Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment

Purpose

Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.35 T pelvis MRIs by evaluating clinical anatomy visualization and generative adversarial network (GAN) auto-segmentation performance.

Materials and methods

3D simulation MRIs from 60 prostate cancer patients treated on MR-Linac (0.35 T) were collected and preprocessed with the N4ITK algorithm for bias field correction. A 3D GAN architecture was trained, validated, and tested on 40, 10, and 10 patients, respectively, to auto-segment the organs at risk (OARs) rectum and bladder. The GAN was trained and evaluated either with the original or the bias-corrected MRIs. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95th) were computed for the segmented volumes of each patient. The Wilcoxon signed-rank test assessed the statistical difference of the metrics within OARs, both with and without bias field correction. Five radiation oncologists blindly scored 22 randomly chosen patients in terms of overall image quality and visibility of boundaries (prostate, rectum, bladder, seminal vesicles) of the original and bias-corrected MRIs. Bennett's S score and Fleiss' kappa were used to assess the pairwise interrater agreement and the interrater agreement among all the observers, respectively.

Results

In the test set, the GAN trained and evaluated on original and bias-corrected MRIs showed DSC/HD95th of 0.92/5.63 mm and 0.92/5.91 mm for the bladder and 0.84/10.61 mm and 0.83/9.71 mm for the rectum. No statistical differences in the distribution of the evaluation metrics were found neither for the bladder (DSC: p = 0.07; HD95th: p = 0.35) nor for the rectum (DSC: p = 0.32; HD95th: p = 0.63). From the clinical visual grading assessment, the bias-corrected MRI resulted mostly in either no change or an improvement of the image quality and visualization of the organs' boundaries compared with the original MRI.

Conclusion

The bias field correction did not improve the anatomy visualization from a clinical point of view and the OARs' auto-segmentation outputs generated by the GAN.

Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images

OBJECTIVES

Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineation. Positron emission tomography (PET) and computed tomography (CT) are commonly used in tumor segmentation. However, current methods still face challenges in handling whole-body scans where a manual selection of a bounding box may be required. Moreover, different institutions might still apply different guidelines for tumor delineation. This study aimed at exploring the auto-localization and segmentation of HNC tumors from entire PET/CT scans and investigating the transferability of trained baseline models to external real world cohorts.

METHODS

We employed 2D Retina Unet to find HNC tumors from whole-body PET/CT and utilized a regular Unet to segment the union of the tumor and involved lymph nodes. In comparison, 2D/3D Retina Unets were also implemented to localize and segment the same target in an end-to-end manner. The segmentation performance was evaluated via Dice similarity coefficient (DSC) and Hausdorff distance 95th percentile (HD95). Delineated PET/CT scans from the HECKTOR challenge were used to train the baseline models by 5-fold cross-validation. Another 271 delineated PET/CTs from three different institutions (MAASTRO, CRO, BERLIN) were used for external testing. Finally, facility-specific transfer learning was applied to investigate the improvement of segmentation performance against baseline models.

RESULTS

Encouraging localization results were observed, achieving a maximum omnidirectional tumor center difference lower than 6.8 cm for external testing. The three baseline models yielded similar averaged cross-validation (CV) results with a DSC in a range of 0.71-0.75, while the averaged CV HD95 was 8.6, 10.7 and 9.8 mm for the regular Unet, 2D and 3D Retina Unets, respectively. More than a 10% drop in DSC and a 40% increase in HD95 were observed if the baseline models were tested on the three external cohorts directly. After the facility-specific training, an improvement in external testing was observed for all models. The regular Unet had the best DSC (0.70) for the MAASTRO cohort, and the best HD95 (7.8 and 7.9 mm) in the MAASTRO and CRO cohorts. The 2D Retina Unet had the best DSC (0.76 and 0.67) for the CRO and BERLIN cohorts, and the best HD95 (12.4 mm) for the BERLIN cohort.

CONCLUSION

The regular Unet outperformed the other two baseline models in CV and most external testing cohorts. Facility-specific transfer learning can potentially improve HNC segmentation performance for individual institutions, where the 2D Retina Unets could achieve comparable or even better results than the regular Unet.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Experimental comparison of linear regression and LSTM motion prediction models for MLC-tracking on an MRI-linac

BACKGROUND

Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction.

PURPOSE

To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities.

METHODS

Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments.

RESULTS

The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%.

CONCLUSIONS

This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.

