PURPOSE Hyperprogressive disease (HPD), fast progression (FP), and early death (ED) have been described in 13.8%, 4.7%, and 5.6% and in 5.1%, 2.8%, and 6.8%, respectively, of patients with non–small-cell lung cancer (NSCLC) treated with single-agent programmed cell death ligand 1 inhibitors (ICI) or chemotherapy, respectively. Whether FP/ED and HPD represent overlapping patterns is unknown. PATIENTS AND METHODS FP, ED, and HPD were retrospectively assessed in patients with NSCLC treated with single-agent ICI or chemotherapy. Eligibility required 2 computed tomography (CT) scans before and 1 CT scan during treatment. (1) HPD, (2) FP, (3) ED were defined as (1) RECIST version 1.1 progression at first CT scan and tumor growth rate variation per month > 50%, (2) ≥ 50% increase in the sum of the longest diameters of target lesions within 6 weeks from baseline, and (3) death as a result of radiologic progression within 12 weeks from baseline CT scan, respectively. RESULTS Of 406 ICI-treated NSCLC, 56 patients (13.8%), 9 patients (2.2%), and 36 patients (8.8%) were HPD, FP, and ED, respectively. Eight (14.2%) and 20 (35.7%) of 56 patients with HPD were also FP and ED. ED significantly correlated with baseline Eastern Cooperative Oncology Group performance status ≥ 2 compared with HPD (33% v 13%, P = .02). Overall survival was significantly longer for HPD (3.4 months [95% CI, 2.7 to 4.0 months]) compared with FP (0.7 months [95% CI, 0.6 to 0.8 months]); HR, 0.18 [95% CI, 0.08 to 0.42]; P < .0001) and ED (1.4 months [95% CI, 1.3 to 1.6 months]); HR, 0.19 [95% CI, 0.11 to 0.34]); P < .0001), whereas it did not differ between FP and ED (HR, 1.3 [95% CI, 0.56 to 3.0]; P = .55). Of 59 patients with NSCLC treated with single-agent chemotherapy, the HPD, FP, and ED rates were 5.1%, 1.7%, and 6.7%, respectively. CONCLUSION FP, ED, and HPD represent distinct progression patterns with limited overlap and different survival outcomes.
P2.16-02 Predicting Pathological Noninvasiveness in T1 Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Learning Algorithm