Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage.

INTRODUCTION: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. METHODS: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. HIGHLIGHTS: Mutation position influences Aß burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aß burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Robotic Anatomical Pulmonary Resections: An Australian Experience.

BACKGROUND: Robotic thoracic surgery is a minimally invasive technique that allows the surgeon to perform delicate, accurate surgical manoeuvres within the chest cavity without rib spreading. Previous studies have suggested potential benefits of the robotic platform in nodal upstaging due to its versatility, seven degrees of freedom of movement, and superior vision. However, there is currently a paucity of robust clinical data from Australia. AIMS: This study aimed to assess the perioperative safety and oncological efficacy of anatomical pulmonary resections performed using the robotic platform. Endpoints included mortality and major morbidity outcomes according to Clavien-Dindo classification and rate of pathological nodal upstaging compared with preoperative imaging using positron emission tomography. METHODS: A single-surgeon retrospective analysis was performed using data collected from two institutions from July 2021 to May 2022, after ethics committee approval. Consecutive patients who underwent anatomical robotic resections were included in the study, with subsequent analysis of patients who had confirmed primary lung cancer. RESULTS: A total of 52 patients underwent robotic anatomical pulmonary resection during the study period. Safety was demonstrated by 0% mortality and a 9.6% major complication rate, which was related to chest tube insertion for prolonged air leak or intensive care unit monitoring during treatment of atrial arrhythmia. After excluding patients who did not have primary lung cancer, 48 patients remained for further analysis; pathological nodal upstaging was observed in nine (18.8%) of these patients. On multivariate analysis, the total number of lymph nodes harvested was found to be a statistically significant predictor of nodal upstaging. Complete microscopic resection (R0) was achieved in 100% of patients. CONCLUSIONS: This study represents the most extensive documentation of robotic thoracic procedures in Australia in the existing literature. It demonstrated a satisfactory safety profile with a relatively high rate of nodal upstaging, possibly reflecting the ability of the robotic instruments to perform comprehensive and complete nodal resection at the time of anatomical pulmonary resection.

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease.

Carriers of mutations responsible for dominantly inherited Alzheimer disease provide a unique opportunity to study potential imaging biomarkers. Biomarkers based on routinely acquired clinical MR images, could supplement the extant invasive or logistically challenging) biomarker studies. We used 1104 longitudinal MR, 324 amyloid beta, and 87 tau positron emission tomography imaging sessions from 525 participants enrolled in the Dominantly Inherited Alzheimer Network Observational Study to extract novel imaging metrics representing the mean (µ) and standard deviation (σ) of standardized image intensities of T1-weighted and Fluid attenuated inversion recovery (FLAIR) MR scans. There was an exponential decrease in FLAIR-µ in mutation carriers and an increase in FLAIR and T1 signal heterogeneity (T1-σ and FLAIR-σ) as participants approached the symptom onset in both supramarginal, the right postcentral and right superior temporal gyri as well as both caudate nuclei, putamina, thalami, and amygdalae. After controlling for the effect of regional atrophy, FLAIR-µ decreased and T1-σ and FLAIR-σ increased with increasing amyloid beta and tau deposition in numerous cortical regions. In symptomatic mutation carriers and independent of the effect of regional atrophy, tau pathology demonstrated a stronger relationship with image intensity metrics, compared with amyloid pathology. We propose novel MR imaging intensity-based metrics using standard clinical T1 and FLAIR images which strongly associates with the progression of pathology in dominantly inherited Alzheimer disease. We suggest that tau pathology may be a key driver of the observed changes in this cohort of patients.

Late-onset epilepsy with cognitive symptoms: Comparison of cognitive and imaging profiles with probable Alzheimer's disease.

