Predictors of salvage therapy for parasagittal meningiomas treated with primary surgery, radiosurgery, or surgery plus adjuvant radiotherapy.

OBJECTIVE: Parasagittal meningiomas (PM) are treated with primary microsurgery, radiosurgery (SRS), or surgery with adjuvant radiation. We investigated predictors of tumor progression requiring salvage surgery or radiation treatment. We sought to determine whether primary treatment modality, or radiologic, histologic, and clinical variables were associated with tumor progression requiring salvage treatment. METHODS: Retrospective study of 109 consecutive patients with PMs treated with primary surgery, radiation (RT), or surgery plus adjuvant RT (2000-2017) and minimum 5 years follow-up. Patient, radiologic, histologic, and treatment data were analyzed using standard statistical methods. RESULTS: Median follow up was 8.5 years. Primary treatment for PM was surgery in 76 patients, radiation in 16 patients, and surgery plus adjuvant radiation in 17 patients. Forty percent of parasagittal meningiomas in our cohort required some form of salvage treatment. On univariate analysis, brain invasion (OR: 6.93, p < 0.01), WHO grade 2/3 (OR: 4.54, p < 0.01), peritumoral edema (OR: 2.81, p = 0.01), sagittal sinus invasion (OR: 6.36, p < 0.01), sagittal sinus occlusion (OR: 4.86, p < 0.01), and non-spherical shape (OR: 3.89, p < 0.01) were significantly associated with receiving salvage treatment. On multivariate analysis, superior sagittal sinus invasion (OR: 8.22, p = 0.01) and WHO grade 2&3 (OR: 7.58, p < 0.01) were independently associated with receiving salvage treatment. There was no difference in time to salvage therapy (p = 0.11) or time to progression (p = 0.43) between patients receiving primary surgery alone, RT alone, or surgery plus adjuvant RT. Patients who had initial surgery were more likely to have peritumoral edema on preoperative imaging (p = 0.01). Median tumor volume was 19.0 cm3 in patients receiving primary surgery, 5.3 cm3 for RT, and 24.4 cm3 for surgery plus adjuvant RT (p < 0.01). CONCLUSION: Superior sagittal sinus invasion and WHO grade 2/3 are independently associated with PM progression requiring salvage therapy regardless of extent of resection or primary treatment modality. Parasagittal meningiomas have a high rate of recurrence with 80.0% of patients with WHO grade 2/3 tumors with sinus invasion requiring salvage treatment whereas only 13.6% of the WHO grade 1 tumors without sinus invasion required salvage treatment. This information is useful when counseling patients about disease management and setting expectations.

Performance of GPT-4 on the American College of Radiology In-training Examination: Evaluating Accuracy, Model Drift, and Fine-tuning.

RATIONALE AND OBJECTIVES: In our study, we evaluate GPT-4's performance on the American College of Radiology (ACR) 2022 Diagnostic Radiology In-Training Examination (DXIT). We perform multiple experiments across time points to assess for model drift, as well as after fine-tuning to assess for differences in accuracy. MATERIALS AND METHODS: Questions were sequentially input into GPT-4 with a standardized prompt. Each answer was recorded and overall accuracy was calculated, as was logic-adjusted accuracy, and accuracy on image-based questions. This experiment was repeated several months later to assess for model drift, then again after the performance of fine-tuning to assess for changes in GPT's performance. RESULTS: GPT-4 achieved 58.5% overall accuracy, lower than the PGY-3 average (61.9%) but higher than the PGY-2 average (52.8%). Adjusted accuracy was 52.8%. GPT-4 showed significantly higher (p = 0.012) confidence for correct answers (87.1%) compared to incorrect (84.0%). Performance on image-based questions was significantly poorer (p < 0.001) at 45.4% compared to text-only questions (80.0%), with adjusted accuracy for image-based questions of 36.4%. When the questions were repeated, GPT-4 chose a different answer 25.5% of the time and there was no change in accuracy. Fine-tuning did not improve accuracy. CONCLUSION: GPT-4 performed between PGY-2 and PGY-3 levels on the 2022 DXIT, significantly poorer on image-based questions, and with large variability in answer choices across time points. Exploratory experiments in fine-tuning did not improve performance. This study underscores the potential and risks of using minimally-prompted general AI models in interpreting radiologic images as a diagnostic tool. Implementers of general AI radiology systems should exercise caution given the possibility of spurious yet confident responses.

