Health Outcomes for Definite Concurrent Chemoradiation in Locally Advanced Non-Small Cell Lung Cancer: A Prospective Study

BACKGROUND: In patients with locally advanced lung cancer treated with concurrent chemoradiation, outcome measurements have been mostly limited to survival. OBJECTIVES: We aimed to measure outcomes that matter to these patients beyond survival in a general clinical practice. METHODS: In a prospective single-centre study, consecutive patients with locally advanced non-small cell lung cancer reported their own outcomes using the EORTC Quality of Life Questionnaire Core 30 at baseline, during therapy, at therapy stop and till 1 year after therapy end every 3 months. Survival, complications, quality of death and case-mix variables were measured. RESULTS: There were 32 consecutive patients included prospectively from June 2013 until September 2016. Median overall survival was 24.3 months (95% CI 12.7-35.9). Severe toxicity (grade III-IV) was frequent (haematologic toxicity III-IV in 59%). Patient-reported outcomes (PROs) documented the burden on global health status and on functional domains (physical, role, social, emotional and cognitive functioning). Deterioration was pronounced during and after treatment with drops over 20 up to 40% points from baseline for physical, role and social functioning. Clinically meaningful negative effects did persist up to 6 and 9 months for physical and role functioning. Fifty-six percent of the deceased patients died in hospital. CONCLUSIONS: The assault on health-related quality of life during concurrent chemoradiation for locally advanced lung cancer is considerable. Loss of physical and role functioning persists up to 6 and 9 months after therapy end, respectively. Measuring PROs can help to identify issues for improvement of the value of care delivered.status: publishe

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Background: Lung cancer can be detected by measuring the patient’s plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient’s metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Patients and methods: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. Results: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86–100]) and a sensitivity of 92% (95% CI [81–98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77–97]) and an AUC value of 0.97 (95% CI [0.93–1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82–99]) and an AUC value of 0.97 (95% CI [0.93–1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. Conclusions: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor’s presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion which relies on the recognition of patterns and clinical context for the detection of specific diseases. In the study, we aimed to explore the accuracy and inter-rater variability of pulmonologists when interpreting PFTs and compared it against that of artificial intelligence (AI)-based software which was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases comprising with PFT and clinical information resulting in 6000 independent interpretations. AI software examined the same data. ATS/ERS guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4% (±5.9) of the cases (range: 56-88%). The inter-rater variability of 0.67 (kappa) pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6% (±8.7) of the cases (range: 24-62%) with a large inter-rater variability (kappa= 0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures). The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice

The genomic landscape of nonsmall cell lung carcinoma in never smokers.

Lung cancer is the number one cause of cancer-related death worldwide with cigarette smoking as its major risk factor. Although the incidence of lung cancer in never smokers is rising, this subgroup of patients is underrepresented in genomic studies of lung cancer. Here, we assembled a prospective cohort of 46 never-smoking, nonsmall cell lung cancer (NSCLC) patients and performed whole-exome and low-coverage whole-genome sequencing on tumors and matched germline DNA. We observed fewer somatic mutations, genomic breakpoints and a smaller fraction of the genome with chromosomal instability in lung tumors from never smokers compared to smokers. The lower number of mutations, enabled us to identify TSC22D1 as a potential driver gene in NSCLC. On the other hand, the frequency of mutations in actionable genes such as EGFR and ERBB2 and of amplifications in MET were higher, while the mutation rate of TP53, which is a negative prognostic factor, was lower in never smokers compared to smokers. Together, these observations suggest a more favorable prognosis for never smokers with NSCLC. Classification of somatic mutations into six-substitution type patterns or into 96-substitution type signatures revealed distinct clusters between smokers and never smokers. Particularly, we identified in never smokers signatures related to aging, homologous recombination damage and APOBEC/AID activity as the most important underlying processes of NSCLC. This further indicates that second-hand smoking is not driving NSCLC pathogenesis in never smokers

The genomic landscape of nonsmall cell lung carcinoma in never smokers

Lung cancer is the number one cause of cancer-related death worldwide with cigarette smoking as its major risk factor. Although the incidence of lung cancer in never smokers is rising, this subgroup of patients is underrepresented in genomic studies of lung cancer. Here, we assembled a prospective cohort of 46 never-smoking, nonsmall cell lung cancer (NSCLC) patients and performed whole-exome and low-coverage whole-genome sequencing on tumors and matched germline DNA. We observed fewer somatic mutations, genomic breakpoints and a smaller fraction of the genome with chromosomal instability in lung tumors from never smokers compared to smokers. The lower number of mutations, enabled us to identify TSC22D1 as a potential driver gene in NSCLC. On the other hand, the frequency of mutations in actionable genes such as EGFR and ERBB2 and of amplifications in MET were higher, while the mutation rate of TP53, which is a negative prognostic factor, was lower in never smokers compared to smokers. Together, these observations suggest a more favorable prognosis for never smokers with NSCLC. Classification of somatic mutations into six-substitution type patterns or into 96-substitution type signatures revealed distinct clusters between smokers and never smokers. Particularly, we identified in never smokers signatures related to aging, homologous recombination damage and APOBEC/AID activity as the most important underlying processes of NSCLC. This further indicates that second-hand smoking is not driving NSCLC pathogenesis in never smokers.status: publishe

Does nivolumab for progressed metastatic lung cancer fulfill its promises? : an efficacy and safety analysis in 20 general hospitals

Objectives: In patients with refractory or recurrent non-small-cell lung cancer (NSCLC) after first line chemotherapy, phase III trials showed superiority of nivolumab, an IgG4 programmed death-1 immune-checkpoint inhibitor antibody, over docetaxel. We evaluated case mix, effectiveness and safety of nivolumab upon implementation in general practice. Materials and methods: In 20 general hospitals, all consecutive NSCLC patients treated with nivolumab within the medical need program (inclusion period 12 months) in Flanders - Belgium were evaluated. Results: There were 267 patients, Eastem Cooperative Oncology Group (ECOG) score was 2 in 24% and 0-1 in 76%. In 48%, two or more systemic regimens were given before nivolumab. The median overall survival was 7.8 months (95% confidence interval (CI) 6.3-9.3). At one year, the overall survival rate was 36.5 +/- 0.34%. Median progression-free survival was 3.7 months (95% CI 2.9-4.5). An objective response was obtained in 23.2%. ECOG score 2 and presence of liver metastasis strongly correlated with worse survival (p < 0.00001). Treatment related adverse events grade 3 or 4 were reported in 21%, colitis (4%) and pneumonitis (7%) were most frequent. Conclusion: Upon implementation of nivolumab therapy in general hospitals, the case mix was characterized by a more heavily pretreated population with a substantial fraction of patients with ECOG score 2. The median overall survival is slightly inferior to what was published in the randomized phase III trials. An ECOG score 2 and the presence of liver metastasis correlated strongly with a worse survival. We report a high prevalence of serious adverse events

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases. 120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests. The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4 +/- 5.9% of the cases (range 56-88%). The interrater variability of kappa=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6 +/- 8.7% of the cases (range 24-62%) with a large interrater variability (kappa=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice.status: publishe