OBJECTIVE: We aimed to (i) compare the clinical, neuropsychological, and neuroimaging characteristics of unprovoked late-onset epilepsy (LOE) patients with cognitive symptoms against probable Alzheimer's disease (AD) patients; (ii) clarify how neurodegeneration and other processes could be implicated in the cognitive symptoms of unprovoked LOE patients; and (iii) characterize the longitudinal trajectory of unprovoked LOE patients with cognitive symptoms. METHODS: Twenty-six unprovoked LOE patients with cognitive symptoms and 26 probable AD were retrospectively recruited from epilepsy and memory clinics at a single tertiary referral center. The patients underwent comprehensive clinical, neuropsychological, and 18Fluorodeoxyglucose PET-CT assessments. All LOE patients had clinical follow-up and a subset of 17 patients had repeat neuropsychological assessments. RESULTS: At baseline, 18% of LOE patients with cognitive symptoms had dementia-range cognitive impairment and one received a diagnosis of probable AD. Compared with the probable AD group, the LOE group did not perform significantly better in global measures of cognition (total ACE-III), neuropsychological tests for fluency, working memory, language, attention, or executive function, but performed better in naming, memory, and visuospatial ability. The commonest patterns of cognitive impairment in the LOE group were frontal and left temporal, whereas all AD patients exhibited parietotemporal patterns. The AD group had more 18Fluorodeoxyglucose PET-CT hypometabolism in the parietal and occipital, but not the temporal and frontal lobes. During the 3.0 ± 3.2 years follow-up, improved seizure frequency in the LOE group covaried with improved total ACE-III score, there was no further conversion to probable AD and no group-level cognitive decline. CONCLUSION: Unprovoked LOE patients with cognitive symptoms had varying severities of cognitive impairment, and different patterns of cognitive and imaging abnormalities compared with AD patients. They were rarely diagnosed with probable AD at presentation or follow-up. Cognitive outcome in LOE may be related to seizure control. Cerebral small vessel disease may play a role in LOE-associated cognitive impairment.

Biomarker clustering in autosomal dominant Alzheimer's disease.

INTRODUCTION: As the number of biomarkers used to study Alzheimer's disease (AD) continues to increase, it is important to understand the utility of any given biomarker, as well as what additional information a biomarker provides when compared to others. METHODS: We used hierarchical clustering to group 19 cross-sectional biomarkers in autosomal dominant AD. Feature selection identified biomarkers that were the strongest predictors of mutation status and estimated years from symptom onset (EYO). Biomarkers identified included clinical assessments, neuroimaging, cerebrospinal fluid amyloid, and tau, and emerging biomarkers of neuronal integrity and inflammation. RESULTS: Three primary clusters were identified: neurodegeneration, amyloid/tau, and emerging biomarkers. Feature selection identified amyloid and tau measures as the primary predictors of mutation status and EYO. Emerging biomarkers of neuronal integrity and inflammation were relatively weak predictors. DISCUSSION: These results provide novel insight into our understanding of the relationships among biomarkers and the staging of biomarkers based on disease progression.

CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations.

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled 'radiogenomics', a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research.

Enhancing medical image registration via appearance adjustment networks.

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs - Voxelmorph (VM), Diffeomorphic Voxelmorph (DifVM), and Laplacian Pyramid Image Registration Network (LapIRN) - on three well-established public datasets of 3D brain magnetic resonance imaging (MRI) - IBSR18, Mindboggle101, and LPBA40. The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs.

Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods.

