Local control of brain metastases with osimertinib alone in patients with EGFR-mutant non-small cell lung cancer.

PURPOSE: Although osimertinib has excellent intracranial activity in metastatic non-small cell lung cancer (NSCLC) with exon 19 deletion or L858R EGFR alterations, measures of local control of brain metastases are less well-reported. We describe lesion-level outcomes of brain metastases treated with osimertinib alone. METHODS: We retrospectively reviewed patients with EGFR-mutant NSCLC with untreated brain metastasis measuring ≥ 5 mm at the time of initiating osimertinib. Cumulative incidence of local recurrence in brain (LRiB) was calculated with death as a competing risk, and univariable and multivariable analyses were conducted to identify factors associated with LRiB. RESULTS: We included 284 brain metastases from 37 patients. Median follow-up was 20.1 months. On initial MRI after starting osimertinib, patient-level response was complete response (CR) in 11 (15%), partial response (PR) in 33 (45%), stable disease (SD) in 18 (25%) and progressive disease (PD) in 11 (15%). The 1-year cumulative incidence of LRiB was 14% (95% CI 9.9-17.9) and was significantly different in patients with a CR (0%), PR (4%), and SD (11%; p = 0.02). Uncontrolled primary tumor (adjusted hazard ratio [aHR] 3.78, 95% CI 1.87-7.66; p < 0.001), increasing number of prior systemic therapies (aHR 2.12, 95% CI 1.49-3.04; p < 0.001), and higher ECOG score (aHR 7.8, 95% CI 1.99-31.81; p = 0.003) were associated with LRiB. CONCLUSIONS: Although 1-year cumulative incidence of LRiB is < 4% with a CR or PR, 1-year cumulative incidence of LRiB is over 10% for patients with less than a PR to osimertinib on initial MRI. These patients should be followed closely for need for additional treatment such as stereotactic radiosurgery.

A National Cancer Disparities Analysis of Predictors for Radiation Therapy Refusal by Race.

PURPOSE: Radiation therapy (RT) refusal by patients with cancer is infrequent but is significant because it portends poor outcomes. No prior study has evaluated all five federally defined racial categories with respects to RT refusal. Here we use a large nationally representative population with cancer to determine: 1) which race of patients refuse RT the most and 2) predictive factors for RT refusal by race. MATERIALS/METHODS: A retrospective study included patients ≥18 years old with diagnostically confirmed cancer between 2004-2017, using the National Cancer Database. All patients included were offered RT for first course treatment. Multivariable logistic regression assessed RT refusal (adjusted odds ratio [aOR]) with 95% confidence intervals (95%CI). Analyses were adjusted for patient factors (age, rurality, income, education, and comorbidities) and cancer characteristics (stage, cancer type, facility type, year of diagnosis, and region). Median overall survival was calculated using the Kaplan-Meier method. RESULTS: Of 11,609,044 patients, 2,759,753 patients were included and recommended for RT by the treating physician. Median follow-up was 50 months. RT was refused by 139,383 patients (5.0%), varying by race: 416 NHPI (7.2%), 489 AIAN (5.8%), 118,186 Non-Hispanic White (5.0%), 17,427 Black (4.8%), and 2,865 Asian (4.8%) patients. The rates of annual RT refusal were increasing, especially among NHPI patients. The populations with the highest likelihood of refusing RT were NHPI (aOR=1.53, 95%CI=1.36-1.71), AIAN (aOR=1.24, 95%CI=1.12-1.37), and Black (aOR=1.11, 95%CI=1.09-1.14) patients, compared to Non-Hispanic White patients. Older age and higher comorbidity burden predicted RT refusal across all races. Median overall survival was 81 months and 144 months for patients who refused RT and received RT, respectively. CONCLUSIONS: Indigenous and Black patients are more likely to refuse RT, which may contribute to inferior cancer outcomes. Understanding NHPI and AIAN patient perspectives and perceptions may elucidate interventions to mitigate these disparities.

Effect of Radiation Schedule on Transportation-Related Carbon Emissions: A Case Study in Rectal Cancer.

