scholarly journals A novel clinical model for predicting malignancy of solitary pulmonary nodules: A multicenter study in Chinese population

2021 ◽  
Author(s):  
Xia He ◽  
Ning Xue ◽  
Xiaohua Liu ◽  
Xuemiao Tang ◽  
Songguo Peng ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Pulmonary Nodules ◽  
Cancer Center ◽  
The Novel ◽  
Solitary Pulmonary Nodules ◽  
Operating Characteristics ◽  
Clinical Model ◽  
Net Reclassification Improvement ◽  
Improvement Index

Abstract Background: This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). Methods: Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort.Results: A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed -0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models.Conclusions: The novel model in our study had a high clinical value in diagnose of MSPNs.

scholarly journals A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xia He ◽  
Ning Xue ◽  
Xiaohua Liu ◽  
Xuemiao Tang ◽  
Songguo Peng ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Pulmonary Nodules ◽  
Cancer Center ◽  
The Novel ◽  
Solitary Pulmonary Nodules ◽  
Operating Characteristics ◽  
Clinical Model ◽  
Net Reclassification Improvement ◽  
Improvement Index

Abstract Background This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). Methods Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort. Results A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models. Conclusions The novel model in our study had a high clinical value in diagnose of MSPNs.

scholarly journals A Novel Clinical Model for Predicting Malignancy of Solitary Pulmonary Nodules: A Multicenter Study in Chinese Population

2020 ◽  
Author(s):  
Xia He ◽  
Ning Xue ◽  
Xiaohua Liu ◽  
Xuemiao Tang ◽  
Songguo Peng ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Pulmonary Nodules ◽  
Cancer Center ◽  
Solitary Pulmonary Nodules ◽  
Operating Characteristics ◽  
Clinical Value ◽  
Clinical Model ◽  
Net Reclassification Improvement ◽  
Improvement Index

Abstract BackgroundThis study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). MethodsRecords from 406 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The data was randomly divided into training cohort and internal validation cohort. Other 190 SPNs patients in Henan Tumor Hospital were used for external validation. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. ResultsA total of 15 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.221). The AUC for our model was 0.799, which was higher than other two reported models. DCA also showed our model was superior to the other two reported models. In our model, sensitivity = 70.06%, specificity = 77.08%. Compared with the PKUPH and Mayo models, the NRI of our model increased by 0.301 and 0.469 respectively, and the IDI improved 0.011 and 0.123, respectively. Furthermore, the model was significant positive correlation with PKUPH and Mayo models. ConclusionsThe novel model in our study had a high clinical value in diagnose of MSPNs.

Solitary Pulmonary Nodules: Clinical Prediction Model Versus Physicians

1999 ◽  
Vol 74 (4) ◽  
pp. 319-329 ◽  
Author(s):  
Stephen J. Swensen ◽  
Marc D. Silverstein ◽  
Eric S. Edell ◽  
Victor F. Trastek ◽  
Gregory L. Aughenbaugh ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pulmonary Nodules ◽  
Clinical Prediction ◽  
Solitary Pulmonary Nodules ◽  
Clinical Prediction Model ◽  
Model Versus

Development and validation of diagnostic prediction model for solitary pulmonary nodules

Respirology ◽  
2007 ◽  
Vol 12 (6) ◽  
pp. 856-862 ◽  
Author(s):  
Kan YONEMORI ◽  
Ukihide TATEISHI ◽  
Hajime UNO ◽  
Yoko YONEMORI ◽  
Koji TSUTA ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pulmonary Nodules ◽  
Solitary Pulmonary Nodules ◽  
Development And Validation

Prediction model for detecting circulating tumor DNA (ctDNA) in metastatic colorectal cancer (mCRC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3590-3590
Author(s):  
Allan Andresson Lima Pereira ◽  
Aparna Raj Parikh ◽  
Emily E. Van Seventer ◽  
Jingquan Jia ◽  
Jonathan M. Loree ◽  
...  
Keyword(s):  
Prediction Model ◽  
Metastatic Disease ◽  
Clinical Laboratory ◽  
External Validation ◽  
Area Under The Curve ◽  
Disease Status ◽  
Circulating Tumor Dna ◽  
Data Sets ◽  
Operating Characteristics ◽  
Node Metastases

