A prospective phase 2 study of expeditious EGFR genotyping and immediate therapeutic initiation through extracellular vesicles (EV)-based bronchoalveolar lavage fluid (BALF) liquid biopsy in advanced NSCLC patients.

Background: In our previous study, epidermal growth factor receptor (EGFR) genotyping using extracellular vesicles (EV)-derived DNA isolated from bronchoalveolar lavage fluid (BALF) was proven to be highly concordant with conventional tissue-based genotyping and its turn-around-time (TAT) was only 1-2 days. On this background, we prospectively validated the performance of EV-based BALF liquid biopsy for EGFR genotyping in the real practice of advanced non-small cell lung cancer (NSCLC) patients. Methods: After screening 120 newly diagnosed stage III-IV NSCLC patients, 51 cases were detected as EGFR-mutated by EV-based BALF EGFR genotyping and 40 patients were enrolled for gefitinib treatment. BALF EV were isolated by ultracentrifuge method and EGFR genotyping was performed with PCR-based PNA-clamping assisted fluorescence melting curve analysis. The objective response rate, progression-free survival (PFS), TAT, time to treatment initiation (TTI), and concordance rate were analyzed with clinical parameters. Results: There was only one false positive case among the 120 screened patients and the overall concordance rate between tissue biopsy and EV-based BALF liquid biopsy was 99.2% including the subtype of EGFR mutations. TAT for EV-based BALF EGFR genotyping was 1.9±1.1 days, while tissue-based TAT was 12.1±7.2 days (P<0.001). EGFR genotyping was determined even before obtaining histopathologic report in most cases. TTI in BALF EGFR genotyping was faster than tissue genotyping (7.8±6.5 vs. 13.8±12.9 days). Therapeutic outcomes of response rate and PFS were almost similar to tissue-based results. Conclusions: We demonstrated, for the first time, that EV-based BALF liquid biopsy should be an excellent platform for expeditious EGFR genotyping and rapid therapeutic intervention even before obtaining the result of histopathology in advanced NSCLC patients.

Nailfold capillaroscopy findings of interstitial pneumonia with autoimmune features.

BACKGROUND/AIMS: We evaluated nailfold capillaroscopy (NFC) of interstitial pneumonia with autoimmune features (IPAF) and compared it with that of patients with connective tissue disease-interstitial lung disease (CTD-ILD) and idiopathic interstitial pneumonia (IIP). METHODS: Patients with newly diagnosed as ILD were evaluated using NFC. Baseline demographic, clinical, serological, and high-resolution CT findings were collected. NFC was semi-quantitatively scored with six domains ranging from 0 to 18. In addition, the overall patterns (scleroderma/non-scleroderma patterns) were determined. RESULTS: A total of 81 patients (31 with CTD-ILD, 18 with IPAF, and 32 with IIP) were included. The non-specific interstitial pneumonia pattern was the most common ILD pattern in the CTD-ILD and IPAF groups, whereas the usual interstitial pneumonia pattern was the most common in the IIP group. The semi-quantitative score of the CTD-ILD group was higher than that of the IPAF or IIP groups (5.8 vs 4.2 vs 3.0, p < 0.001, respectively). Giant capillaries and haemorrhages were more frequently present in the CTD-ILD and IPAF groups than in the IIP group. A scleroderma pattern was present in 27.8% of the IPAF group, whereas none of the IIP patients showed a scleroderma pattern. CONCLUSION: NFC findings may be useful in classifying patients with ILD into CTD-ILD/IPAF/IIP.

Effect of a Team Approach to Pressure Injury Management over 5 Years in a Tertiary Hospital.

OBJECTIVE: The authors' facility established a novel integrated wound care team (IWCT), which included the implementation of a strict treatment algorithm by the patients' attending providers and a specialized wound care team led by a plastic surgeon. Investigators then retrospectively analyzed clinical outcomes of pressure injury (PI) management by the IWCT over 5 years. METHODS: The authors performed a retrospective chart review and periodic statistical analysis of the data for all patients with PI referred to the IWCT in the authors' center from May 2015 to April 2019. Data including patients' demographic information, first and last consultation dates, referring department, PI stage, site of PI, and Braden Scale scores were collected and analyzed. RESULTS: Patients (N = 15,556) did not differ significantly in age, sex, or Braden Scale score. A preimplementation/postimplementation analysis of PI data before and after establishing the IWCT showed that the incidence of stage 3 or 4 PIs had significantly decreased during the study period (19.1% vs 15.2%, P < .05). Conversely, the incidence of stage 1 PIs significantly increased in the same period (38.0% vs 57.4%, P < .05). The proportion of completely healed PIs also increased, and the median treatment period was significantly shortened (P < .05). CONCLUSIONS: Implementation of the IWCT in a tertiary hospital setting led to a significant increase in early-stage PI detection and a decrease in severe PIs.

