[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program.

Background and purpose: The [18]F-fluoroethyl-l-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TVMR) and hybrid target volumes (TVMR+FET) incorporating pre-defined FET PET biological tumour volumes (BTVs). Materials and methods: Central review and analysis of TVMR and TVMR+FET was undertaken across three benchmarking cases. BTVs were pre-defined by a sole nuclear medicine expert. Intraclass correlation coefficient (ICC) confidence intervals (CIs) evaluated volume agreement. RO contour spatial and boundary agreement were evaluated (Dice similarity coefficient [DSC], Jaccard index [JAC], overlap volume [OV], Hausdorff distance [HD] and mean absolute surface distance [MASD]). Dose plan generation (one case per site) was assessed. Results: Data from 19 ROs across 10 trial sites (54 initial submissions, 8 resubmissions requested, 4 conditional passes) was assessed with an initial pass rate of 77.8 %; all resubmissions passed. TVMR+FET were significantly larger than TVMR (p < 0.001) for all cases. RO gross tumour volume (GTV) agreement was moderate-to-excellent for GTVMR (ICC = 0.910; 95 % CI, 0.708-0.997) and good-to-excellent for GTVMR+FET (ICC = 0.965; 95 % CI, 0.871-0.999). GTVMR+FET showed greater spatial overlap and boundary agreement compared to GTVMR. For the clinical target volume (CTV), CTVMR+FET showed lower average boundary agreement versus CTVMR (MASD: 1.73 mm vs. 1.61 mm, p = 0.042). All sites passed the planning exercise. Conclusions: The credentialing program demonstrated feasibility in successful credentialing of 19 ROs across 10 sites, increasing national expertise in TVMR+FET delineation.

Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy.

Objective. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit (HU) accuracy, and image quality. To achieve these goals, a vision-transformer and anatomically sensitive loss functions are described. Better quantification of image quality is achieved using the alignment-invariant Fréchet inception distance (FID), and uncertainty estimation for sCT risk prediction is implemented in a scalable plug-and-play manner.Approach. Baseline U-Net, generative adversarial network (GAN), and CycleGAN models were trained to identify shortcomings in each approach. The proposed CycleGAN-Best model was empirically optimized based on a large ablation study and evaluated using classical image quality metrics, FID, gamma index, and a segmentation analysis. Two uncertainty estimation methods, Monte-Carlo Dropout (MCD) and test-time augmentation (TTA), were introduced to model epistemic and aleatoric uncertainty.Main results. FID was correlated to blind observer image quality scores with a Correlation Coefficient of -0.83, validating the metric as an accurate quantifier of perceived image quality. The FID and mean absolute error (MAE) of CycleGAN-Best was 42.11 ± 5.99 and 25.00 ± 1.97 HU, compared to 63.42 ± 15.45 and 31.80 HU for CycleGAN-Baseline, and 144.32 ± 20.91 and 68.00 ± 5.06 HU for the CBCT, respectively. Gamma 1%/1 mm pass rates were 98.66 ± 0.54% for CycleGAN-Best, compared to 86.72 ± 2.55% for the CBCT. TTA and MCD-based uncertainty maps were well spatially correlated with poor synthesis outputs.Significance. Anatomical accuracy was achieved by suppressing CycleGAN-related artefacts. FID better discriminated image quality, where alignment-based metrics such as MAE erroneously suggest poorer outputs perform better. Uncertainty estimation for sCT was shown to correlate with poor outputs and has clinical relevancy toward model risk assessment and quality assurance. The proposed model and accompanying evaluation and risk assessment tools are necessary additions to achieve clinically robust sCT generation models.

Patient Independent Interictal Epileptiform Discharge Detection.

