Background: In non-small-cell lung cancers with programmed death-ligand 1 (PD-L1) expression on 50% of tumor cells, firstline treatment with the PD-1 inhibitor pembrolizumab improves survival compared with platinum-doublet chemotherapy. Whether higher PD-L1 levels within the expression range of 50%-100% predict for even greater benefit to pembrolizumab is currently unknown.Patients and methods: In this multicenter retrospective analysis, we analyzed the impact of PD-L1 expression levels on the overall response rate (ORR), median progression-free survival (mPFS), and median overall survival (mOS) in patients who received commercial pembrolizumab as first-line treatment of non-small-cell lung cancer (NSCLC) with a PD-L1 expression of 50% and negative for genomic alterations in the EGFR and ALK genes .Results: Among 187 patients included in this analysis, the ORR was 44.4% [95% confidence interval (CI) 37.1% to 51.8%], the mPFS was 6.5 months (95% CI 4.5-8.5), and the mOS was not reached. The median PD-L1 expression level among patients who experienced a response to pembrolizumab was significantly higher than among patients with stable or progressive disease (90% versus 75%, P < 0.001). Compared with patients with PD-L1 expression of 50%-89% (N ¼ 107), patients with an expression level of 90%-100% (N ¼ 80) had a significantly higher ORR (60.0% versus 32.7%, P < 0.001), a significantly longer mPFS [14.5 versus 4.1 months, hazard ratio (HR) 0.50 (95% CI 0.33-0.74), P < 0.01], and a significantly longer mOS [not reached versus 15.9 months, HR 0.39 (95% CI 0.21-0.70), P ¼ 0.002]. Conclusion:Among patients with NSCLC and PD-L1 expression of 50% treated with first-line pembrolizumab, clinical outcomes are significantly improved in NSCLCs with a PD-L1 expression of 90%. These findings have implications for treatment selection as well as for clinical trial interpretation and design.

Importance Shared decision-making is associated with improved patient-reported outcomes, but not all patients prefer to participate in medical decisions. Studies of the effect of matching between actual and preferred medical decision roles on cancer patients’ perceptions of care quality have been conflicting. Objective To determine whether shared decision-making was associated with patient ratings of care quality and physician communication, and whether patients’ preferred decision roles modified those associations. Design We surveyed lung and colorectal cancer patients, diagnosed from 2003–2005, participating in the Cancer Care Outcomes Research and Surveillance Consortium (CanCORS) study. We asked patients about their preferred roles in medical decisions and actual roles in decisions about surgery, chemotherapy, and radiation. We assessed associations of patients’ decision roles with patient-reported quality of care and physician communication. Setting A population- and health-system-based cohort of lung and colorectal cancer patients, treated in integrated care delivery systems, academic institutions, private offices, and Veterans Affairs hospitals. Participants The CanCORS study included 9737 patients (cooperation rate among patients contacted, 59.9%). We analyzed 5315 patients (56% with colorectal, 40% with non-small cell lung, and 5% with small cell lung cancer) who completed baseline surveys and reported decision roles for a total of 10817 treatment decisions. Main Outcome Measures The outcomes (identified before data collection) included patient-reported “excellent” quality of care and top ratings (highest score) of physician communication scale. Results After adjustment, patients describing physician-controlled (versus shared) decisions were less likely to report excellent quality of care (odds ratio, OR=0.64, 95%CI=0.54–0.75; P<0.001); patients’ preferred decision roles did not modify this effect (P for interaction=0.29). Both actual and preferred physician-controlled (versus shared) roles were associated with lower ratings of physician communication (OR=0.55, 95%CI 0.45–0.66, P<0.001, and 0.67, 95%CI 0.51–0.87, P=0.002 respectively); preferred role did not modify the effect of actual role (P for interaction=0.76). Conclusions and Relevance Physician-controlled decisions regarding lung or colorectal cancer treatment were associated with lower ratings of care quality and physician communication. These effects were independent of patients’ preferred decision roles, underscoring the importance of seeking to involve all patients in decision-making about their treatment.

