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IMPORTANCETemporal shifts in clinical knowledge and practice need to be adjusted for in treatment outcome assessment in clinical evidence. OBJECTIVE To use electronic health record (EHR) data to (1) assess the temporal trends in treatment decisions and patient outcomes and (2) emulate a randomized clinical trial (RCT) using EHR data with proper adjustment for temporal trends. DESIGN, SETTING, AND PARTICIPANTS The Clinical Outcomes of Surgical Therapy (COST) StudyGroup Trial assessing overall survival of patients with stages I to III early-stage colon cancer was chosen as the target trial. The RCT was emulated using EHR data of patients from a single health care system cohort who underwent colectomy for early-stage colon cancer from January 1, 2006, to

Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.

Background Glioblastoma is associated with fatal outcomes and devastating neurological presentations especially impacting the elderly. Management remains controversial and representation in clinical trials poor. We generated two nomograms and a clinical decision making webtool using real world data. Methods Patients ≥ 60 years of age with histologically confirmed glioblastoma (ICD-O-3 histology codes 9440/3, 9441/3, 9442/3) diagnosed 2005 – 2015 were identified from the BC Cancer Registry (n = 822). 729 patients for which performance status was captured were included in the analysis.. Age, performance and resection status, administration of radiation therapy (RT) and chemotherapy were reviewed. Nomograms predicting 6- and 12-month OS probability were developed using Cox proportional hazards regression internally validated by c-index. A web tool powered by JavaScript was developed to calculate the survival probability. Results Median overall survival (OS) was 6.6 months (95% Confidence Interval (CI) 6-7.2 months). Management involved concurrent chemoradiation (CRT) (34%), RT alone (42%), chemo alone (2.3%). 21% of patients did not receive treatment beyond surgical intervention. Age, performance status, extent of resection, chemotherapy and RT administration were all significant independent predictors of OS. Patients <80 years old who received RT had a significant survival advantage, regardless of extent of resection (HR range from 0.22-0.60, CI 0.15-0.95). A nomogram was constructed from all 729 patients (Harrell’s Concordance Index = 0.78 (CI 0.71-0.84)) with a second nomogram based on subgroup analysis of the 452 patients who underwent RT (Harrell’s Concordance Index = 0.81 (CI 0.70-0.90)). An online calculator based on both nomograms was generated for clinical use. Conclusions Two nomograms and accompanying web tool incorporating commonly captured clinical features were generated based on real-world data to optimize decision making in the clinic.

Purpose/Objective(s): Patients undergoing concurrent chemoradiotherapy (CRT) for head and neck cancer often experience significant toxicities. Proton therapy permits decreased dose delivery to non-target structures and the potential for superior organs-at-risk sparing. The aim of this study was to evaluate if proton therapy is associated with decreased acute grade ≥3 toxicities compared to photon therapy in the setting of concurrent CRT for head and neck cancers. Materials/Methods: This study included 654 adult patients with nonmetastatic, locally advanced head and neck cancer treated with definitive concurrent CRT from January 1, 2011 to April 30, 2019 at one institution. 90-day toxicity data were prospectively collected as defined by Common Terminology Criteria for Adverse Events, version 4. Acute toxicities were grouped into five categories for analysis: mucosal (mucosal infection, mucositis), oral cavity (dry mouth, dysgeusia), swallow (aspiration, dysphagia), constitutional (fatigue, anorexia, dehydration), and skin (dermatitis, edema, superficial soft tissue necrosis, alopecia) toxicities. Modified Poisson regression models with covariate adjustment were used to model toxicity risk following multiple imputation for missing covariate values. Ten hypotheses were tested. Thus, the Bonferroni multiple testing correction was applied and statistical significance was assessed at the 0.005 level. Results: One hundred and six patients received proton therapy and 548 received photon therapy. Patients receiving proton therapy were older (mean age [SD] 61.2 [12.2] vs 58.9 [9.30], P = 0.029) and were more likely to be HPV-positive (67.0% vs 57.0%, P = 0.036). Proton therapy patients had lower dose to the parotid glands (right mean [SD], 21.8 Gy [12.9] vs 27.9 [11.3], P < 0.001; left 23.1 Gy [12.7] vs 27.2 [11.0], P = 0.001), oral cavity (13.