Amphetamines in Workplace Testing Hair Samples over the Years 2011-2020.

Amphetamines (AMPs) in hair were investigated with thousands of workplace testing head and body hair samples collected and analyzed over 10 years and tabulated by year. All samples were washed by a published extensive method prior to confirmation by liquid chromatography-mass spectrometry-mass spectrometry. Presented are concentrations of parent methamphetamine (METH), 3,4-methylenedioxymethamphetamine (MDMA) and methylenedioxyamphetamine as metabolite and AMP as metabolite and without the presence of parent drug. Some differences in METH concentrations from year to year were significant, and some ratios of metabolite to parent drug for both METH and MDMA also varied significantly. While rates of METH use may not have changed significantly, some aspects of the drugs ingested as demonstrated by hair analysis varied over the 10-year period.

Clinical Management of Supratentorial Non-Skull Base Meningiomas.

Supratentorial non-skull base meningiomas are the most common primary central nervous system tumor subtype. An understanding of their pathophysiology, imaging characteristics, and clinical management options will prove of substantial value to the multi-disciplinary team which may be involved in their care. Extensive review of the broad literature on the topic is conducted. Narrowing the scope to meningiomas located in the supratentorial non-skull base anatomic location highlights nuances specific to this tumor subtype. Advances in our understanding of the natural history of the disease and how findings from both molecular pathology and neuroimaging have impacted our understanding are discussed. Clinical management and the rationale underlying specific approaches including observation, surgery, radiation, and investigational systemic therapies is covered in detail. Future directions for probable advances in the near and intermediate term are reviewed.

Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy.

Glioblastoma (GBM) continues to be one of the most lethal malignancies and is almost always fatal. In this review article, the role of radiation therapy, systemic therapy, as well as the molecular basis of classifying GBM is described. Technological advances in the treatment of GBM are outlined as well as the diagnostic imaging characteristics of this tumor. In addition, factors that affect prognosis such as differentiating progression from treatment effect is discussed. The role of MRI guided radiation therapy and how this technology may provide a mechanism to improve the care of patients with this disease are described.

Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective.

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

Identifying contamination versus ingestion in hair testing, with cocaine as example.

When developing a procedure to identify external contamination of hair as opposed to drug that is in hair from ingestion, there are components of the process that must be considered in the final method. A method that does not achieve the objective may be missing one or more of these elements: choice of solvent, a drug-binding agent, ratio of solvent to hair, temperature, time, intactness of the hair, and establishing, for the chosen method, a criterion based on the drug contents of the wash and hair that indicates the hair may be contaminated.

Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.

The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect. Specifically, we present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH). This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect. This involves non-rigidly aligning the patients' MRI scans to a healthy atlas via diffeomorphic registration. The resulting inverse mapping is used to obtain the deformation field magnitudes in the normal parenchyma. These measurements are then combined with a 3D texture descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (COLLAGE), which captures the morphological heterogeneity and infiltration within the tumor confines, on MRI scans. In this work, we extensively evaluated r-DepTH for survival risk-stratification on a total of 207 GBM cases from 3 different cohorts (Cohort 1 ( n1 = 53 ), Cohort 2 ( n2 = 75 ), and Cohort 3 ( n3 = 79 )), where each of these three cohorts was used as a training set for our model separately, and the other two cohorts were used for testing, independently, for each training experiment. When employing Cohort 1 for training, r-DepTH yielded Concordance indices (C-indices) of 0.7 and 0.65, hazard ratios (HR) and Confidence Intervals (CI) of 10 (6 - 19) and 5 (3 - 8) on Cohorts 2 and 3, respectively. Similarly, training on Cohort 2 yielded C-indices of 0.6 and 0.7, HR and CI of 1 (0.7 - 2) and 3 (2 - 5) on Cohorts 1 and 3, respectively. Finally, training on Cohort 3 yielded C-indices of 0.75 and 0.63, HR and CI of 24 (10 - 57) and 12 (6 - 21) on Cohorts 1 and 2, respectively. Our results show that r-DepTH descriptor may serve as a comprehensive and a robust MRI-based prognostic marker of disease aggressiveness and survival in solid tumors.

Cannabinoids Tetrahydrocannabinol, Cannabinol, Cannabidiol, Tetrahydrocannabivarin and 11-nor-9-carboxy-∆9-THC in Hair.

