Sensitivity For Detection Of Disease Research Articles

Predictive Value of Serum Lactate Levels for Mortality in Patients with Hepatitis B-Related Decompensated Cirrhosis: A Retrospective Analysis

Aims/Background Decompensated cirrhosis is characterized by the progression of cirrhosis from an asymptomatic state to elevated portal pressure and marked deterioration of liver function. This pathological condition progresses rapidly following onset, significantly raising the risk for mortality. The aim of this study is to explore the association between serum lactate concentrations and mortality rates in individuals with hepatitis B-induced decompensated cirrhosis and to evaluate its potential as a clinical prognostic indicator. Methods This retrospective analysis involved 200 individuals (134 men and 66 women) diagnosed with decompensated cirrhosis related to hepatitis B and hospitalized between March 2017 and May 2023. Out of these patients, 162 survived while 38 did not. Clinical information and laboratory results, including the Model for End-Stage Liver Disease (MELD) score, Child-Pugh score, and serum lactate levels, were collected. Logistic regression was applied to identify mortality risk factors from the patient sample and groups categorized according to gender. The predictive value of serum lactate levels for mortality was assessed using the area under the curve (AUC), i.e., receiver operating characteristic (ROC) curve. Results The surviving patient group showed significantly lowered MELD scores, Child-Pugh scores, and serum lactate levels compared to those who were deceased (p < 0.05). Multivariate analysis revealed that the MELD score, Child-Pugh score, and serum lactate levels were independent predictors of mortality in patients with decompensated hepatitis B cirrhosis, with odds ratios (OR) of 1.321, 1.432, and 49.082, respectively (p = 0.012, 0.028, and <0.001, respectively). Additionally, the OR for serum lactate levels was notably higher in female patients compared to male patients (46.824 vs. 30.451). Thus, the MELD score, Child-Pugh score, and serum lactate levels are effective predictors for mortality in cirrhosis patients (AUCs = 0.628, 0.675, and 0.809; p = 0.014, 0.001, and <0.001, respectively), with serum lactate levels showing the most excellent predictive efficacy profile (sensitivity 65.8% and specificity 97.5%). Additionally, the AUC value for serum lactate levels was lower in male patients (0.785) compared to female patients (0.875), indicating that changes in serum lactate levels were more sensitive in female patients. In summary, serum lactate concentration is a prognostic indicator of mortality in individuals with decompensated cirrhosis due to hepatitis B, exhibiting higher predictive significance in female patients. Conclusion Deceased patients with decompensated cirrhosis linked to hepatitis B exhibit markedly increased serum lactate levels. Thus, monitoring serum lactate levels offers an effective tool for predicting patient prognosis, exhibiting higher sensitivity for disease detection in female patients.

Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques

This study evaluates the effectiveness of deep learning and canonical machine learning models for detecting diseases in crayfish from an imbalanced dataset. In this study, measurements such as weight, size, and gender of healthy and diseased crayfish individuals were taken, and at least five photographs of each individual were used. Deep learning models outperformed canonical models, but combining both approaches proved the most effective. Utilizing the ResNet50 model for automatic feature extraction and subsequent training of the RF algorithm with these extracted features led to a hybrid model, RF-ResNet50, which achieved the highest performance in diseased sample detection. This result underscores the value of integrating canonical machine learning algorithms with deep learning models. Additionally, the ConvNeXt-T model, optimized with AdamW, performed better than those using SGD, although its disease detection sensitivity was 1.3% lower than the hybrid model. McNemar’s test confirmed the statistical significance of the performance differences between the hybrid and the ConvNeXt-T model with AdamW. The ResNet50 model’s performance was improved by 3.2% when combined with the RF algorithm, demonstrating the potential of hybrid approaches in enhancing disease detection accuracy. Overall, this study highlights the advantages of leveraging both deep learning and canonical machine learning techniques for early and accurate detection of diseases in crayfish populations, which is crucial for maintaining ecosystem balance and preventing population declines.

