Examining the Relationship and Prognostic Significance of Cell-Free DNA Levels and the PSMA-Positive Tumor Volume in Men with Prostate Cancer: A Retrospective-Prospective [68Ga]Ga-PSMA-11 PET/CT Study

Functional imaging with prostate-specific membrane antigen (PSMA) ligands has emerged as the standard imaging method for prostate cancer (PCA). In parallel, the analysis of blood-derived, cell-free DNA (cfDNA) has been shown to be a promising quantitative biomarker of PCA aggressiveness and patient outcome. This study aimed to evaluate the relationship and prognostic value of cfDNA concentrations and the PSMA-positive tumor volume (PSMA-TV) in men with PCA undergoing [68Ga]Ga-PSMA-11 PET/CT imaging. Methods: We recruited 148 men with histologically proven PCA (mean age, 70.7 ± 7.7 y) who underwent [68Ga]Ga-PSMA-11 PET/CT (184.9 ± 18.9 MBq) and blood sampling between March 2019 and August 2021. Among these, 74 (50.0%) had hormone-sensitive PCA and 74 (50.0%) had castration-resistant PCA (CRPC). All patients provided written informed consent before blood sample collection and imaging. The cfDNA was extracted and quantified, and PSMA-expressing tumor lesions were delineated to extract the PSMA-TVs. The Spearman coefficient assessed correlations between PSMA-TV and cfDNA concentrations and cfDNA's relation with clinical parameters. The Kruskal-Wallis test examined the mean cfDNA concentration differences based on PSMA-TV quartiles for significantly correlated patient groups. Log-rank and multivariate Cox regression analyses evaluated the prognostic significance of high and low cfDNA and PSMA-TV levels for overall survival. Results: Weak positive correlations were found between cfDNA concentration and PSMA-TV in the overall group (r = 0.16, P = 0.049) and the CRPC group (r = 0.31, P = 0.007) but not in hormone-sensitive PCA patients (r = -0.024, P = 0.837). In the CRPC cohort, cfDNA concentrations significantly differed between PSMA-TV quartiles 4 and 1 (P = 0.002) and between quartiles 4 and 2 (P = 0.016). Survival outcomes were associated with PSMA-TV (P < 0.0001, P = 0.004) but not cfDNA (P = 0.174, P = 0.12), as per the log-rank and Cox regression analysis. Conclusion: These findings suggest that cfDNA might serve as a biomarker of advanced, aggressive CRPC but does not reliably reflect total tumor burden or prognosis. In comparison, [68Ga]Ga-PSMA-11 PET/CT provides a highly granular and prognostic assessment of tumor burden across the spectrum of PCA disease progression.

The Benefits and Cost-Effectiveness of Arteriovenous (AV) Fistula Screening in Haemodialysis Patients

Background Ultrasound (US) monitoring of arteriovenous fistulas (AVFs) presents contradictory findings. These differences may be attributed to variances in the chosen surveillance strategy, the specific type of fistula being monitored, and the precise technique used for ultrasound scanning. In this study, we are trying to assess the benefits and cost-effectiveness of US scanning of AVF. Patients and methods This was a descriptive, retrospective, and observational study. The study sample consisted of patients diagnosed with end-stage renal disease (ESRD) on hemodialysis who had AVF for dialysis either by native vein or using prosthetic grafts. We excluded all the patients whose fistula failed to mature, failed to attend the surveillance scan at six weeks, or had absent records or incomplete data. We retrieved the data of the patients who underwent AVF creation at Glan Clwyd Hospital between April 2020 and April 2023. The data was analysed using statistical software (SPSS) version 21 (IBM Corp., Armonk, NY, USA). Results Ninety-eight patients were studied. Stenosis 43.9% (n = 43) was the predominant complication, followed by thrombosis (15.3%; n = 15) while the remaining complications (bleeding, pseudoaneurysm) were less prominent. On the other hand, a total of 37.8% (n = 37) did not experience any complications. Primary patency ranged from 2 to 87 months with a mean of 9 ± 13.2 months SD, and secondary patency ranged from 1 to 24 months with a mean of 1.3 ± 3.9 months SD. The mean cost of a surveillance scan for AVF is 2520 USD, and the mean cost of intervention is 1332 + 1258 USD SD. Out of all the patients, 52 (53%) underwent intervention to salvage the AVF, 2 (2%) received open surgical intervention, and 50 (51%) underwent endovascular intervention. Considering the AVF failure to work, 29.6% (n = 29) had fistulas that failed to work, and 70.4% (n = 69) were still working. Conclusion Routine duplex scanning in six-month periods to diagnose failing AV fistulas is not cost-effective when compared to diagnosing failing or failed AV fistulas based on clinical symptoms.

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

Error mitigation enables PET radiomic cancer characterization on quantum computers

BACKGROUND

Cancer is a leading cause of death worldwide. While routine diagnosis of cancer is performed mainly with biopsy sampling, it is suboptimal to accurately characterize tumor heterogeneity. Positron emission tomography (PET)-driven radiomic research has demonstrated promising results when predicting clinical endpoints. This study aimed to investigate the added value of quantum machine learning both in simulator and in real quantum computers utilizing error mitigation techniques to predict clinical endpoints in various PET cancer patients.