Planning CT Identifies Patients at Risk of High Prostate Intrafraction Motion

Prostate motion (standard deviation, range of motion, and diffusion coefficient) was calculated from 4D ultrasound data of 1791 fractions of radiation therapy in N = 100 patients. The inner diameter of the lesser pelvis was obtained from transversal slices through the pubic symphysis in planning CTs. On the lateral and craniocaudal axes, motility increases significantly (t-test, p < 0.005) with the inner diameter of the lesser pelvis. A diameter of >106 mm (ca. 6th decile) is a good predictor for high prostate intrafraction motion (ca. 9th decile). The corresponding area under the receiver operator curve (AUROC) is 80% in the lateral direction, 68% to 80% in the craniocaudal direction, and 62% to 70% in the vertical direction. On the lateral x-axis, the proposed test is 100% sensitive and has a 100% negative predictive value for all three characteristics (standard deviation, range of motion, and diffusion coefficient). On the craniocaudal z-axis, the proposed test is 79% to 100% sensitive and reaches 95% to 100% negative predictive value. On the vertical axis, the proposed test still delivers 98% negative predictive value but is not particularly sensitive. Overall, the proposed predictor is able to help identify patients at risk of high prostate motion based on a single planning CT.

ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients

Background and purpose: In radiotherapy, dose calculations based on 4D cone beam CTs (4DCBCTs) require image intensity corrections. This retrospective study compared the dose calculation accuracy of a deep learning, projection-based scatter correction workflow (ScatterNet), to slower workflows: conventional 4D projection-based scatter correction (CBCTcor) and a deformable image registration (DIR)-based method (4DvCT). Materials and methods: For 26 lung cancer patients, planning CTs (pCTs), 4DCTs and CBCT projections were available. ScatterNet was trained with pairs of raw and corrected CBCT projections. Corrected projections from ScatterNet and the conventional workflow were reconstructed using MA-ROOSTER, yielding 4DCBCTSN and 4DCBCTcor. The 4DvCT was generated by 4DCT to 4DCBCT DIR, as part of the 4DCBCTcor workflow. Robust intensity modulated proton therapy treatment plans were created on free-breathing pCTs. 4DCBCTSN was compared to 4DCBCTcor and the 4DvCT in terms of image quality and dose calculation accuracy (dose-volume-histogram parameters and 3 % /3 mm gamma analysis). Results: 4DCBCTSN resulted in an average mean absolute error of 87 HU and 102 HU when compared to 4DCBCTcor and 4DvCT respectively. High agreement was observed in targets with median dose differences of 0.4 Gy (4DCBCTSN-4DCBCTcor) and 0.3 Gy (4DCBCTSN-4DvCT). The gamma analysis showed high average 3 % /3 mm pass rates of 96 % for both 4DCBCTSN vs. 4DCBCTcor and 4DCBCTSN vs. 4DvCT. Conclusions: Accurate 4D dose calculations are feasible for lung cancer patients using ScatterNet for 4DCBCT correction. Average scatter correction times could be reduced from 10 min (4DCBCTcor) to 3.9 s , showing the clinical suitability of the proposed deep learning-based method.

A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [Formula: see text]F]FDG PET/CT

PURPOSE

PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to interobserver variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients.

METHODS

Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [Formula: see text]F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [Formula: see text]F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and lymph node (LN) metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively.

RESULTS

In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classification accuracy was 98.0% and 97.9% in cross-validation and external data, respectively. Univariate Cox analysis in the cross-validation and the external testing reveals that manually and automatically derived total MTVs are both highly prognostic with respect to overall survival, yielding essentially identical hazard ratios (HR) ([Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in cross-validation and [Formula: see text]; [Formula: see text] vs. [Formula: see text]; [Formula: see text] in external testing).

CONCLUSION

To the best of our knowledge, this work presents the first CNN model for successful MTV delineation and lesion classification in HNC. In the vast majority of patients, the network performs satisfactory delineation and classification of primary tumor and lymph node metastases and only rarely requires more than minimal manual correction. It is thus able to massively facilitate study data evaluation in large patient groups and also does have clear potential for supervised clinical application.

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy

BACKGROUND AND PURPOSE

Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500ms into the future.

MATERIALS AND METHODS

Models were trained (52 patients, 3.1h of motion), validated (18 patients, 0.6h) and tested (18 patients, 1.1h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour.