Influence of diffusion weighted imaging and contrast enhanced T1 sequences on the diagnostic accuracy of magnetic resonance enterography for Crohn’s disease

ObjectivesTo evaluate the additional diagnostic benefit of diffusion weighted imaging (DWI) and contrast enhanced (CE) images during MR enterography (MRE) of Crohn’s disease. MethodsDatasets from 73 patients (mean age 32; 40 male) (28 new-diagnosis, 45 relapsed) were read independently by two radiologists selected from a pool of 13. Radiologists interpreted datasets using three sequential sequence blocks: (1) T2 weighted and steady state free precession gradient echo (SSFP) images alone (T2^); (2) T2 weighted and SSFP images with DWI (T2 + DWI^) and; (3) T2 weighted images, SSFP, DWI and post-contrast enhanced (CE) T1 images (T2 + DWI + CE^), documenting presence, location, and activity of small bowel disease. For each sequence block, sensitivity and specificity (readers combined) was calculated against an outcome-based construct reference standard. Results59/73 patients had small bowel disease. Per-patient sensitivity for disease detection was essentially identical (80 % [95 % CI 72, 86], 81 % [73,87], and 79 % [71,86] for T2^, T2 + DWI^and T2 + DWI + CE^respectively). Specificity was identical (82 % [64 to 92]). Per patient sensitivity for disease extent was 56 % (47,65), 56 % (47,65) and 52 % (43 to 61) respectively, and specificity was 82 % (64 to 92) for all blocks. Sensitivity for active disease was 97 % (90,99), 97 % (90,99) and 98 % (92,99), and specificity was also comparable between all sequence combination reads. Results were consistent across segments and newly diagnosed/relapse patients. ConclusionThere is no additional diagnostic benefit of adding either DWI or CE to T2 FSE and SSFP sequences for evaluating small bowel Crohn’s disease, suggesting MRE protocols can be simplified safely.

Ultrasensitive detection and monitoring of central nervous system tumors from plasma using personalized whole-genome ctDNA profiling.

2064 Background: Patients with the central nervous system (CNS) tumors are largely followed up by imaging. Current plasma-based liquid biopsy techniques have limited utility in neuro-oncology due to a low circulating cell-free tumor DNA (ctDNA) burden, blood-brain barrier, and low number of mutations in coding regions. Whole genome sequencing (WGS)-derived patient specific mutational signature from a matched tumor-normal WGS can provide a personalized, highly sensitive and specific approach to detect mutations in ctDNA and provide blood-based monitoring in brain tumor patients. Furthermore, it can be performed on lower amount of peripheral blood since WGS requires less sequencing depth compared to targeted ctDNA panels. Methods: We have profiled a cohort of 28 extra- and intra-axial adult and pediatric brain tumors including adult and pediatric low- and high-grade glioma (9), meningiomas (11), medulloblastomas (5), ependymomas (2), neurocytoma (1). Tumor DNA was extracted from archival pathology tissue, normal DNA from unsorted white blood cells, and ctDNA from 1-2 mL of post-surgery plasma. WGS was performed with 40x coverage for Tumor-Normal DNA and 20x for ctDNA. Using WGS of matched Tumor-Normal and plasma samples, we derived a personalized mutational pattern using SNVs, indels, and copy numbers for quantification and ultra-sensitive detection of ctDNA in plasma samples. An AI-based error suppression model was implemented to filter out the noise in the cell-free DNA (cfDNA) while the personalized mutational signature was used to detect the ctDNA in the cfDNA and to amplify the somatic signal to determine the Tumor Fraction at the time of diagnosis, during the therapy or surveillance period. The ctDNA Tumor Fraction (TF) was compared to the clinical status and MRI-based imaging. Results: All subtypes of brain tumors contained enough mutations to derive personalized mutational signatures. Most mutations were distributed in the noncoding DNA. TF correlated with clinical status and with the disease course on imaging at given time points reaching a 10-4 minimal residual disease detection sensitivity. We were able to detect ctDNA across all WHO grades ranging from WHO 1 meningioma to WHO 4 glioblastoma and medulloblastoma. Furthermore, we were able to detect tumor-specific copy number aberrations such as MYCN amplification in plasma samples and mutational signatures. Conclusions: Here we demonstrate that patient-specific WGS tumor signature in ctDNA from plasma can be used for sensitive monitoring of adults and children with primary low- and high-grade CNS tumors.