Extracellular Vesicle-Based Bronchoalveolar Lavage Fluid Liquid Biopsy for EGFR Mutation Testing in Advanced Non-Squamous NSCLC.

To overcome the limitations of the tissue biopsy and plasma cfDNA liquid biopsy, we performed the EV-based BALF liquid biopsy of 224 newly diagnosed stage III-IV NSCLC patients and compared it with tissue genotyping and 110 plasma liquid biopsies. Isolation of EVs from BALF was performed by ultracentrifugation. EGFR genotyping was performed through peptide nucleic acid clamping-assisted fluorescence melting curve analysis. Compared with tissue-based genotyping, BALF liquid biopsy demonstrated a sensitivity, specificity, and concordance rates of 97.8%, 96.9%, and 97.7%, respectively. The performance of BALF liquid biopsy was almost identical to that of standard tissue-based genotyping. In contrast, plasma cfDNA-based liquid biopsy (n = 110) demonstrated sensitivity, specificity, and concordance rates of 48.5%, 86.3%, and 63.6%, respectively. The mean turn-around time of BALF liquid biopsy was significantly shorter (2.6 days) than that of tissue-based genotyping (13.9 days; p < 0.001). Therefore, the use of EV-based BALF shortens the time for confirmation of EGFR mutation status for starting EGFR-TKI treatment and can hence potentially improve clinical outcomes. As a result, we suggest that EV-based BALF EGFR testing in advanced lung NSCLC is a highly accurate rapid method and can be used as an alternative method for lung tissue biopsy.

A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea.

We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.

Ultra-Low-Dose Spectral CT Based on a Multi-level Wavelet Convolutional Neural Network.

Spectral computed tomography (CT) based on a photon-counting detector (PCD) is a promising technique with the potential to improve lesion detection, tissue characterization, and material decomposition. PCD-based scanners have several technical issues including operation in the step-and-scan mode and long data acquisition time. One straightforward solution to these issues is to reduce the number of projection views. However, if the projection data are under-sampled or noisy, it would be challenging to produce a correct solution without precise prior information. Recently, deep-learning approaches have demonstrated impressive performance for under-sampled CT reconstruction. In this work, the authors present a multilevel wavelet convolutional neural network (MWCNN) to address the limitations of PCD-based scanners. Data properties of the proposed method in under-sampled spectral CT are analyzed with respect to the proposed deep-running-network-based image reconstruction using two measures: sampling density and data incoherence. This work presents the proposed method and four different methods to restore sparse sampling. We investigate and compare these methods through a simulation and real experiments. In addition, data properties are quantitatively analyzed and compared for the effect of sparse sampling on the image quality. Our results indicate that both sampling density and data incoherence affect the image quality in the studied methods. Among the different methods, the proposed MWCNN shows promising results. Our method shows the highest performance in terms of various evaluation parameters such as the structural similarity, root mean square error, and resolution. Based on the results of imaging and quantitative evaluation, this study confirms that the proposed deep-running network structure shows excellent image reconstruction in sparse-view PCD-based CT. These results demonstrate the feasibility of sparse-view PCD-based CT using the MWCNN. The advantage of sparse view CT is that it can significantly reduce the radiation dose and obtain images with several energy bands by fusing PCDs. These results indicate that the MWCNN possesses great potential for sparse-view PCD-based CT.

Targeted Next-Generation Sequencing Analysis Predicts the Recurrence in Resected Lung Adenocarcinoma Harboring EGFR Mutations.