RESULTS

The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2mm and 1.0mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models.

CONCLUSION

LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking.

Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis

OBJECTIVES

Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC). However, lesion segmentation is typically required, resulting in a predictive power susceptible to variations in primary and lymph node gross tumor volume (GTV) segmentation. This study aimed at achieving prognosis without GTV segmentation, and extending single modality prognosis to joint PET/CT to allow investigating the predictive performance of combined- compared to single-modality inputs.

METHODS

We employed a 3D-Resnet combined with a time-to-event outcome model to incorporate censoring information. We focused on the prognosis of DM and OS for HNC patients. For each clinical endpoint, five models with PET and/or CT images as input were compared: PET-GTV, PET-only, CT-GTV, CT-only, and PET/CT-GTV models, where -GTV indicates that the corresponding images were masked using the GTV contour. Publicly available delineated CT and PET scans from 4 different Canadian hospitals (293) and the MAASTRO clinic (74) were used for training by 3-fold cross-validation (CV). For independent testing, we used 110 patients from a collaborating institution. The predictive performance was evaluated via Harrell's Concordance Index (HCI) and Kaplan-Meier curves.

RESULTS

In a 5-year time-to-event analysis, all models could produce CV HCIs with median values around 0.8 for DM and 0.7 for OS. The best performance was obtained with the PET-only model, achieving a median testing HCI of 0.82 for DM and 0.69 for OS. Compared with the PET/CT-GTV model, the PET-only still had advantages of up to 0.07 in terms of testing HCI. The Kaplan-Meier curves and corresponding log-rank test results also demonstrated significant stratification capability of our models for the testing cohort.

CONCLUSION

Deep learning-based DM and OS time-to-event models showed predictive capability and could provide indications for personalized RT. The best predictive performance achieved by the PET-only model suggested GTV segmentation might be less relevant for PET-based prognosis.

18F-Fluorodeoxyglucose Positron Emission Tomography of Head and Neck Cancer: Location and HPV Specific Parameters for Potential Treatment Individualization

Purpose

18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis.

Methods

Original analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUV_max, SUV_mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM).

Results

FDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUV_max was well suited to identify patients with excellent LRC for organ preservation. Patients with SUV_max of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer.

Conclusion

FDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging approaches including radiomics or artificial intelligence methods should implement the identified cut-off values as benchmark routine imaging parameters.

Offline and online LSTM networks for respiratory motion prediction in MR-guided radiotherapy

Objective. Gated beam delivery is the current clinical practice for respiratory motion compensation in MR-guided radiotherapy, and further research is ongoing to implement tracking. To manage intra-fractional motion using multileaf collimator tracking the total system latency needs to be accounted for in real-time. In this study, long short-term memory (LSTM) networks were optimized for the prediction of superior–inferior tumor centroid positions extracted from clinically acquired 2D cine MRIs. Approach. We used 88 patients treated at the University Hospital of the LMU Munich for training and validation (70 patients, 13.1 h), and for testing (18 patients, 3.0 h). Three patients treated at Fondazione Policlinico Universitario Agostino Gemelli were used as a second testing set (1.5 h). The performance of the LSTMs in terms of root mean square error (RMSE) was compared to baseline linear regression (LR) models for forecasted time spans of 250 ms, 500 ms and 750 ms. Both the LSTM and the LR were trained with offline (offline LSTM and offline LR) and online schemes (offline+online LSTM and online LR), the latter to allow for continuous adaptation to recent respiratory patterns. Main results. We found the offline+online LSTM to perform best for all investigated forecasts. Specifically, when predicting 500 ms ahead it achieved a mean RMSE of 1.20 mm and 1.00 mm, while the best performing LR model achieved a mean RMSE of 1.42 mm and 1.22 mm for the LMU and Gemelli testing set, respectively. Significance. This indicates that LSTM networks have potential as respiratory motion predictors and that continuous online re-optimization can enhance their performance.

MR-guided radiotherapy in node-positive non-small cell lung cancer and severely limited pulmonary reserve: a report proposing a new clinical pathway for the management of high-risk patients

INTRODUCTION

Online MR-guided radiotherapy (MRgRT) is a relatively novel advancement in the field of radiation oncology, ensuring superior soft-tissue visualisation, allowing for online plan adaptation to anatomical and functional interfractional changes and improved motion management. Platinum-based chemoradiation followed by durvalumab is the recommended treatment for stage IIB(N1)/III NSCLC. However, this is only the case for patients with favourable risk factors and sufficient pulmonary function and reserve.