METHODS

Previously published PET radiomics datasets including 11C-MET PET glioma, 68GA-PSMA-11 PET prostate and lung 18F-FDG PET with 3-year survival, low-vs-high Gleason risk and 2-year survival as clinical endpoints respectively were utilized in this study. Redundancy reduction with 0.7, 0.8, and 0.9 Spearman rank thresholds (SRT), followed by selecting 8 and 16 features from all cohorts, was performed, resulting in 18 dataset variants. Quantum advantage was estimated by Geometric Difference (GDQ) score in each dataset variant. Five classic machine learning (CML) and their quantum versions (QML) were trained and tested in simulator environments across the dataset variants. Quantum circuit optimization and error mitigation were performed, followed by training and testing selected QML methods on the 21-qubit IonQ Aria quantum computer. Predictive performances were estimated by test balanced accuracy (BACC) values.

RESULTS

On average, QML outperformed CML in simulator environments with 16-features (BACC 70% and 69%, respectively), while with 8-features, CML outperformed QML with + 1%. The highest average QML advantage was + 4%. The GDQ scores were ≤ 1.0 in all the 8-feature cases, while they were > 1.0 when QML outperformed CML in 9 out of 11 cases. The test BACC of selected QML methods and datasets in the IonQ device without error mitigation (EM) were 69.94% BACC, while EM increased test BACC to 75.66% (76.77% in noiseless simulators).

CONCLUSIONS

We demonstrated that with error mitigation, quantum advantage can be achieved in real existing quantum computers when predicting clinical endpoints in clinically relevant PET cancer cohorts. Quantum advantage can already be achieved in simulator environments in these cohorts when relying on QML.

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters

Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NNs challenging to apply in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.

Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts

BACKGROUND

Hybrid imaging became an instrumental part of medical imaging, particularly cancer imaging processes in clinical routine. To date, several radiomic and machine learning studies investigated the feasibility of in vivo tumor characterization with variable outcomes. This study aims to investigate the effect of recently proposed fuzzy radiomics and compare its predictive performance to conventional radiomics in cancer imaging cohorts. In addition, lesion vs. lesion+surrounding fuzzy and conventional radiomic analysis was conducted.

METHODS

Previously published 11C Methionine (MET) positron emission tomography (PET) glioma, 18F-FDG PET/computed tomography (CT) lung, and 68GA-PSMA-11 PET/magneto-resonance imaging (MRI) prostate cancer retrospective cohorts were included in the analysis to predict their respective clinical endpoints. Four delineation methods including manually defined reference binary (Ref-B), its smoothed, fuzzified version (Ref-F), as well as extended binary (Ext-B) and its fuzzified version (Ext-F) were incorporated to extract imaging biomarker standardization initiative (IBSI)-conform radiomic features from each cohort. Machine learning for the four delineation approaches was performed utilizing a Monte Carlo cross-validation scheme to estimate the predictive performance of the four delineation methods.

RESULTS

Reference fuzzy (Ref-F) delineation outperformed its binary delineation (Ref-B) counterpart in all cohorts within a volume range of 938-354987 mm³ with relative cross-validation area under the receiver operator characteristics curve (AUC) of  +4.7-10.4. Compared to Ref-B, the highest AUC performance difference was observed by the Ref-F delineation in the glioma cohort (Ref-F: 0.74 vs. Ref-B: 0.70) and in the prostate cohort by Ref-F and Ext-F (Ref-F: 0.84, Ext-F: 0.86 vs. Ref-B: 0.80). In addition, fuzzy radiomics decreased feature redundancy by approx. 20%.

CONCLUSIONS

Fuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared to conventional binary radiomics in PET. We hypothesize that this effect is due to the ability of fuzzy radiomics to model partial volume effects and delineation uncertainties at small lesion boundaries. In addition, we consider that the lower redundancy of fuzzy radiomic features supports the identification of imaging biomarkers in future studies. Future studies shall consider systematically analyzing lesions and their surroundings with fuzzy and binary radiomics.

Sex-specific radiomic features of L-[S-methyl-11C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability

Introduction

Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl-¹¹Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status.

Methods

MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated.

Results

There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well.

Discussion

We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers.

Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer

PURPOSE

Head and neck squamous cell carcinomas (HNSCCs) are a molecularly, histologically, and clinically heterogeneous set of tumors originating from the mucosal epithelium of the oral cavity, pharynx, and larynx. This heterogeneous nature of HNSCC is one of the main contributing factors to the lack of prognostic markers for personalized treatment. The aim of this study was to develop and identify multi-omics markers capable of improved risk stratification in this highly heterogeneous patient population.

METHODS

In this retrospective study, we approached this issue by establishing radiogenomics markers to identify high-risk individuals in a cohort of 127 HNSCC patients. Hybrid in vivo imaging and whole-exome sequencing were employed to identify quantitative imaging markers as well as genetic markers on pathway-level prognostic in HNSCC. We investigated the deductibility of the prognostic genetic markers using anatomical and metabolic imaging using positron emission tomography combined with computed tomography. Moreover, we used statistical and machine learning modeling to investigate whether a multi-omics approach can be used to derive prognostic markers for HNSCC.