3590 Background: While tissue-based assays have yields above 90% in solid tumors, there is less known about factors that influence the sensitivity of ctDNA for detecting mutations. Methods:We retrospectively evaluated mCRC patients (pts) who had plasma-derived NGS utilizing a highly-sensitive targeted 68-73-gene ctDNA assay. In a case-control design, pts with a known mutation on tissue and radiologic evidence of metastatic disease but no detectable ctDNA mutation were matched 1:3 with randomly selected pts with detectable mutations and compared according to clinical, laboratory, and radiologic characteristics. A prediction score for ctDNA detection was built using a binary logistic backward stepwise regression analysis and tested in two independent data sets from different institutions. Area under the curve (AUC) from receiver operating characteristics curves (ROC) were used for internal and external validation. Results: From 416 pts who met inclusion criteria, plasma-derived NGS did not find tumor mutations in 66 cases (15.9%); 198 pts with detectable alterations were selected as controls. After multivariate analysis, the detection of ctDNA was associated with increasing age (OR 1.05; 95%CI 1.02-1.09; p = .001), presence of liver (OR 5.82; 95%CI 2.55-12.49; p < .001) and lymph node metastases (OR 3.28; 95%CI 1.51-7.60; p = .004), archival TP53 mutations (OR = 2.88; 95%CI 1.37-6.17; p = .006). A key determinant was timing of collection relative to disease status: plasma collected in newly diagnosed metastatic disease or after evidence of progression was substantially more likely to have detectable alterations (OR 9.24; 95%CI 4.11-22.40; p < .001); The simplified prediction model performed well in internal (AUC = 0.88) and external validation (AUC = 0.95; 163 pts). Conclusions: Our validated prediction model provides clinicians and researchers with a tool to screen for patients in whom ctDNA testing can outperform tissue-based testing in detecting genomic alterations.

Long-term follow-up analysis of a highly characterized arrhythmogenic cardiomyopathy cohort with classical and non-classical phenotypes–a real-world assessment of a novel prediction model: does the subtype really matter

EP Europace ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 797-805 ◽  
Author(s):  
Michela Casella ◽  
Alessio Gasperetti ◽  
Fassini Gaetano ◽  
Mattia Busana ◽  
Elena Sommariva ◽  
...  
Keyword(s):  
Prediction Model ◽  
Goodness Of Fit ◽  
External Validation ◽  
Outcome Data ◽  
The Novel ◽  
Arrhythmogenic Cardiomyopathy ◽  
Term Survival ◽  
Long Term Outcome ◽  
Classical Form

Abstract Aims To provide long-term outcome data on arrhythmogenic cardiomyopathy (ACM) patients with non-classical forms [left dominant ACM (LD-ACM) and biventricular ACM (Bi-ACM)] and an external validation of a recently proposed algorithm for ventricular arrhythmia (VA) prediction in ACM patients. Methods and results Demographic, clinical, and outcome data were retrieved from all ACM patients encountered at our institution. Patients were classified according to disease phenotype (R-ACM; Bi-ACM; LD-ACM). Overall and by phenotype long-term survival were calculated; the novel Cadrin-Tourigny et al. algorithm was used to calculate the a priori predicted VA risk, and it was compared with the observed outcome to test its reliability. One hundred and one patients were enrolled; three subgroups were defined (R-ACM, n = 68; Bi-ACM, n = 14; LD-ACM, n = 19). Over a median of 5.41 (2.59–8.37) years, the non-classical form cohort experienced higher rates of VAs than the classical form [5-year freedom from VAs: 0.58 (0.43–0.78) vs. 0.76 (0.66–0.89), P = 0.04]. The Cadrin-Tourigny et al. predictive model adequately described the overall cohort risk [mean observed-predicted risk difference (O-PRD): +6.7 (−4.3, +17.7) %, P = 0.19]; strafing by subgroup, excellent goodness-of-fit was demonstrated for the R-ACM subgroup (mean O-PRD, P = 0.99), while in the Bi-ACM and LD-ACM ones the real observed risk appeared to be underestimated [mean O-PRD: −20.0 (−1.1, −38.9) %, P &lt; 0.0001; −22.6 (−7.8, −37.5) %, P &lt; 0.0001, respectively]. Conclusion Non-classical ACM forms appear more prone to VAs than classical forms. The novel prediction model effectively predicted arrhythmic risk in the classical R-ACM cohort, but seemed to underestimate it in non-classical forms.

scholarly journals External Validation of an MRI-Derived Radiomics Model to Predict Biochemical Recurrence after Surgery for High-Risk Prostate Cancer