BACKGROUND: Myopic maculopathy (MM) is the most serious and irreversible complication of pathologic myopia, which is a major cause of visual impairment and blindness. Clinic proposed limited number of factors related to MM. To explore additional features strongly related with MM from optic disc region, we employ a machine learning based radiomics analysis method, which could explore and quantify more hidden or imperceptible MM-related features to the naked eyes and contribute to a more comprehensive understanding of MM and therefore may assist to distinguish the high-risk population in an early stage. METHODS: A total of 457 eyes (313 patients) were enrolled and were divided into severe MM group and without severe MM group. Radiomics analysis was applied to depict features significantly correlated with severe MM from optic disc region. Receiver Operating Characteristic were used to evaluate these features' performance of classifying severe MM. RESULTS: Eight new MM-related image features were discovered from the optic disc region, which described the shapes, textural patterns and intensity distributions of optic disc region. Compared with clinically reported MM-related features, these newly discovered features exhibited better abilities on severe MM classification. And the mean values of most features were markedly changed between patients with peripapillary diffuse chorioretinal atrophy (PDCA) and macular diffuse chorioretinal atrophy (MDCA). CONCLUSIONS: Machine learning and radiomics method are useful tools for mining more MM-related features from the optic disc region, by which complex or even hidden MM-related features can be discovered and decoded. In this paper, eight new MM-related image features were found, which would be useful for further quantitative study of MM-progression. As a nontrivial byproduct, marked changes between PDCA and MDCA was discovered by both new image features and clinic features.

Imaging of patients with multiple myeloma and associated plasma cell disorders: consensus practice statement by the Medical Scientific Advisory Group to Myeloma Australia.

Imaging modalities for multiple myeloma (MM) have evolved to enable earlier detection of disease. Furthermore, the diagnosis of MM requiring therapy has recently changed to include disease prior to bone destruction, specifically the detection of focal bone lesions. Focal lesions are early, abnormal areas in the bone marrow, which may signal the development of subsequent lytic lesions that typically occur within the next 18-24 months. Cross-sectional imaging modalities are more sensitive for the detection and monitoring of bone and bone marrow disease and are now included in the International Myeloma Working Group current consensus criteria for initial diagnosis and treatment response assessment. The aim of this consensus practice statement is to review the evidence supporting these modalities. A more detailed Position Statement can be found on the Myeloma Australia website.

Modeling autosomal dominant Alzheimer's disease with machine learning.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients. 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) has unique value in tumor staging, predicting prognosis, and evaluating treatment response. Our aim was to determine if we could identify radiomic predictors from PET/CT in breast cancer patient therapeutic efficacy prior to NAC. METHODS: This retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage. CONCLUSION: Radiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

Adapted Treatment Guided by Interim PET-CT Scan in Advanced Hodgkin's Lymphoma.

BACKGROUND: We tested interim positron-emission tomography-computed tomography (PET-CT) as a measure of early response to chemotherapy in order to guide treatment for patients with advanced Hodgkin's lymphoma. METHODS: Patients with newly diagnosed advanced classic Hodgkin's lymphoma underwent a baseline PET-CT scan, received two cycles of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) chemotherapy, and then underwent an interim PET-CT scan. Images were centrally reviewed with the use of a 5-point scale for PET findings. Patients with negative PET findings after two cycles were randomly assigned to continue ABVD (ABVD group) or omit bleomycin (AVD group) in cycles 3 through 6. Those with positive PET findings after two cycles received BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone). Radiotherapy was not recommended for patients with negative findings on interim scans. The primary outcome was the difference in the 3-year progression-free survival rate between randomized groups, a noninferiority comparison to exclude a difference of 5 or more percentage points. RESULTS: A total of 1214 patients were registered; 937 of the 1119 patients (83.7%) who underwent an interim PET-CT scan according to protocol had negative findings. With a median follow-up of 41 months, the 3-year progression-free survival rate and overall survival rate in the ABVD group were 85.7% (95% confidence interval [CI], 82.1 to 88.6) and 97.2% (95% CI, 95.1 to 98.4), respectively; the corresponding rates in the AVD group were 84.4% (95% CI, 80.7 to 87.5) and 97.6% (95% CI, 95.6 to 98.7). The absolute difference in the 3-year progression-free survival rate (ABVD minus AVD) was 1.6 percentage points (95% CI, -3.2 to 5.3). Respiratory adverse events were more severe in the ABVD group than in the AVD group. BEACOPP was given to the 172 patients with positive findings on the interim scan, and 74.4% had negative findings on a third PET-CT scan; the 3-year progression-free survival rate was 67.5% and the overall survival rate 87.8%. A total of 62 patients died during the trial (24 from Hodgkin's lymphoma), for a 3-year progression-free survival rate of 82.6% and an overall survival rate of 95.8%. CONCLUSIONS: Although the results fall just short of the specified noninferiority margin, the omission of bleomycin from the ABVD regimen after negative findings on interim PET resulted in a lower incidence of pulmonary toxic effects than with continued ABVD but not significantly lower efficacy. (Funded by Cancer Research UK and Others; ClinicalTrials.gov number, NCT00678327.).