RESULTS

Radiogenomic analysis revealed a significant influence of genetic pathway alterations on imaging markers. A highly prognostic radiogenomic marker based on cellular senescence was identified. Furthermore, the radiogenomic biomarkers designed in this study vastly outperformed the prognostic value of markers derived from genetics and imaging alone.

CONCLUSION

Using the identified markers, a clinically meaningful stratification of patients is possible, guiding the identification of high-risk patients and potentially aiding in the development of effective targeted therapies.

Improved Measurement of Solar Neutrinos from the Carbon-Nitrogen-Oxygen Cycle by Borexino and Its Implications for the Standard Solar Model

We present an improved measurement of the carbon-nitrogen-oxygen (CNO) solar neutrino interaction rate at Earth obtained with the complete Borexino Phase-III dataset. The measured rate, R_{CNO}=6.7_{-0.8}^{+2.0}  counts/(day×100  tonnes), allows us to exclude the absence of the CNO signal with about 7σ C.L. The correspondent CNO neutrino flux is 6.6_{-0.9}^{+2.0}×10^{8}  cm^{-2} s^{-1}, taking into account the neutrino flavor conversion. We use the new CNO measurement to evaluate the C and N abundances in the Sun with respect to the H abundance for the first time with solar neutrinos. Our result of N_{CN}=(5.78_{-1.00}^{+1.86})×10^{-4} displays a ∼2σ tension with the "low-metallicity" spectroscopic photospheric measurements. Furthermore, our result used together with the ^{7}Be and ^{8}B solar neutrino fluxes, also measured by Borexino, permits us to disfavor at 3.1σ C.L. the "low-metallicity" standard solar model B16-AGSS09met as an alternative to the "high-metallicity" standard solar model B16-GS98.

Automated data preparation for in vivo tumor characterization with machine learning

Background

This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts.

Methods

A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts.

Results

Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps.

Conclusions

This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.

Positron range in combination with point-spread-function correction: an evaluation of different implementations for [124I]-PET imaging

AIM

To evaluate the effect of combining positron range correction (PRC) with point-spread-function (PSF) correction and to compare different methods of implementation into iterative image reconstruction for ¹²⁴I-PET imaging.

MATERIALS AND METHODS

Uniform PR blurring kernels of ¹²⁴I were generated using the GATE (GEANT4) framework in various material environments (lung, water, and bone) and matched to a 3D matrix. The kernels size was set to 11 × 11 × 11 based on the maximum PR in water and the voxel size of the PET system. PET image reconstruction was performed using the standard OSEM algorithm, OSEM with PRC implemented before the forward projection (OSEM+PRC simplified) and OSEM with PRC implemented in both forward- and back-projection steps (full implementation) (OSEM+PRC). Reconstructions were repeated with resolution recovery, point-spread function (PSF) included. The methods and kernel variation were validated using different phantoms filled with ¹²⁴I acquired on a Siemens mCT PET/CT system. The data was evaluated for contrast recovery and image noise.

RESULTS

Contrast recovery improved by 2-10% and 4-37% with OSEM+PRC simplified and OSEM+PRC, respectively, depending on the sphere size of the NEMA IQ phantom. Including PSF in the reconstructions further improved contrast by 4-19% and 3-16% with the PSF+PRC simplified and PSF+PRC, respectively. The benefit of PRC was more pronounced within low-density material. OSEM-PRC and OSEM-PSF as well as OSEM-PSF+PRC in its full- and simplified implementation showed comparable noise and convergence. OSEM-PRC simplified showed comparably faster convergence but at the cost of increased image noise.

CONCLUSIONS

The combination of the PSF and PRC leads to increased contrast recovery with reduced image noise compared to stand-alone PSF or PRC reconstruction. For OSEM-PRC reconstructions, a full implementation in the reconstruction is necessary to handle image noise. For the combination of PRC with PSF, a simplified PRC implementation can be used to reduce reconstruction times.

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

OBJECTIVES

This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients.

METHODS

A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis.

RESULTS

The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS.

CONCLUSION

ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy.

KEY POINTS

• Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma. • Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens. • Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.

A multi-modality physical phantom for mimicking tumour heterogeneity patterns in PET/CT and PET/MRI

Background

Hybrid imaging (e.g., positron emission tomography [PET]/computed tomography [CT], PET/magnetic resonance imaging [MRI]) helps one to visualize and quantify morphological and physiological tumor characteristics in a single study. The noninvasive characterization of tumor heterogeneity is essential for grading, treatment planning, and following‐up oncological patients. However, conventional (CONV) image‐based parameters, such as tumor diameter, tumor volume, and radiotracer activity uptake, are insufficient to describe tumor heterogeneities. Here, radiomics shows promise for a better characterization of tumors. Nevertheless, the validation of such methods demands imaging objects capable of reflecting heterogeneities in multi‐modality imaging. We propose a phantom to simulate tumor heterogeneity repeatably in PET, CT, and MRI.