Evaluation of the Clinical Impact of PSMA PET-CT Imaging in the Post-radical Treatment Setting in Clinical Practice at Royal Surrey County Hospital

Purpose: PSMA imaging has a higher diagnostic sensitivity for disease detection when compared with conventional imaging, particularly at lower PSA levels [1]. We reviewed whether PSMA PET-CT led to changes in clinical decision making for patients with biochemical recurrence. We included patients who had undergone radical prostatectomy, external beam radiotherapy or brachytherapy.

Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence

ObjectivesThe purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. BackgroundCoronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. MethodsAn automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. study. How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. ResultsAcceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. ConclusionsAutomated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence.

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

Objectives: To employ a deep learning technique that would sort the fruits into normal and abnormal based on the features such as fruit colour, number of fruit spots, and shape of the fruit. Methods: A combined CNN LSTM deep learning model is applied to classify a set of 6519 fruits into two classes namely normal and abnormal. The dataset is an excel file with each record consisting of 9 features. Convolutional Neural Networks (CNN) are applied for deep feature extraction and Long-Short Term Memory (LSTM) is used to detect the class based on extracted features. Findings: The proposed system achieved an accuracy of 98.17%, specificity of 98.65%, sensitivity of 97.77%, and an F1-score of 98.39%. Novelty: The sensitivity of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with these various existing algorithms is that the accuracy was reduced since some sources of errors were not eliminated. Deep Learning delivers methodologies, approaches, and functionalities that can help to resolve analytic and predictive analysis accurately. Keywords: Deep Learning; CNN LSTM; Classification; Hyperparameters

Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform

Manual interpretation of heart sounds is insensitive and prone to subjectivity. Automated diagnosis systems incorporating artificial intelligence and advanced signal processing tools can potentially increase the sensitivity of disease detection and reduce the subjectiveness. This study proposes a novel method for the automated binary classification of heart sound signals using the Fano-factor constrained tunable quality wavelet transform (TQWT) technique. Optimal TQWT based decomposition can reveal significant information in subbands for the reconstruction of events of interest. While transforming heart sound signals using TQWT, the Fano-factor is applied as a thresholding parameter to select the subbands for the clinically relevant reconstruction of signals. TQWT parameters and threshold of the Fano-factor are tuned using a genetic algorithm (GA) to adapt to the underlying optimal detection performance. The time and frequency domain features are extracted from the reconstructed signals. Overall 15 unique features are extracted from each sub-frame resulting in a total feature set of 315 features for each epoch. The resultant features are fed to Light Gradient Boosting Machine model to perform binary classification of the heart sound recordings. The proposed framework is validated using a ten-fold cross-validation scheme and attained sensitivity of 89.30%, specificity of 91.20%, and overall score of 90.25%. Further, synthetic minority over-sampling technique (SMOTE) is applied to produce balanced data set which yielded sensitivity and specificity of 86.32% and 99.44% respectively and overall score of 92.88%. Our developed model can be used in digital stethoscopes to automatically detect abnormal heart sounds and aid the clinicians in their diagnosis.

Evaluation of public and animal health risks in case of a delayed post-mortem inspection in ungulates.

The potential effects of a 24 or 72‐h delay in post‐mortem inspection (PMI) of ungulates on public health and monitoring of animal health and welfare was evaluated. The assessment used a survey of meat inspectors, expert opinion, literature search and a stochastic model for Salmonella detection sensitivity. Disease detection sensitivity at a delayed PMI is expected to reduce detection sensitivity to a variable extent, depending on the hazard and on the signs/lesions and organs involved. No reduction is expected for Trichinella detection in meat from susceptible animal species and any decrease in detection of transmissible spongiform encephalopathies (TSEs) will not exceed the current tolerance for fallen stock. A 24‐h delay in PMI could result in a small reduction in sensitivity of detection for tuberculosis, echinococcosis and cysticercosis. A greater reduction is expected for the detection of pyaemia and Rift valley fever. For the detection of Salmonella, the median model estimates are a reduction of sensitivity of 66.5% (90% probability interval (PI) 0.08–99.75%) after 24‐h delay and 94% (90% PI 0.83–100%) after 72‐h delay of PMI. Laboratory testing for tuberculosis following a sampling delay of 24–72 h could result in no, or a moderate, decrease in detection depending on the method of confirmation used (PCR, culture, histopathology). For chemical contaminants, a delay in meat inspection of 24 or 72 h is expected to have no impact on the effectiveness of detection of persistent organic pollutants and metals. However, for certain pharmacologically active substances, there will be a reduced effectiveness to detect some of these substances due to potential degradation in the available matrices (tissues and organs) and the non‐availability of specific preferred matrices of choice.

