Diagnosis of Lung Cancer: What Metabolomics Can Contribute

The reprogrammed metabolism of cancer cells reflects itself in an alteration of metabolite concentrations, which in turn can be used to define a specific metabolic phenotype or fingerprint for cancer. In this contribution, a metabolism-based discrimination between lung cancer patients and healthy controls, derived from an analysis of human blood plasma by proton nuclear magnetic resonance (1H-NMR) spectroscopy, is described. This technique is becoming widely used in the field of metabolomics because of its ability to provide a highly informative spectrum, representing the relative metabolite concentrations. Cancer types are characterized by decreased or increased levels of specific plasma metabolites, such as glucose or lactate, compared to controls. Data analysis by multivariate statistics provides a classification model with high levels of sensitivity and specificity. Nuclear magnetic resonance (NMR) metabolomics might not only contribute to the diagnosis of lung cancer but also shows potential for treatment follow-up as well as for paving the way to a better understanding of disease-related diverting biochemical pathways

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Background: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). Conclusion: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation

Safety and Immunogenicity of MAGE-A3 Cancer Immunotherapeutic with or without Adjuvant Chemotherapy in Patients with Resected Stage IB to III MAGE-A3-Positive Non-Small-Cell Lung Cancer

Introduction: To assess the safety and immunogenicity of MAGE-A3 immunotherapeutic in patients with stage IB-III MAGE-A3-positive non-small-cell lung cancer (NSCLC) who were or were not undergoing standard cisplatin/vinorelbine chemotherapy. Methods: This open, prospective, multicenter, parallel-group phase I study (NCT00455572) enrolled patients with resected (cohorts 1-3) or unresectable (cohort 4) MAGE-A3-positive NSCLC. MAGE-A3 immunotherapeutic (300 g recombinant MAGE-A3 formulated with AS15) was administered (eight doses, 3 weeks apart) concurrent with (cohort 1), after (cohort 2), or without (cohort 3) standard-adjuvant chemotherapy, or after standard radiotherapy and/or chemotherapy (cohort 4). Results: Sixty-seven patients received greater than or equal to 1 dose of MAGE-A3 immunotherapeutic. Grade 3/4 adverse events (AEs) were reported for 16 out of 19 (84%), 2 out of 18 (11%), 5 out of 18 (28%), and 1 out of 12 (8%) patients in cohorts 1, 2, 3, and 4, respectively. Many grade 3/4 AEs in cohort 1 (e.g., neutropenia) were typical of chemotherapy. Six patients, including three in cohort 1, reported study treatment-related grade 3/4 AEs (injection-site reactions or musculoskeletal/back pain, which resolved within 5 days). One patient (in cohort 4) died, but this and the other serious adverse events were not study treatment related. MAGE-A3-specific antibody responses to immunotherapy were induced in all patients evaluated in all cohorts. MAGE-A3-specific CD4(+) T-cell responses to immunotherapy were detected in 4 out of 11 (36%), 4 out of 15 (27%), 2 out of 8 (25%), and 5 out of 6 (83%) evaluated patients in cohorts 1, 2, 3, and 4, respectively; and CD8(+) T-cell responses were only detected in four patients. Conclusion: In resected and unresectable NSCLC patients and irrespective of whether standard chemotherapy was concurrent or not, MAGE-A3 immunotherapeutic is well tolerated and induces MAGE-A3-specific immune responses. GlaxoSmithKline Biologicals SA sponsored the clinical trial and covered the costs associated with the development and publishing of the manuscript, including scientific writing assistance. adjuvant chemotherapy; immunotherapy; immunostimulant; MAGE-A3; non–small cell lung carcinoma; vaccin

Effects of the DICE Method to Improve Timely Recognition and Treatment of Neuropsychiatric Symptoms in Early Alzheimer's Disease at the Memory Clinic:The BEAT-IT Study