Rheumatoid leptomeningitis presenting with an acute neuropsychiatric disorder.

Leptomeningitis is a rare central nervous system manifestation of rheumatoid arthritis, generally in patients with established chronic rheumatoid disease. We report a 41-year-old man without previous rheumatoid arthritis or psychiatric disorder who presented with an acute neuropsychiatric disturbance and polyarthralgia. His MR scan of brain showed asymmetric bifrontal leptomeningitis, confirmed on (18F)-fluoro-D-glucose-positron emission tomography. Other investigations showed highly positive serum and cerebrospinal fluid anti-cyclic citrullinated peptide. A leptomeningeal biopsy showed necrotising leptomeningeal inflammation with ill-defined granulomas and lymphoplasmacytic infiltrate without organisms. Prolonged high-dose corticosteroids and then rituximab resulted in recovery. Chronic leptomeningitis can present with an acute neuropsychiatric disorder. We highlight that early rheumatoid disease can, rarely, cause a chronic leptomeningitis, reversible with immunotherapy.

PET-CT for staging and early response: results from the Response-Adapted Therapy in Advanced Hodgkin Lymphoma study.

International guidelines recommend that positron emission tomography-computed tomography (PET-CT) should replace CT in Hodgkin lymphoma (HL). The aims of this study were to compare PET-CT with CT for staging and measure agreement between expert and local readers, using a 5-point scale (Deauville criteria), to adapt treatment in a clinical trial: Response-Adapted Therapy in Advanced Hodgkin Lymphoma (RATHL). Patients were staged using clinical assessment, CT, and bone marrow biopsy (RATHL stage). PET-CT was performed at baseline (PET0) and after 2 chemotherapy cycles (PET2) in a response-adapted design. PET-CT was reported centrally by experts at 5 national core laboratories. Local readers optionally scored PET2 scans. The RATHL and PET-CT stages were compared. Agreement among experts and between expert and local readers was measured. RATHL and PET0 stage were concordant in 938 (80%) patients. PET-CT upstaged 159 (14%) and downstaged 74 (6%) patients. Upstaging by extranodal disease in bone marrow (92), lung (11), or multiple sites (12) on PET-CT accounted for most discrepancies. Follow-up of discrepant findings confirmed the PET characterization of lesions in the vast majority. Five patients were upstaged by marrow biopsy and 7 by contrast-enhanced CT in the bowel and/or liver or spleen. PET2 agreement among experts (140 scans) with a κ (95% confidence interval) of 0.84 (0.76-0.91) was very good and between experts and local readers (300 scans) at 0.77 (0.68-0.86) was good. These results confirm PET-CT as the modern standard for staging HL and that response assessment using Deauville criteria is robust, enabling translation of RATHL results into clinical practice.

Safety and activity of microRNA-loaded minicells in patients with recurrent malignant pleural mesothelioma: a first-in-man, phase 1, open-label, dose-escalation study.

