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Liao Z, Chen G, Cao X, Liu L, Li J, Zhu B, Cao Z. Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study. Forensic Sci Med Pathol 2025:10.1007/s12024-024-00920-6. [PMID: 39797964 DOI: 10.1007/s12024-024-00920-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/14/2024] [Indexed: 01/13/2025]
Abstract
Forensic diagnosis of sudden cardiac death (SCD) is an extremely important part of routine forensic practice. The present study aimed to develop and validate nomograms for predicting the probability of SCD with special regards to ischemic heart disease-induced SCD (IHD-induced SCD) based on multiple autopsy variables. A total of 3322 cases, were enrolled and randomly assigned into a training cohort (n = 2325) and a validation cohort (n = 997), respectively. Prediction models of SCD and IHD-induced SCD were developed through multivariable logistic regression based on variables selected by LASSO regression or ridge regression, and prediction model with higher area under the curve (AUC) of the receiver operating characteristic (ROC) curve in the validation cohort was used to establish nomograms. For SCD prediction, discrimination of the nomogram was determined based on the ROC with AUC of 0.751 (95% CI, 0.726-0.775) and 0.735 (95% CI, 0.696-0.774) in the training cohort and validation cohort respectively. The AUC of IHD-induced SCD prediction nomogram in the training cohort and validation cohort were 0.742 (95% CI, 0.716-0.768) and 0.738 (95% CI, 0.698-0.777). To facilitate the use of nomograms in routine casework in forensic practice, web calculators ( https://forensic.shinyapps.io/Forensic_SCD/ , https://forensic.shinyapps.io/Forensic_IHDinducedSCD/ ) were constructed. In conclusion, the present study developed and validated simple and practical nomograms for predicting the probability of SCD and IHD-induced SCD based on multiple autopsy variables. The nomograms have certain efficiency for discrimination and calibration to provide a novel approach to diagnose cause of death, and may become a valuable tool in forensic practice.
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Affiliation(s)
- Zihan Liao
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China
| | - Gaohan Chen
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China
| | - Xingrui Cao
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China
| | - Longqiao Liu
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China
| | - Jiatong Li
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China
| | - Baoli Zhu
- Academy of Forensic Science, Liaoning University, No. 111, Nujiang Street, Huanggu Area, Shenyang, 110031, P. R. China.
| | - Zhipeng Cao
- Department of Forensic Pathology, School of Forensic Medicine, China Medical University, Shenyang, 110122, P. R. China.
- Liaoning Province Key Laboratory of Forensic Bio-evidence Sciences, Shenyang, 110122, P. R. China.
- Center of Forensic Investigation, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, P. R. China.
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Zhang Y, Chen Y, Su Q, Huang X, Li Q, Yang Y, Zhang Z, Chen J, Xiao Z, Xu R, Zu Q, Du S, Zheng W, Ye W, Xiang J. The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers. BMC Public Health 2024; 24:3269. [PMID: 39587532 PMCID: PMC11587756 DOI: 10.1186/s12889-024-20713-4] [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: 05/27/2024] [Accepted: 11/12/2024] [Indexed: 11/27/2024] Open
Abstract
BACKGROUND Workplace may not only increase the risk of heat-related illnesses and injuries but also compromise work efficiency, particularly in a warming climate. This study aimed to utilize machine learning (ML) and deep learning (DL) algorithms to quantify the impact of temperature discomfort on productivity loss among petrochemical workers and to identify key influencing factors. METHODS A cross-sectional face-to-face questionnaire survey was conducted among petrochemical workers between May and September 2023 in Fujian Province, China. Initial feature selection was performed using Lasso regression. The dataset was divided into training (70%), validation (20%), and testing (10%) sets. Six predictive models were evaluated: support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP), and logistic regression (LR). The most effective model was further analyzed with SHapley Additive exPlanations (SHAP). RESULTS Among the 2393 workers surveyed, 58.4% (1,747) reported productivity loss when working in high temperatures. Lasso regression identified twenty-seven predictive factors such as educational level and smoking. All six models displayed strong prediction accuracy (SVM = 0.775, RF = 0.760, XGBoost = 0.727, GNB = 0.863, MLP = 0.738, LR = 0.680). GNB model showed the best performance, with a cutoff of 0.869, accuracy of 0.863, precision of 0.897, sensitivity of 0.918, specificity of 0.715, and an F1-score of 0.642, indicating its efficacy as a predictive tool. SHAP analysis showed that occupational health training (SHAP value: -3.56), protective measures (-2.61), and less physically demanding jobs (-1.75) were negatively associated with heat-attributed productivity loss, whereas lack of air conditioning (1.92), noise (2.64), vibration (1.15), and dust (0.95) increased the risk of heat-induced productivity loss. CONCLUSIONS Temperature discomfort significantly undermined labor productivity in the petrochemical sector, and this impact may worsen in a warming climate if adaptation and prevention measures are insufficient. To effectively reduce heat-related productivity loss, there is a need to strengthen occupational health training and implement strict controls for occupational hazards, minimizing the potential combined effects of heat with other exposures.
