Added survival benefit of whole brain radiotherapy in brain metastatic non-small cell lung cancer: Development and external validation of an individual prediction model

BackgroundThe heterogeneous survival benefit of whole brain radiotherapy (WBRT) in brain metastatic non-small cell lung cancer (NSCLC) was prospectively evidenced in the Quality of Life after Treatment for Brain Metastases (QUARTZ) trial, resulting in inconsistent guideline recommendations and diverse clinical practices for giving WBRT. The objective of this study was to develop and externally validate an individual prediction model to demonstrate the added survival benefit of WBRT to assist decision making when giving WBRT is undetermined.MethodsFor model development, we collected 479 brain metastatic NSCLC patients unfit for surgery or stereotactic radiotherapy techniques at Siriraj Hospital. Potential predictors were age, sex, performance status, histology, genetic mutation, neurological symptoms, extracranial disease, previous systemic treatment, measurable lesions, further systemic treatment, and WBRT. Cox proportional hazard regression was used for survival analysis. We used multiple imputations to handle missing data and a backward selection method for predictor selection. Bootstrapping was used for internal validation, while model performance was assessed with discrimination (c-index) and calibration prediction accuracy. The final model was transformed into a nomogram and a web-based calculator. An independent cohort from Sawanpracharak Hospital was used for external validation.ResultsIn total, 452 patients in the development cohort died. The median survival time was 4.4 (95% CI, 3.8–4.9) months, with 5.1 months for patients who received WBRT and 2.3 months for those treated with optimal supportive care (OSC). The final model contained favorable predictors: female sex, KPS > 70, receiving additional systemic treatment, and WBRT. Having active extracranial disease, experiencing neurological symptoms, and receiving previous systemic treatment were adverse predictors. After optimism correction, the apparent c-index dropped from 0.71 (95% CI, 0.69–0.74) to 0.70 (95% CI, 0.69–0.73). The predicted and observed values agreed well in all risk groups. Our model performed well in the external validation cohort, with a c-index of 0.66 (95% CI, 0.59–0.73) and an acceptable calibration.ConclusionsThis model (https://siriraj-brainmetscore.netlify.app/) predicted the added survival benefit of WBRT for individual brain metastatic NSCLC patients, with satisfactory performance in the development and validation cohorts. The results certify its value in aiding treatment decision-making when the administration of WBRT is unclear

Final data set for submission13nov2017.xls

Data for manuscript : Neoadjuvant chemotherapy followed by concurrent chemoradiotherapy versus concurrent chemoradiotherapy followed by adjuvant chemotherapy in locally advanced nasopharyngeal carcinoma<br><br>from Division of Radiation Oncology, Department of Radiology, Faculty of Medicine, Siriraj hospital, Mahidol University, Bangkok, Thailand<br

3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility

This paper proposes a time-series deep-learning 3D Kinect camera scheme to classify the respiratory phases with a lung tumor and predict the lung tumor displacement. Specifically, the proposed scheme is driven by two time-series deep-learning algorithmic models: the respiratory-phase classification model and the regression-based prediction model. To assess the performance of the proposed scheme, the classification and prediction models were tested with four categories of datasets: patient-based datasets with regular and irregular breathing patterns; and pseudopatient-based datasets with regular and irregular breathing patterns. In this study, ‘pseudopatients’ refer to a dynamic thorax phantom with a lung tumor programmed with varying breathing patterns and breaths per minute. The total accuracy of the respiratory-phase classification model was 100%, 100%, 100%, and 92.44% for the four dataset categories, with a corresponding mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2) of 1.2–1.6%, 0.65–0.8%, and 0.97–0.98, respectively. The results demonstrate that the time-series deep-learning classification and regression-based prediction models can classify the respiratory phases and predict the lung tumor displacement with high accuracy. Essentially, the novelty of this research lies in the use of a low-cost 3D Kinect camera with time-series deep-learning algorithms in the medical field to efficiently classify the respiratory phase and predict the lung tumor displacement