METHODS

Herein, we present a technical report on tumour motion and breathing curve analyses of the first patient with node-positive stage IIB NSCLC and severely compromised pulmonary function and reserve [total lung capacity (TLC) 8.78L/132% predicted, residual volume (RV) 6.35L/271% predicted, vital capacity (VC) max 2.43L/58% predicted, FEV1 1.19L/38% predicted, DLCO-SB corrected for hemoglobin 2.76 mmol/min/kPa/30% predicted] treated in a prospective observational study with moderately hypofractionated MRgRT to a total dose of 48.0 Gy/16 daily fractions on the MRIdian system (Viewray Inc, Oakwood, USA).

RESULTS

Radiotherapy was well tolerated with no relevant toxicity. First follow-up imaging at 3 months post-radiotherapy showed a partial remission. The distinctive features of this case are the patient's severely compromised pulmonary function and the first online MR-guided accelerated hypofractionated radiotherapy treatment for primary node-positive NSCLC.

CONCLUSIONS

This technical report describes the first patient treated in a prospective observational study evaluating the feasibility of this relatively novel technology in stage IIB(N1)/III disease, proposing a clinical pathway for the management of high-risk patients.

Risk Stratification Using 18F-FDG PET/CT and Artificial Neural Networks in Head and Neck Cancer Patients Undergoing Radiotherapy

This study retrospectively analyzed the performance of artificial neural networks (ANN) to predict overall survival (OS) or locoregional failure (LRF) in HNSCC patients undergoing radiotherapy, based on 2-[¹⁸F]FDG PET/CT and clinical covariates. We compared predictions relying on three different sets of features, extracted from 230 patients. Specifically, (i) an automated feature selection method independent of expert rating was compared with (ii) clinical variables with proven influence on OS or LRF and (iii) clinical data plus expert-selected SUV metrics. The three sets were given as input to an artificial neural network for outcome prediction, evaluated by Harrell’s concordance index (HCI) and by testing stratification capability. For OS and LRF, the best performance was achieved with expert-based PET-features (0.71 HCI) and clinical variables (0.70 HCI), respectively. For OS stratification, all three feature sets were significant, whereas for LRF only expert-based PET-features successfully classified low vs. high-risk patients. Based on 2-[¹⁸F]FDG PET/CT features, stratification into risk groups using ANN for OS and LRF is possible. Differences in the results for different feature sets confirm the relevance of feature selection, and the key importance of expert knowledge vs. automated selection.

Human adipose tissue-derived mesenchymal stem cells abrogate plasmablast formation and induce regulatory B cells independently of T helper cells

Mesenchymal or stromal stem cells (MSC) interact with cells of the immune system in multiple ways. Modulation of the immune system by MSC is believed to be a therapeutic option for autoimmune disease and transplant rejection. In recent years, B cells have moved into the focus of the attention as targets for the treatment of immune disorders. Current B-cell targeting treatment is based on the indiscriminate depletion of B cells. The aim of this study was to examine whether human adipose tissue-derived MSC (ASC) interact with B cells to affect their proliferation, differentiation, and immune function. ASC supported the survival of quiescent B cells predominantly via contact-dependent mechanisms. Coculture of B cells with activated T helper cells led to proliferation and differentiation of B cells into CD19(+) CD27(high) CD38(high) antibody-producing plasmablasts. ASC inhibited the proliferation of B cells and this effect was dependent on the presence of T cells. In contrast, ASC directly targeted B-cell differentiation, independently of T cells. In the presence of ASC, plasmablast formation was reduced and IL-10-producing CD19(+) CD24(high) CD38(high) B cells, known as regulatory B cells, were induced. These results demonstrate that ASC affect B cell biology in vitro, suggesting that they can be a tool for the modulation of the B-cell response in immune disease.

Anatomical features and clinical correlations in Caucasian patients with definite arrhythmogenic right ventricular dysplasia/cardiomyopathy

AIM

Arrhythmogenic right ventrticular dysplasia/cardiomyopathy (ARVD/C) is an inherited cardiomyopathy characterized by fibrofatty replacement and a high risk of ventricular arrhythmias (VA) and sudden cardiac death (SCD). The aim of the present investigation is to examine the pathological profile and the clinical correlations in a group of ARVD/C patients.