Prevalence of Macropod Progressive Periodontal Disease (“lumpy jaw”) in wild western grey kangaroos (Macropus fuliginosus)

Macropod Progressive Periodontal Disease (MPPD), colloquially referred to as “lumpy jaw”, is a commonly observed disease in captive macropods. However, the prevalence of this disease in the wild is largely unknown. A systematic study of MPPD in wild macropods would provide an indication of the endemic presence of this disease in wild populations, and could assist those managing disease in captive populations, by highlighting potential risk factors for disease development. Utilising kangaroos culled as part of a population management program, this study used visual observation and computer tomography (CT) of skulls to investigate the prevalence of MPPD in wild western grey kangaroos (Macropus fuliginosus) from the Perth metropolitan region, Western Australia. The sample suitable for visual and CT analysis comprised 121 specimens, 71 (58.7%) male and 50 (41.3%) female, with the mean age for all 121 specimens being 4.5 years (±2.63 SD). No evidence of MPPD was detected in any of the specimens examined. Overabundance may not be associated with the development of MPPD, as previously considered, and age-related factors should not be eliminated. This results may reflect low susceptibility to MPPD in western grey kangaroos, given low prevalence is reported in this species in captive populations. Further investigation into species-specificity is recommended, and should include samples with soft tissue to improve sensitivity of disease detection. Surveillance of MPPD in wild populations of macropods helps to improve our understanding of the biological significance, development and potential spread of this disease. Notably, this information may assist in the management of MPPD in captive populations, and may have a positive impact on both the welfare and conservation of macropods in captivity.

The early detection of cervical cancer. The current and changing landscape of cervical disease detection.

Cervical cancer prevention has undergone dramatic changes over the past decade. With the introduction of human papillomavirus (HPV) vaccination, some countries have seen a dramatic decline in HPV-mediated cervical disease. However, widespread implementation has been limited by economic considerations and the varying healthcare priorities of different countries, as well as by vaccine availability and, in some instances, vaccine hesitancy amongst the population/government. In this environment, it is clear that cervical screening will retain a critical role in the prevention of cervical cancer and will in due course need to adapt to the changing incidence of HPV-associated neoplasia. Cervical screening has, for many years, been performed using Papanicolaou staining of cytology samples. As our understanding of the role of HPV in cervical cancer progression has advanced, and with the availability of sensitive detection systems, cervical screening now incorporates HPV testing. Although such tests improve disease detection, they are not specific, and cannot discriminate high-grade from low-grade disease. This has necessitated the development of effective triage approaches to stratify HPV-positive women according to their risk of cancer progression. Although cytology triage remains the mainstay of screening, novel strategies under evaluation include DNA methylation, biomarker detection and the incorporation of artificial intelligence systems to detect cervical abnormalities. These tests, which can be partially anchored in a molecular understanding of HPV pathogenesis, will enhance the sensitivity of disease detection and improve patient outcomes. This review will provide insight on these innovative methodologies while explaining their scientific basis drawing from our understanding of HPV tumour biology.