Methods

The phantom consists of three 50‐ml plastic tubes filled partially with acrylic spheres of S1: 1.6 mm, S2: 50%(1.6 mm)/50%(6.3 mm), or S3: 6.3‐mm diameter. The spheres were fixed to the bottom of each tube by a plastic grid, yielding one sphere free homogeneous region and one heterogeneous (S1, S2, or S3) region per tube. A 3‐tube phantom and its replica were filled with a fluorodeoxyglucose (18F) solution for test–retest measurements in a PET/CT Siemens TPTV and a PET/MR Siemens Biograph mMR system. A number of 42 radiomic features (10 first order and 32 texture features) were calculated for each phantom region and imaging modality. Radiomic features stability was evaluated through coefficients of variation (COV) across phantoms and scans for PET, CT, and MRI. Further, the Wilcoxon test was used to assess the capability of stable features to discriminate the simulated phantom regions.

Results

The different patterns (S1–S3) did present visible heterogeneity in all imaging modalities. However, only for CT and MRI, a clear visual difference was present between the different patterns. Across all phantom regions in PET, CT, and MR images, 10, 16, and 21 features out of 42 evaluated features in total had a COV of 10% or less. In particular, CONV, histogram, and gray‐level run length matrix features showed high repeatability for all the phantom regions and imaging modalities. Several of repeatable texture features allowed the image‐based discrimination of the different phantom regions (p < 0.05). However, depending on the feature, different pattern discrimination capabilities were found for the different imaging modalities.

Conclusion

The proposed phantom appears suitable for simulating heterogeneities in PET, CT, and MRI. We demonstrate that it is possible to select radiomic features for the readout of the phantom. Most of these features had been shown to be relevant in previous clinical studies.

Implementation of a Spatially-Variant and Tissue-Dependent Positron Range Correction for PET/CT Imaging

Aim

To develop and evaluate a new approach for spatially variant and tissue-dependent positron range (PR) correction (PRC) during the iterative PET image reconstruction.

Materials and Methods

The PR distributions of three radionuclides (¹⁸F, ⁶⁸Ga, and ¹²⁴I) were simulated using the GATE (GEANT4) framework in different material compositions (lung, water, and bone). For every radionuclide, the uniform PR kernel was created by mapping the simulated 3D PR point cloud to a 3D matrix with its size defined by the maximum PR in lung (¹⁸F) or water (⁶⁸Ga and ¹²⁴I) and the PET voxel size. The spatially variant kernels were composed from the uniform PR kernels by analyzing the material composition of the surrounding medium for each voxel before implementation as tissue-dependent, point-spread functions into the iterative image reconstruction. The proposed PRC method was evaluated using the NEMA image quality phantom (¹⁸F, ⁶⁸Ga, and ¹²⁴I); two unique PR phantoms were scanned and evaluated following OSEM reconstruction with and without PRC using different metrics, such as contrast recovery, contrast-to-noise ratio, image noise and the resolution evaluated in terms of full width at half maximum (FWHM).

Results

The effect of PRC on ¹⁸F-imaging was negligible. In contrast, PRC improved image contrast for the 10-mm sphere of the NEMA image quality phantom filled with ⁶⁸Ga and ¹²⁴I by 33 and 24%, respectively. While the effect of PRC was less noticeable for the larger spheres, contrast recovery still improved by 5%. The spatial resolution was improved by 26% for ¹²⁴I (FWHM of 4.9 vs. 3.7 mm).

Conclusion

For high energy positron-emitting radionuclides, the proposed PRC method helped recover image contrast with reduced noise levels and with improved spatial resolution. As such, the PRC approach proposed here can help improve the quality of PET data in clinical practice and research.

First Directional Measurement of Sub-MeV Solar Neutrinos with Borexino

We report the measurement of sub-MeV solar neutrinos through the use of their associated Cherenkov radiation, performed with the Borexino detector at the Laboratori Nazionali del Gran Sasso. The measurement is achieved using a novel technique that correlates individual photon hits of events to the known position of the Sun. In an energy window between 0.54 to 0.74 MeV, selected using the dominant scintillation light, we have measured 10 887_{-2103}^{+2386}(stat)±947(syst) (68% confidence interval) solar neutrinos out of 19 904 total events. This corresponds to a ^{7}Be neutrino interaction rate of 51.6_{-12.5}^{+13.9}  counts/(day·100  ton), which is in agreement with the standard solar model predictions and the previous spectroscopic results of Borexino. The no-neutrino hypothesis can be excluded with >5σ confidence level. For the first time, we have demonstrated the possibility of utilizing the directional Cherenkov information for sub-MeV solar neutrinos, in a large-scale, high light yield liquid scintillator detector. This measurement provides an experimental proof of principle for future hybrid event reconstruction using both Cherenkov and scintillation signatures simultaneously.

Clinical data classification with noisy intermediate scale quantum computers

Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique ([Formula: see text]) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the [Formula: see text] encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.

