1
|
Jiang X, Luo C, Peng X, Zhang J, Yang L, Liu LZ, Cui YF, Liu MW, Miao L, Jiang JM, Ren JL, Yang XT, Li M, Zhang L. Incidence rate of occult lymph node metastasis in clinical T 1-2N 0M 0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research. Respir Res 2024; 25:226. [PMID: 38811960 PMCID: PMC11138070 DOI: 10.1186/s12931-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
Collapse
Affiliation(s)
- Xu Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Chao Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xin Peng
- Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, 610031, China
- Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Jing Zhang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Lin Yang
- Department of Pathology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Yan-Fen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Meng-Wen Liu
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lei Miao
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiu-Ming Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, 100176, China
| | - Xiao-Tang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China.
| | - Meng Li
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Li Zhang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| |
Collapse
|
2
|
He Z, Chen M, Luo Z. Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma. Aging (Albany NY) 2024; 16:911-927. [PMID: 38217549 PMCID: PMC10817386 DOI: 10.18632/aging.205427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/04/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study was conducted to screen out immune-related genes in connection with the prognosis of melanoma, construct a prognosis model and explore the relevant mechanisms. METHODS AND MATERIALS 1973 genes associated with immune system were derived from the Immport database, and RNA-seq data of melanoma and information of patients were searched from the Xena database. Cox univariate analysis, Lasso analysis and Cox multivariate analysis were used to screen out six genes to construct the model. Then the risk scores were estimated for patients based on our constructed prognosis model. Estimate was used to affirm that the model was about immune infiltration, and CIBERSORT was used to screen out immune cells associated with prognosis. TIDE was applied to predict the efficacy of immunotherapy. Finally, GSE65904 and GSE19234 were used to confirm the effectiveness of the model. RESULTS ADCYAP1R1, GPI, NTS might cause poor prognosis while IFITM1, KIR2DL4, LIF were more likely conductive to prognosis of melanoma patients and a model of prognosis was constructed on the basis of these six genes. The effectiveness of the model has been proven by the ROC curve, and the miRNAs targeting the screened genes were found out, suggesting that the immune system might impact on the prognosis of melanoma by T cell CD8+, T cell CD4+ memory and NK cells. CONCLUSIONS In this study, the screened six genes were associated with the prognosis of melanoma, which was conductive to clinical prognostic prediction and individualized treatment strategy.
Collapse
Affiliation(s)
- Zhenghao He
- Department of Plastic Surgery, Zhongshan City People's Hospital, Zhongshan 528403, Guangdong, China
| | - Manli Chen
- Department of Plastic Surgery, Zhongshan City People's Hospital, Zhongshan 528403, Guangdong, China
| | - Zhijun Luo
- Department of Plastic Surgery, Zhongshan City People's Hospital, Zhongshan 528403, Guangdong, China
| |
Collapse
|
3
|
He W, Fu X, Chen S. Advancing polytrauma care: developing and validating machine learning models for early mortality prediction. J Transl Med 2023; 21:664. [PMID: 37743498 PMCID: PMC10518974 DOI: 10.1186/s12967-023-04487-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Rapid identification of high-risk polytrauma patients is crucial for early intervention and improved outcomes. This study aimed to develop and validate machine learning models for predicting 72 h mortality in adult polytrauma patients using readily available clinical parameters. METHODS A retrospective analysis was conducted on polytrauma patients from the Dryad database and our institution. Missing values pertinent to eligible individuals within the Dryad database were compensated for through the k-nearest neighbor algorithm, subsequently randomizing them into training and internal validation factions on a 7:3 ratio. The patients of our institution functioned as external validation cohorts. The predictive efficacy of random forest (RF), neural network, and XGBoost models was assessed through an exhaustive suite of performance indicators. The SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) methods were engaged to explain the supreme-performing model. Conclusively, restricted cubic spline analysis and multivariate logistic regression were employed as sensitivity analyses to verify the robustness of the findings. RESULTS Parameters including age, body mass index, Glasgow Coma Scale, Injury Severity Score, pH, base excess, and lactate emerged as pivotal predictors of 72 h mortality. The RF model exhibited unparalleled performance, boasting an area under the receiver operating characteristic curve (AUROC) of 0.87 (95% confidence interval [CI] 0.84-0.89), an area under the precision-recall curve (AUPRC) of 0.67 (95% CI 0.61-0.73), and an accuracy of 0.83 (95% CI 0.81-0.86) in the internal validation cohort, paralleled by an AUROC of 0.98 (95% CI 0.97-0.99), an AUPRC of 0.88 (95% CI 0.83-0.93), and an accuracy of 0.97 (95% CI 0.96-0.98) in the external validation cohort. It provided the highest net benefit in the decision curve analysis in relation to the other models. The outcomes of the sensitivity examinations were congruent with those inferred from SHAP and LIME. CONCLUSIONS The RF model exhibited the best performance in predicting 72 h mortality in adult polytrauma patients and has the potential to aid clinicians in identifying high-risk patients and guiding clinical decision-making.
Collapse
Affiliation(s)
- Wen He
- Reproductive Medicine Center, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, No. 100, Minjiang Avenue, Quzhou, 324000, Zhejiang, China
| | - Xianghong Fu
- Reproductive Medicine Center, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, No. 100, Minjiang Avenue, Quzhou, 324000, Zhejiang, China
| | - Song Chen
- Department of Orthopedics, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, No. 100, Minjiang Avenue, Quzhou, 324000, Zhejiang, China.
| |
Collapse
|
4
|
Chen HY, Deng XY, Pan Y, Chen JY, Liu YY, Chen WJ, Yang H, Zheng Y, Yang YB, Liu C, Shao GL, Yu RS. Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis. Front Oncol 2022; 11:745001. [PMID: 35004272 PMCID: PMC8733460 DOI: 10.3389/fonc.2021.745001] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open
Abstract
Objective To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). Materials and Methods Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. Results Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. Conclusion This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.
