ImportanceImproper 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.ObjectiveTo 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 ParticipantsThis 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 MeasuresThe primary end points were comorbidity burden by Charlson-Deyo Comorbidity Index and overall survival (OS).ResultsIn 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 RelevanceIn 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.

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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 signi cantly 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 risk of LRiB is <4% with a CR or PR, 1-year risk of LRiB is over 10% for patients with less than a PR to osimertinib. These patients should be followed closely for need for additional treatment such as stereotactic radiosurgery.

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 risk of LRiB is <4% with a CR or PR, 1-year risk of LRiB is over 10% for patients with less than a PR to osimertinib. These patients should be followed closely for need for additional treatment such as 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.