Liquid Argon Instrumentation and Monitoring in LEGEND-200

LEGEND is the next-generation experiment searching for the neutrinoless double beta decay in 76Ge. The first stage, LEGEND-200, takes over the cryogenic infrastructure of GERDA at LNGS: an instrumented water tank surrounding a 64 m3 liquid argon cryostat. Around 200 kg of Ge detectors will be deployed in the cryostat, with the liquid argon acting as cooling medium, high-purity passive shielding and secondary detection medium. For the latter purpose, a liquid argon instrumentation is developed, based on the system used in GERDA Phase II. Wavelength shifting fibers coated with TPB are arranged in two concentric barrels. Both ends are read out by SiPM arrays. A wavelength shifting reflector surrounds the array in order to enhance the light collection far from the array. The LLAMA is installed in the cryostat to permanently monitor the optical parameters and to provide in-situ inputs for modeling purposes.

The design of all parts of the LEGEND-200 LAr instrumentation is presented. An overview of the geometry, operation principle, and off-line data analysis of the LLAMA is shown.

A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.

Descending Dysploidy and Bidirectional Changes in Genome Size Accompanied Crepis (Asteraceae) Evolution

The evolution of the karyotype and genome size was examined in species of Crepis sensu lato. The phylogenetic relationships, inferred from the plastid and nrITS DNA sequences, were used as a framework to infer the patterns of karyotype evolution. Five different base chromosome numbers (x = 3, 4, 5, 6, and 11) were observed. A phylogenetic analysis of the evolution of the chromosome numbers allowed the inference of x = 6 as the ancestral state and the descending dysploidy as the major direction of the chromosome base number evolution. The derived base chromosome numbers (x = 5, 4, and 3) were found to have originated independently and recurrently in the different lineages of the genus. A few independent events of increases in karyotype asymmetry were inferred to have accompanied the karyotype evolution in Crepis. The genome sizes of 33 Crepis species differed seven-fold and the ancestral genome size was reconstructed to be 1C = 3.44 pg. Both decreases and increases in the genome size were inferred to have occurred within and between the lineages. The data suggest that, in addition to dysploidy, the amplification/elimination of various repetitive DNAs was likely involved in the genome and taxa differentiation in the genus.

Toward Quantitative in vivo Label-Free Tracking of Lipid Distribution in a Zebrafish Cancer Model

Cancer cells often adapt their lipid metabolism to accommodate the increased fatty acid demand for membrane biogenesis and energy production. Upregulation of fatty acid uptake from the environment of cancer cells has also been reported as an alternative mechanism. To investigate the role of lipids in tumor onset and progression and to identify potential diagnostic biomarkers, lipids are ideally imaged directly within the intact tumor tissue in a label-free way. In this study, we investigated lipid accumulation and distribution in living zebrafish larvae developing a tumor by means of coherent anti-Stokes Raman scattering microscopy. Quantitative textural features based on radiomics revealed higher lipid accumulation in oncogene-expressing larvae compared to healthy ones. This high lipid accumulation could reflect an altered lipid metabolism in the hyperproliferating oncogene-expressing cells.

Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging

Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.

Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

PURPOSE

Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning.

METHODS

Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.

RESULTS

The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively.

CONCLUSION

Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

Simple Summary

Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [¹⁸F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models.

Abstract

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [¹⁸F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUV_max model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [¹⁸F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.

Assessment of the kidney function parameters split function, mean transit time, and outflow efficiency using dynamic FDG-PET/MRI in healthy subjects

Background

Traditionally, isotope nephrography is considered as the method of choice to assess kidney function parameters in nuclear medicine. We propose a novel approach to determine the split function (SF), mean transit time (MTT), and outflow efficiency (OE) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) dynamic positron emission tomography (PET).

Materials and methods

Healthy adult subjects underwent dynamic simultaneous FDG-PET and magnetic resonance imaging (PET/MRI). Time-activity curves (TACs) of total kidneys, renal cortices, and the aorta were prospectively obtained from dynamic PET series. MRI images were used for anatomical correlation. The same individuals were subjected to dynamic renal Technetium-99 m-mercaptoacetyltriglycine (MAG3) scintigraphy and TACs of kidneys; the perirenal background and the left ventricle were determined. SF was calculated on the basis of integrals over the TACs, MTT was determined from renal retention functions after deconvolution analysis, and OE was determined from MTT. Values obtained from PET series were compared with scintigraphic parameters, which served as the reference.

Results

Twenty-four subjects underwent both examinations. Total kidney SF, MTT, and OE as estimated by dynamic PET/MRI correlated to their reference values by r = 0.75, r = 0.74 and r = 0.81, respectively, with significant difference (p < 0.0001) between the means of MTT and OE. No correlations were found for cortex FDG values.

Conclusions

The study proofs the concept that SF, MTT, and OE can be estimated with dynamic FDG PET/MRI scans in healthy kidneys. This has advantages for patients receiving a routine PET/MRI scan, as kidney parameters can be estimated simultaneously to functional and morphological imaging with high accuracy.