Collapse
Affiliation(s)
- Hai-Yan Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Xue-Ying Deng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jie-Yu Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yun-Ying Liu
- Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Wu-Jie Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Hong Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yao Zheng
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Yong-Bo Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
| | - Cheng Liu
- Research Institute of Artificial Intelligence in Healthcare, Hangzhou YITU Healthcare Technology Co. Ltd., Hangzhou, China
| | - Guo-Liang Shao
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, China
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
5
|
Zhu Y, Mao Y, Chen J, Qiu Y, Guan Y, Wang Z, He J. Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep 2021; 11:18347. [PMID: 34526604 PMCID: PMC8443588 DOI: 10.1038/s41598-021-97796-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/30/2021] [Indexed: 01/04/2023] Open
Abstract
To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.
Collapse
Affiliation(s)
- Yong Zhu
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210097, Jiangsu Province, China
| | - Yingfan Mao
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Jun Chen
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Yudong Qiu
- Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China
| | - Yue Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, No.1954 Huashan Road, Shanghai, 200000, China
| | - Zhongqiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210097, Jiangsu Province, China.
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu Province, China.
| |
Collapse
|
6
|
Zhuo Y, Feng M, Yang S, Zhou L, Ge D, Lu S, Liu L, Shan F, Zhang Z. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol 2020; 13:100820. [PMID: 32622312 PMCID: PMC7334418 DOI: 10.1016/j.tranon.2020.100820] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 12/23/2022] Open
Abstract
To evaluate the clinical features and radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of the presence of spread through air spaces (STAS) in patients with lung adenocarcinoma. A total of 107 STAS-positive lung adenocarcinomas were selected and matched to 105 STAS-negative lung adenocarcinomas. Thin-slice CT imaging annotation and region of interest (ROI) segmentation were performed with semi-automatic in-house software. Radiomics features were extracted from all nodules and incremental distances of 5, 10, and 15 mm outside the lesion segmentation. A radiomics nomogram was established with multivariable logistic regression based on clinical and radiomics features. The maximum diameter of the solid component and mediastinal lymphadenectasis were selected as independent predictors of STAS. The radiomics nomogram of lung nodules showed especially good prediction in the training set [area under the curve (AUC), 0.98; 95% confidence interval (CI), 0.97–1.00] and test set (AUC, 0.99; 95% CI, 0.97–1.00). The radiomics nomogram of peritumoral regions also showed good prediction, but the fitting degrees of the calibration curves were not good. Our study may provide guidance for surgical methods in patients with lung adenocarcinoma.
Collapse
Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China
| | - Mingxiang Feng
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
| | - Shuyi Yang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China
| | - Lingxiao Zhou
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China.
| | - Di Ge
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
| | - Shaohua Lu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lei Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China.
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, No. 2901 Caolang Road, Jinshan, Shanghai 201508, China; Research Institude of Big Data, Fudan University, Shanghai 200032, China; Headmaster's Office, Fudan University, Shanghai 200433, China; Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
| |
Collapse
|
7
|
Agier L, Slama R, Basagaña X. Relying on repeated biospecimens to reduce the effects of classical-type exposure measurement error in studies linking the exposome to health. ENVIRONMENTAL RESEARCH 2020; 186:109492. [PMID: 32330767 DOI: 10.1016/j.envres.2020.109492] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 03/11/2020] [Accepted: 04/04/2020] [Indexed: 05/27/2023]
Abstract
The exposome calls for assessing numerous exposures, typically using biomarkers with varying amounts of measurement error, which can be assumed to be of classical type. We evaluated the impact of classical-type measurement error on the performance of exposome-health studies, and the efficiency of two measurement error correction methods relying on the collection of repeated biospecimens: within-subject biospecimens pooling and regression calibration. In a simulation study, we generated 237 exposures from a realistic correlation matrix, with various amounts of classical-type measurement error, and a continuous health outcome linearly influenced by exposures. Measurement error decreased the sensitivity to identify exposures influencing health from a value of 75% down to 46%, increased false discovery proportion from 26% to 49% and increased attenuation bias in the slope of true predictors from 45% to 66%. Assuming that repeated biospecimens were available, within-subject pooling and regression calibration improved sensitivity (which increased to 63%), false discovery proportion (down to 37%) and bias (down to 49%) compared to an error-prone study with a single biospecimen per subject. Performances were poorer for the exposures with the largest amount of measurement error, and increased with the number of available biospecimens. Relying on repeated biospecimens only for the exposures with the largest amount of measurement error provided similar performance improvement. Exposome studies relying on spot exposure biospecimens suffer from decreased performances if some biomarkers suffer from measurement error due to their temporal variability; performances can be improved by collecting repeated biospecimens per subject, in particular for non persistent chemicals.
Collapse
Affiliation(s)
- Lydiane Agier
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Inserm, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France.
| | - Rémy Slama
- Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Inserm, CNRS, University Grenoble Alpes, Institute for Advanced Biosciences (IAB), U1209 Joint Research Center, Grenoble, France.
| | - Xavier Basagaña
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| |
Collapse
|