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IMPORTANCE A rapid learning health care system for oncology will require scalable methods for extracting clinical end points from electronic health records (EHRs). Outside of clinical trials, end points such as cancer progression and response are not routinely encoded into structured data. OBJECTIVE To determine whether deep natural language processing can extract relevant cancer outcomes from radiologic reports, a ubiquitous but unstructured EHR data source. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study evaluated 1112 patients who underwent tumor genotyping for a diagnosis of lung cancer and participated in the Dana-Farber Cancer Institute PROFILE study from June 26, 2013, to July 2, 2018. EXPOSURES Patients were divided into curation and reserve sets. Human abstractors applied a structured framework to radiologic reports for the curation set to ascertain the presence of cancer and changes in cancer status over time (ie, worsening/progressing vs improving/responding). Deep learning models were then trained to capture these outcomes from report text and subsequently evaluated in a 10% held-out test subset of curation patients. Cox proportional hazards regression models compared human and machine curations of disease-free survival, progression-free survival, and time to improvement/ response in the curation set, and measured associations between report classification and overall survival in the curation and reserve sets. MAIN OUTCOMES AND MEASURES The primary outcome was area under the receiver operating characteristic curve (AUC) for deep learning models; secondary outcomes were time to improvement/response, disease-free survival, progression-free survival, and overall survival. RESULTS A total of 2406 patients were included (mean [SD] age, 66.5 [10.8] years; 1428 female [59.7%]; 2170 [90.2%] white). Radiologic reports (n = 14 230) were manually reviewed for 1112 patients in the curation set. In the test subset (n = 109), deep learning models identified the presence of cancer, improvement/response, and worsening/ progression with accurate discrimination (AUC >0.90). Machine and human curation yielded similar measurements of disease-free survival (hazard ratio [HR] for machine vs human curation, 1.18; 95% CI, 0.71-1.95); progression-free survival (HR, 1.11; 95% CI, 0.71-1.71); and time to improvement/response (HR, 1.03; 95% CI, 0.65-1.64). Among 15 000 additional reports for 1294 reserve set patients, algorithm-detected cancer worsening/progression was associated with decreased overall survival (HR for mortality, 4.04; 95% CI, 2.78-5.85), and improvement/response was associated with increased overall survival (HR, 0.41; 95% CI, 0.22-0.77). CONCLUSIONS AND RELEVANCE Deep natural language processing appears to speed curation of relevant cancer outcomes and facilitate rapid learning from EHR data.

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Among patients with lung or colorectal cancer, frequent physician tumor board engagement was associated with patient clinical trial participation and higher rates of curative-intent surgery for stage I to II NSCLC but not with overall survival.

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Data are limited regarding whether the availability of biomarker-directed therapy for lung cancer exacerbates racial and socioeconomic disparities. Patients diagnosed with stage IV lung adenocarcinoma from 2008 to 2013 were identified using Surveillance, Epidemiology, and End Results Program-Medicare. The primary outcome was a Medicare claim for molecular testing within 60 days of diagnosis, analyzed using multivariable logistic regression; the secondary outcome was overall survival, analyzed using multivariable Cox proportional hazards models. All statistical tests were two-sided. Of 5556 patients, 1437 (25.9%) had molecular testing. Testing rates were 14.1% among black, 26.2% among white, and 32.8% among patients of Asian/other descent (adjusted P < .001); 20.6% among patients with Medicaid eligibility vs 28.4% among those without (adjusted P ¼ .01); and 19.9% among patients in the highest census tract-level poverty rate quintile vs 30.7% among patients in the lowest quintile (for all quintiles, adjusted P ¼ .18). Median survival from 60 days was 8.2 months among patients with molecular testing within 60 days of diagnosis and 6.1 months among those without (hazard ratio ¼ 0.92, 95% confidence interval ¼ 0.86 to 0.99; adjusted P ¼ .02). Equitable precision medicine requires concerted implementation efforts.