Centrifugal filtration of serum for FTIR spectroscopy does not improve stratification of brain tumours

Discrimination of brain cancer versus non-cancer patients using serum-based attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy diagnostics was first developed by Hands et al . Cameron et al . then went on to stratifying between specific brain tumour types: glioblastoma multiforme (GBM) vs. primary cerebral lymphoma. Expanding on these studies, 30 GBM, 30 lymphoma and 30 non-cancer patients were selected to investigate the influence on test performance by focusing on specific molecular weight regions of the patient serum. Membrane filters with molecular weight cut offs of 100 kDa, 50 kDa, 30 kDa, 10 kDa and 3 kDa were purchased in order to remove the most abundant high molecular weight components. Three groups were classified using both partial least squares-discriminate analysis (PLS-DA) and random forest (RF) machine learning algorithms; GBM versus non-cancer, lymphoma versus non-cancer and GBM versus lymphoma. For all groups, once the serum was filtered the sensitivity, specificity and overall balanced accuracies decreased. This illustrates that the high molecular weight components are required for discrimination between cancer and non-cancer as well as between tumour types. From a clinical application point of view, this is preferable as less sample preparation is required

Investigating centrifugal filtration of serum-based FTIR spectroscopy for the stratification of brain tumours

Discrimination of brain cancer versus non-cancer patients using serum-based attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy diagnostics was first developed by Hands et al with a reported sensitivity of 92.8% and specificity of 91.5%. Cameron et al. then went on to stratifying between specific brain tumour types: glioblastoma multiforme (GBM) vs. primary cerebral lymphoma with a sensitivity of 90.1% and specificity of 86.3%. Expanding on these studies, 30 GBM, 30 lymphoma and 30 non-cancer patients were selected to investigate the influence on test performance by focusing on specific molecular weight regions of the patient serum. Membrane filters with molecular weight cut offs of 100 kDa, 50 kDa, 30 kDa, 10 kDa and 3 kDa were purchased in order to remove the most abundant high molecular weight components. Three groups were classified using both partial least squares-discriminate analysis (PLS-DA) and random forest (RF) machine learning algorithms; GBM versus non-cancer, lymphoma versus non-cancer and GBM versus lymphoma. For all groups, once the serum was filtered the sensitivity, specificity and overall balanced accuracies decreased. This illustrates that the high molecular weight components are required for discrimination between cancer and non-cancer as well as between tumour types. From a clinical application point of view, this is preferable as less sample preparation is required

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients’ average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy

Pubic bone injuries in primiparous women: magnetic resonance imaging in detection and differential diagnosis of structural injury

Objective To evaluate the utility of magnetic resonance imaging (MRI) in diagnosing structural injury in primiparous women at risk for pelvic floor injury. Methods This was an observational study of 77 women who underwent 3T MRI after delivery. Women were operationally defined as high risk ( n = 45) for levator ani muscle tears (risk factors: second‐stage labor > 150 min or 35 years and birth weight > 4000 g) or low risk ( n = 32): vaginally delivered without these risk factors ( n = 12); delivered by Cesarean section after second‐stage labor > 150 min ( n = 14) or delivered by Cesarean section without labor ( n = 6). All women were imaged using fluid‐sensitive MRI sequences. Two musculoskeletal radiologists reviewed images for bone marrow edema, fracture, pubic symphysis measurements and levator ani tear. Results MRI showed pubic bone fractures in 38% of women at high risk for pelvic floor injury and in 13% of women at low risk for pelvic floor injury (χ 2 (3) = 9.27, P = 0.03). Levator ani muscle tears were present in 44% of the high‐risk women and in 9% of the low‐risk women (χ 2 (3) = 11.57, P = 0.010). Bone marrow edema in the pubic bones was present in 61% of women studied across delivery categories. Complex patterns of injury included combinations of bone marrow edema, fractures, levator ani tears and pubic symphysis injuries. No MRI‐documented injuries were present in 18% of women at high risk and 44% at low risk for pelvic floor injury (χ 2 (1) = 6.2, P = 0.013). Conclusions Criteria identifying primiparous women at risk for pelvic floor injury can predict increased risk of bone and soft tissue changes at the pubic symphysis. Fluid‐sensitive MRI has utility for differential diagnosis of structural injury in postpartum women. Copyright © 2012 ISUOG. Published by John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90593/1/9082_ftp.pd

Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients

Rapid Spectroscopic Liquid Biopsy for the Universal Detection of Brain Tumours

Background: To support the early detection and diagnosis of brain tumours we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumour diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumour genetic material) report a lower classification accuracy for early-stage tumours. In this manuscript we present an investigation into the link between brain tumour volume and liquid biopsy test performance. Methods: In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligoastrocytoma and oligodendroglioma)) tumour volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumour patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Results: Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumour volumes as small as 0.2 cm3 were correctly identified. Conclusions: Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis.</jats:p

Quality by Any Other Name?: A Comparison of Three Profiling Systems for Assessing Health Care Quality

Many performance measurement systems are designed to identify differences in the quality provided by health plans or facilities. However, we know little about whether different methods of performance measurement provide similar answers about the quality of care of health care organizations. To examine this question, we used three different measurement approaches to assess quality of care delivered in veteran affairs (VA) facilities. Data Sources/Study Setting . Medical records for 621 patients at 26 facilities in two VA regions. Study Design . We examined agreements in quality conclusions using: focused explicit (38 measures for six conditions/prevention), global explicit (372 measures for 26 conditions/prevention), and structured implicit review physician-rated care (a single global rating of care for three chronic conditions and overall acute, chronic and preventive care). Trained nurse abstractors and physicians reviewed all medical records. Correlations between scores from the three systems were adjusted for measurement error in each using multilevel regression models. Results . Intercorrelations of scores were generally moderate to high across all three systems, and rose with adjustment for measurement error. Site-level correlations for prevention and diabetes care were particularly high. For example, adjusted for measurement error at the site level, prevention quality was correlated at 0.89 between the implicit and global systems, 0.67 between implicit and focused, and 0.73 between global and focused systems. Conclusions . We found moderate to high agreement in quality scores across the three profiling systems for most clinical areas, indicating that all three were measuring a similar construct called “quality.” Adjusting for measurement error substantially enhanced our ability to identify this underlying construct.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/1/HESR_730_sm_Appendix1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/2/HESR+730+Appendix+2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/3/j.1475-6773.2007.00730.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/4/HESR_730_sm_Appendix2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/5/HESR+730+Appendix+1.pd