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INTRODUCTION: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. METHODS: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. RESULTS: For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. CONCLUSIONS: Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.
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Importance: Therapies for patients with advanced well-differentiated neuroendocrine tumors (NETs) have expanded but remain inadequate, with patients dying of disease despite recent advances in NET therapy. While patients with other cancers have seen long-term disease control and tumor regression with the application of immunotherapies, initial prospective studies of single-agent programmed cell death 1 inhibitors in NET have been disappointing. Objective: To evaluate the response rate following treatment with the combination of the vascular endothelial growth factor inhibitor bevacizumab with the programmed cell death 1 ligand 1 inhibitor atezolizumab in patients with advanced NETs. Design, Setting, and Participants: This single-arm, open-label nonrandomized clinical study in patients with rare cancers included 40 patients with advanced, progressive grade 1 to 2 NETs (20 with pancreatic NETs [pNETs] and 20 with extrapancreatic NETs [epNETs]) treated at a tertiary care referral cancer center between March 31, 2017, and February 19, 2019. Data were analyzed from June to September 2021. Interventions: Patients received intravenous bevacizumab and atezolizumab at standard doses every 3 weeks until progression, death, or withdrawal. Main Outcomes and Measures: The primary end point was objective radiographic response using Response Evaluation Criteria in Solid Tumors, version 1.1, with progression-free survival (PFS) as a key secondary end point. Results: Following treatment of the 40 study patients with bevacizumab and atezolizumab, objective response was observed in 4 patients with pNETs (20%; 95% CI, 5.7%-43.7%) and 3 patients with epNETs (15%; 95% CI, 3.2%-37.9%). The PFS was 14.9 (95% CI, 4.4-32.0) months and 14.2 (95% CI, 10.2-19.6) months in these cohorts, respectively. Conclusions and Relevance: In this nonrandomized clinical trial, findings suggest that clinical responses in patients with NET may follow treatment with the combination of bevacizumab and atezolizumab, with a PFS consistent with effective therapies. Trial Registration: ClinicalTrials.gov Identifier: NCT03074513.