METHODS

We conducted a multicenter study evaluating 47 patients (31 men; mean age 37±14 years) with definite ARVD/C. Diagnosis was established according to the actual clinicomorphologic criteria at autopsy or clinically. We divided the study population in 2 different groups. First group included 28 alive patients and the second 19 patients dead suddenly.

RESULTS

Age at presentation was different in the two groups (P=0.0015). We observed an important association regarding the risk of sudden death and the history of physical exercise (P=0.0017). Moreover patients with negative outcome (i.e., SCD, cardiac transplantation, congestive heart failure) had a significantly association with biventricular form of ARVD/C (P=0.0034) and age presentation (P=0.003). Left ventricular (LV) involvement was frequently observed in the two groups (17% and 32% respectively). Post-mortem examination revealed frequent inflammatory infiltrates (26%) indicating active myocarditis, which probably justify the fatal arrhythmic events occurred in these patients.

CONCLUSION

Frequent LV involvement justifies the recent adoption of the broad term Arrhythmogenic Cardiomyopathy. Early age presentation, sport activity and the biventricular form of ARVD/C represent important predictors of adverse outcome that can be useful to early identify patients at high risk.

Effect of haemin on growth, protein content and the antioxidant defence system inTrypanosoma cruzi

A nutritional characteristic of trypanosomatid protozoa is thatin vitrothey need a haem-compound as a growth factor, which is supplied as haemoglobin, haematin or haemin. Because haemin and related porphyrins are an important source of oxidative stress in biological systems, the effect of haemin on growth, protein content and the antioxidant defence system inTrypanosoma cruziwas evaluated. We have observed that, in epimastigotes grown under different haemin concentrations in the culture medium (0–30 mg/l), 5 mg/l was the haemin concentration yielding optimum growth. Above 15 mg/l there was a clear decrease in growth rate, producing the epimastigote to amastigote transformation. Such morphological change was observed together with a marked injury of the enzymatic machinery of the parasite, leading to diminished protein synthesis as well as lower activity of the antioxidant enzymes (superoxide dismutase, ascorbate peroxidase and trypanothione reductase), reduced total thiol content and a marked increase in the HaemOx-1 activity and expression. The current work demonstrates that there is a correlation between higher haemin concentrations in the culture medium and oxidative damage in the cells. Under these conditions induction of HaemOx-1 would indicate the important role of this enzyme as an antioxidant defence response inTrypanosoma cruzi.

Recent trends of ophthalmic diseases in Italy: are official data reliable?

PURPOSE

The aim of the study was to investigate the epidemiological trends of ocular diseases using hospital discharge forms as our data source.

MATERIALS AND METHODS

We examined official Italian Statistical Institute (ISTAT) data concerning patients discharged from ophthalmologic wards. Our analysis was limited to the years when ISTAT managed health service data (1986, 1990 and 1994); whilst currently this duty has been transferred to the Ministry of Health.

RESULTS

The diseases encountered most frequently were cataract, glaucoma and retinal detachment. Patients over 65 years of age represented 54% in 1986 and 67% in 1994. We observed a general trend toward shorter hospital stays (10.5 days in 1986 and 6.4 days in 1994). We applied an indirect analysis that included masculinity and chi 2 homogeneity that were aimed to evaluate results de spite the questionable reliability of ISTAT gross data.

CONCLUSIONS

The results of indirect analysis (masculinity and chi 2 homogeneity, of data combined with the significant number of charts presenting incomplete diagnostic information strongly question the reliability of official documentation. The increase of hospitalisation for ophthalmic surgery and a higher average age of affected subjects were confirmed. We observed a reduction of mean hospital stay per admission, indicating a positive achievement in terms of cost reduction.

C terminus of infectious bursal disease virus major capsid protein VP2 is involved in definition of the T number for capsid assembly