Multiple haploids, triploids, and tetraploids found in modern-day "living fossil" Ginkgo biloba

Ginkgo biloba, the last extant representative of a lineage of Mesozoic gymnosperms, is one of the few seed plants with an exceptionally long (~300 Myr) evolutionary history free of genome-wide duplications (polyploidy). Despite this genome conservatism, we have recently found a viable spontaneous tetraploid Ginkgo sapling during routine screening of several plants, demonstrating that natural polyploidy is possible in Ginkgo. Here we provide a much wider flow cytometry survey of ploidy in some European Ginkgo collections, and own seedlings (>2200 individuals and ~200 cultivars). We found a surprisingly high level of ploidy variation in modern-day Ginkgo and documented altogether 13 haploid, 3 triploid, and 10 tetraploid Ginkgo plants or cultivars, most of them being morphologically distinct from common diploids. Haploids frequently produced polyploid (dihaploid) buds or branches. Tetraploids showed some genome size variation. The surveyed plants provide a unique resource for future Ginkgo research and breeding, and they might be used to accelerate the modern diversification of this nearly extinct plant lineage.

Assessing the kidney function parameters glomerular filtration rate and effective renal plasma flow with dynamic FDG-PET/MRI in healthy subjects

Background

A method was developed to assess the kidney parameters glomerular filtration rate (GFR) and effective renal plasma flow (ERPF) from 2-deoxy-2-[¹⁸F]fluoro-d-glucose (FDG) concentration behavior in kidneys, measured with positron emission tomography (PET) scans.

Twenty-four healthy adult subjects prospectively underwent dynamic simultaneous PET/magnetic resonance imaging (MRI) examination. Time activity curves (TACs) were obtained from the dynamic PET series, with the guidance of MR information. Patlak analysis was performed to determine the GFR, and based on integrals, ERPF was calculated. Results were compared to intra-individually obtained reference values determined from venous blood samples.

Results

Total kidney GFR and ERPF as estimated by dynamic PET/MRI were highly correlated to their reference values (r = 0.88/p < 0.0001 and r = 0.82/p < 0.0001, respectively) with no significant difference between their means.

Conclusions

The study is a proof of concept that GFR and ERPF can be assessed with dynamic FDG PET/MRI scans in healthy kidneys. This has advantages for patients getting a routine scan, where additional examinations for kidney function estimation could be avoided. Further studies are required for transferring this PET/MRI method to PET/CT applications.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0389-1) contains supplementary material, which is available to authorized users.

Recent Borexino results and perspectives of the SOX measurement

Borexino is a liquid scintillator detector sited underground in the Laboratori Nazionali del Gran Sasso (Italy). Its physics program, until the end of this year, is focussed on the study of solar neutrinos, in particular from the Beryllium, pp, pep and CNO fusion reactions. Knowing the reaction chains in the sun provides insights towards physics disciplines such as astrophysics (star physics, star formation, etc.), astroparticle and particle physics. Phase II started in 2011 and its aim is to improve the phase I results, in particular the measurements of the neutrino fluxes from the pep and CNO processes. By the end of this year, data taking from the sun will be over and a new project is scheduled to launch: Short distance Oscillation with boreXino (SOX), which uses a Cerium source for neutrinos (100÷150 kCi of activity) and aims to confirm or rule out the presence of sterile neutrinos. This particle is hypothesised to justify the reactor, Gallium and LSND anomalies found and can reject extensions to the standard model. The work presented is a summary of the solar neutrino results achieved so far, which lead not only to a precise study of the processes in the sun, but also to more Standard Model oriented measurements (such as the stability of the charge, i.e. the life time of the electron). Furthermore, the perspectives of the SOX program are discussed showing the experiment sensitivity to a fourth neutrino state covering almost entirely 3σ of the preferred region of the anomalous neutrino experiments, and additional applications of the detector such as the study of geo-neutrinos.

Impact of the Fukushima accident on tritium, radiocarbon and radiocesium levels in seawater of the western North Pacific Ocean: A comparison with pre-Fukushima situation

Tritium, radiocarbon and radiocesium concentrations in water column samples in coastal waters offshore Fukushima and in the western North Pacific Ocean collected in 2011-2012 during the Ka'imikai-o-Kanaloa (KoK) cruise are compared with other published results. The highest levels in surface seawater were observed for ¹³⁴Cs and ¹³⁷Cs in seawater samples collected offshore Fukushima (up to 1.1 Bq L^-1), which represent an increase by about three orders of magnitude when compared with the pre-Fukushima concentration. Tritium levels were much lower (up to 0.15 Bq L^-1), representing an increase by about a factor of 6. The impact on the radiocarbon distribution was measurable, but the observed levels were only by about 9% above the global fallout background. The ¹³⁷Cs (and similarly ¹³⁴Cs) inventory in the water column of the investigated western North Pacific region was (2.7 ± 0.4) PBq, while for ³H it was only (0.3 ± 0.2) PBq. Direct releases of highly contaminated water from the damaged Fukushima NPP, as well as dry and wet depositions of these radionuclides over the western North Pacific considerably changed their distribution patterns in seawater. Presently we can distinguish Fukushima labeled waters from global fallout background thanks to short-lived ¹³⁴Cs. However, in the long-term perspective when ¹³⁴Cs will decay, new distribution patterns of ³H, ¹⁴C and ¹³⁷Cs in the Pacific Ocean should be established for future oceanographic and climate change studies in the Pacific Ocean.