BACKGROUND: TargomiRs are minicells (EnGeneIC Dream Vectors) loaded with miR-16-based mimic microRNA (miRNA) and targeted to EGFR that are designed to counteract the loss of the miR-15 and miR-16 family miRNAs, which is associated with unsuppressed tumour growth in preclinical models of malignant pleural mesothelioma. We aimed to assess the safety, optimal dosing, and activity of TargomiRs in patients with malignant pleural mesothelioma. METHODS: In this first-in-man, open-label, dose-escalation phase 1 trial at three major cancer centres in Sydney (NSW, Australia), we recruited adults (aged ≥18 years) with a confirmed diagnosis of malignant pleural mesothelioma, measurable disease, radiological signs of progression after previous chemotherapy, Eastern Cooperative Oncology Group performance status of 0 or 1, life expectancy of 3 months or more, immunohistochemical evidence of tumour EGFR expression, and adequate bone marrow, liver, and renal function. Patients were given TargomiRs via 20 min intravenous infusion either once or twice a week (3 days apart) in a traditional 3 + 3 dose-escalation design in five dose cohorts. The dose-escalation steps planned were 5 × 109, 7 × 109, and 9 × 109 TargomiRs either once or twice weekly, but after analysis of data from the first eight patients, all subsequent patients started protocol treatment at 1 × 109 TargomiRs. The primary endpoints were to establish the maximum tolerated dose of TargomiRs as measured by dose-limiting toxicity, define the optimal frequency of administration, and objective response (defined as the percentage of assessable patients with a complete or partial response), duration of response (defined as time from the first evidence of response to disease progression in patients who achieved a response), time to response (ie, time from start of treatment to the first evidence of response) and overall survival (defined as time from treatment allocation to death from any cause). Analyses were based on the full analysis set principle, including every patient who received at least one dose of TargomiRs. The study was closed for patient entry on Jan 3, 2017, and registered with ClinicalTrials.gov, number NCT02369198, and the Australian Registry of Clinical Trials, number ACTRN12614001248651. FINDINGS: Between Sept 29, 2014, and Nov 24, 2016, we enrolled 27 patients, 26 of whom received at least one TargomiR dose (one patient died before beginning treatment). Overall, five dose-limiting toxicities were noted: infusion-related inflammatory symptoms and coronary ischaemia, respectively, in two patients given 5 × 109 TargomiRs twice weekly; anaphylaxis and cardiomyopathy, respectively, in two patients given 5 × 109 TargomiRs once weekly but who received reduced dexamethasone prophylaxis; and non-cardiac pain in one patient who received 5 × 109 TargomiRs once weekly. We established that 5 × 109 TargomiRs once weekly was the maximum tolerated dose. TargomiR infusions were accompanied by transient lymphopenia (25 [96%] of 26 patients), temporal hypophosphataemia (17 [65%] of 26 patients), increased aspartate aminotransferase or alanine aminotranferase (six [23%] of 26 patients), and increased alkaline phosphatase blood concentrations (two [8%]). Cardiac events occurred in five patients: three patients had electrocardiographic changes, one patient had ischaemia, and one patient had Takotsubo cardiomyopathy. Of the 22 patients who were assessed for response by CT, one (5%) had a partial response, 15 (68%) had stable disease, and six (27%) had progressive disease. The proportion of patients who achieved an objective response was therefore one (5%) of 22, and the duration of the objective response in that patient was 32 weeks. Median overall survival was 200 days (95% CI 94-358). During the trial, 21 deaths occurred, of which 20 were related to tumour progression and one was due to bowel perforation. INTERPRETATION: The acceptable safety profile and early signs of activity of TargomiRs in patients with malignant pleural mesothelioma support additional studies of TargomiRs in combination with chemotherapy or immune checkpoint inhibitors. FUNDING: Asbestos Diseases Research Foundation.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

The relationship between neuropsychological functioning and FDG-PET hypometabolism in intractable mesial temporal lobe epilepsy.