Infectious bursal disease virus (IBDV), a member of the Birnaviridae family, is a double-stranded RNA virus. The IBDV capsid is formed by two major structural proteins, VP2 and VP3, which assemble to form a T=13 markedly nonspherical capsid. During viral infection, VP2 is initially synthesized as a precursor, called VPX, whose C end is proteolytically processed to the mature form during capsid assembly. We have computed three-dimensional maps of IBDV capsid and virus-like particles built up by VP2 alone by using electron cryomicroscopy and image-processing techniques. The IBDV single-shelled capsid is characterized by the presence of 260 protruding trimers on the outer surface. Five classes of trimers can be distinguished according to their different local environments. When VP2 is expressed alone in insect cells, dodecahedral particles form spontaneously; these may be assembled into larger, fragile icosahedral capsids built up by 12 dodecahedral capsids. Each dodecahedral capsid is an empty T=1 shell composed of 20 trimeric clusters of VP2. Structural comparison between IBDV capsids and capsids consisting of VP2 alone allowed the determination of the major capsid protein locations and the interactions between them. Whereas VP2 forms the outer protruding trimers, VP3 is found as trimers on the inner surface and may be responsible for stabilizing functions. Since elimination of the C-terminal region of VPX is correlated with the assembly of T=1 capsids, this domain might be involved (either alone or in cooperation with VP3) in the induction of different conformations of VP2 during capsid morphogenesis.

VP5, the nonstructural polypeptide of infectious bursal disease virus, accumulates within the host plasma membrane and induces cell lysis

Infectious bursal disease virus (IBDV) encodes a 17-kDa nonstructural polypeptide known as VP5. This polypeptide is not essential for virus replication in vitro but it plays an important role in in vivo dissemination and pathogenesis. We have characterized the expression of VP5 in three eukaryotic systems: (i) IBDV-infected chicken embryo fibroblasts; (ii) BSC-1 cells infected with a recombinant vaccinia virus vector; and (iii) Cos-1 cells transiently transfected with a plasmid vector. Immunofluorescence analyses showed that upon expression VP5 accumulates within the plasma membrane. This finding was consistent with sequence-based topology predictions, indicating that VP5 is a class II membrane protein with a cytoplasmic N-terminus and an extracellular C-terminal domain. Brefeldin A treatment of VP5-expressing cells prevented the accumulation of this polypeptide in the plasma membrane, thus showing the requirement of an active exocytic pathway to reach that compartment. Expression of VP5 was shown to be highly cytotoxic. Induction of VP5 expression resulted in the alteration of cell morphology, the disruption of the plasma membrane, and a drastic reduction of cell viability. VP5-induced cytotoxicity was prevented by blocking its transport to the membrane with Brefeldin A. Our findings suggest that VP5 plays an important role in the release of the IBDV progeny.

A beta-stranded motif drives capsid protein oligomers of the parvovirus minute virus of mice into the nucleus for viral assembly

The determinants of nuclear import in the VP-1 and VP-2 capsid proteins of the parvovirus minute virus of mice strain i (MVMi) synthesized in human fibroblasts were sought by genetic analysis in an infectious plasmid. Immunofluorescence of transfected cells revealed that the two proteins were involved in cooperative cytoplasmic interactions for nuclear cotransport. However, while VP-1 translocated regardless of extension of deletions and did not form capsid epitopes by itself, VP-2 seemed to require cytoplasmic folding and the overall conformation for nuclear transport. The sequence (528)KGKLTMRAKLR(538) was found necessary for nuclear uptake of VP-2, even though it was not sufficient to confer a nuclear localization capacity on a heterologous protein. In the icosahaedral MVMi capsid, this sequence forms the carboxy end of the amphipathic beta-strand I (betaI), and all its basic residues are contiguously positioned at the face that in the unassembled subunit would be exposed to solvent. Mutations in singly expressed VP-2 that either decrease the net basic charge of the exposed face (K530N-R534T), perturb the hydrophobicity of the opposite face (L531E), or distort the betaI conformation (G529P) produced cytoplasmic subviral oligomers. Particle formation by betaI mutants indicated that the basic residues clustered at one face of betaI drive VP oligomers into the nucleus preceding and uncoupled to assembly and that the nuclear environment is required for MVMi capsid formation in the infected cell. The degree of VP-1/VP-2 transport cooperativity suggests that VP trimers are the morphogenetic intermediates translocating through the nuclear pore. The results support a model in which nuclear transport signaling preserves the VP-1/VP-2 stoichiometry necessary for efficient intranuclear assembly and in which the beta-stranded VP-2 nuclear localization motif contributes to the quality control of viral morphogenesis.