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Affiliation(s)
- Yilin Zhang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Yifeng Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Qingling Su
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China
| | - Xiaoyin Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China
| | - Qingyu Li
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Yan Yang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Zitong Zhang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Jiake Chen
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Zhihong Xiao
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China
| | - Rong Xu
- Minnan Branch of the First Affiliated Hospital of Fujian Medical University, Quangang, Quanzhou, 362100, Fujian Province, China
| | - Qing Zu
- Minnan Branch of the First Affiliated Hospital of Fujian Medical University, Quangang, Quanzhou, 362100, Fujian Province, China
| | - Shanshan Du
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China
| | - Wei Zheng
- Minnan Branch of the First Affiliated Hospital of Fujian Medical University, Quangang, Quanzhou, 362100, Fujian Province, China.
| | - Weimin Ye
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China.
| | - Jianjun Xiang
- Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China.
- School of Public Health, The University of Adelaide, North Terrace Campus, Adelaide, South Australia, 5005, Australia.
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Connor M, Salans M, Karunamuni R, Unnikrishnan S, Huynh-Le MP, Tibbs M, Qian A, Reyes A, Stasenko A, McDonald C, Moiseenko V, El-Naqa I, Hattangadi-Gluth JA. Fine Motor Skill Decline After Brain Radiation Therapy-A Multivariate Normal Tissue Complication Probability Study of a Prospective Trial. Int J Radiat Oncol Biol Phys 2023; 117:581-593. [PMID: 37150258 PMCID: PMC10911396 DOI: 10.1016/j.ijrobp.2023.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/20/2023] [Accepted: 04/29/2023] [Indexed: 05/09/2023]
Abstract
PURPOSE Brain radiation therapy can impair fine motor skills (FMS). Fine motor skills are essential for activities of daily living, enabling hand-eye coordination for manipulative movements. We developed normal tissue complication probability (NTCP) models for the decline in FMS after fractionated brain radiation therapy (RT). METHODS AND MATERIALS On a prospective trial, 44 patients with primary brain tumors received fractioned RT; underwent high-resolution volumetric magnetic resonance imaging, diffusion tensor imaging, and comprehensive FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS]; and Grooved Pegboard dominant/nondominant hands) at baseline and 6 months postRT. Regions of interest subserving motor function (including cortex, superficial white matter, thalamus, basal ganglia, cerebellum, and white matter tracts) were autosegmented using validated methods and manually verified. Dosimetric and clinical variables were included in multivariate NTCP models using automated bootstrapped logistic regression, least absolute shrinkage and selection operator logistic regression, and random forests with nested cross-validation. RESULTS Half of the patients showed a decline on grooved pegboard test of nondominant hands, 17 of 42 (40.4%) on grooved pegboard test of -dominant hands, and 11 of 44 (25%) on DKEFS-MS. Automated bootstrapped logistic regression selected a 1-term model including maximum dose to dominant postcentral white matter. The least absolute shrinkage and selection operator logistic regression selected this term and steroid use. The top 5 variables in the random forest were all dosimetric: maximum dose to dominant thalamus, mean dose to dominant caudate, mean and maximum dose to the dominant corticospinal tract, and maximum dose to dominant postcentral white matter. This technique performed best with an area under the curve of 0.69 (95% CI, 0.68-0.70) on nested cross-validation. CONCLUSIONS We present the first NTCP models for FMS impairment after brain RT. Dose to several supratentorial motor-associated regions of interest correlated with a decline in dominant-hand fine motor dexterity in patients with primary brain tumors in multivariate models, outperforming clinical variables. These data can guide prospective fine motor-sparing strategies for brain RT.