Purpose: The health care sector is a major contributor of worldwide greenhouse gas (GHG) emissions. Indirect emissions, including those associated with transportation, make up 82% of the US health care sector's environmental footprint. Radiation therapy (RT) treatment regimens present an opportunity for environmental health care-based stewardship owing to the high incidence of cancer diagnosis, significant utilization of RT, and myriad treatment days required for curative regimens. Because the use of short-course RT (SCRT) in the treatment of rectal cancer has demonstrated noninferior clinical outcomes compared with conventional, long-course RT (LCRT), we investigate the environmental and health equity-related outcomes. Methods and Materials: Patients treated with curative, preoperative RT for newly diagnosed rectal cancer at our institution between 2004 and 2022 and living in-state were included. Travel distance was estimated using patients' reported home address. Associated GHG emissions were calculated and reported in carbon dioxide equivalents (CO2e). Results: Of 334 patients included, the total distance traveled for the treatment course was significantly greater in patients treated with LCRT versus SCRT (median, 1417 vs 319 miles; P < .001). Total CO2e emissions for those undergoing LCRT (n = 261) and SCRT (n = 73) were 665.3 kg CO2e and 149.9 kg CO2e, respectively, per treatment course (P < .001), with a net difference of 515.4 kg CO2e. Relatively, this suggests that LCRT is associated with 4.5 times greater GHG emissions from patient transportation. Conclusions: Using treatment of rectal cancer as proof-of-principle, we advocate for the inclusion of environmental considerations in the creation of climate-resilient oncologic RT practices, especially in the context of equivocal clinical outcomes between RT fractionation schedules.

SUPPORT: SUrvey of Parental Leave POlicies of RadiaTion Oncology Programs and Residency Applicants.

Purpose: Recruitment to radiation oncology training programs has recently declined, and gender inequities persist in radiation oncology. Policies that promote inclusivity, such as the updated American College of Graduate Medical Education parental leave policy establishing minimum parental leave requirements, may support recruitment to radiation oncology. Methods and Materials: We surveyed 2021-2022 radiation oncology residency applicants and program directors (PDs) about program-specific parental leave policies, transparency of parental leave information during the residency application and interview process, and perceptions of the effect of parenthood on residency training, career advancement, and well-being. Results: Of 89 radiation oncology PDs, 29 (33%) completed the survey. Of 154 residency applicants (current fourth-year medical students, international applicants, or postdoctoral fellows) surveyed, 62 (40%) completed the survey. Most applicants planned to start a family during residency (53%) and reported perceived flexibility to start a family influenced their decision to pursue radiation oncology over other career specialties (55%). Many applicants viewed time in residency (nonresearch, 22%), in research (33%), and as early career faculty (24%) as the best time to start a family. A small number of applicants used program-specific parental leave policy information in determining their rank list (11%), and many applicants sought information regarding fertility health care benefits (55%). Many applicants obtained parental leave information verbally, despite expressing a preference for objective means (slide deck, 63%; website, 50%; or handout, 42%) of information sharing. PDs were all supportive of a 6-week maternity leave policy (100% agree or strongly agree with the policy) and did not feel parental leave would negatively affect a resident's ability to pursue an academic (100%) or private practice career (100%). Conclusions: Many radiation oncology residency applicants plan to start families during training, seek and value program-specific parental leave information and health benefits, and prefer objective means of information sharing. These findings likely reflect those who have strong views of parental leave policies.

Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.

PURPOSE: Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery (SRS). VBrain by Vysioneer Inc. is a deep learning algorithm with recent FDA clearance to assist in brain tumor contouring. We aimed to assess the performance of this tool by various demographic and clinical characteristics among patients with brain metastases treated with SRS. MATERIALS AND METHODS: We randomly selected 100 patients with brain metastases who underwent initial SRS on the CyberKnife from 2017 to 2020 at a single institution. Cases with resection cavities were excluded from the analysis. Computed tomography (CT) and axial T1-weighted post-contrast magnetic resonance (MR) image data were extracted for each patient and uploaded to VBrain. A brain metastasis was considered "detected" when the VBrain- "predicted" contours overlapped with the corresponding physician contours ("ground-truth" contours). We evaluated performance of VBrain against ground-truth contours using the following metrics: lesion-wise Dice similarity coefficient (DSC), lesion-wise average Hausdorff distance (AVD), false positive count (FP), and lesion-wise sensitivity (%). Kruskal-Wallis tests were performed to assess the relationships between patient characteristics including sex, race, primary histology, age, and size and number of brain metastases, and performance metrics such as DSC, AVD, FP, and sensitivity. RESULTS: We analyzed 100 patients with 435 intact brain metastases treated with SRS. Our cohort consisted of patients with a median number of 2 brain metastases (range: 1 to 52), median age of 69 (range: 19 to 91), and 50% male and 50% female patients. The primary site breakdown was 56% lung, 10% melanoma, 9% breast, 8% gynecological, 5% renal, 4% gastrointestinal, 2% sarcoma, and 6% other, while the race breakdown was 60% White, 18% Asian, 3% Black/African American, 2% Native Hawaiian or other Pacific Islander, and 17% other/unknown/not reported. The median tumor size was 0.112 c.c. (range: 0.010-26.475 c.c.). We found mean lesion-wise DSC to be 0.723, mean lesion-wise AVD to be 7.34% of lesion size (0.704 mm), mean FP count to be 0.72 tumors per case, and lesion-wise sensitivity to be 89.30% for all lesions. Moreover, mean sensitivity was found to be 99.07%, 97.59%, and 96.23% for lesions with diameter equal to and greater than 10 mm, 7.5 mm, and 5 mm, respectively. No other significant differences in performance metrics were observed across demographic or clinical characteristic groups. CONCLUSION: In this study, a commercial deep learning algorithm showed promising results in segmenting brain metastases, with 96.23% sensitivity for metastases with diameters of 5 mm or higher. As the software is an assistive AI, future work of VBrain integration into the clinical workflow can provide further clinical and research insights.