VP1, the putative RNA-dependent RNA polymerase of infectious bursal disease virus, forms complexes with the capsid protein VP3, leading to efficient encapsidation into virus-like particles

A cDNA corresponding to the coding region of VP1, the putative RNA-dependent RNA polymerase, of infectious bursal disease virus (IBDV) was cloned and inserted into the genome of a vaccinia virus inducible expression vector. The molecular mass and antigenic reactivity of VP1 expressed in mammalian cells are identical to those of its counterpart expressed in IBDV-infected cells. The results presented here demonstrate that VP1 is efficiently incorporated into IBDV virus-like particles (VLPs) produced in mammalian cells coexpressing the IBDV polyprotein and VP1. Incorporation of VP1 into VLPs requires neither the presence of IBDV RNAs nor that of the nonstructural polypeptide VP5. Immunofluorescence, confocal laser scanning microscopy, and immunoprecipitation analyses conclusively showed that VP1 forms complexes with the structural polypeptide VP3. Formation of VP1-VP3 complexes is likely to be a key step for the morphogenesis of IBDV particles.

[In vivo dosimetry and radiographic confirmation in radiotherapy of Hodgkin's lymphoma]

In September 1991 a protocol for quality control of large shaped irradiation fields was started in our department. In vivo dosimetry with semiconductor detectors was used to measure the absorbed dose and patient positioning was checked with portal films weekly. First, we set a computed dosimetric system yielding dosimetric values in real time and allowing their easy storage. Then, we calibrated the diodes and determined the correction factors for each of them outside standard conditions. Entrance dose, exit dose and midline dose were measured in 62 patients undergoing supradiaphragmatic radiation therapy for Hodgkin's lymphoma. The exist dose was measured weekly to assess treatment repeatability. High agreement was observed between measured and calculated doses; repeatability was also high, since only 6% of exit dose measurements exceeded 5% of the first determination. In 33 patients portal films were obtained in the first treatment session, and thereafter weekly, to assess mispositioning relative to simulation (reproducibility) and from one session to another (repeatability). A small systematic error was detected in both longitudinal (x = -3 mm; SD = 3.7 mm) and transverse (x = -2 mm; SD = 3.4 mm) directions. Statistically significant errors (> 6 mm) were observed in 14% of patients. Reproducibility was excellent. The protocol reported on in this paper not only helps avoid systematic dosimetric and/or positioning errors in the patients, but also helps identify the main causes of uncertainty and thus remove them.

Attitudes of elementary, middle and high school teachers toward mainstreaming: implications for job satisfaction

Little attention has been paid to the job satisfaction experienced by human services personnel, including classroom teachers. This study examines teacher's response to postal questionnaires seeking information about job satisfaction and attitudes to the mainstreaming of children with special needs. Results suggest that teachers at secondary or high school level experience lower job satisfaction than those working in middle or elementary schools. Comparison between teachers with or without experiences of mainstreaming reveals further differences between these groups with regard to their attitude toward the value of special educator involvement in the classroom; the importance of special assessments; discipline; the role of mainstreaming in enhancing peer relationships; and appreciation of school administrators' awareness of the implications of mainstreaming.

[Prognostic value of echocardiographic finding of valvular vegetations in patients with infectious endocarditis]

42 consecutive patients with infective endocarditis on native valves, according to Pelletier and Petersdorf's criteria of definite (13 pts), probable (12 pts.) and possible (17 pts) endocarditis, were identified and prospectively followed-up with M-mode and two-dimensional echocardiography, since 1980. We compared: 1) these three groups; 2) survivors not referred for surgery versus surgical patients plus nonsurvivors; 3) patients who suffered embolic events versus those who did not; 4) patients with severe-moderate heart failure versus those with no failure or mild failure; 5) patients with aortic valve echocardiographic vegetations versus those with mitral valve vegetations. Furthermore 11 of these patients who did not undergo surgery (9 with mitral and 2 with mitro-aortic vegetations on echo) were serially followed-up with echocardiography for 6-42 months (average: 32 months). The presence of ultrasound detectable vegetations itself and their size, without considering their site, did not identify a major risk of embolization, heart failure, death or need of surgery. The site of vegetations was the only significant feature in our series. It identified a high-risk group and a relatively low-risk group. Aortic valve involvement, with echocardiographic vegetations, was related to severe or moderate heart failure (P less than 0.01), death or need of surgery (P less than 0.05). Mitral valve involvement carried on a relatively low risk. The 9 patients with mitral valve vegetations only, not referred for surgery and followed-up, did well on medical treatment and returned to work. They did not have relapses or embolization. On serial echocardiographic examinations, mitral vegetations become smaller in the long run. Two years after the acute episode, usually echocardiography did not allow identification of vegetations.