Fast Quantitative Magnetic Resonance Imaging

Among medical imaging modalities, magnetic resonance imaging (MRI) stands out for its excellent soft-tissue contrast, anatomical detail, and high sensitivity for disease detection. However, as proven by the continuous and vast effort to develop new MRI techniques, limitations and open challenges remain. The primary source of contrast in MRI images are the various relaxation parameters associated with the nuclear magnetic resonance (NMR) phenomena upon which MRI is based. Although it is possible to quantify these relaxation parameters (qMRI) they are rarely used in the clinic, and radiological interpretation of images is primarily based upon images that are relaxation time weighted. The clinical adoption of qMRI is mainly limited by the long acquisition times required to quantify each relaxation parameter as well as questions around their accuracy and reliability. More specifically, the main limitations of qMRI methods have been the difficulty in dealing with the high inter-parameter correlations and a high sensitivity to MRI system imperfections. Recently, new methods for rapid qMRI have been proposed. The multi-parametric models at the heart of these techniques have the main advantage of accounting for the correlations between the parameters of interest as well as system imperfections. This holistic view on the MR signal makes it possible to regress many individual parameters at once, potentially with a higher accuracy. Novel, accurate techniques promise a fast estimation of relevant MRI quantities, including but not limited to longitudinal (T1) and transverse (T2) relaxation times. Among these emerging methods, MR Fingerprinting (MRF), synthetic MR (syMRI or MAGIC), and T1‒T2 Shuffling are making their way into the clinical world at a very fast pace. However, the main underlying assumptions and algorithms used are sometimes different from those found in the conventional MRI literature, and can be elusive at times. In this book, we take the opportunity to study and describe the main assumptions, theoretical background, and methods that are the basis of these emerging techniques. Quantitative transient state imaging provides an incredible, transformative opportunity for MRI. There is huge potential to further extend the physics, in conjunction with the underlying physiology, toward a better theoretical description of the underlying models, their application, and evaluation to improve the assessment of disease and treatment efficacy.

Spatially-resolved Brillouin spectroscopy reveals biomechanical abnormalities in mild to advanced keratoconus in vivo

Mounting evidence connects the biomechanical properties of tissues to the development of eye diseases such as keratoconus, a disease in which the cornea thins and bulges into a conical shape. However, measuring biomechanical changes in vivo with sufficient sensitivity for disease detection has proven challenging. Here, we demonstrate the diagnostic potential of Brillouin light-scattering microscopy, a modality that measures longitudinal mechanical modulus in tissues with high measurement sensitivity and spatial resolution. We have performed a study of 85 human subjects (93 eyes), consisting of 47 healthy volunteers and 38 keratoconus patients at differing stages of disease, ranging from stage I to stage IV. The Brillouin data in vivo reveal increasing biomechanical inhomogeneity in the cornea with keratoconus progression and biomechanical asymmetry between the left and right eyes at the onset of keratoconus. The receiver operating characteristic analysis of the stage-I patient data indicates that mean Brillouin shift of the cone performs better than corneal thickness and maximum curvature respectively. In conjunction with morphological patterns, Brillouin microscopy may add value for diagnosis of keratoconus and potentially for screening subjects at risk of complications prior to laser eye surgeries.

Disease Detection Methodologies in Acute Lymphoblastic Leukemia (ALL): Opportunities for Improvement

Background Minimal residual disease (MRD) is highly prognostic for outcomes in ALL, particularly prior to HCT. New methods for MRD determination in bone marrow (BM) increase the sensitivity of disease detection but remain dependent on the quality of the specimen obtained. Residual disease testing in the cerebrospinal fluid (CSF) is rarely performed. With the dual objectives to identify the role of BM biopsy in conjunction with BM aspirate and flow cytometry (FC), and also the role of CSF FC alongside cytopathology (CP), we evaluated the frequency and degree of discrepancies in disease detection based on methodologies of BM and CSF disease assessments. Design In this retrospective study of children and young adults with relapsed ALL who were enrolled on one or more phase I/II trials at the NCI between 2012 and 2018, we systematically analyzed all BM and CSF assessments that were performed. All CSF analyses included cell count, CP, and frequently FC. BM samples needed to have a biopsy, aspirate, and FC results to be included in the study. BM and CNS status were classified based on established definitions. For BM samples, a discrepancy was defined as any difference in marrow status (M1, M2, or M3). For CSF, a discrepancy was noted if the CP and the FC were discordant. BM specimens were further analyzed by extent of hemodilution and cellularity with MRD threshold set at Results From 98 patients, a total of 410 BM samples were analyzed, amongst which 61 (14.8%) samples had at least one discordant result (Figure 1). This included 26 discrepancies between biopsy and aspirate, 47 between biopsy and FC, and 39 between aspirate and FC. From these, 26 (6.3%) were deemed clinically relevant, defined as changing the assessment from a morphologic remission to more extensive involvement, and had a median discrepancy of 30.5% blasts (range, 7-91%). In 17 of these cases, the biopsy was essential to determine the full extent of disease. In 35 cases, the aspirate was reported as poor quality and in 30% the flow specimen had hemodilution deemed to be clinically significant. 351 of 418 CSF samples had a concurrent FC analysis of which, 32 (9.1%) had disease discrepancies; all cases demonstrated FC positivity with a negative CP report, with the highest discrepancy at 60% blasts (CSF WBC: 2). Conclusion MRD testing in ALL is critical, however, assessing the depth of remission is dependent on the quality of the sample acquired. This retrospective analysis highlights the frequency and degree of discrepancies observed between traditional histopathologic methods of disease assessment and FC. While the incorporation of highly-sensitive MRD testing will increase disease detection, these data support expanding methodologies for complimentary quantitation of BM and CNS involvement in patients with ALL.

