scholarly journals Development and Analysis of a Predictive Nomogram Assessing Cancer Risk in a Chinese Cohort of Patients Presenting with Pulmonary Nodules

2021 ◽  
Author(s):  
Zufang Liao ◽  
Rongjiong Zheng ◽  
Ni Li ◽  
Guofeng Shao
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Pulmonary Nodule ◽  
Validation Cohort ◽  
External Validation ◽  
Pulmonary Nodules ◽  
Hypertension Status ◽  
Internal Validation ◽  
Nodule Size ◽  
Chinese Cohort

Abstract Background: Lung cancer is a major global threat to public health for which a novel prognostic nomogram is urgently needed.Patients and methods: Here, we designed a novel prognostic nomogram using a training dataset consisting of 178 pulmonary nodules for design and 124nodules for external validation. The R ‘caret’ package was used to separate patients for design into two groups, including a training cohort (n=126) for model construction and an internal validation cohort (n=52). Optimal feature selection for this model was achieved using the least absolute shrinkage and selection operator regression (LASSO) model. C-index values, calibration plots, and decision curve analyses were used to gauge the discrimination, calibration, and clinical utility, respectively, of this predictive model. Validation was then performed with the validation cohort.Results: A predictive nomogram was successfully constructed incorporating hypertension status, plasma fibrinogen levels, serum uric acid (SUA) levels, triglyceride (TG) and high-density lipoprotein (HDL) levels, density, spicule sign, ground-glass opacity (GGO), and pulmonary nodule size. This model exhibited good discriminative ability, with a C-index value of 0.795 (95% CI: 0.720–0.870), and was well-calibrated. When we used the validation cohort to evaluate the model, the C-indexes were 0.886 (95% CI: 0.800–0.972) and 0.817 (95% CI: 0.747–0.897) for internal validation and external validation, respectively. Decision curve analyses indicated the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 9%.Conclusion: The nomogram constructed in this study, which incorporates hypertension status, plasma fibrinogen levels, SUA, TG, HDL, density, spicule sign, GGO status, and pulmonary nodule size was able to reliably predict lung cancer risk in this Chinese cohort of patients presenting with pulmonary nodules.

scholarly journals Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhong Zhang ◽  
Juan Pu ◽  
Haijun Zhang
Keyword(s):  
Risk Factors ◽  
Pancreatic Adenocarcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
Significant Risk ◽  
Early Death ◽  
Seer Database ◽  
Data Set ◽  
Internal Validation ◽  
Chinese Cohort

BackgroundPancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.MethodsPatients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.ResultsA total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.ConclusionThe nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.

scholarly journals Effect of a pulmonary nodule fact sheet on patient anxiety and knowledge: a quality improvement initiative

2018 ◽  
Vol 7 (3) ◽  
pp. e000437 ◽  
Author(s):  
Matthew T Koroscil ◽  
Mitchell H Bowman ◽  
Michael J Morris ◽  
Andrew J Skabelund ◽  
Andrew M Hersh
Keyword(s):  
Lung Cancer ◽  
Quality Improvement ◽  
Pulmonary Nodule ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Low Risk ◽  
Quality Improvement Initiative ◽  
Patient Anxiety ◽  
Patient Understanding ◽  
Fact Sheet

IntroductionThe utilisation of chest CT for the evaluation of pulmonary disorders, including low-dose CT for lung cancer screening, is increasing in the USA. As a result, the discovery of both screening-detected and incidental pulmonary nodules has become more frequent. Despite an overall low risk of malignancy, pulmonary nodules are a common cause of emotional distress among adult patients.MethodsWe conducted a multi-institutional quality improvement (QI) initiative involving 101 participants to determine the effect of a pulmonary nodule fact sheet on patient knowledge and anxiety. Males and females aged 35 years or older, who had a history of either screening-detected or incidental solid pulmonary nodule(s) sized 3–8 mm, were included. Prior to an internal medicine or pulmonary medicine clinic visit, participants were given a packet containing a pre-fact sheet survey, a pulmonary nodule fact sheet and a post-fact sheet survey.ResultsOf 101 patients, 61 (60.4%) worried about their pulmonary nodule at least once per month with 18 (17.8%) worrying daily. The majority 67/101 (66.3%) selected chemotherapy, chemotherapy and radiation, or radiation as the best method to cure early-stage lung cancer. Despite ongoing radiographic surveillance, 16/101 (15.8%) stated they would not be interested in an intervention if lung cancer was diagnosed. Following review of the pulmonary nodule fact sheet, 84/101 (83.2%) reported improved anxiety and 96/101 (95.0%) reported an improved understanding of their health situation. Patient understanding significantly improved from 4.2/10.0 to 8.1/10.0 (p<0.01).ConclusionThe incorporation of a standardised fact sheet for subcentimeter solid pulmonary nodules improves patient understanding and alleviates anxiety. We plan to implement pulmonary nodule fact sheets into the care of our patients with low-risk subcentimeter pulmonary nodules.

scholarly journals Malignant melanoma without primary, presenting as solitary pulmonary nodule: a case report