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 814 ◽  
Author(s):  
Vincent Bourbonne ◽  
Georges Fournier ◽  
Martin Vallières ◽  
François Lucia ◽  
Laurent Doucet ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Predictive Accuracy ◽  
External Validation ◽  
Specific Antigen ◽  
Area Under The Curve ◽  
Recurrence Risk ◽  
Clinical Model ◽  
Adc Map

Adjuvant radiotherapy after prostatectomy was recently challenged by early salvage radiotherapy, which highlighted the need for biomarkers to improve risk stratification. Therefore, we developed an MRI ADC map-derived radiomics model to predict biochemical recurrence (BCR) and BCR-free survival (bRFS) after surgery. Our goal in this work was to externally validate this radiomics-based prediction model. Experimental Design: A total of 195 patients with a high recurrence risk of prostate cancer (pT3-4 and/or R1 and/or Gleason’s score > 7) were retrospectively included in two institutions. Patients with postoperative PSA (Prostate Specific Antigen) > 0.04 ng/mL or lymph node involvement were excluded. Radiomics features were extracted from T2 and ADC delineated tumors. A total of 107 patients from Institution 1 were used to retrain the previously published model. The retrained model was then applied to 88 patients from Institution 2 for external validation. BCR predictions were evaluated using AUC (Area Under the Curve), accuracy, and bRFS using Kaplan–Meier curves. Results: With a median follow-up of 46.3 months, 52/195 patients experienced BCR. In the retraining cohort, the clinical prediction model (combining the number of risk factors and postoperative PSA) demonstrated moderate predictive power (accuracy of 63%). The radiomics model (ADC-based SZEGLSZM) predicted BCR with an accuracy of 78% and allowed for significant stratification of patients for bRFS (p < 0.0001). In Institution 2, this radiomics model remained predictive of BCR (accuracy of 0.76%) contrary to the clinical model (accuracy of 0.56%). Conclusions: The recently developed MRI ADC map-based radiomics model was validated in terms of its predictive accuracy of BCR and bRFS after prostatectomy in an external cohort.

scholarly journals Construction of a MicroRNA-Based Nomogram for Prediction of Lung Metastasis in Breast Cancer Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Leyi Zhang ◽  
Jun Pan ◽  
Zhen Wang ◽  
Chenghui Yang ◽  
Jian Huang
Keyword(s):  
Breast Cancer ◽  
Lung Metastasis ◽  
Risk Score ◽  
Validation Cohort ◽  
Molecular Taxonomy ◽  
External Validation ◽  
Tnm Stage ◽  
Functional Enrichment ◽  
Operating Characteristics ◽  
Net Reclassification Improvement

The lung is one of the most common sites of distant metastasis in breast cancer (BC). Identifying ideal biomarkers to construct a more accurate prediction model than conventional clinical parameters is crucial. MicroRNAs (miRNAs) data and clinicopathological data were acquired from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database. miR-663, miR-210, miR-17, miR-301a, miR-135b, miR-451, miR-30a, and miR-199a-5p were screened to be highly relevant to lung metastasis (LM) of BC patients. The miRNA-based risk score was developed based on the logistic coefficient of the individual miRNA. Univariate and multivariate logistic regression selected tumor node metastasis (TNM) stage, age at diagnosis, and miRNA-risk score as independent predictive parameters, which were used to construct a nomogram. The Cancer Genome Atlas (TCGA) database was used to validate the signature and nomogram. The predictive performance of the nomogram was compared to that of the TNM stage. The area under the receiver operating characteristics curve (AUC) of the nomogram was higher than that of the TNM stage in all three cohorts (training cohort: 0.774 vs. 0.727; internal validation cohort: 0.763 vs. 0.583; external validation cohort: 0.925 vs. 0.840). The calibration plot of the nomogram showed good agreement between predicted and observed outcomes. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA) of the nomogram showed that its performances were better than that of the TNM classification system. Functional enrichment analyses suggested several terms with a specific focus on LM. Subgroup analysis showed that miR-30a, miR-135b, and miR-17 have unique roles in lung metastasis of BC. Pan-cancer analysis indicated the significant importance of eight predictive miRNAs in lung metastasis. This study is the first to establish and validate a comprehensive lung metastasis predictive nomogram based on the METABRIC and TCGA databases, which provides a reliable assessment tool for clinicians and aids in appropriate treatment selection.

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