Successful Application of the Gamma-Interferon Assay in a Bovine Tuberculosis Eradication Program: The French Bullfighting Herd Experience.

In the French Camargue region, where bovine tuberculosis had been enzootic for several years in bullfighting cattle herds, the gamma-interferon (IFN) assay was used since 2003 in parallel with the intradermal test in order to increase overall disease detection sensitivity in infected herds. This study presents the results of a field-evaluation of the assay during a 10-year period (2004–2014) of disease control and surveillance program and explores the particular pattern of IFN assay results in bullfight herds in comparison to cattle from other regions of France. The low sensitivity [59.2% (50.6; 67.3)] of IFN assay using the tuberculin stimulation could be related to the poor gamma-IFN production from bullfight cattle blood cells which is significantly lower than in animals of conventional breeds. The characteristics of the assay were progressively adapted to the epidemiological situation and the desired strategic applications. Data analysis with a receiver operating characteristic curve based on a simple S/P value algorithm allowed for the determination of a new cutoff adapted for a global screening, giving a high specificity of 99.9% results and a high accuracy of the assay. Having regularly risen to above 5% since 2005, with a peak around 10% in 2010, the annual incidence dropped to under 1% in 2014. The positive predictive value relative to the bacteriological confirmation evolved during the years, from 33% in 2009 to 12% during the last screening period, a normal trend in a context of decreasing prevalence. The estimated rate of false-positive reactions during screening campaigns was 0.67%, confirming the high specificity of the test, measured in bTB negative herds, in this epidemiological context. The proportion of false-positive reactions decreased with the age and was higher in males than in females. Although these results indicate that the IFN assay is accurate in the field, it also emphasizes great differences between interferon quantities produced by bullfight cattle blood samples compared to those of classical bovine breeds, which underlines the necessity to adapt the algorithms and combinations of the assay according to local epidemiological contexts.

Abnormal Heavy/Light Chain Ratio and Matched Pair Suppression Increase Residual Disease Detection Sensitivity in Patients With Multiple Myeloma With Deep Responses

BackgroundHeavy/light chain (HLC) assay can quantify involved as well as uninvolved immunoglobulin pairs and is used to detect monoclonal proteins. Patients and MethodsWe compared the sensitivity between HLC assay and serum protein electrophoresis, serum immunofixation electrophoresis (IFE), and free light chain (FLC) assay in patients with symptomatic multiple myeloma (n = 111) whose responses were stable disease or better. ResultsAmong patients with negative IFE and normal FLC ratios, 84.4% (38 of 45) and 80% (36 of 45) exhibited normal HLC ratios and no pair suppression, respectively (13.3% [6 of 45], moderate pair suppression and 6.7% [3 of 45], severe pair suppression). The lower the monoclonal protein levels, the more the possibility that the patients had normal HLC ratios and no matched pair suppression (both P < .000001). HLC ratios or pair suppression combined with IFE results and FLC ratios were more sensitive for detecting monoclonal proteins than were IFE results and FLC ratios alone (P = .016 and .0039, respectively). A combination of all 4 methods (IFE, FLC, HLC, and pair suppression) was far more sensitive than were IFE findings plus FLC ratios alone (P = .00024). ConclusionAbnormal HLC ratios and HLC-matched pair suppression can increase the sensitivity for detecting residual disease in patients with multiple myeloma with deep responses.