Disparities in Survival and Comorbidity Burden Between Asian and Native Hawaiian and Other Pacific Islander Patients With Cancer.

Importance: Improper aggregation of Native Hawaiian and other Pacific Islander individuals with Asian individuals can mask Native Hawaiian and other Pacific Islander patient outcomes. A comprehensive assessment of cancer disparities comparing Asian with Native Hawaiian and other Pacific Islander populations is lacking. Objective: To compare comorbidity burden and survival among East Asian, Native Hawaiian and other Pacific Islander, South Asian, and Southeast Asian individuals with non-Hispanic White individuals with cancer. Design, Setting, and Participants: This retrospective cohort study used a national hospital-based oncology database enriched with Native Hawaiian and other Pacific Islander and Asian populations. Asian, Native Hawaiian and other Pacific Islander, and White individuals diagnosed with the most common cancers who received treatment from January 1, 2004, to December 31, 2017, were included. Patients younger than 18 years, without pathologic confirmation of cancer, or with metastatic disease were excluded. Data were analyzed from January to May 2022. Main Outcomes and Measures: The primary end points were comorbidity burden by Charlson-Deyo Comorbidity Index and overall survival (OS). Results: In total, 5 955 550 patients were assessed, including 60 047 East Asian, 11 512 Native Hawaiian and other Pacific Islander, 25 966 South Asian, 42 815 Southeast Asian, and 5 815 210 White patients. The median (IQR) age was 65 (56-74) years, median (IQR) follow-up was 58 (30-96) months, and 3 384 960 (57%) were women. Patients were predominantly from metropolitan areas (4 834 457 patients [84%]) and the Southern United States (1 987 506 patients [34%]), with above median education (3 576 460 patients [65%]), and without comorbidities (4 603 386 patients [77%]). Cancers included breast (1 895 351 patients [32%]), prostate (948 583 patients [16%]), kidney or bladder (689 187 patients [12%]), lung (665 622 patients [11%]), colorectal (659 165 patients [11%]), melanoma (459 904 patients [8%]), endometrial (307 401 patients [5%]), lymphoma (245 003 patients [4%]), and oral cavity (85 334 patients [1%]) malignant neoplasms. Native Hawaiian and other Pacific Islander patients had the highest comorbidity burden (adjusted odds ratio [aOR], 1.70; 95% CI, 1.47-1.94) compared with Asian and White groups. Asian patients had superior OS compared with White patients for most cancers; only Southeast Asian patients with lymphoma had inferior survival (adjusted hazard ratio [aHR], 1.26; 95% CI, 1.16-1.37). In contrast, Native Hawaiian and other Pacific Islander patients demonstrated inferior OS compared with Asian and White patients for oral cavity cancer (aHR, 1.56; 95% CI, 1.14-2.13), lymphoma (aHR, 1.35; 95% CI, 1.11-1.63), endometrial cancer (aHR, 1.30; 95% CI, 1.12-1.50), prostate cancer (aHR, 1.29; 95% CI, 1.14-1.46), and breast cancer (aHR, 1.09; 95% CI, 1.00-1.18). No cancers among Native Hawaiian and other Pacific Islander patients had superior OS compared with White patients. Conclusions and Relevance: In this cohort study, compared with White patients with the most common cancers, Asian patients had superior survival outcomes while Native Hawaiian and other Pacific Islander patients had inferior survival outcomes. Native Hawaiian and other Pacific Islander patients had significantly greater comorbidity burden compared with Asian and White patients, but this alone did not explain the poor survival outcomes. These results support the disaggregation of these groups in cancer studies.