The cannabinoids tetrahydrocannabinol (THC), tetrahydrocannabivarin (THCV), cannabidiol (CBD), cannabinol (CBN) and (-)-11-nor-9-carboxy-∆9-tetrahydrocannabinol (THC-COOH) were determined in 4,773 hair samples. Confirmation of THC-COOH was by GC-MS-MS (gas chromatography--mass spectrometry-mass spectrometry). Confirmation of THC, THCV, CBN and CBD was by LC-MS-MS (liquid chromatoraphy--mass spectrometry-mass spectrometry) on an AB Sciex QTRAP 6500+ LC-MS-MS. The purpose of this work was not to utilize any analyte other than THC-COOH as indicative of ingestion, but to assess the absence or presence, and relative concentrations, of the other cannabinoid analytes in hair of marijuana users vs. primarily CBD users. In this regard, 10% of samples contained significantly higher concentrations of CBD relative to THC than the other 90%. A concentration of CBD that is five times greater than that of THC was proposed as good evidence of primarily CBD ingestion. THC concentrations in the samples ranged from below the limit of detection (5 pg/mg) to 47,808 pg/mg hair, varying widely in the relationship between parent THC and the metabolite THC-COOH. CBN was present in most samples, but concentrations relative to THC decreased with increasing THC concentrations. Only 26% of the samples contained THCV detectable by the method. When present, THCV concentrations averaged 1.77% of THC. A limitation of this study is the lack of subject histories to determine the types and amounts of products used and the mode of ingestion. Also, not all THC from external contamination may be removed. Nonetheless, the data provide a useful guide as to what cannabinoids may be found in hair, at what concentrations and under conditions of marijuana use vs. likely primarily CBD use.

Minimalistic mycoplasmas harbor different functional toxin-antitoxin systems.

Mycoplasmas are minute bacteria controlled by very small genomes ranging from 0.6 to 1.4 Mbp. They encompass several important medical and veterinary pathogens that are often associated with a wide range of chronic diseases. The long persistence of mycoplasma cells in their hosts can exacerbate the spread of antimicrobial resistance observed for many species. However, the nature of the virulence factors driving this phenomenon in mycoplasmas is still unclear. Toxin-antitoxin systems (TA systems) are genetic elements widespread in many bacteria that were historically associated with bacterial persistence. Their presence on mycoplasma genomes has never been carefully assessed, especially for pathogenic species. Here we investigated three candidate TA systems in M. mycoides subsp. capri encoding a (i) novel AAA-ATPase/subtilisin-like serine protease module, (ii) a putative AbiEii/AbiEi pair and (iii) a putative Fic/RelB pair. We sequence analyzed fourteen genomes of M. mycoides subsp. capri and confirmed the presence of at least one TA module in each of them. Interestingly, horizontal gene transfer signatures were also found in several genomic loci containing TA systems for several mycoplasma species. Transcriptomic and proteomic data confirmed differential expression profiles of these TA systems during mycoplasma growth in vitro. While the use of heterologous expression systems based on E. coli and B. subtilis showed clear limitations, the functionality and neutralization capacities of all three candidate TA systems were successfully confirmed using M. capricolum subsp. capricolum as a host. Additionally, M. capricolum subsp. capricolum was used to confirm the presence of functional TA system homologs in mycoplasmas of the Hominis and Pneumoniae phylogenetic groups. Finally, we showed that several of these M. mycoides subsp. capri toxins tested in this study, and particularly the subtilisin-like serine protease, could be used to establish a kill switch in mycoplasmas for industrial applications.

Radiological and Clinical Value of 7T MRI for Evaluating 3T-Visible Lesions in Pharmacoresistant Focal Epilepsies.