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
George Tsaknis ◽  
Muhammad Naeem ◽  
Advitya Singh ◽  
Siddharth Vijayakumar
Keyword(s):  
Lung Cancer ◽  
Malignant Melanoma ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Past History ◽  
White Male ◽  
Pulmonary Nodules ◽  
Metastatic Malignant Melanoma ◽  
Unknown Primary ◽  
Solitary Pulmonary Nodules

Abstract Background Solitary pulmonary nodules are the most common incidental finding on chest imaging. Their management is very well defined by several guidelines, with risk calculators for lung cancer being the gold standard. Solitary intramuscular metastasis combined with a solitary pulmonary nodule from malignant melanoma without a primary site is rare. Case presentation A 57-year-old white male was referred to our lung cancer service with solitary pulmonary nodule. After positron-emission tomography, we performed an ultrasound-guided core needle biopsy of an intramuscular solitary lesion, not identified on computed tomography scan, and diagnosed metastatic malignant melanoma. The solitary pulmonary nodule was resected and also confirmed metastatic melanoma. There was no primary skin lesion. The patient received oral targeted therapy and is disease-free 5 years later. Conclusions Clinicians dealing with solitary pulmonary nodules must remain vigilant for other extrathoracic malignancies even in the absence of obvious past history. Lung metastasectomy may have a role in metastatic malignant melanoma with unknown primary.

Age-adapted percentiles of measured glomerular filtration in healthy individuals: extrapolation to living kidney donors over 65 years

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pierre Delanaye ◽  
François Gaillard ◽  
Jessica van der Weijden ◽  
Geir Mjøen ◽  
Ingela Ferhman-Ekholm ◽  
...  
Keyword(s):  
Decision Making ◽  
Glomerular Filtration ◽  
Validation Cohort ◽  
External Validation ◽  
Healthy Individuals ◽  
Kidney Donors ◽  
Internal Validation ◽  
Living Kidney Donors ◽  
Multi Center Study ◽  
Elderly Living

Abstract Objectives Most data on glomerular filtration rate (GFR) originate from subjects <65 years old, complicating decision-making in elderly living kidney donors. In this retrospective multi-center study, we calculated percentiles of measured GFR (mGFR) in donors <65 years old and extrapolated these to donors ≥65 years old. Methods mGFR percentiles were calculated from a development cohort of French/Belgian living kidney donors <65 years (n=1,983), using quantiles modeled as cubic splines (two linear parts joining at 40 years). Percentiles were extrapolated and validated in an internal cohort of donors ≥65 years (n=147, France) and external cohort of donors and healthy subjects ≥65 years (n=329, Germany, Sweden, Norway, France, The Netherlands) by calculating percentages within the extrapolated 5th–95th percentile (P5–P95). Results Individuals in the development cohort had a higher mGFR (99.9 ± 16.4 vs. 86.4 ± 14 and 82.7 ± 15.5 mL/min/1.73 m2) compared to the individuals in the validation cohorts. In the internal validation cohort, none (0%) had mGFR below the extrapolated P5, 12 (8.2%) above P95 and 135 (91.8%) between P5–P95. In the external validation cohort, five subjects had mGFR below the extrapolated P5 (1.5%), 25 above P95 (7.6%) and 299 (90.9%) between P5–P95. Conclusions We demonstrate that extrapolation of mGFR from younger donors is possible and might aid with decision-making in elderly donors.

scholarly journals A Modified TNM Classification for Primary Operable Colorectal Cancer

2020 ◽  
Author(s):  
Chundong Zhang ◽  
Zubing Mei ◽  
Junpeng Pei ◽  
Masanobu Abe ◽  
Xiantao Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Fitting ◽  
Information Criteria ◽  
Stage I ◽  
Internal Validation ◽  
Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P &lt;0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

scholarly journals C-Reactive Protein (CRP) Levels in Immune Checkpoint Inhibitor Response and Progression in Advanced Non-Small Cell Lung Cancer: A Bi-Center Study

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2319
Author(s):  
Jakob M. Riedl ◽  
Dominik A. Barth ◽  
Wolfgang M. Brueckl ◽  
Gloria Zeitler ◽  
Vasile Foris ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Small Cell ◽  
C Reactive Protein ◽  
Small Cell Lung ◽  
Discovery Cohort ◽  
Reactive Protein ◽  
Progression Risk ◽  
Center Study

