Assessment of NETest Clinical Utility in a U.S. Registry-Based Study.

BACKGROUND: The clinical relevance of molecular biomarkers in oncology management has been recognized in breast and lung cancers. We evaluated a blood-based multigene assay for management of neuroendocrine tumors (NETs) in a real-world study (U.S. registry NCT02270567). Diagnostic accuracy and relationship to clinical disease status in two cohorts (treated and watch-and-wait) were evaluated. MATERIALS AND METHODS: Patients with NETs (n = 100) were followed for 6-12 months. Patients' primary tumors were gastroenteropancreatic (68%), lung 20%, and of unknown origin (12%). Characteristics included well-differentiated, low-grade tumors (97%), stage IV disease (96%); treatment with surgery (70%); and drug treatment (56%). NETest was measured at each visit and disease status determined by RECIST. Scores categorized as low (NETest 14%-40%) or high (≥80%) defined disease as stable or progressive. Multivariate analyses determined the strength of the association with progression-free survival (PFS). RESULTS: NETest diagnostic accuracy was 96% and concordant (95%) with image-demonstrable disease. Scores were reproducible (97%) and concordant with clinical status (98%). The NETest was the only feature linked to PFS (odds ratio, 6.1; p < .0001). High NETest correlated with progressive disease (81%; median PFS, 6 months), and low NETest correlated with stable disease (87%; median PFS, not reached). In the watch-and-wait cohort, low NETest was concordant with stable disease in 100% of patients, and high NETest was associated with management changes in 83% of patients. In the treated cohort, all low NETest patients (100%) remained stable. A high NETest was linked to intervention and treatment stabilization (100%). Use of NETest was associated with reduced imaging (biannual to annual) in 36%-38% of patients. CONCLUSION: Blood NETest is an accurate diagnostic and can be of use in monitoring disease status and facilitating management change in both watch-and-wait and treatment cohorts. IMPLICATIONS FOR PRACTICE: A circulating multigene molecular biomarker to guide neuroendocrine tumor (NET) management has been developed because current biomarkers have limited clinical utility. NETest is diagnostic (96%) and in real time defines the disease status (>95%) as stable or progressive. It is >90% effective in guiding treatment decisions in conjunction with diagnostic imaging. Monitoring was effective in watch-and-wait or treatment groups. Low levels supported no management change and reduced the need for imaging. High levels indicated the need for management intervention. Real-time liquid biopsy assessment of NETs has clinical utility and can contribute additional value to patient management strategies and outcomes.

Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography.

PURPOSE: Diagnosis of obstructive sleep apnea by the gold-standard of polysomnography (PSG), or by home sleep testing (HST), requires numerous physical connections to the patient which may restrict use of these tools for early screening. We hypothesized that normal and disturbed breathing may be detected by a consumer smartphone without physical connections to the patient using novel algorithms to analyze ambient sound. METHODS: We studied 91 patients undergoing clinically indicated PSG. Phase I: In a derivation cohort (n = 32), we placed an unmodified Samsung Galaxy S5 without external microphone near the bed to record ambient sounds. We analyzed 12,352 discrete breath/non-breath sounds (386/patient), from which we developed algorithms to remove noise, and detect breaths as envelopes of spectral peaks. Phase II: In a distinct validation cohort (n = 59), we tested the ability of acoustic algorithms to detect AHI < 15 vs AHI > 15 on PSG. RESULTS: Smartphone-recorded sound analyses detected the presence, absence, and types of breath sound. Phase I: In the derivation cohort, spectral analysis identified breaths and apneas with a c-statistic of 0.91, and loud obstruction sounds with c-statistic of 0.95 on receiver operating characteristic analyses, relative to adjudicated events. Phase II: In the validation cohort, automated acoustic analysis provided a c-statistic of 0.87 compared to whole-night PSG. CONCLUSIONS: Ambient sounds recorded from a smartphone during sleep can identify apnea and abnormal breathing verified on PSG. Future studies should determine if this approach may facilitate early screening of SDB to identify at-risk patients for definitive diagnosis and therapy. CLINICAL TRIALS: NCT03288376; clinicaltrials.org.