Spiky gold shells on magnetic particles for DNA biosensors

Combined separation and detection of biomolecules has the potential to speed up and improve the sensitivity of disease detection, environmental testing, and biomolecular analysis. In this work, we synthesized magnetic particles coated with spiky nanostructured gold shells and used them to magnetically separate out and detect oligonucleotides using SERS. The distance dependence of the SERS signal was then harnessed to detect DNA hybridization using a Raman label bound to a hairpin probe. The distance of the Raman label from the surface increased upon complementary DNA hybridization, leading to a decrease in signal intensity. This work demonstrates the use of the particles for combined separation and detection of oligonucleotides without the use of an extrinsic tag or secondary hybridization step.

The Utility of PET/CT in the Planning of External Radiation Therapy for Prostate Cancer.

Radiotherapy and radical prostatectomy are the definitive treatment options for patients with localized prostate cancer. A rising level of prostate-specific antigen after radical prostatectomy indicates prostate cancer recurrence, and these patients may still be cured with salvage radiotherapy. To maximize chance for cure, the irradiated volumes should completely encompass the extent of disease. Therefore, accurate estimation of the location of disease is critical for radiotherapy planning in both the definitive and the salvage settings. Current first-line imaging for prostate cancer has limited sensitivity for detection of disease both at initial staging and at biochemical recurrence. Integration of PET into routine evaluation of prostate cancer patients may improve both staging accuracy and radiotherapy planning. 18F-FDG PET/CT is now routinely used in radiation planning for several cancer types. However, 18F-FDG PET/CT has low sensitivity for prostate cancer. Additional PET probes evaluated in prostate cancer include 18F-sodium fluoride, 11C-acetate, 11C- or 18F-choline, 18F-fluciclovine, and 68Ga- or 18F-labeled ligands that bind prostate-specific membrane antigen (PSMA). PSMA ligands appear to be the most sensitive and specific but have not yet received Food and Drug Administration New Drug Application approval for use in the United States. Retrospective and prospective investigations suggest a potential major impact of PET/CT on prostate radiation treatment planning. Prospective trials randomizing patients to routine radiotherapy planning versus PET/CT-aided planning may show meaningful clinical outcomes. Prospective clinical trials evaluating the addition of 18F-fluciclovine PET/CT for planning of salvage radiotherapy with clinical endpoints are under way. Prospective trials evaluating the clinical impact of PSMA PET/CT on prostate radiation planning are indicated.

Radiolabeled inorganic nanoparticles for positron emission tomography imaging of cancer: an overview.

Over the last few years, a plethora of radiolabeled inorganic nanoparticles have been developed and evaluated for their potential use as probes in positron emission tomography (PET) imaging of a wide variety of cancers. Inorganic nanoparticles represent an emerging paradigm in molecular imaging probe design, allowing the incorporation of various imaging modalities, targeting ligands, and therapeutic payloads into a single vector. A major challenge in this endeavor is to develop disease-specific nanoparticles with facile and robust radiolabeling strategies. Also, the radiolabeled nanoparticles should demonstrate adequate in vitro and in vivo stability, enhanced sensitivity for detection of disease at an early stage, optimized in vivo pharmacokinetics for reduced non-specific organ uptake, and improved targeting for achieving high efficacy. Owing to these challenges and other technological and regulatory issues, only a single radiolabeled nanoparticle formulation, namely "C-dots" (Cornell dots), has found its way into clinical trials thus far. This review describes the available options for radiolabeling of nanoparticles and summarizes the recent developments in PET imaging of cancer in preclinical and clinical settings using radiolabeled nanoparticles as probes. The key considerations toward clinical translation of these novel PET imaging probes are discussed, which will be beneficial for advancement of the field.

Utility of MR enterography and ultrasound for the investigation of small bowel Crohn's disease.

5 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2017;45:1573-1588.