Background: Biomarkers for predicting response to immune checkpoint inhibitors (ICI) are scarce and often lack external validation. This study provides a comprehensive investigation of pretreatment C-reactive protein (CRP) levels as well as its longitudinal trajectories as a marker of treatment response and disease outcome in patients with advanced non-small cell lung cancer (NSCLC) undergoing immunotherapy with anti PD-1 or anti PD-L1 agents. Methods: We performed a retrospective bi-center study to assess the association between baseline CRP levels and anti PD-(L)1 treatment outcomes in the discovery cohort (n = 90), confirm these findings in an external validation cohort (n = 101) and explore the longitudinal evolution of CRP during anti PD-(L)1 treatment and the potential impact of dynamic CRP changes on treatment response and disease outcome in the discovery cohort. Joint models were implemented to evaluate the association of longitudinal CRP trajectories and progression risk. Primary treatment outcomes were progression-free survival (PFS) and overall survival (OS), while the objective response rate (ORR) was a secondary outcome, respectively. Results: In the discovery cohort, elevated pretreatment CRP levels emerged as independent predictors of worse PFS (HR per doubling of baseline CRP = 1.37, 95% CI: 1.16–1.63, p < 0.0001), worse OS (HR per doubling of baseline CRP = 1.42, 95% CI: 1.18–1.71, p < 0.0001) and a lower ORR ((odds ratio (OR) of ORR per doubling of baseline CRP = 0.68, 95% CI: 0.51–0.92, p = 0.013)). In the validation cohort, pretreatment CRP could be fully confirmed as a predictor of PFS and OS, but not ORR. Elevated trajectories of CRP during anti PD-(L)1 treatment (adjusted HR per 10 mg/L increase in CRP = 1.22, 95% CI: 1.15–1.30, p < 0.0001), as well as a faster increases of CRP over time (HR per 10 mg/L/month faster increase in CRP levels = 13.26, 95% CI: 1.14–154.54, p = 0.039) were strong predictors of an elevated progression risk, whereas an early decline of CRP was significantly associated with a reduction in PFS risk (HR = 0.91, 95% CI: 0.83–0.99, p = 0.036), respectively. Conclusion: These findings support the concept that CRP should be further explored by future prospective studies as a simple non-invasive biomarker for assessing treatment benefit during anti PD-(L)1 treatment in advanced NSCLC.

scholarly journals Screen-detected subsolid pulmonary nodules: long-term follow-up and application of the PanCan lung cancer risk prediction model

2016 ◽  
Vol 89 (1060) ◽  
pp. 20160016 ◽  
Author(s):  
Henry Zhao ◽  
Henry M Marshall ◽  
Ian A Yang ◽  
Rayleen V Bowman ◽  
John Ayres ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Lung Cancer Risk ◽  
Pulmonary Nodules ◽  
Risk Prediction Model ◽  
Long Term Follow Up

scholarly journals Clinically Applicable Deep Learning Strategy for Pulmonary Nodule Risk Prediction: Insights into HONORS

2020 ◽  
Author(s):  
Wenhui Lv ◽  
Yang Wang ◽  
Changsheng Zhou ◽  
Sheng Huang ◽  
Xiangming Fang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Medical Errors ◽  
Pulmonary Nodule ◽  
Large Scale ◽  
Learning Algorithm ◽  
Pulmonary Nodules ◽  
Youden Index ◽  
List Type ◽  
Clinical Scenarios

AbstractBackground and PurposeLimited optimization was clinically applicable for reducing missed diagnosis, misdiagnosis and inter-reader variability in pulmonary nodule diagnosis. We aimed to propose a deep learning-based algorithm and a practical strategy to better stratify the risk of pulmonary nodules, thus reducing medical errors and optimizing the clinical workflow.Materials and MethodsA total of 2,348 pulmonary nodules (1,215 with lung cancer) containing screened nodules from National Lung Cancer Screening Trial (NLST) and incidentally detected nodules from Jinling Hospital (JLH) were used to train and evaluate a deep learning algorithm, Filter-guided pyramid network (FGP-NET). Internal and external test of FGP-NET were performed on two independent datasets (n=542). The performance of FGP-NET at Youden point which maximizing the Youden index was compared with 126 board-certificated radiologists. We further proposed Hierarchical Ordered Network ORiented Strategy (HONORS), which manipulates the emphasis either on sensitivity or specificity to target risk-stratified clinical scenarios, directly making decisions for some patients.ResultsFGP-NET achieved a high area under the curve (AUC) of 0.969 and 0.855 for internal and external testing, and was comparable or even outperformed the radiologists when considering sensitivity. HONORS-guided FGP-NET identified benign nodules with a high sensitivity (95.5%) in the screening scenario, and demonstrated satisfactory performance for the rest ambiguous nodules with 0.945 of AUC by the Youden point. FGP-NET also detected lung cancer with a high specificity of 94.5% in routine diagnostic scenario; an AUC of 0.809 was achieved for the rest nodules.ConclusionThe combination of HONORS and FGP-NET provides well-organized stratification for pulmonary nodules and also offers the potential for reducing medical errors.HighlightsPulmonary nodules were managed for both screening and diagnostic scenariosProposal of a hierarchical strategy for targeting risk-stratified clinical scenariosA large scale Human-deep learning contest for reliable performance evaluation

scholarly journals Effect of Aging-Related Genes on the Prognosis of Colon Cancer

2021 ◽  
Author(s):  
Yushu Liu ◽  
Jiantao Gong ◽  
Yanyi Huang ◽  
Qunguang Jiang
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Predictive Ability ◽  
Lasso Regression ◽  
Internal Validation ◽  
Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

Sign in / Sign up

Export Citation Format

Share Document