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Background: Despite the widespread implementation of telemedicine, there are limited data regarding its impact on key components of care for patients with incurable or high‐risk cancer. For these patients, high‐quality care requires detailed conversations regarding treatment priorities (advance care planning) and clinical care to minimize unnecessary acute care (unplanned hospitalizations). Whether telemedicine affects these outcomes relative to in‐person clinic visits was examined among patients with cancer at high risk for 6‐month mortality. Methods: This retrospective cohort study included adult patients with cancer with any tumor type treated at the University of Pennsylvania who were newly identified between April 1 and December 31, 2020, to be at high risk for 6‐month mortality via a validated machine learning algorithm. Separate modified Poisson regressions were used to assess the occurrence of advance care planning and unplanned hospitalizations for telemedicine as compared to in‐person visits. Additional analyses were done comparing telemedicine type (video or phone) as compared to in‐person clinic visits. Results: The occurrence of advance care planning was similar between telemedicine and in‐person visits (6.8% vs. 6.0%; adjusted risk ratio [aRR], 1.25; 95% CI, 0.92–1.69). In regard to telemedicine subtype, patients exposed to video encounters were modestly more likely to have documented advance care planning in comparison to those seen in person (7.5% vs. 6.0%; aRR, 1.48; 95% CI, 1.03–2.11). The 3‐month risk for unplanned hospitalization was comparable for telemedicine compared to in‐person clinic encounters (21% vs. 18%; aRR, 1.06; 95% CI, 0.81–1.38). Conclusions: In this study, care delivered by telemedicine, compared to in‐person clinic visits, produced comparable rates of advance care planning conversations without increasing hospitalizations, which suggests that vulnerable patients can be managed safely by telemedicine. In this study, care delivered by telemedicine, compared to in‐person clinic visits, produced comparable rates of advance care planning conversations without increasing hospitalizations, which suggests that vulnerable patients can be managed safely by telemedicine. [ABSTRACT FROM AUTHOR]

Background: We evaluated if proton therapy is associated with decreased acute toxicities compared to intensity‐modulated radiation therapy (IMRT) in patients receiving concurrent chemoradiotherapy for head and neck cancers. Methods: We analyzed 580 patients with nonmetastatic head and neck cancers. Primary endpoint was any 90‐day grade ≥3 toxicity, prospectively collected and graded per CTCAEv4. Modified Poisson regression models were used. Results: Ninety‐five patients received proton and 485 IMRT. The proton group had more HPV‐positive tumors (65.6 vs. 58.0%, p = 0.049), postoperative treatment (76.8 vs. 62.1%, p = 0.008), unilateral neck treatment (18.9 vs. 6.6%, p < 0.001) and significantly lower doses to organs‐at‐risk compared to IMRT group. Adjusted for patient and treatment characteristics, the proton group had decreased grade 2 dysgeusia (RR0.67, 95%CI 0.53–0.84, p = 0.004) and a trend toward lower grade ≥3 toxicities (RR0.60, 95%CI 0.41–0.88, p = 0.06). Conclusions: Proton therapy was associated with significantly reduced grade 2 dysgeusia and nonstatistically significant decrease in acute grade ≥3 toxicities compared to IMRT. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single‐model methods (e.g., logistic regression and decision trees) in predictive modeling of post‐RT treatment failure and adverse events (AEs) for breast cancer patients using automatically extracted EMR data. Methods: Data from 1967 consecutive breast radiotherapy (RT) courses at one institution between 2008 and 2015 were automatically extracted from EMRs and oncology information systems using extraction software. Over 230 variables were extracted spanning the following variable segments: patient demographics, medical/surgical history, tumor characteristics, RT treatment history, and AEs tracked using CTCAEv4.0. Treatment failure was extracted algorithmically by searching posttreatment encounters for evidence of local, nodal, or distant failure. Individual models were trained using decision trees, logistic regression, random forests, and boosted decision trees to predict treatment failures and AEs. Models were fit on 75% of the data and evaluated for probability calibration and area under the ROC curve (AUC) on the remaining test set. The impact of each variable segment was assessed by retraining without the segment and measuring change in AUC (ΔAUC). Results: All AUC values were statistically significant (P < 0.05). Ensemble methods outperformed single‐model methods across all outcomes. The best ensemble method outperformed decision trees and logistic regression by an average AUC of 0.053 and 0.034, respectively. Model probabilities were well calibrated as evidenced by calibration curves. Excluding the patient medical history variable segment led to the largest AUC reduction in all models (Average ΔAUC = −0.025), followed by RT treatment history (−0.021) and tumor information (−0.015). Conclusion: In this largest such study in breast cancer performed to date, automatically extracted EMR data provided a basis for reliable outcome predictions across multiple statistical methods. Ensemble methods provided substantial advantages over single‐model methods. Patient medical history contributed the most to prediction quality. [ABSTRACT FROM AUTHOR]

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision‐making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data. [ABSTRACT FROM AUTHOR]

Discusses the effects of host government attitudes on foreign investment in less developed countries. Role of unrestricted admission of direct investment from abroad on the establishment of foreign-controlled firms; Influence of the perceived foreign domination on the political pressures exerted by domestic firms; Behavior of the explicit cost of foreign investment to the host economy.