Objective: The recent FDA approval of the first 7T MRI scanner for clinical diagnostic use in October 2017 will likely increase the utilization of 7T for epilepsy presurgical evaluation. This study aims at accessing the radiological and clinical value of 7T in patients with pharmacoresistant focal epilepsy and 3T-visible lesions. Methods: Patients with pharmacoresistant focal epilepsy were included if they had a lesion on pre-operative standard-of-care 3T MRI and also a 7T research MRI. An epilepsy protocol was used for the acquisition of the 7T MRI. Prospective visual analysis of 7T MRI was performed by an experienced board-certified neuroradiologist and communicated to the patient management team. The clinical significance of the additional 7T findings was assessed by intracranial EEG (ICEEG) ictal onset, surgical resection, post-operative seizure outcome and histopathology. A subset of lesions were demarked with arrows for subsequent, retrospective comparison between 3T and 7T by 7 neuroradiologists using a set of quantitative scales: lesion presence, conspicuity, boundary, gray-white tissue contrast, artifacts, and the most helpful sequence for diagnosis. Conger's kappa for multiple raters was performed for chance-adjusted agreement statistics. Results: A total of 47 patients were included, with the main pathology types of focal cortical dysplasia (FCD), hippocampal sclerosis, periventricular nodular heterotopia (PVNH), tumor and polymicrogyria (PMG). 7T detected additional smaller lesions in 19% (9/47) of patients, who had extensive abnormalities such as PMG and PVNH; however, these additional findings were not necessarily epileptogenic. 3T-7T comparison by the neuroradiologist team showed that lesion conspicuity and lesion boundary were significantly better at 7T (p < 0.001), particularly for FCD, PVNH and PMG. Chance-adjusted agreement was within the fair range for lesion presence, conspicuity and boundary. Gray-white contrast was significantly improved at 7T (p < 0.001). Significantly more artifacts were encountered at 7T (p < 0.001). Significance: For patients with 3T-visible lesions, 7T MRI may better elucidate the extent of multifocal abnormalities such as PVNH and PMG, providing potential targets to improve ICEEG implantation. Patients with FCD, PVNH and PMG would likely benefit the most from 7T due to improved lesion conspicuity and boundary. Pathologies in the antero-inferior temporal regions likely benefit less due to artifacts.

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a 3-dimensional convolutional neural network. The 3-dimensional convolutional neural network was trained using transfer learning; in the source task, normal control and DAT scans were used to pretrain the model. This pretrained model was then retrained on the target task of classifying which MCI patients converted to DAT. Our model resulted in 82.4% classification accuracy at the target task, outperforming current models in the field. Next, we visualized brain regions that significantly contribute to the prediction of MCI conversion using an occlusion map approach. Contributory regions included the pons, amygdala, and hippocampus. Finally, we showed that the model's prediction value is significantly correlated with rates of change in clinical assessment scores, indicating that the model is able to predict an individual patient's future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI.

Sexually dimorphic radiogenomic models identify distinct imaging and biological pathways that are prognostic of overall survival in glioblastoma.

BACKGROUND: Recent epidemiological studies have suggested that sexual dimorphism influences treatment response and prognostic outcome in glioblastoma (GBM). To this end, we sought to (i) identify distinct sex-specific radiomic phenotypes-from tumor subcompartments (peritumoral edema, enhancing tumor, and necrotic core) using pretreatment MRI scans-that are prognostic of overall survival (OS) in GBMs, and (ii) investigate radiogenomic associations of the MRI-based phenotypes with corresponding transcriptomic data, to identify the signaling pathways that drive sex-specific tumor biology and treatment response in GBM. METHODS: In a retrospective setting, 313 GBM patients (male = 196, female = 117) were curated from multiple institutions for radiomic analysis, where 130 were used for training and independently validated on a cohort of 183 patients. For the radiogenomic analysis, 147 GBM patients (male = 94, female = 53) were used, with 125 patients in training and 22 cases for independent validation. RESULTS: Cox regression models of radiomic features from gadolinium T1-weighted MRI allowed for developing more precise prognostic models, when trained separately on male and female cohorts. Our radiogenomic analysis revealed higher expression of Laws energy features that capture spots and ripple-like patterns (representative of increased heterogeneity) from the enhancing tumor region, as well as aggressive biological processes of cell adhesion and angiogenesis to be more enriched in the "high-risk" group of poor OS in the male population. In contrast, higher expressions of Laws energy features (which detect levels and edges) from the necrotic core with significant involvement of immune related signaling pathways was observed in the "low-risk" group of the female population. CONCLUSIONS: Sexually dimorphic radiogenomic models could help risk-stratify GBM patients for personalized treatment decisions.

Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study.