We examined the relationship between baseline neuropsychological functioning and 18-fluorodeoxyglucose positron emission tomography (FDG-PET) in intractable mesial temporal lobe epilepsy (MTLE). We hypothesized relationships between dominant temporal lobe hypometabolism and verbal memory and between nondominant temporal lobe hypometabolism and nonverbal memory in line with the lateralized material-specific model of memory deficits in MTLE. We also hypothesized an association between performance on frontal lobe neuropsychological tests and prefrontal hypometabolism. Thirty-two patients who had undergone temporal lobectomy for treatment of MTLE and who completed both presurgical FDG-PET and comprehensive neuropsychological investigations with widely used standardized measures were included. Age-adjusted composite measures were calculated for verbal memory, nonverbal memory, relative material-specific memory, IQ, executive function, attention/working memory, and psychomotor speed. Fluorodeoxyglucose positron emission tomography was analyzed with statistical parametric mapping (SPM) to identify hypometabolism relative to healthy controls. Pearson's correlation was used to determine the relationship between regions of hypometabolism and neuropsychological functioning. Dominant temporal lobe hypometabolism was associated with relatively inferior verbal memory, while nondominant temporal lobe hypometabolism was associated with inferior nonverbal memory. No relationship was found between performance on any frontal lobe measures and prefrontal hypometabolism. Statistical parametric mapping-quantified lateralized temporal lobe hypometabolism correlates with material-specific episodic memory impairment in MTLE. In contrast, prefrontal hypometabolism is not associated with performance on frontal lobe measures. We suggest that this is because frontal lobe neuropsychology tests may not be good measures of isolated frontal lobe functioning.

PET/CT assessment in follicular lymphoma using standardized criteria: central review in the PRIMA study.

PURPOSE: We aimed to compare the standardized central review of (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT scans performed after induction therapy for follicular lymphoma (FL) in the PRIMA study (Salles et al., Lancet 377:42-51, 2011; Trotman et al., J Clin Oncol 29:3194-3200, 2011) to scan review at local centres. METHODS: PET/CT scans were independently evaluated by two nuclear medicine physicians using the 2007 International Harmonization Project (IHP) criteria (Cheson et al., J Clin Oncol 25:579-586, 2007; Juweid et al., J Clin Oncol 25:571-578, 2007; Shankar et al., J Nucl Med 47:1059-1066, 2006) and Deauville 5-point scale (5PS) criteria (Meignan et al., Leuk Lymphoma 50:1257-1260, 2009; Meignan et al., Leuk Lymphoma 51:2171-2180, 2010; Barrington et al., Eur J Nucl Med Mol Imaging 37:1824-1833, 2010). PET/CT status was compared with prospectively recorded patient outcomes. RESULTS: Central evaluation was performed on 119 scans. At diagnosis, 58 of 59 were recorded as positive, with a mean maximum standardized uptake value (SUVmax) of 11.7 (range 4.6-35.6). There was no significant association between baseline SUVmax and progression-free survival (PFS). Sixty post-induction scans were interpreted using both the IHP criteria and 5PS. Post-induction PET-positive status failed to predict progression when applying the IHP criteria [p = 0.14; hazard ratio (HR) 1.9; 95 % confidence interval (CI) 0.8-4.6] or 5PS with a cut-off ≥3 (p = 0.12; HR 2.0; 95% CI 0.8-4.7). However, when applying the 5PS with a cut-off ≥4, there was a significantly inferior 42-month PFS in PET-positive patients of 25.0% (95% CI 3.7-55.8%) versus 61.4% (95% CI 45.4-74.1%) in PET-negative patients (p = 0.01; HR 3.1; 95% CI 1.2-7.8). The positive predictive value (PPV) of post-induction PET with this liver cut-off was 75%. The 42-month PFS for patients remaining PET-positive by local assessment was 31.1% (95% CI 10.2-55.0%) vs 64.6% (95% CI 47.0-77.6%) for PET-negative patients (p = 0.002; HR 3.3; 95% CI 1.5-7.4), with a PPV of 66.7%. CONCLUSION: We confirm that FDG PET/CT status when applying the 5PS with a cut-off ≥4 is strongly predictive of outcome after first-line immunochemotherapy for FL. Further efforts to refine the criteria for assessing minimal residual FDG uptake in FL should provide a reproducible platform for response assessment in future prospective studies of a PET-adapted approach.