A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

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Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17217923 ◽

2020 ◽

Vol 17 (21) ◽

pp. 7923

Author(s):

Davide Barbieri ◽

Nitesh Chawla ◽

Luciana Zaccagni ◽

Tonći Grgurinović ◽

Jelena Šarac ◽

...

Keyword(s):

Data Mining ◽

Decision Making ◽

Cardiovascular Risk ◽

Clinical Decision Making ◽

Characteristic Curve ◽

Area Under The Curve ◽

Clinical Decision ◽

Classification Performance ◽

Biomedical Data ◽

Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

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Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics –Based Nomogram

Frontiers in Oncology ◽

10.3389/fonc.2021.637794 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wei Du ◽

Yu Wang ◽

Dongdong Li ◽

Xueming Xia ◽

Qiaoyue Tan ◽

...

Keyword(s):

Cervical Cancer ◽

Clinical Decision Making ◽

Area Under The Curve ◽

Predictive Performance ◽

Clinical Decision ◽

Tumor Stage ◽

Support Vector ◽

Training Cohort ◽

Clinical Model ◽

Radiomics Signature

PurposeTo build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation.MethodThis study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models.ResultThe radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%).ConclusionRadiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.

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The Development and Validation of a CT-Based Radiomics Nomogram to Preoperatively Predict Lymph Node Metastasis in High-Grade Serous Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.711648 ◽

2021 ◽

Vol 11 ◽

Author(s):

Hui-zhu Chen ◽

Xin-rong Wang ◽

Fu-min Zhao ◽

Xi-jian Chen ◽

Xue-sheng Li ◽

...

Keyword(s):

Ovarian Cancer ◽

Lymph Node ◽

Clinical Decision Making ◽

Tumor Resection ◽

Area Under The Curve ◽

Clinical Decision ◽

High Grade ◽

Serous Ovarian Cancer ◽

Training Cohort ◽

Portal Phase

PurposeTo develop and validate a radiomics model for predicting preoperative lymph node (LN) metastasis in high-grade serous ovarian cancer (HGSOC).Materials and MethodsFrom May 2008 to January 2018, a total of 256 eligible HGSOC patients who underwent tumor resection and LN dissection were divided into a training cohort (n=179) and a test cohort (n=77) in a 7:3 ratio. A Radiomics Model was developed based on a training cohort of 179 patients. A radiomics signature (defined as the Radscore) was selected by using the random forest method. Logistics regression was used as the classifier for modeling. An Integrated Model that incorporated the Radscore and CT_reported LN status (CT_LN_report) was developed and presented as a radiomics nomogram. Its performance was determined by the area under the curve (AUC), calibration, and decision curve. The radiomics nomogram was internally tested in an independent test cohort (n=77) and a CT-LN-report negative subgroup (n=179) using the formula derived from the training cohort.ResultsThe AUC value of the CT_LN_report was 0.688 (95% CI: 0.626, 0.759) in the training cohort and 0.717 (95% CI: 0.630, 0.804) in the test cohort. The Radiomics Model yielded an AUC of 0.767 (95% CI: 0.696, 0.837) in the training cohort and 0.753 (95% CI: 0.640, 0.866) in the test. The radiomics nomogram demonstrated favorable calibration and discrimination in the training cohort (AUC=0.821), test cohort (AUC=0.843), and CT-LN-report negative subgroup (AUC=0.82), outperforming the Radiomics Model and CT_LN_report alone.ConclusionsThe radiomics nomogram derived from portal phase CT images performed well in predicting LN metastasis in HGSOC and could be recommended as a new, convenient, and non-invasive method to aid in clinical decision-making.

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Radiomics for intracerebral hemorrhage: are all small hematomas benign?

British Journal of Radiology ◽

10.1259/bjr.20201047 ◽

2020 ◽

pp. 20201047

Author(s):

Chenyi Zhan ◽

Qian Chen ◽

Mingyue Zhang ◽

Yilan Xiang ◽

Jie Chen ◽

...

Keyword(s):

Intracerebral Hemorrhage ◽

Clinical Decision Making ◽

Characteristic Curve ◽

Clinical Information ◽

Clinical Decision ◽

Hematoma Expansion ◽

Clinical Factors ◽

Combined Model ◽

Clinical Model ◽

Discrimination Ability

Objectives: We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas. Methods: We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated the developed radiomic score and clinical factors was then constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Results: The addition of radiomics to clinical factors significantly improved the prediction performance of HE compared with the clinical model alone in both the training {AUC, 0.762 [95% CI (0.665–0.859)] versus AUC, 0.651 [95% CI (0.556–0.745)], p = 0.007} and test {AUC, 0.776 [95% CI (0.655–0.897) versus AUC, 0.631 [95% CI (0.451–0.810)], p = 0.001} cohorts. Moreover, the radiomic-based model achieved good discrimination ability of poor outcomes in the 3–10 ml group (AUCs 0.720 and 0.701). Conclusion: Compared with clinical information alone, combined model had greater potential for discriminating between benign and malignant course in patients with small ICH, particularly 3–10 ml hematomas. Advances in knowledge: Radiomics can be used as a supplement to conventional medical imaging, improving clinical decision-making and facilitating personalized treatment in small ICH.