Epilepsy is a highly prevalent brain condition with many serious complications arising from it. The majority of patients which present to a clinic and undergo electroencephalogram (EEG) monitoring would be unlikely to experience seizures during the examination period, thus the presence of interictal epileptiform discharges (IEDs) become effective markers for the diagnosis of epilepsy. Furthermore, IED shapes and patterns are highly variable across individuals, yet trained experts are still able to identify them through EEG recordings - meaning that commonalities exist across IEDs that an algorithm can be trained on to detect and generalise to the larger population. This research proposes an IED detection system for the binary classification of epilepsy using scalp EEG recordings. The proposed system features an ensemble based deep learning method to boost the performance of a residual convolutional neural network, and a bidirectional long short-term memory network. This is implemented using raw EEG data, sourced from Temple University Hospital's EEG Epilepsy Corpus, and is found to outperform the current state of the art model for IED detection across the same dataset. The achieved accuracy and Area Under Curve (AUC) of 94.92% and 97.45% demonstrates the effectiveness of an ensemble method, and that IED detection can be achieved with high performance using raw scalp EEG data, thus showing promise for the proposed approach in clinical settings.

Quantitative [68Ga]Ga-PSMA-11 PET biomarkers for the analysis of lesion-level progression in biochemically recurrent prostate cancer: a multicentre study.

[68Ga]Ga-PSMA-11 PET has become the standard imaging modality for biochemically recurrent (BCR) prostate cancer (PCa). However, its prognostic value in assessing response at this stage remains uncertain. The study aimed to assess the prognostic significance of radiographic patient-level patterns of progression derived from lesion-level biomarker quantitation in metastatic disease sites. A total of 138 BCR PCa patients with both baseline and follow-up [68Ga]Ga-PSMA-11 PET scans were included in this analysis. Tumour response was quantified at the lesion level using commonly used quantitative parameters (SUVmean, SUVmax, SUVpeak, volume), and patients were classified as systemic, mixed, or no-progression based on these response classifications. A total of 328 matched lesions between baseline and follow-up scans were analysed. The results showed that systemic progressors had a significantly higher risk of death than patients with no progression with SUVmean demonstrating the highest prognostic value (HR = 5.70, 95% CI = 2.63-12.37, p < 0.001, C-Index = 0.69). Moreover, progressive disease as measured by SUVmean using the radiographic PSMA PET Progression Criteria (rPPP) was found to be significantly prognostic for patient overall survival (HR = 3.67, 95% CI = 1.82-7.39, p < 0.001, C-Index = 0.65). This work provides important evidence supporting the prognostic utility of PSMA response quantitation in the BCR setting.

Prospective inter- and intra-tracer repeatability analysis of radiomics features in [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET scans in metastatic prostate cancer.

OBJECTIVE: This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [18F]F-PSMA-1007 positron emission tomography (PET) scans. METHODS: Eighteen patients with metastatic prostate cancer (mPCa) were prospectively recruited for the study and randomised to one of three test-retest groups: (i) intratracer [68Ga]Ga-PSMA-11 PET, (ii) intratracer [18F]F-PSMA-1007 PET or (iii) intertracer between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET. Four conventional PET metrics (standardised uptake value (SUV)max, SUVmean, SUVtotal and volume) and 107 radiomics features were extracted from 75 lesions and assessed using the repeatability coefficient (RC) and the ICC. Radiomic feature repeatability was also quantified after the application of 16 filters to the PET image. RESULTS: Test-retest scans were taken a median of 5 days apart (range: 2-7 days). SUVmean demonstrated the lowest RC limits of the conventional features, with RCs of 7.9%, 14.2% and 24.7% for the [68Ga]Ga-PSMA-11 PET, [18F]F-PSMA-1007 PET, and intertracer groups, respectively. 69%, 66% and 9% of all radiomics features had good or excellent ICC values (ICC ≥ 0.75) for the same groups. Feature repeatability therefore diminished considerably for the intertracer group relative to intratracer groups. CONCLUSION: In this study, robust biomarkers for each tracer group that can be used in subsequent clinical studies were identified. Overall, the repeatability of conventional and radiomic features were found to be substantially lower for the intertracer group relative to both intratracer groups, suggesting that assessing patient response quantitatively should be done using the same radiotracer where possible. ADVANCES IN KNOWLEDGE: Intertracer biomarker repeatability limits are significantly larger than intratracer limits.

Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [68Ga]Ga-PSMA-11 PET images.

PURPOSE: This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model. METHODS: One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen's kappa statistic. RESULTS: Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96-7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23-11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31). CONCLUSION: RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.

Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study-TROG 18.06.

PURPOSE: The O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in Glioblastoma (FIG) trial is an Australian prospective, multi-centre study evaluating FET PET for glioblastoma patient management. FET PET imaging timepoints are pre-chemoradiotherapy (FET1), 1-month post-chemoradiotherapy (FET2), and at suspected progression (FET3). Before participant recruitment, site nuclear medicine physicians (NMPs) underwent credentialing of FET PET delineation and image interpretation. METHODS: Sites were required to complete contouring and dynamic analysis by ≥ 2 NMPs on benchmarking cases (n = 6) assessing biological tumour volume (BTV) delineation (3 × FET1) and image interpretation (3 × FET3). Data was reviewed by experts and violations noted. BTV definition includes tumour-to-background ratio (TBR) threshold of 1.6 with crescent-shaped background contour in the contralateral normal brain. Recurrence/pseudoprogression interpretation (FET3) required assessment of maximum TBR (TBRmax), dynamic analysis (time activity curve [TAC] type, time to peak), and qualitative assessment. Intraclass correlation coefficient (ICC) assessed volume agreement, coefficient of variation (CoV) compared maximum/mean TBR (TBRmax/TBRmean) across cases, and pairwise analysis assessed spatial (Dice similarity coefficient [DSC]) and boundary agreement (Hausdorff distance [HD], mean absolute surface distance [MASD]). RESULTS: Data was accrued from 21 NMPs (10 centres, n ≥ 2 each) and 20 underwent review. The initial pass rate was 93/119 (78.2%) and 27/30 requested resubmissions were completed. Violations were found in 25/72 (34.7%; 13/12 minor/major) of FET1 and 22/74 (29.7%; 14/8 minor/major) of FET3 reports. The primary reasons for resubmission were as follows: BTV over-contour (15/30, 50.0%), background placement (8/30, 26.7%), TAC classification (9/30, 30.0%), and image interpretation (7/30, 23.3%). CoV median and range for BTV, TBRmax, and TBRmean were 21.53% (12.00-30.10%), 5.89% (5.01-6.68%), and 5.01% (3.37-6.34%), respectively. BTV agreement was moderate to excellent (ICC = 0.82; 95% CI, 0.63-0.97) with good spatial (DSC = 0.84 ± 0.09) and boundary (HD = 15.78 ± 8.30 mm; MASD = 1.47 ± 1.36 mm) agreement. CONCLUSION: The FIG study credentialing program has increased expertise across study sites. TBRmax and TBRmean were robust, with considerable variability in BTV delineation and image interpretation observed.

Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM.

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet-Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.

Development and validation of gas chromatography with electron capture detection method using QuEChERS for pesticide residue determination in cucumber.

A modified quick, easy, cheap, efficient, rugged, and safe (QuEChERS) method coupled to gas chromatography with electron capture detection was developed for the simultaneous determination of selected electronegative pesticides, namely, chlorpyrifos-methyl (1), chlorpyrifos (2), quinolphos (3), profenofos (4), myclobutanil (5), ethion (6), fenpropathrin (7), and cypermethrin (8), in vegetables with high water content. The selected compounds and some of their metabolites have even been found in human body fluids. In addition, some of them are known or suspected carcinogens according to the World Health Organization. Extraction and cleanup parameters were optimized; thus, the original QuEChERS method was modified to minimize solvent usage by making the study eco-friendly. The developed method was validated for selectivity, specificity, linearity, precision, and accuracy using SANTE guidelines. Calibration curves showed good linearity (r > 0.99) within the test range. Precision was evaluated by intra- and inter-day experiments with an acceptable range of less than 20.0% of relative standard deviation. Recovery was evaluated at limit of quantification and was found to be in the range of 70-120%, with relative standard deviations lower than 4.21%. The proposed method is applicable for detection and monitoring of selected pesticides in one run not only in fruits and vegetables with high water content but also in samples containing large quantities of pigments/dyes.

Spontaneous NETosis in diabetes: A role of hyperglycemia mediated ROS and autophagy.

