Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Using patients' own knowledge of early sensations and symptoms to develop an interactive, individualized e-questionnaire to facilitate early diagnosis of lung cancer.

BACKGROUND: One reason for the often late diagnosis of lung cancer (LC) may be that potentially-indicative sensations and symptoms are often diffuse, and may not be considered serious or urgent, making their interpretation complicated. However, with only a few exceptions, efforts to use people's own in-depth knowledge about prodromal bodily experiences has been a missing link in efforts to facilitate early LC diagnosis. In this study, we describe and discuss facilitators and challenges in our process of developing and initial testing an interactive, self-completion e-questionnaire based on patient descriptions of experienced prodromal sensations and symptoms, to support early identification of lung cancer (LC). METHODS: E-questionnaire items were derived from in-depth, detailed explorative interviews with individuals undergoing investigation for suspected LC. The descriptors of sensations/symptoms and the background items obtained were the basis for developing an interactive, individualized instrument, PEX-LC, which was refined for usability through think-aloud and other interviews with patients, members of the public, and clinical staff. RESULTS: Major challenges in the process of developing PEX-LC related to collaboration among many actors, and design/user interface problems including technical issues. Most problems identified through the think-aloud interviews related to design/user interface problems and technical issues rather than content, for example we re-ordered questions to be in line with patients' chronological, rather than retrospective, descriptions of their experiences. PEX-LC was developed into a final e-questionnaire on a touch-screen smart tablet with one background module covering sociodemographic characteristics, 10 interactive, individualized modules covering early sensations and symptoms, and a 12th assessing current symptoms. CONCLUSIONS: Close collaboration with patients throughout the process was intrinsic for developing PEX-LC. Similarly, we recognized the extent to which clinicians and technical experts were also important in this process. Similar endeavors should assure all necessary competence is included in the core research team, to facilitate timely progress. Our experiences developing PEX-LC combined with new empirical research suggest that this individualized, interactive e-questionnaire, developed through systematizing patients' own formulations of their prodromal symptom experiences, is both feasible for use and has potential value in the intended group

Afatinib versus gefitinib in patients with EGFR mutation-positive advanced non-small-cell lung cancer: Overall survival data from the phase IIb LUX-Lung 7 trial

<b>Background</b>\ud \ud In LUX-Lung 7, the irreversible ErbB family blocker, afatinib, significantly improved progression-free survival (PFS), time-to-treatment failure (TTF) and objective response rate (ORR) versus gefitinib in patients with epidermal growth factor receptor (EGFR) mutation-positive non-small-cell lung cancer (NSCLC). Here, we present primary analysis of mature overall survival (OS) data.\ud \ud <b>Patients and methods</b>\ud \ud LUX-Lung 7 assessed afatinib 40 mg/day versus gefitinib 250 mg/day in treatment-naïve patients with stage IIIb/IV NSCLC and a common EGFR mutation (exon 19 deletion/L858R). Primary OS analysis was planned after ∼213 OS events and ≥32-month follow-up. OS was analysed by a Cox proportional hazards model, stratified by EGFR mutation type and baseline brain metastases.\ud \ud <b>Results</b>\ud \ud Two-hundred and twenty-six OS events had occurred at the data cut-off (8 April 2016). After a median follow-up of 42.6 months, median OS (afatinib versus gefitinib) was 27.9 versus 24.5 months [hazard ratio (HR) = 0.86, 95% confidence interval (CI) 0.66‒1.12, P = 0.2580]. Prespecified subgroup analyses showed similar OS trends (afatinib versus gefitinib) in patients with exon 19 deletion (30.7 versus 26.4 months; HR, 0.83, 95% CI 0.58‒1.17, P = 0.2841) and L858R (25.0 versus 21.2 months; HR 0.91, 95% CI 0.62‒1.36, P = 0.6585) mutations. Most patients (afatinib, 72.6%; gefitinib, 76.8%) had at least one subsequent systemic anti-cancer treatment following discontinuation of afatinib/gefitinib; 20 (13.7%) and 23 (15.2%) patients received a third-generation EGFR tyrosine kinase inhibitor. Updated PFS (independent review), TTF and ORR data were significantly improved with afatinib.\ud \ud <b>Conclusion</b>\ud \ud In LUX-Lung 7, there was no significant difference in OS with afatinib versus gefitinib. Updated PFS (independent review), TTF and ORR data were significantly improved with afatinib