, 0.87-0.96], P < .0001). Conclusion This exploratory study suggests SFA may provide a useful method to identify dysphagia after HNC treatment. Furthermore, SFA may offer a simple, objective measure of swallowing function change in HNC over the C/RT trajectory.

scholarly journals Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

10.2196/16981 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e16981
Author(s):  
Yang Xiang ◽  
Hangyu Ji ◽  
Yujia Zhou ◽  
Fang Li ◽  
Jingcheng Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Asthma Exacerbation ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Model Parameters ◽  
Supporting Evidence ◽  
Contributing Factors ◽  
Clinical Variables ◽  
Personalized Risk

Background Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. Objective This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. Methods We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. Results The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. Conclusions The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

scholarly journals PEMERIKSAAN TINGKAT sdLDL SERUM SEBAGAI PETANDA DIAGNOSTIK STENOSIS KORONER (Serum sdLDL Level as A Diagnostic Marker of Coronary Stenosis)

2018 ◽  
Vol 22 (1) ◽  
pp. 9
Author(s):  
Indranila K Samsuria ◽  
Laily Adninta
Keyword(s):  
Predictive Value ◽  
Coronary Stenosis ◽  
Area Under The Curve ◽  
Clinical Pathology ◽  
Diagnostic Value ◽  
T Test ◽  
Cross Sectional ◽  
Increased Risk ◽  
Test Kit ◽  
Vascular Stenosis

Small dense LDL (sdLDL) is the LDL which particles are small and dense, it is pro-atherogenic. Increased levels of serum sdLDL areassociated with an increased risk of coronary stenosis. The aim of this study was to examine the diagnostic value of sd LDL in coronarystenosis. An analytical observational study with cross sectional approach was conducted at the Department of Clinical Pathology, MedicalFaculty of Diponegoro University/Dr. Kariadi Hospital and the Unit of Cardiac diseases during the period of March-October 2013. Thesubjects were 39 patients suspected of suffering a coronary stenosis. The diagnosis of coronary stenosis, degree of stenosis and numberof vascular stenosis was established at the time of cardiac catheterization. SdLDL assessment used a test kit. The statistical analysis usedwere unpaired t-test, Spearman correlation test, ROC analysis and diagnostic test. LDL levels in stenosis subjects, 35.4±9.01 mg/dL weresignificantly higher compared to levels in subjects that had no stenosis, 20.7±7.10 mg/dL (p<0.001; unpaired t-test). Correlation testresults showed a correlation between levels of serum sdLDL with severe degree of stenosis (correlation coefficient -0.64, p <0.001) and amoderate positive correlation between the number of vascular stenosis (Coefficient correlation 0.46; p=0.003; Spearman Correlation’sTest). The area under the curve of ROC was 0.9 (p <0.001). The cut off levels sdLDL were used to detect stenosis. The results showeda sensitivity of 85.2%, specificity of 75%, positive predictive value of 88.5%, negative predictive value of 69.2% and accuracy of 82%.Levels of serum sdLDL were associated with severe to extensive stenosis degree, and showed a good diagnostic value, thus, it can beused for screening to determine the presence of coronary stenosis.

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