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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. [ABSTRACT FROM AUTHOR]

Background: Progressive, metastatic non-small cell lung cancer (NSCLC) often requires the initiation of new systemic therapy. However, in patients with NSCLC that is oligoprogressive (≤3 lesions), local radiotherapy (RT) may allow for the eradication of resistant microclones and, therefore, the continuation of otherwise effective systemic therapy.Methods: Patients treated from 2008 to 2019 with definitive doses of RT to all sites of intracranial or extracranial oligoprogression without a change in systemic therapy were identified. Radiographic progression-free survival (rPFS) and time to new therapy (TNT) were measured. Associations between baseline clinical and treatment-related variables were correlated with progression-free survival via Cox proportional hazards modeling.Results: Among 198 unique patients, 253 oligoprogressive events were identified. Intracranial progression occurred in 51% of the patients, and extracranial progression occurred in 49%. In the entire cohort, the median rPFS was 7.9 months (95% CI, 6.5-10.0 months), and the median TNT was 8.8 months (95% CI, 7.2-10.9 months). On adjusted modeling, patients with the following disease characteristics were associated with better rPFS: better performance status (P = .003), fewer metastases (P = .03), longer time to oligoprogression (P = .009), and fewer previous systemic therapies (P = .02). Having multiple sites of oligoprogression was associated with worse rPFS (P < .001).Conclusions: In select patients with oligoprogression, definitive RT is a feasible treatment option to delay the initiation of next-line systemic therapies, which have more limited response rates and efficacy. Further randomized prospective data may help to validate these findings and identify which patients are most likely to benefit. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose This study evaluates outcomes and patterns of care among patients receiving radiation therapy (RT) for bone metastases at a high-volume academic institution. Methods and Materials Records of all patients whose final RT course was for bone metastases from April 2007 to July 2012 were identified from electronic medical records. Chart review yielded demographic and clinical data. Rates of complicated versus uncomplicated bone metastases were not analyzed. Results We identified 339 patients whose final RT course was for bone metastases. Of these, 52.2% were male; median age was 65 years old. The most common primary was non-small-cell lung cancer (29%). Most patients (83%) were prescribed ≤10 fractions; 8% received single-fraction RT. Most patients (52%) had a documented goals of care (GOC) discussion with their radiation oncologist; hospice referral rates were higher when patients had such discussions (66% with vs 50% without GOC discussion, P=.004). Median life expectancy after RT was 96 days. Median survival after RT was shorter based on inpatient as opposed to outpatient status at the time of consultation (35 vs 136 days, respectively, P<.001). Hospice referrals occurred for 56% of patients, with a median interval between completion of RT and hospice referral of 29 days and a median hospice stay of 22 days. Conclusions These data document excellent adherence to American Society for Radiation Oncolology Choosing Wisely recommendation to avoid routinely using >10 fractions of palliative RT for bone metastasis. Nonetheless, single-fraction RT remains relatively uncommon. Participating in GOC discussions with a radiation oncologist is associated with higher rates of hospice referral. Inpatient status at consultation is associated with short survival. [ABSTRACT FROM AUTHOR]