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Affiliation(s)
- Michael Connor
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Mia Salans
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Soumya Unnikrishnan
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | | | - Michelle Tibbs
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Alexander Qian
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Anny Reyes
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Alena Stasenko
- Department of Psychiatry, University of California San Diego, San Diego, California
| | - Carrie McDonald
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California
| | - Issam El-Naqa
- Department of Radiation Oncology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Jona A Hattangadi-Gluth
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California.
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Qin J, Hu C, Cao X, Gao J, Chen Y, Yan M, Chen J. Development and validation of a nomogram model to predict primary graft dysfunction in patients after lung transplantation based on the clinical factors. Clin Transplant 2023; 37:e15039. [PMID: 37256785 DOI: 10.1111/ctr.15039] [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: 03/11/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Primary graft dysfunction (PGD), a significant complication that can affect patients' prognosis and quality of life, develops within 72 h post lung transplantation (LTx). Early detection and prevention of PGD should be given special consideration. The purpose of this study was to create a clinical prediction model to forecast the occurrence of PGD. METHODS We collected information on 622 LTx patients from Wuxi People's Hospital from 2016 to 2020 and used the data to construct the prediction model. Information on 224 patients from 2021 to June 2022 was used for external validation. We used LASSO regression for variable screening. A nomogram was developed for model presentation. Distinctness, fit, and calibration were used to evaluate the performance of the model. RESULTS Subjects with respiratory failure, who received fresh frozen plasma, donor age, donor gender, donor mechanism of death, donor smoking, donor ventilator use time, and donor PaO 2/FiO 2 ratio were independent predictor variables for the occurrence of PGD. The area under the curve of the nomogram was .779. The Hosmer-Lemeshow test showed a good model fit (P = .158). The calibration curve of the nomogram is fairly close to the ideal diagonal. Moreover, the decision curve analysis revealed a positive net benefit of the model. External validation also confirmed the reliability of the model. CONCLUSIONS The nomogram of PGD based on clinical risk factors in postoperative LTx patients was established with high reliability. It provides clinicians and nurses with a new and effective tool for early prediction of PGD and early intervention.
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Affiliation(s)
- Jianan Qin
- School of Nursing, Fudan University, Shanghai, China
- Operation Department, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Chunxiao Hu
- Wuxi Lung Transplant Center, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Xiaodong Cao
- Department of Nursing, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Jian Gao
- Department of Nutrition, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuan Chen
- Wuxi Lung Transplant Center, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
| | - Meiqiong Yan
- Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jingyu Chen
- Wuxi Lung Transplant Center, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China
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Zhao DW, Teng F, Meng LL, Fan WJ, Luo YR, Jiang HY, Chen NX, Zhang XX, Yu W, Cai BN, Zhao LJ, Wang PG, Ma L. Development and validation of a nomogram for prediction of recovery from moderate-severe xerostomia post-radiotherapy in nasopharyngeal carcinoma patients. Radiother Oncol 2023; 184:109683. [PMID: 37120102 DOI: 10.1016/j.radonc.2023.109683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/28/2023] [Accepted: 04/21/2023] [Indexed: 05/01/2023]
Abstract
PURPOSE Aim to create and validate a comprehensive nomogram capable of accurately predicting the transition from moderate-severe to normal-mild xerostomia post-radiotherapy (postRT) in patients with nasopharyngeal carcinoma (NPC). Materials and methods We constructed and internally verified a prediction model using a primary cohort comprising 223 patients who were pathologically diagnosed with NPC from February 2016 to December 2019. LASSO regression model was used to identify the clinical factors and relevant variables (the pre-radiotherapy (XQ-preRT) and immediate post-radiotherapy (XQ-postRT) xerostomia questionnaire scores, as well as the mean dose (Dmean) delivered to the parotid gland (PG), submandibular gland (SMG), sublingual gland (SLG), tubarial gland (TG), and oral cavity). Cox proportional hazards regression analysis was performed to develop the prediction model, which was presented as a nomogram. The models' performance with regard to calibration, discrimination, and clinical usefulness was evaluated. The external validation cohort comprised 78 patients. Results Due to better discrimination and calibration in the training cohort, age, gender, XQ-postRT, and Dmean of PG, SMG, and TG were included in the individualized prediction model (C-index of 0.741 (95% CI:0.717 to 0.765). Verification of the nomogram's performance in internal and external validation cohorts revealed good discrimination (C-index of 0.729 (0.692 to 0.766) and 0.736 (0.702 to 0.770), respectively) and calibration. Decision curve analysis revealed that the nomogram was clinically useful. The 12-month and 24-month moderate-severe xerostomia rate was statistically lower in the SMG-spared arm (28.4% (0.230 to 35.2) and 5.2% (0.029 to 0.093), respectively) than that in SMG-unspared arm (56.8% (0.474 to 0.672) and 12.5% (0.070 to 0.223), respectively), with an HR of 1.84 (95%CI: 1.412 to 2.397, p= 0.000). The difference in restricted mean survival time for remaining moderate-severe xerostomia between the two arms at 24 months was 5.757 months (95% CI, 3.863 to 7.651; p=0.000). Conclusion The developed nomogram, incorporating age, gender, XQ-postRT, and Dmean to PG, SMG, and TG, can be used for predicting recovery from moderate-severe xerostomia post-radiotherapy in NPC patients. Sparing SMG is highly important for the patient's recovery.