Type 2-diabetes, particularly poorly controlled diabetes, is a risk factor for several infections such as lower respiratory tract and skin infections. Hyperglycemia, a characteristic downstream effect of poorly controlled diabetes, has been shown to impair the function of immune cells, in particular neutrophils. Several studies have demonstrated that hyperglycemia-mediated priming of NADPH oxidase results in subsequent elevated levels of reactive oxygen species (ROS). In healthy neutrophils, ROS plays an important role in pathogen killing by phagocytosis and by induction of Neutrophil Extracellular Traps (NETs). Given the key role of ROS in autophagy, phagocytosis and NETosis, the relationship between these pathways and the role of diabetes in the modulation of these pathways has not been explored previously. Therefore, our study aimed to understand the relationship between autophagy, phagocytosis and NETosis in diabetes. We hypothesized that hyperglycemia-associated oxidative stress alters the balance between phagocytosis and NETosis by modulating autophagy. Using whole blood samples from individuals with and without type 2-diabetes (in the presence and absence of hyperglycemia), we demonstrated that (i) hyperglycemia results in elevated levels of ROS in neutrophils from those with diabetes, (ii) elevated levels of ROS increase LCIII (a marker for autophagy) and downstream NETosis. (iii) Diabetes was also found to be associated with low levels of phagocytosis and phagocytic killing of S. pneumoniae. (iv) Blocking either NADPH oxidase or cellular pathways upstream of autophagy led to a significant reduction in NETosis. This study is the first to demonstrate the role of ROS in altering NETosis and phagocytosis by modulating autophagy in type 2-diabetes. GRAPHICAL ABSTRACT.

Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [68Ga]Ga-PSMA-11 PET/CT images.

PURPOSE: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [68Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. METHODS: Three hundred thirty-seven [68Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLVauto) and total lesional uptake (TLUauto) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival. RESULTS: At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLVauto and TLUauto showed they were significantly associated with patient overall survival (both p < 0.005). CONCLUSION: The fully automated assessment of whole-body [68Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival. TRIAL REGISTRATION: This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings-with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state-of-the-art methods utilized in radiation oncology.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections.

Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to demonstrate how deep learning based on phantom data can be used effectively for CBCT intensity correction in patient images. Four anthropomorphic phantoms were scanned on a CBCT and conventional fan-beam CT system. Intensity correction is performed by estimating the cone-beam intensity deviations from prior information contained in the CT. Residual projections were extracted by subtraction of raw cone-beam projections from virtual CT projections. An improved version of U-net is utilized to train on a total of 2001 projection pairs. Once trained, the network could estimate intensity deviations from input patient head and neck raw projections. The results from our novel method showed that corrected CBCT images improved the (contrast-to-noise ratio) with respect to uncorrected reconstructions by a factor of 2.08. The mean absolute error and structural similarity index improved from 318 HU to 74 HU and 0.750 to 0.812 respectively. Visual assessment based on line-profile measurements and difference image analysis indicate the proposed method reduced noise and the presence of beam-hardening artefacts compared to uncorrected and manufacturer reconstructions. Projection domain intensity correction for cone-beam acquisitions of patients was shown to be feasible using a convolutional neural network trained on phantom data. The method shows promise for further improvements which may eventually facilitate dose monitoring and ART in the clinical radiotherapy workflow.

Comprehensive investigation into the stability of Varian and Elekta kV imaging systems during arc delivery.

In radiotherapy treatments utilizing accelerator gantry rotation, gantry-mounted kilovoltage (kV) imaging systems have become integral to treatment verification. The accuracy of such verification depends on the stability of the imaging components during gantry rotation. In this study, a simple measurement method and accurate algorithm are introduced for investigation of the kV panel and source movement during gantry rotation. The method is based on images of a ball-bearing phantom combined with a Winston-Lutz phantom, and determines the movements of all the mechanical parameters of the kV imaging system relative to the reference at zero gantry angle. Analysis was performed on different linear accelerators and both gantry rotation directions. The precision of the method was tested and was less than 0.04 mm. This method is suitable to be included in the quality assurance testing of linacs to monitor the kV imaging system performance and provides additional mechanical information that previous tests cannot.

