A Risk Prediction Model by LASSO for Radiation-Induced Xerostomia in Patients With Nasopharyngeal Carcinoma Treated With Comprehensive Salivary Gland-Sparing Helical Tomotherapy Technique

Cohort-based nomogram for forensic prediction of SCD: a single-center pilot study

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.

The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers

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.

Fine Motor Skill Decline After Brain Radiation Therapy-A Multivariate Normal Tissue Complication Probability Study of a Prospective Trial

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.

Development and validation of a nomogram model to predict primary graft dysfunction in patients after lung transplantation based on the clinical factors

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.

Development and validation of a nomogram for prediction of recovery from moderate-severe xerostomia post-radiotherapy in nasopharyngeal carcinoma patients

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 (D_mean) 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 D_mean 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 D_mean 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.

Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer

Machine 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.

Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma

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 D_20cc 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.

Radiotherapy for head and neck tumours using an oral fixation and parameter acquisition device and TOMO technology: a randomised controlled study

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.

A nomogram for predicting late radiation-induced xerostomia among locoregionally advanced nasopharyngeal carcinoma in intensity modulated radiation therapy era

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.