Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy.

PURPOSE: Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS: CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS: CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION: Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation therapy (IMRT) was measured between one month prior to and one week after the start of IMRT. Weight change between one week and two months after the commencement of IMRT was analyzed, and dichotomized at 5% WL. Each patient had a planning CT and contours of gross tumor volume (GTV) and esophagus (ESO). A total of 355 features including clinical parameter (CP), GTV and ESO (GTV&ESO) dose-volume histogram (DVH), GTV radiomics, and GTV&ESO dosiomics features were extracted. R&D features were categorized as first- (L1), second- (L2), higher-order (L3) statistics, and three combined groups, L1 + L2, L2 + L3 and L1 + L2 + L3. Multi-view texture analysis was performed to identify optimal R&D input features. In the training set (194 earlier patients), feature selection was performed using Boruta algorithm followed by collinearity removal based on variance inflation factor. Machine-learning models were developed using Laplacian kernel support vector machine (lpSVM), deep neural network (DNN) and their averaged ensemble classifiers. Prediction performance was tested on an independent test set (194 more recent patients), and compared among seven different input conditions: CP-only, DVH-only, R&D-only, DVH + CP, R&D + CP, R&D + DVH and R&D + DVH + CP. Combined GTV L1 + L2 + L3 radiomics and GTV&ESO L3 dosiomics were identified as optimal input features, which achieved the best performance with an ensemble classifier (AUC = 0.710), having statistically significantly higher predictability compared with DVH and/or CP features (p < 0.05). When this performance was compared to that with full R&D-only features which reflect traditional single-view data, there was a statistically significant difference (p < 0.05). Using optimized multi-view R&D input features is beneficial for predicting early WL in lung cancer radiotherapy, leading to improved performance compared to using conventional DVH and/or CP features.

Utility of a Clinical Decision Support System in Weight Loss Prediction After Head and Neck Cancer Radiotherapy.

PURPOSE: To evaluate the utility of a clinical decision support system (CDSS) using a weight loss prediction model. METHODS: A prediction model for significant weight loss (loss of greater than or equal to 7.5% of body mass at 3-month post radiotherapy) was created with clinical, dosimetric, and radiomics predictors from 63 patients in an independent training data set (accuracy, 0.78; area under the curve [AUC], 0.81) using least absolute shrinkage and selection operator logistic regression. Four physicians with varying experience levels were then recruited to evaluate 100 patients in an independent validation data set of head and neck cancer twice (ie, a pre-post design): first without and then with the aid of a CDSS derived from the prediction model. At both evaluations, physicians were asked to predict the development (yes/no) and probability of significant weight loss for each patient on the basis of patient characteristics, including pretreatment dysphagia and weight loss and information from the treatment plan. At the second evaluation, physicians were also provided with the prediction model's results for weight loss probability. Physicians' predictions were compared with actual weight loss, and accuracy and AUC were investigated between the two evaluations. RESULTS: The mean accuracy of the physicians' ability to identify patients who will experience significant weight loss (yes/no) increased from 0.58 (range, 0.47 to 0.63) to 0.63 (range, 0.58 to 0.72) with the CDSS ( P = .06). The AUC of weight loss probability predicted by physicians significantly increased from 0.56 (range, 0.46 to 0.64) to 0.69 (range, 0.63 to 0.73) with the aid of the CDSS ( P < .05). Specifically, more improvement was observed among less-experienced physicians ( P < .01). CONCLUSION: Our preliminary results demonstrate that physicians' decisions may be improved by a weight loss CDSS model, especially among less-experienced physicians. Additional study with a larger cohort of patients and more participating physicians is thus warranted for understanding the usefulness of CDSSs.

The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System.