Validation of an automated morphological MRI-based (123)I-FP-CIT SPECT evaluation method

INTRODUCTION

Dopamine transporter imaging with (123)I-FP-CIT single photon emission computed tomography (SPECT) is helpful for the differential diagnosis between Parkinsonian syndrome (PS) and essential tremor (ET). Although visual assessment and time-consuming manual evaluation techniques are readily available, a fully objective and automated dopamine transporter quantification technique is always preferable, at least in research and follow-up investigations. Our aim was to develop a novel automated magnetic resonance imaging (MRI)-based evaluation technique of dopamine transporter SPECT images and to compare its diagnostic accuracy with those of the gold-standard visual grading and manual dopamine transporter binding quantification methods.

METHODS

(123)I-FP-CIT SPECT and MRI sessions were conducted in 33 patients with PS (15 men; mean age: 60.3 ± 9.7 years) and 15 patients with ET (8 men; mean age: 54.7 ± 16.3 years). Striatal dopamine transporter binding was visually classified by 2 independent experts as normal or abnormal grade I, II and III. Caudal and putaminal specific uptake ratios were calculated by both automated MRI-based and manual evaluation techniques.

RESULTS

We found almost perfect agreement (κ = 0.829) between the visual scores by the 2 observers. The automated method showed strong correlation with the visual and manual evaluation techniques and its diagnostic accuracy (sensitivity = 97.0%; specificity = 93.3%) was also comparable to these methods. The automatically determined uptake parameters showed negative correlation with the clinical severity of parkinsonism. Based on ordinal regression modelling, the automated MRI-based method could reliably determine the visual grading scores.

CONCLUSION

The novel MRI-based evaluation of (123)I-FP-CIT SPECT images is useful for the differentiation of PS from ET.

Test of Electric Charge Conservation with Borexino

Borexino is a liquid scintillation detector located deep underground at the Laboratori Nazionali del Gran Sasso (LNGS, Italy). Thanks to the unmatched radio purity of the scintillator, and to the well understood detector response at low energy, a new limit on the stability of the electron for decay into a neutrino and a single monoenergetic photon was obtained. This new bound, τ≥6.6×10^{28}  yr at 90% C.L., is 2 orders of magnitude better than the previous limit.

Lifetimes of (214)Po and (212)Po measured with Counting Test Facility at Gran Sasso National Laboratory

The decays of (214)Po into (210)Pb and of (212)Po into (208)Pb tagged by the previous decays from (214)Bi and (212)Bi have been studied inserting quartz vials inside the Counting Test Facility (CTF) at the underground laboratory in Gran Sasso (LNGS). We find that the mean lifetime of (214)Po is (236.00 ± 0.42(stat) ± 0.15(syst)) μs and that of (212)Po is (425.1 ± 0.9(stat) ± 1.2(syst)) ns. Our results are compatible with previous measurements, have a much better signal to background ratio, and reduce the overall uncertainties.

Growth regulator requirement for in vitro embryogenic cultures of snowdrop (Galanthus nivalis L.) suitable for germplasm preservation

In this study, we report on the production of bulb scale-derived tissue cultures capable of efficient shoot and plant regeneration in three genotypes of snowdrop (Galanthus nivalis L., Amaryllidaceae), a protected ornamental plant. For culture line A, high auxin and low cytokinin concentration is required for callus production and plant regeneration. The type of auxin is of key importance: α-naphthaleneacetic acid (NAA) in combination with indole-3-acetic acid (IAA) at concentrations of 2 mg L-1 or 2-10 mg L-1 NAA with 1 mg L-1 N6-benzyladenine (BA), a cytokinin on full-strength media are required for regeneration. Cultures showing regeneration were embryogenic. When lines B and C were induced and maintained with 2 mg L-1 NAA and 1 mg L-1 BA, they produced mature bulblets with shoots, without roots. Line A produced immature bulblets with shoots under the above culture condition. Amplified Fragment Length Polymorphism (AFLP) analysis showed that (i) genetic differences between line A and its bulb explants were not significant, therefore these tissue cultures are suitable for germplasm preservation, and (ii) different morphogenetic responses of lines A, B and C originated from genetic differences. Culture line A is suitable for field-growing, cultivation and germplasm preservation of G. nivalis and for the production of Amaryllidaceae alkaloids.