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Affiliation(s)
- Da-Wei Zhao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China; Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China
| | - Feng Teng
- Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing, China
| | - Ling-Ling Meng
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wen-Jun Fan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China; Department of Radiation Oncology, Armed Police Forces Corps Hospital of Henan Province, Zhengzhou, 450052, China
| | - Yan-Rong Luo
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Hua-Yong Jiang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Nan-Xiang Chen
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin-Xin Zhang
- Department of Otolaryngology, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wei Yu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bo-Ning Cai
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Lu-Jun Zhao
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Pei-Guo Wang
- Department of Radiation Oncology, First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Lin Ma
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
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Adeoye J, Hui L, Su YX. Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer. JOURNAL OF BIG DATA 2023; 10:28. [DOI: 10.1186/s40537-023-00703-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/23/2023] [Indexed: 01/03/2025]
Abstract
AbstractMachine learning models have been increasingly considered to model head and neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of the disease. As the concept of data-centric artificial intelligence is still incipient in healthcare systems, little is known about the data quality of the models proposed for clinical utility. This is important as it supports the generalizability of the models and data standardization. Therefore, this study overviews the quality of structured and unstructured data used for machine learning model construction in head and neck cancer. Relevant studies reporting on the use of machine learning models based on structured and unstructured custom datasets between January 2016 and June 2022 were sourced from PubMed, EMBASE, Scopus, and Web of Science electronic databases. Prediction model Risk of Bias Assessment (PROBAST) tool was used to assess the quality of individual studies before comprehensive data quality parameters were assessed according to the type of dataset used for model construction. A total of 159 studies were included in the review; 106 utilized structured datasets while 53 utilized unstructured datasets. Data quality assessments were deliberately performed for 14.2% of structured datasets and 11.3% of unstructured datasets before model construction. Class imbalance and data fairness were the most common limitations in data quality for both types of datasets while outlier detection and lack of representative outcome classes were common in structured and unstructured datasets respectively. Furthermore, this review found that class imbalance reduced the discriminatory performance for models based on structured datasets while higher image resolution and good class overlap resulted in better model performance using unstructured datasets during internal validation. Overall, data quality was infrequently assessed before the construction of ML models in head and neck cancer irrespective of the use of structured or unstructured datasets. To improve model generalizability, the assessments discussed in this study should be introduced during model construction to achieve data-centric intelligent systems for head and neck cancer management.
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Fu G, Xie Y, Pan J, Qiu Y, He H, Li Z, Li J, Feng Y, Lv X. Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma. Radiother Oncol 2022; 173:277-284. [PMID: 35718009 DOI: 10.1016/j.radonc.2022.06.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 06/04/2022] [Accepted: 06/12/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. METHODS 36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. RESULTS Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. CONCLUSION Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.
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Affiliation(s)
- Gui Fu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yuanyao Xie
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jie Pan
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Haoqiang He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China; Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.