Pyopneumothorax secondary to Aspergillus infection: a case report.

A 32 -year- old male presented with complaints of fever, dry cough, breathlessness and right sided chest pain of two months duration. Chest radiograph showed right sided hydropneumothorax which revealed frank pus on diagnostic thoracocentesis, for which tube thoracostomy was done. Despite vigorous broad spectrum antibiotic coverage, postural drainage and chest physiotherapy, there was no clinical improvement. Further work up included serology, pleural fluid culture, closed as well as thoracoscopic guided pleural biopsy revealed growth of Aspergillus fumigatus. Patient was prescribed antifungal medication (Voriconazole) and subsequent thoracotomy with right sided pneumonectomy showed good clinical recovery.

Extra-pulmonary primary multidrug-resistant tubercular lymphadenitis in an HIV negative patient.

A 28-year-old woman without any history of prior antituberculosis treatment presented with cervical lymphadenopathy and a cold abscess near medial end of clavicle of 5 months duration. Pus culture and sensitivity revealed Mycobacterium tuberculosis resistant to rifampicin and isoniazid. Thus she was diagnosed as a case of primary multidrug-resistant tuberculosis and treated with second line drugs according to culture susceptibility pattern. On completion of therapy, patent showed good clinical response. This case highlights the observation that even extra-pulmonary primary multidrug-resistant tuberculosis can be successfully treated with currently available second line drugs.

Swyer-James-MacLeod syndrome with ipsilateral herniation of hyperinflated hyperlucent lung.

Swyer-James-MacLeod syndrome is characterised by unilateral hyperlucency on chest radiograph with small or normal-sized lung on the affected side and compensatory hyperinflation of opposite lung. Hyperinflation of the affected lung is a very rarely reported entity. An adult female patient, who presented with exertional breathlessness and diagnosed to have hypoplastic left pulmonary artery with hyperlucent, hyperinflated and herniated left lung is described.

Multidrug-resistant and extensively drug resistant tuberculosis in Kashmir, India.

BACKGROUND: To study the profile of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) in tertiary care hospital setting, representing almost the whole affected population in Kashmir valley of India. METHODOLOGY: A total of 910 cases of pulmonary tuberculosis were enrolled over four years. Among these, cases of MDR-TB and XDR-TB were meticulously studied for drug susceptibility, treatment, adverse effects profile and overall survival. RESULTS: Fifty-two (5.7%) cases of MDR-TB were identified, among which eight (15.3%) were diagnosed as XDR-TB on the basis of drug susceptibility testing, using the prescribed definition. The cases were sensitive to 2, 3, 4, 5 and more than 5 drugs in almost equal proportions. Thirty-seven (71.1%) cases were successfully cured; eleven (21.1%) patients died; and only four (7.6%) cases defaulted, indicating overall satisfactory adherence to treatment. CONCLUSION: For effective treatment of MDR-TB and XDR-TB, early case detection, improved laboratory facilities, availability of appropriate treatment regimens, and financial assistance in resource-limited settings through effective political intervention are necessary for better patient adherence and overall cure.

Ciliopathy with special emphasis on kartageners syndrome.

Cilia are hair-like structures extending from the cell membrane, perform diverse biological functions. Primary defects in the structure and function of sensory and motile cilia result in multiple ciliopathies. The most prominent genetic abnormality involving motile cilia is primary ciliary dyskinesia (PCD) or Kartageners syndrome. PCD is a rare, usually autosomal recessive, genetically heterogeneous disorder characterized by sino-pulmonary disease, laterality defects and male infertility. One of the important components of cilia is the Dynein. Ciliary ultrastructural defects are identified in approximately 90% of PCD patients and involve the outer dynein arms, inner dynein arms, or both. Diagnosing PCD is challenging and requires a compatible clinical phenotype together with tests such as ciliary ultrastructural analysis, immunofluorescent staining, ciliary beat assessment, and/or nasal nitric oxide measurements. Increased understanding of the pathogenesis will aid in better diagnosis and treatment of PCD. The aim of the article is to present the basic defect involved in the etiology of this interesting syndrome.