A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression vs. tumor recurrence? In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed. First, enhancing lesion on Gd-T1w MRI and peri-lesional hyperintensities on T2w/FLAIR were segmented by experts and then registered to a brain atlas. Using patients from the two phenotypes, we construct two atlases by quantifying frequency of occurrence of enhancing lesion and peri-lesion hyperintensities, by averaging voxel intensities across the population. Analysis of differential involvement was then performed to compute voxel-wise significant differences (p-value < 0.05) across the atlases. Statistically significant clusters were finally mapped to a structural atlas to provide anatomic localization of their location. Our results demonstrate that patients with tumor recurrence showed prominence of their initial tumor in the parietal lobe, while patients with PsP showed a multi-focal distribution of the initial tumor in the frontal and temporal lobes, insula, and putamen. These preliminary results suggest that lateralization of pre-treatment lesions toward certain anatomical areas of the brain may allow to provide early cues regarding assessing likelihood of occurrence of pseudo-progression from tumor recurrence on MRI scans.

Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study.

PURPOSE: To identify radiomic features extracted from the tumor habitat on routine MR images that are prognostic for progression-free survival (PFS) and to assess their morphologic basis with corresponding histopathologic attributes in glioblastoma (GBM). MATERIALS AND METHODS: In this retrospective study, 156 pretreatment GBM MR images (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery [FLAIR] images) were curated. Of these 156 images, 122 were used for training (90 from The Cancer Imaging Archive and 32 from the Cleveland Clinic, acquired between December 1, 2011, and May 1, 2018) and 34 were used for validation. The validation set was obtained from the Ivy Glioblastoma Atlas Project database, for which the percentage extent of 11 histologic attributes was available on corresponding histopathologic specimens of the resected tumor. Following expert annotations of the tumor habitat (necrotic core, enhancing tumor, and FLAIR-hyperintense subcompartments), 1008 radiomic descriptors (eg, Haralick texture features, Laws energy features, co-occurrence of local anisotropic gradient orientations [CoLIAGe]) were extracted from the three MRI sequences. The top radiomic features were obtained from each subcompartment in the training set on the basis of their ability to risk-stratify patients according to PFS. These features were then concatenated to create a radiomics risk score (RRS). The RRS was independently validated on a holdout set. In addition, correlations (P < .05) of RRS features were computed, with the percentage extent of the 11 histopathologic attributes, using Spearman correlation analysis. RESULTS: RRS yielded a concordance index of 0.80 on the validation set and constituted radiomic features, including Laws (capture edges, waves, ripple patterns) and CoLIAGe (capture disease heterogeneity) from enhancing tumor and FLAIR hyperintensity. These radiomic features were correlated with histopathologic attributes associated with disease aggressiveness in GBM, particularly tumor infiltration (P = .0044) and hyperplastic blood vessels (P = .0005). CONCLUSION: Preliminary findings demonstrated significant associations of prognostic radiomic features with disease-specific histologic attributes, with implications for risk-stratifying patients with GBM for personalized treatment decisions. Supplemental material is available for this article. © RSNA, 2020.

Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.

PURPOSE: The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites. ACQUISITION AND VALIDATION METHODS: From TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. DATA FORMAT AND USAGE NOTES: We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis. POTENTIAL APPLICATIONS: The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.

Hydroxycocaines as Metabolic Indicators of Cocaine Ingestion.

Hydroxycocaines in hair were investigated with many hundreds of head and body hair samples. All samples were washed by a published extensive aqueous method prior to confirmation by LC-MS/MS. Concentrations, percent of cocaine, and ratios of para- and meta-hydroxycocaines to ortho-hydroxycocaine are presented. Hydroxycocaines as percent of cocaine did not appear to be affected by cocaine concentrations, but were shown to increase with cocaethylene concentrations. Stability of hydroxycocaines over a year of ambient storage was demonstrated. Ortho-hydroxycocaine was shown to be formed by exposure of cocaine-positive hair to peroxide, while para- and meta-hydroxycocaines were not. Presence of para- or meta-hydroxycocaine at > 0.05% of cocaine is proposed as indicating ingestion of cocaine. This indicator prevents black hair from being more likely interpreted as positive for ingestion than lighter colored hair.

Identification of Photorhabdus symbionts by MALDI-TOF MS.