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8
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Zhang X, Wang T, Xiao X, Li X, Wang CY, Huang B, He L, Song Y. Radiotherapy for head and neck tumours using an oral fixation and parameter acquisition device and TOMO technology: a randomised controlled study. BMJ Open 2021; 11:e052542. [PMID: 34772753 PMCID: PMC8593711 DOI: 10.1136/bmjopen-2021-052542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 10/19/2021] [Indexed: 11/08/2022] Open
Abstract
INTRODUCTION Radiotherapy has become one of the main methods used for the treatment of malignant tumours of the head and neck. Spiral tomographic intensity-modulated radiotherapy has the many advantages of precision radiotherapy, which puts forward high requirements for postural reproducibility and accuracy. We will aim to ensure that the accurate positioning of the tumour will reduce the side effects of radiotherapy caused by positioning errors. We will design and implement this clinical trial using the patent of 'a radiotherapy oral fixation and parameter acquisition device (patent number: ZL201921877986.5)'. METHODS AND ANALYSIS This will be a randomised, controlled, prospective study with 120 patients with head and neck tumours. Using the random number table method, a random number sequence will be generated, and the patients will be enrolled in the experimental group (oral fixation device) and the control group (conventional fixation) in a 2:1 ratio. The primary outcome will be the progression-free survival time after the treatment. Secondary outcomes will include the oral mucosal reaction and the quality of life. Follow-ups will be carried out according to the plan. This is V.1.0 of protocol on 1 April 2021. The recruitment process for this clinical trial commenced on 1 May 2021, and will end on 1 October 2022. ETHICS AND DISSEMINATION The trial received ethical approval from Medical Ethics Committee of Liaoning Provincial Cancer Hospital (number 20210131X). The final results will be presented at a scientific conference and published in a peer-reviewed journal in accordance with the journal's guidelines. TRIAL REGISTRATION NUMBER ChiCTR2100045096.
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Affiliation(s)
- Xiaofang Zhang
- Department of Radiotherapy, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianlu Wang
- Department of Radiotherapy, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xinyan Xiao
- China Medical University, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xia Li
- Department of Radiotherapy, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Chen Yu Wang
- Department of Information Management, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Bo Huang
- Department of Pathology, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Lei He
- Department of Radiotherapy Physics, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yingqiu Song
- Department of Radiotherapy, Cancer Hospital of China Medical University, Shenyang, Liaoning, China
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Yang K, Xie W, Zhang X, Wang Y, Shou A, Wang Q, Tian J, Yang J, Li G. A nomogram for predicting late radiation-induced xerostomia among locoregionally advanced nasopharyngeal carcinoma in intensity modulated radiation therapy era. Aging (Albany NY) 2021; 13:18645-18657. [PMID: 34282056 PMCID: PMC8351700 DOI: 10.18632/aging.203308] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 06/29/2021] [Indexed: 02/05/2023]
Abstract
Background: Dry mouth sensation cannot be improved completely even though parotids are spared correctly. Our purpose is to develop a nomogram to predict the moderate-to-severe late radiation xerostomia for patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) in intensity modulated radiation therapy (IMRT) / volumetric modulated arc radiotherapy (VMAT) era. Methods: A dataset of 311 patients was retrospectively collected between January 2010 and February 2013. The binary logistic regression was to estimate each factor’s prognostic value for development of moderate-to-severe patient-reported xerostomia at least 2 years (Xer2y) after completion of radiotherapy. Therefore, we can develop a nomogram according to binary logistic regression coefficients. This novel model was validated by bootstrapping analyses. Results: Contralateral Parotid mean dose (coMD<24.4Gy), VMAT (yes), and platinum-based concurrent chemoradiotherapy (no) were significantly related to patient-reported xerostomia at least 2 years (Xer2y) (all p < 0.001), and were included in the nomogram. Receiver operating characteristic (ROC) analysis revealed AUC (area under the ROC curve) with the value of 0.811 (0.710-0.912) of the nomogram, which was significantly higher than coMD 0.698 (0.560-0.840) from QUANTEC2010 (p<0.001). Calibration plots illustrated that the predicted Xer2y was close to the actual observation, and decision curve analyses (DCA) indicated valid positive net benefits. Conclusion: We developed a feasible nomogram to predict patient-rated Xer2y based on comprehensive individual data in patients with LA-NPC in the real world. The proposed model is able to facilitate the development of treatment plan and quality of life improvement.
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Affiliation(s)
- Kaixuan Yang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.,Department of Radiation Oncology, West China Second University Hospital and Key Laboratory of Obstetrics and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Wenji Xie
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Xiangbin Zhang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Yu Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, Sichuan, China
| | - Arthur Shou
- School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, China
| | - Qiang Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jiangfang Tian
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Jiangping Yang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
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