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Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽

10.3390/cancers13081963 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1963

Author(s):

Daimantas Milonas ◽

Tomas Ruzgas ◽

Zilvinas Venclovas ◽

Mindaugas Jievaltas ◽

Steven Joniau

Keyword(s):

Clinical Decision Making ◽

Area Under The Curve ◽

Clinical Decision ◽

Competing Risk ◽

Grade Group ◽

Tertiary Center ◽

Stable Performance ◽

Cancer Specific Mortality ◽

Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

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Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis

10.1101/2020.06.23.20138552 ◽

2020 ◽

Author(s):

Angela Mc Ardle ◽

Anna Kwasnik ◽

Agnes Szenpetery ◽

Melissa Jones ◽

Belinda Hernandez ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Serum Protein ◽

Clinical Decision Making ◽

Multiple Reaction Monitoring ◽

Area Under The Curve ◽

Clinical Decision ◽

Additional Patient ◽

Second Phase ◽

Protein Biomarkers ◽

Protein Discovery

AbstractObjectivesTo identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) to provide an accurate diagnosis and support appropriate early intervention.MethodsIn an initial protein discovery phase, the serum proteome of a cohort of patients with PsA and RA was interrogated using unbiased liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Subsequently, analytically validated targeted multiple reaction monitoring (MRM) assays were developed to further evaluate those proteins identified as discriminatory during the discovery. During an initial verification phase, MRM assays were developed to a panel of 150 proteins (by measuring a total of 233 peptides) and used to re-evaluate the discovery cohort (n=60). During a second verification phase, the panel of proteins was expanded to include an additional 23 proteins identified in other proteomic discovery analyses of arthritis patients. The expanded panel was evaluated using a second, independent cohort of PsA and RA patients (n=167).ResultsMultivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. During the initial verification phase, random forest models confirmed that proteins measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and during the second phase of verification the expanded panel could segregate the two disease groups with an AUC of 0.85.ConclusionWe report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making and with continued evaluation and refinement using additional patient cohorts will support the development of a diagnostic test for patients with PsA.

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CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

Frontiers in Oncology ◽

10.3389/fonc.2021.621520 ◽

2021 ◽

Vol 11 ◽

Author(s):

Tiansong Xie ◽

Xuanyi Wang ◽

Zehua Zhang ◽

Zhengrong Zhou

Keyword(s):

Predictive Value ◽

Cross Validation ◽

Clinical Decision Making ◽

Area Under The Curve ◽

Clinical Decision ◽

Cystic Neoplasm ◽

Cystic Neoplasms ◽

Serous Cystadenomas ◽

Radiological Model ◽

Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

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Gallbladder Polyps: Real or Imagined?

The American Surgeon ◽

10.1177/000313481808401027 ◽

2018 ◽

Vol 84 (10) ◽

pp. 1670-1674 ◽

Cited By ~ 2

Author(s):

Yiping Li ◽

Talar Tejirian ◽

J. Craig Collins

Keyword(s):

Clinical Decision Making ◽

Clinical Decision ◽

Imaging Data ◽

Final Pathology ◽

Gallbladder Polyps ◽

Imaging Results ◽

Unreliable Method ◽

Us Findings ◽

Risk Of Cancer ◽

Selection Of

The finding of gallbladder polyps on imaging studies prompts further workup. Imaging results are often discordant with final pathology. The goal of this study is to compare polypoid lesions of the gallbladder found on preoperative ultrasound (US) with final pathologic diagnosis after cholecystectomy to help guide clinical decision-making. A retrospective study was conducted identifying adult patients who were diagnosed with polyps via US and who underwent cholecystectomy from 2008 through 2015. Imaging data, final pathology, and demographics were manually reviewed. A total of 2290 cholecystectomy patients had US-based polyps. Of these, 1661 patients (73%) did not have polyps on final pathology; primarily, stones or sludge were identified. Adenomyosis was diagnosed in 61 patients (2.7%). A total of 556 patients (24.2%) had pathologic polypoid lesions with the following breakdown: 463 (20.2%) cholesterol polyps, 43 other benign polyps (1.8%), 40 adenomas (1.7%), and 10 adenocarcinomas (0.4%). All patients with adenocarcinoma were older than 40 years and 91 per cent had US findings of polyps >10 mm. Ultrasound alone is an unreliable method of detecting real gallbladder polyps. This large database study found a very low risk of cancer. Size on US and patient age should be considered in the selection of appropriate surgical candidates with sonographic “polyps.”

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