DataSheet_1_Added survival benefit of whole brain radiotherapy in brain metastatic non-small cell lung cancer: Development and external validation of an individual prediction model.docx

BackgroundThe heterogeneous survival benefit of whole brain radiotherapy (WBRT) in brain metastatic non-small cell lung cancer (NSCLC) was prospectively evidenced in the Quality of Life after Treatment for Brain Metastases (QUARTZ) trial, resulting in inconsistent guideline recommendations and diverse clinical practices for giving WBRT. The objective of this study was to develop and externally validate an individual prediction model to demonstrate the added survival benefit of WBRT to assist decision making when giving WBRT is undetermined.MethodsFor model development, we collected 479 brain metastatic NSCLC patients unfit for surgery or stereotactic radiotherapy techniques at Siriraj Hospital. Potential predictors were age, sex, performance status, histology, genetic mutation, neurological symptoms, extracranial disease, previous systemic treatment, measurable lesions, further systemic treatment, and WBRT. Cox proportional hazard regression was used for survival analysis. We used multiple imputations to handle missing data and a backward selection method for predictor selection. Bootstrapping was used for internal validation, while model performance was assessed with discrimination (c-index) and calibration prediction accuracy. The final model was transformed into a nomogram and a web-based calculator. An independent cohort from Sawanpracharak Hospital was used for external validation.ResultsIn total, 452 patients in the development cohort died. The median survival time was 4.4 (95% CI, 3.8–4.9) months, with 5.1 months for patients who received WBRT and 2.3 months for those treated with optimal supportive care (OSC). The final model contained favorable predictors: female sex, KPS > 70, receiving additional systemic treatment, and WBRT. Having active extracranial disease, experiencing neurological symptoms, and receiving previous systemic treatment were adverse predictors. After optimism correction, the apparent c-index dropped from 0.71 (95% CI, 0.69–0.74) to 0.70 (95% CI, 0.69–0.73). The predicted and observed values agreed well in all risk groups. Our model performed well in the external validation cohort, with a c-index of 0.66 (95% CI, 0.59–0.73) and an acceptable calibration.ConclusionsThis model (https://siriraj-brainmetscore.netlify.app/) predicted the added survival benefit of WBRT for individual brain metastatic NSCLC patients, with satisfactory performance in the development and validation cohorts. The results certify its value in aiding treatment decision-making when the administration of WBRT is unclear.</p

The Impact of Active Nutritional Support for Head and Neck Cancer Patients Receiving Concurrent Chemoradiotherapy

Objective: Malnutrition is the most common problem in head and neck cancer (HNC) patients receiving concurrent chemoradiotherapy. The radiation toxicities cause decreased food intake, with resultant severe weight loss and malnutrition. This study sought to determine whether an active nutrition improvement counseling program before and during concurrent chemoradiotherapy for HNC patients could increase the treatment completion rate without the interruptions caused by the side effects of chemoradiotherapy. Methods: The findings of a prospective study of the effects of an active nutrition improvement program before and during concurrent chemoradiotherapy (study, n = 32) was compared with those of a retrospective chart review of HNC patients who had received definite or postoperative concurrent chemoradiotherapy (control, n = 80). The correlations between nutritional status and the number of treatment completions, number of tube feeding insertions during treatment, RTOG toxicity, nutritional status, and quality of life were obtained. Results: There was no statistically significant difference between the concurrent chemoradiotherapy completion rates of both groups (p = 0.121; 95% CI, 0.226-1.188). The major cause of delayed or discontinued chemotherapy was oral mucositis. No significant differences were found in the tube feeding insertion rates and RTOG toxicities of both groups. However, the data showed a clinically significant difference in the concurrent chemoradiotherapy completion rate for the study group (56%), more than 15 percentage points higher than the control group’s rate (40%). Conclusion: An active nutrition improvement program before and during concurrent chemoradiotherapy is clinically beneficial for HNC patients, providing a higher treatment completion rate than otherwise