PURPOSE: Clinical data collection and development of outcome prediction models by machine learning can form the foundation for a learning health system offering precision radiation therapy. However, changes in clinical practice over time can affect the measures and patient outcomes and, hence, the collected data. We hypothesize that regular prediction model updates and continuous prospective data collection are important to prevent the degradation of a model's predication accuracy. METHODS AND MATERIALS: Clinical and dosimetric data from head and neck patients receiving intensity modulated radiation therapy from 2008 to 2015 were prospectively collected as a routine clinical workflow and anonymized for this analysis. Prediction models for grade ≥2 xerostomia at 3 to 6 months of follow-up were developed by bivariate logistic regression using the dose-volume histogram of parotid and submandibular glands. A baseline prediction model was developed with a training data set from 2008 to 2009. The selected predictor variables and coefficients were updated by 4 different model updating methods. (A) The prediction model was updated by using only recent 2-year data and applied to patients in the following test year. (B) The model was updated by increasing the training data set yearly. (C) The model was updated by increasing the training data set on the condition that the area under the curve (AUC) of the recent test year was less than 0.6. (D) The model was not updated. The AUC of the test data set was compared among the 4 model updating methods. RESULTS: Dose to parotid and submandibular glands and grade of xerostomia showed decreasing trends over the years (2008-2015, 297 patients; P < .001). The AUC of predicting grade ≥2 xerostomia for the initial training data set (2008-2009, 41 patients) was 0.6196. The AUC for the test data set (2010-2015, 256 patients) decreased to 0.5284 when the initial model was not updated (D). However, the AUC was significantly improved by model updates (A: 0.6164; B: 0.6084; P < .05). When the model was conditionally updated, the AUC was 0.6072 (C). CONCLUSIONS: Our preliminary results demonstrate that updating prediction models with prospective data collection is effective for maintaining the performance of xerostomia prediction. This suggests that a machine learning framework can handle the dynamic changes in a radiation oncology clinical practice and may be an important component for the construction of a learning health system.

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

OBJECTIVE: We explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. METHODS AND MATERIALS: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data. RESULTS: Among 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume-larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively. CONCLUSIONS: We demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system.

Preventive effects of a fermented dairy product against Alzheimer's disease and identification of a novel oleamide with enhanced microglial phagocytosis and anti-inflammatory activity.

Despite the ever-increasing number of patients with dementia worldwide, fundamental therapeutic approaches to this condition have not been established. Epidemiological studies suggest that intake of fermented dairy products prevents cognitive decline in the elderly. However, the active compounds responsible for the effect remain to be elucidated. The present study aims to elucidate the preventive effects of dairy products on Alzheimer's disease and to identify the responsible component. Here, in a mouse model of Alzheimer's disease (5xFAD), intake of a dairy product fermented with Penicillium candidum had preventive effects on the disease by reducing the accumulation of amyloid ß (Aß) and hippocampal inflammation (TNF-α and MIP-1α production), and enhancing hippocampal neurotrophic factors (BDNF and GDNF). A search for preventive substances in the fermented dairy product identified oleamide as a novel dual-active component that enhanced microglial Aß phagocytosis and anti-inflammatory activity towards LPS stimulation in vitro and in vivo. During the fermentation, oleamide was synthesized from oleic acid, which is an abundant component of general dairy products owing to lipase enzymatic amidation. The present study has demonstrated the preventive effect of dairy products on Alzheimer's disease, which was previously reported only epidemiologically. Moreover, oleamide has been identified as an active component of dairy products that is considered to reduce Aß accumulation via enhanced microglial phagocytosis, and to suppress microglial inflammation after Aß deposition. Because fermented dairy products such as camembert cheese are easy to ingest safely as a daily meal, their consumption might represent a preventive strategy for dementia.

The utility of galactomannan antigen in the bronchial washing and serum for diagnosing pulmonary aspergillosis.

BACKGROUND: The diagnosis of pulmonary aspergillosis is difficult because the sensitivity of the conventional methods for the detection of Aspergillus such as culture and cytology, is poor. To improve the sensitivity for Aspergillus detection, the detection of galactomannan antigen has been investigated. The serum galactomannan (GM) antigen has been recognized to be a useful tool for the diagnosis of invasive pulmonary aspergillosis. However, the utility of the galactomannan antigen for the diagnosis of pulmonary aspergillosis other than invasive pulmonary aspergillosis (IPA) has been unclear. METHODS: The GM antigen using serum and bronchial washing (BW) using bronchofiberscopy for the diagnosis of pulmonary aspergillosis other than IPA were measured. RESULTS: In 45 enrolled patients, 7 patients had pulmonary aspergillosis, 5 of these patients had chronic necrotizing pulmonary aspergillosis and 2 patients had allergic bronchopulmonary aspergillosis. The area under the receiver operating characteristic (ROC) curve was 0.89 for the BW GM antigen detection test, and 0.41 for the serum GM antigen detection test, suggesting that the BW GM antigen detection test exhibits a better diagnostic performance than the serum GM antigen detection test. The BW GM antigen detection test had a sensitivity of 85.7% and a specificity of 76.3% at a cut-off level of ≥0.5, which was the optimal cut-off level obtained by the ROC curve. CONCLUSION: The BW GM antigen detection test is thought to be a promising test for the diagnosis of pulmonary aspergillosis other than IPA.