Phylogenetic and comparative functional analysis of the cell-separation α-glucanase Agn1p in Schizosaccharomyces

The post-cytokinetic separation of cells in cell-walled organisms involves enzymic processes that degrade a specific layer of the division septum and the region of the mother cell wall that edges the septum. In the fission yeast Schizosaccharomyces pombe, the 1,3-α-glucanase Agn1p, originally identified as a mutanase-like glycoside hydrolase family 71 (GH71) enzyme, dissolves the mother cell wall around the septum edge. Our search in the genomes of completely sequenced fungi identified GH71 hydrolases in Basidiomycota, Taphrinomycotina and Pezizomycotina, but not in Saccharomycotina. The most likely Agn1p orthologues in Pezizomycotina species are not mutanases having mutanase-binding domains, but experimentally non-characterized hypothetical proteins that have no carbohydrate-binding domains. The analysis of the GH71 domains corroborated the phylogenetic relationships of the Schizosaccharomyces species determined by previous studies, but suggested a closer relationship to the Basidiomycota proteins than to the Ascomycota proteins. In the Schizosaccharomyces genus, the Agn1p proteins are structurally conserved: their GH71 domains are flanked by N-terminal secretion signals and C-terminal sequences containing the conserved block YNFNA(Y)/HTG. The inactivation of the agn1(Sj) gene in Schizosaccharomyces japonicus, the only true dimorphic member of the genus, caused a severe cell-separation defect in its yeast phase, but had no effect on the hyphal growth and yeast-to-mycelium transition. It did not affect the mycelium-to-yeast transition either, only delaying the separation of the yeast cells arising from the fragmenting hyphae. The heterologous expression of agn1(Sj) partially rescued the separation defect of the agn1Δ cells of Schizosaccharomyces pombe. The results presented indicate that the fission yeast Agn1p 1,3-α-glucanases of Schizosaccharomyces japonicus and Schizosaccharomyces pombe share conserved functions in the yeast phase.

Monitoring of tritium, 60Co and 137Cs in the vicinity of the warm water outlet of the Paks Nuclear Power Plant, Hungary

Danube water, sediment and various aquatic organisms (snail, mussel, predatory and omnivorous fish) were collected upstream (at a background site) and downstream of the outlet of the warm water channel of Paks Nuclear Power Plant. Gamma emitters, tissue free-water tritium (TFWT) and total organically-bound tritium (T-OBT) measurements were performed. A slight contribution of the power plant to the natural tritium background concentration was measured in water samples from the Danube section downstream of the warm water channel. Sediment samples also contained elevated tritium concentrations, along with a detectable amount of (60)Co. In the case of biota samples, TFWT exhibited only a very slight difference compared to the tritium concentration of the Danube water, however, the OBT was higher than the tritium concentration in the Danube, independent of the origin of the samples. The elevated OBT concentration in the mollusc samples downstream of the warm water channel may be attributed to the excess emission from the nuclear power plant. The whole data set obtained was used for dose rate calculations and will be contributed to the development of the ERICA database.

PET-CT based automated lung nodule detection

An automatic method is presented in order to detect lung nodules in PET-CT studies. Using the foreground and background mean ratio independently in every nodule, we can detect the region of the nodules properly. The size and intensity of the lesions do not affect the result of the algorithm, although size constraints are present in the final classification step. The CT image is also used to classify the found lesions built on lung segmentation. We also deal with those cases when nearby and similar nodules are merged into one by a split-up post-processing step. With our method the time of the localization can be decreased from more than one hour to maximum five minutes. The method had been implemented and validated on real clinical cases in Interview Fusion clinical evaluation software (Mediso). Results indicate that our approach is very effective in detecting lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.

New workflows and algorithms of bone scintigraphy based on SPECT-CT

Gold standard bone scintigraphy workflow contains acquisition of planar anterior and posterior images and if necessary, additional SPECTs as well. Planar acquisitions are time consuming and not enough for accurately locating hotspots. Current paper proposes a novel workflow for fast whole body bone SPECT scintigraphy. We present a novel stitching method to generate a whole body SPECT based on the SPECT projections. Our stitching method is performed on the projection series not on the reconstructed SPECTs, thus stitching artifacts are greatly reduced. Our workflow does not require any anterior-posterior image pairs, since these images are derived from the reconstructed whole body SPECT automatically. Our stitching method has been validated on real clinical data performed by medical physicians. Results show that our method is very effective for whole body SPECT generations leaving no signs of artifacts. Our workflow required overall 16 minutes to acquire a whole body SPECT which is comparable to the 60 minutes acquisition time required for gold standard techniques including planar images and additional SPECT acquisitions.

Automated material map generation from MRI scout pairs for preclinical PET attenuation correction

A novel method is presented to perform material map segmentation from preclinical MRI for corresponding PET attenuation correction. MRI does not provide attenuation ratio, hence segmenting a material map from it is challenging. Furthermore the MRI images often suffer from ghost artifacts. On the contrary MRI has no radiation dose. Our method operated with fast spin echo scout pairs that had perpendicular frequency directions. This way the direction of the ghost artifacts were perpendicular as well. Our body-air segmentation method built on this a priori information and successfully erased the ghost artifacts from the final binary mask. Visual and quantitative validation was performed by two preclinical specialists. Results indicate that our method is effective against MRI scout ghost artifacts and that PET attenuation correction based on MRI makes sense even on preclinical images.

A new species of Lygistorrhina Skuse (Diptera: Sciaroidea: Lygistorrhinidae) from South Africa

A new species of Lygistorrhina (Diptera, Sciaroidea, Lygistorrhinidae) from South Africa is described and a key for Afrotropical species of the genus is provided.