BACKGROUND: Neuropsychiatric symptoms (NPS) are highly prevalent in Alzheimer's disease (AD) and are associated with negative outcomes. However, NPS are currently underrecognized at the memory clinic and non-pharmacological interventions are scarcely implemented.OBJECTIVE: To evaluate the effectiveness of the Describe, Investigate, Create, Evaluate (DICE) method™ to improve the care for NPS in AD at the memory clinic.METHODS: We enrolled sixty community-dwelling people with mild cognitive impairment or AD dementia and NPS across six Dutch memory clinics with their caregivers. The first wave underwent care as usual (n = 36) and the second wave underwent the DICE method (n = 24). Outcomes were quality of life (QoL), caregiver burden, NPS severity, NPS-related distress, competence managing NPS, and psychotropic drug use. Reliable change index was calculated to identify responders to the intervention. A cost-effectiveness analysis was performed and semi-structured interviews with a subsample of the intervention group (n = 12).RESULTS: The DICE method did not improve any outcomes over time compared to care as usual. Half of the participants of the intervention group (52%) were identified as responders and showed more NPS and NPS-related distress at baseline compared to non-responders. Interviews revealed substantial heterogeneity among participants regarding NPS-related distress, caregiver burden, and availability of social support. The intervention did not lead to significant gains in quality-adjusted life years and well-being years nor clear savings in health care and societal costs.CONCLUSION: The DICE method showed no benefits at group-level, but individuals with high levels of NPS and NPS-related distress may benefit from this intervention.</p

Feasibility study of computed tomography colonography using limited bowel preparation at normal and low-dose levels study

The purpose was to evaluate low-dose CT colonography without cathartic cleansing in terms of image quality, polyp visualization and patient acceptance. Sixty-one patients scheduled for colonoscopy started a low-fiber diet, lactulose and amidotrizoic-acid for fecal tagging 2 days prior to the CT scan (standard dose, 5.8–8.2 mSv). The original raw data of 51 patients were modified and reconstructed at simulated 2.3 and 0.7 mSv levels. Two observers evaluated the standard dose scan regarding image quality and polyps. A third evaluated the presence of polyps at all three mSv levels in a blinded prospective way. All observers were blinded to the reference standard: colonoscopy. At three times patients were given questionnaires relating to their experiences and preference. Image quality was sufficient in all patients, but significantly lower in the cecum, sigmoid and rectum. The two observers correctly identified respectively 10/15 (67%) and 9/15 (60%) polyps ≥10 mm, with 5 and 8 false-positive lesions (standard dose scan). Dose reduction down to 0.7 mSv was not associated with significant changes in diagnostic value (polyps ≥10 mm). Eighty percent of patients preferred CT colonography and 13% preferred colonoscopy (P<0.001). CT colonography without cleansing is preferred to colonoscopy and shows sufficient image quality and moderate sensitivity, without impaired diagnostic value at dose-levels as low as 0.7 mSv

Early recognition and treatment of neuropsychiatric symptoms to improve quality of life in early Alzheimer's disease

__Background:__ Neuropsychiatric symptoms (NPS) are very common in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and are associated with various disadvantageous clinical outcomes including a negative impact on quality of life, caregiver burden, and accelerated disease progression. Despite growing evidence of the efficacy of (non)pharmacological interventions to reduce these symptoms, NPS remain underrecognized and undertreated in memory clinics. The BEhavioural symptoms in Alzheimer's disease Towards early Identification and Treatment (BEAT-IT) study is developed to (1) investigate the neurobiological etiology of NPS in AD and (2) study the effectiveness of the Describe, Investigate, Create, Evaluate (DICE) approach to structure and standardize the current care of NPS in AD. By means of the DICE method, we aim to improve the quality of life of AD patients with NPS and their caregivers who visit the memory clinic. This paper describes the protocol for the intervention study that incorporates the latter aim. __Methods:__ We aim to enroll a total of 150 community-dwelling patients with MCI or AD and their caregivers in two waves. First, we will recruit a control group who will receive care as usual. Next, the second wave of participants will undergo the DICE method. This approach consists of the following steps: (1) describe the context in which NPS occur, (2) investigate the possible causes, (3) create and implement a treatment pl

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (18F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up