Histological types and localizations of lung cancers in patients with combined pulmonary fibrosis and emphysema.

BACKGROUND: Most patients with combined pulmonary fibrosis and emphysema (CPFE) are males, and heavy smokers. CPFE is more prevalent than fibrosis in patients with lung cancer, and patients with CPFE usually have a poor prognosis. This study reviewed the differences in the prevalence of lung cancer among patients with normal, fibrosis, emphysema and CPFE via chest computed tomography (CT), and the relationship between histopathology and the localizations of lung cancer. METHODS: Patients that were diagnosed with lung cancer confirmed by pathological examinations between 2003 and 2011 were retrospectively reviewed to obtain clinical, pathological, and radiological data. These patients were categorized into four groups based on chest CT findings: normal, fibrosis, emphysema and CPFE. RESULTS: Two hundred and seventy-four patients with lung cancer were classified into 146 normal, 14 fibrosis, 78 emphysema, and 36 CPFE groups. Combined centriacinar and paraseptal emphysema was common in the CPFE group. The prevalence of squamous cell carcinoma in the CPFE group was significantly higher in comparison to the normal group. The rate of peripheral localization of lung cancer in the CPFE group was significantly higher in comparison to the normal, fibrosis, and emphysema groups. The prevalence of squamous cell carcinoma of peripheral areas in the CPFE group was significantly higher in the normal and emphysema groups. CONCLUSIONS: CPFE patients demonstrated histopathological and radiological differences concerning the histological types and localization of lung cancers.

The relationship between the clinical course and cytokine in a patient with cigarette smoking-induced acute eosinophilic pneumonia - A case report.

A 19-year-old female was admitted to our hospital because of a sudden onset fever and cough, and she was diagnosed to have acute eosinophilic pneumonia (AEP). The cause was thought to be cigarette smoking, because she had started smoking just before the development of AEP and her condition improved after cigarette smoking cessation, without corticosteroid treatment. The cytokines which are thought to be involved in eosinophilic accumulation in the lungs were analyzed using bronchoalveolar lavage fluid (BALF) and serum. Of the analyzed cytokines, only regulated on activation, normal T cell expressed and secreted (RANTES) increased in the serum after the improvement. RANTES is a unique chemokine which attracts not only eosinophils, but also T cells. Interestingly, in this case, the eosinophil count in the blood increased in parallel with the lymphocyte count after the improvement. These findings are interesting because it may help to understand the pathogenesis of AEP and the role of RANTES.

Effects of inhaled tiotropium plus transdermal tulobuterol versus tiotropium alone on impulse oscillation system (IOS)-assessed measures of peripheral airway resistance and reactance, lung function and quality of life in patients with COPD: a randomized crossover study.

BACKGROUND: The addition of transdermal tulobuterol (Tulo) to inhaled tiotropium bromide (Tio) produced beneficial effects on spirometry-assessed parameters of respiratory function, disease-related symptoms and quality of life in patients with chronic obstructive pulmonary disease (COPD). AIM: To compare the effects of Tio plus Tulo versus Tio alone on peripheral airway obstruction and quality of life in Japanese patients with COPD using impulse oscillation system (IOS)-assessed measures. PATIENTS AND METHODS: Patients aged 50-80 years with clinically stable COPD and a forced expiratory volume in 1 s (FEV(1)) that was 30-80% of the predicted value were randomized to receive Tio 18 µg once daily, or combination therapy with Tio 18 µg once daily plus Tulo 2 mg once daily for 4 weeks. Patients then switched treatments for a further 4 weeks. RESULTS: Sixteen patients completed the study. Tio plus Tulo was associated with significantly greater improvements than Tio in IOS-assessed markers of resistance (R5 and R5-R20), reactance and reactance area, from baseline to week 4. Both treatments significantly improved these markers over the 4-week treatment period, with the exception of R20 for which improvements were not significant. Tio plus Tulo improved symptoms of dyspnea to a significantly greater extent than Tio alone. St. George's Respiratory Questionnaire Score-Total was not significantly different between the two groups, but improvement from baseline in the 'impact' component was significantly greater with Tio plus Tulo than with Tio alone. CONCLUSIONS: Coadministration of transdermal Tulo with inhaled Tio, as well as Tio alone, is associated with beneficial effects on IOS-assessed measures of peripheral airway obstruction in patients with COPD.