Species of the bacterial genus Photorhabus live in a symbiotic relationship with Heterorhabditis entomopathogenic nematodes. Besides their use as biological control agents against agricultural pests, some Photorhabdus species are also a source of natural products and are of medical interest due to their ability to cause tissue infections and subcutaneous lesions in humans. Given the diversity of Photorhabdus species, rapid and reliable methods to resolve this genus to the species level are needed. In this study, we evaluated the potential of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for the identification of Photorhabdus species. To this end, we established a collection of 54 isolates consisting of type strains and multiple field strains that belong to each of the validly described species and subspecies of this genus. Reference spectra for the strains were generated and used to complement a currently available database. The extended reference database was then used for identification based on the direct transfer sample preparation method and the protein fingerprint of single colonies. High-level discrimination of distantly related species was observed. However, lower discrimination was observed with some of the most closely related species and subspecies. Our results therefore suggest that MALDI-TOF MS can be used to correctly identify Photorhabdus strains at the genus and species level, but has limited resolution power for closely related species and subspecies. Our study demonstrates the suitability and limitations of MALDI-TOF-based identification methods for assessment of the taxonomic position and identification of Photorhabdus isolates.

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.

PURPOSE: To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways. EXPERIMENTAL DESIGN: Two hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features. RESULTS: Twenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM. CONCLUSIONS: Our findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM.

Development of an UPLC-MS/MS method for the analysis of 16 synthetic opioids in segmented hair, and evaluation of the polydrug history in fentanyl analogue users.

Seizures of synthetic opioids have increased since 2012, with a 45 % increase in synthetic opioid related deaths between 2016 and 2017 in US. Recently, concerns have arisen around these substances and their illicit use also in several European countries. Our aim was to develop and validate an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method for the analysis of 16 synthetic opioids in segmented hair, including fentanyl, norfentanyl, acetylfentanyl, U-47700, AH-7921, acrylfentanyl, crotonylfentanyl, butyrylfentanyl, methoxacetylfentanyl, U-49900, valeryfentanyl, 4-fluoro-iso-butyrylfentanyl, ocfentanyl, furanylfentanyl, tetrahydrofuranylfentanyl, and alfetanyl. Sample preparation involved washing the hair in dichloromethane, water and methanol, and extraction in methanol, followed by solid phase extraction clean-up. This method was validated for linearity, limit of quantification (LLOQ), precision and bias, selectivity, stability, matrix effects, extraction efficiency of the clean up procedure, and carryover. LLOQs ranged from 0.15-1pg/mg, and the calibration ranged from the LLOQ up to 500pg/mg. Intra and inter-day precision were evaluated at low and high concentrations, with spiked QCs, during 8 days and the results were satisfactory with RSD<15 % for all the compounds except for norfentanyl (22 %) and alfentanyl (19 %). Two external certified QCs containing fentanyl at 11 and 105pg/mg were also analysed within each batch and the RSD and bias were lower than 16 % and 10 %, respectively. Matrix effects compensated by internal standard fentanyl-d5 (MEIS), were between 77-115 % (RSD<10 %) and extraction efficiency of the clean-up procedure was between 66-93 % (RSD<21 %). Processed sample stability and carryover were acceptable for all of the compounds. The method was applied to 17 authentic hair samples (body or head hair) from US fentanyl analogue users. When head hair was available, the hair strands were analysed in 1cm/segment. Concentrations ranges were as follows: fentanyl (n=16) 2->ULOQ (500) pg/mg, norfentanyl (n=14) 1-38pg/mg, acetylfentanyl (n=7) 0.6->ULOQ (250) pg/mg, furanylfentanyl (n=5) 2-123pg/mg, tetrahydrofuranylfentanyl (n=1) 0.5-63pg/mg and valerylfentanyl (n=1) 2.1->ULOQ (50) pg/mg, along the hair strands. To our knowledge, this is the first time where concentrations of tetrahydrofuranylfentanyl, and valerylfentanyl in hair are reported. The same samples were also analysed for the determination of other drugs of abuse using our routine method (also in 1cm/segment for head hair when available). The results demonstrated poly-drug use in these fentanyl-analogue users population (mean drugs: n=5): amphetamine and/or methamphetamine (n=10), buprenorphine (n=5), cocaine (n=8), methadone (n=8), 6-MAM (n=17), meperidine (n=1), oxycodone (n=11), tramadol (n=3). Evaluation of the concentrations of these drugs, together with the fentanyl analogues is discussed in the present paper. Two authentic samples from two Belgian post-mortem cases, were also analysed showing fentanyl use and in one case polydrug use. The results demonstrated multi-analyte quantitative methods, including fentanyl analogues, are becoming useful in forensic laboratories involved in hair analysis, and in particular when polydrug use is suspected.