Targeted NGS, widely applied to identify driver oncogenes in advanced lung adenocarcinoma, may also be applied to resected early stage cancers. We investigated resected EGFR-mutated lung adenocarcinoma mutation profiles to evaluate prognostic impacts. Tissues from 131 patients who had complete resection of stage I-IIIA EGFR-mutated lung adenocarcinoma were analyzed by targeted NGS for 207 cancer-related genes. Recurrence free survival (RFS) was estimated according to genetic alterations using the Kaplan-Meier method and Cox proportional regression analysis. The relapse rate was 25.2% (33/131). Five-year RFS of stages IA, IB, II, and IIIA were 82%, 75%, 35%, and 0%, respectively (p < 0.001). RFS decreased with the number of co-mutations (p = 0.025). Among co-mutations, the CTNNB1 mutation was associated with short RFS in a multivariate analysis (hazard ratio: 5.4, 95% confidence interval: 2.1-14.4; p = 0.001). TP53 mutations were associated with short RFS in stage IB-IIIA (p = 0.01). RFS was shorter with EGFR exon 19 deletion (19-del) than with mutation 21-L858R in stage IB-IIIA tumors (p = 0.008). Among 19-del subtypes, pL747_P753delinS (6/56, 8.9%) had shorter RFS than pE746_A750del (39/56, 69.6%), the most frequent subtype (p = 0.004).

Genomic profiling of extracellular vesicle-derived DNA from bronchoalveolar lavage fluid of patients with lung adenocarcinoma.

BACKGROUND: Extracellular vesicles (EVs) are membrane-bound and nanometer-sized particles released from most types of cells, containing double-stranded DNA reflecting mutational status of the parental tumor cells. Furthermore, epidermal growth factor receptor (EGFR) genotyping using EV-derived DNA (EV DNA) in bronchoalveolar lavage fluid (BALF) showed almost 100% sensitivity in patients with advanced non-small cell lung cancer (NSCLC). METHODS: We assessed the technical performance of DNA derived from BALF-EV (BALF EV DNA) in targeted next-generation sequencing (NGS) for detection and quantification of mutations compared with the matching tissue DNA in 20 lung adenocarcinomas. RESULTS: DNA yields, tumor purity, and depth of coverage were higher using the tissue DNA than using the BALF EV DNA. However, estimated library size was not significantly different between the two samples, and BALF EV DNA yielded longer fragments than tissue DNA. Overall mutation concordance between the two samples were 56% for nonsynonymous somatic mutations and increased to 81% for clinically significant mutations. By-variant sensitivity for clinically significant somatic mutations increased from 62% to 83% in the NGS of BALF EV DNA. Allele frequencies of EGFR and TP53 were higher in tissue DNA (10-25%) than in BALF EV DNA (<5%). Tumor mutation burden of BALF EV DNA correlated with that of tissue DNA. CONCLUSIONS: Our findings demonstrate, for the first time, that BALF EV DNA in patients with NSCLC can be a reliable DNA source for targeted NGS for the identification of actionable genetic alterations and that this approach has high clinical feasibility and utility.

Targeted Next-Generation Sequencing Analysis for Recurrence in Early-Stage Lung Adenocarcinoma.

BACKGROUND: Despite surgical resection, early lung adenocarcinoma has a recurrence rate of 20-50%. No clear predictive markers for recurrence of early lung adenocarcinoma are available. Targeted next-generation sequencing (NGS) is rarely used to identify recurrence-related genes. We aimed to identify genetic alterations that can predict recurrence, by comparing the molecular profiles of patient groups with and without recurrence. METHODS: Tissues from 230 patients with resected stage I-II lung adenocarcinoma (median follow-up: 49 months) were analyzed via targeted NGS for 207 cancer-related genes. The recurrence-free survival according to the number and type of mutation was estimated using the Kaplan-Meier method. Independent predictive biomarkers related to recurrence were identified using the Cox proportional hazards model. RESULTS: Recurrence was observed in 64 patients (27.8%). In multivariate analysis adjusted for age, sex, smoking history, stage, surgical mode, and visceral pleural invasion, the CTNNB1 mutation and fusion genes (ALK, ROS1, RET) were negative prognostic factors for recurrence in early-stage lung adenocarcinoma (HR 4.47, p = 0.001; HR 2.73, p = 0.009). EGFR mutation was a favorable factor (HR 0.51, p = 0.016), but the CTNNB1/EGFR co-mutations were negative predictors (HR 19.2, p < 0.001). TP53 mutation was a negative predictor compared with EGFR mutation for recurrence (HR 5.24, p = 0.02). CONCLUSIONS: Targeted NGS can provide valuable information to predict recurrence and identify patients at high recurrence risk, facilitating selection of the treatment strategy among close monitoring and adjuvant-targeted therapy. Larger datasets are required to validate these findings.

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network.

Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of particular interest among researchers within the CT community. Advanced reconstruction algorithms for sparse-view CT, such as iterative algorithms with total-variation (TV), have been studied along with the problem of increasing computational burden and the blurring of artifacts in the reconstructed images. Studies on deep-learning-based approaches applying U-NET have recently achieved remarkable outcomes in various domains including low-dose CT. In this study, we propose a new method for sparse-view CT reconstruction based on a multi-level wavelet convolutional neural network (MWCNN). First, a filtered backprojection (FBP) was used to reconstruct a sparsely sampled sinogram from 60, 120, and 180 projections. Subsequently, the sparse-view data obtained from FBP were fed to a deep-learning network, i.e., the MWCNN. Our network architecture combines a wavelet transform and modified U-NET without pooling. By replacing the pooling function with the wavelet transform, the receptive field is enlarged to improve the performance. We qualitatively and quantitatively evaluated the interpolation, iterative TV method, and standard U-NET in terms of a reduction in the streaking artifacts and a preservation of the anatomical structures. When compared with other methods, the proposed method showed the highest performance based on various evaluation parameters such as the structural similarity, root mean square error, and resolution. These results indicate that the MWCNN possesses a powerful potential for achieving a sparse-view CT reconstruction.

Liquid biopsy using extracellular vesicle-derived DNA in lung adenocarcinoma.

Blood liquid biopsy has emerged as a way of overcoming the clinical limitations of repeat biopsy by testing for the presence of acquired resistance mutations to therapeutic agents. Despite its merits of repeatability and non-invasiveness, this method is currently only used as a supplemental test due to a relatively low sensitivity rate of 50%-60%, and cannot replace tissue biopsy. The circulating tumor DNAs used in blood liquid biopsies are passive products of fragmented DNA with a short half-life released following tumor cell death; the low sensitivity seen with liquid blood biopsy results from this instability, which makes increasing the sensitivity of this test fundamentally difficult. Extracellular vesicles (EVs) are ideal carriers of cancer biomarkers, as cancer cells secret an abundance of EVs, and the contents of tumor cell-originated EVs reflect the molecular and genetic composition of parental cells. In addition, EV-derived DNAs (EV DNAs) consist of large-sized genomic DNAs and tumor-specific oncogenic mutant DNAs. For these reasons, liquid biopsy using EV DNA has the potential to overcome issues arising from tissue shortages associated with small biopsies, which are often seen in lung cancer patients, and the biopsy product can be used in other diagnostic methods, such as epidermal growth factor receptor (EGFR) mutation testing and next-generation sequencing (NGS). A higher sensitivity can be achieved when EV DNAs obtained from bronchoalveolar lavage fluid (BALF) are used rather than those from blood. BALF, when obtained close to the tumor site, is a promising liquid biopsy tool, as it enables the gathering of both cellular and non-cellular fractions of the tumor microenvironment, and provides increased diagnostic sensitivity when compared to blood.

Development of a senior-specific, citizen-oriented healthcare service system in South Korea based on the Canadian 48/6 model of care.

BACKGROUND: In the age of aging, Korea's current medical delivery system threatens to increase the number of medical and caring refugees. This study attempts to develop an integrated senior citizen-oriented healthcare service system in which daily care, professional care, and rehabilitation are organically organized between medical institutions and local communities, thereby meeting the daily life needs of the elderly and inducing well-being, wellness, and well-dying. METHODS: To develop the integrated healthcare system, data collection and analyses were conducted through a systematic review, literature review, benchmarking, focus group interviews, and expert consultation. RESULTS: The senior-specific, citizen-oriented healthcare service system developed in this study is designed to screen patients aged 65 or older within 24 h of being admitted, using the Geriatric Screening for Care-10. If there is reason for concern as a result of the screening, further evaluation is performed through assessment. Doctors and nurses create a care plan and a discharge plan based on the results from the screening and assessment. The nurse further uses the screening to monitor the patient's condition before discharge. Based on the screening results at the time of discharge, a transitional care plan is prepared and provided to elderly patients and/or their families. This process enables a systematic link between medical institutions and community resources, aiming for the continuous management of health issues. It also establishes a multidisciplinary treatment plan that considers patients and their families so that diseases common to the elderly are diagnosed and treated promptly. CONCLUSIONS: The most important issue for the elderly is to be able to live healthily and independently for the rest of their lives through well-being, wellness, and well-dying. The senior-specific, citizen-oriented healthcare service proposed in this study is an integrated medical treatment system for elderly users the implementation of which requires the daily care, professional care, and rehabilitation of elderly members of society to be organically organized according to the role of the patients, their families, and the caregiver.

Improved Quantification of 18F-FDG PET during 131I-Rituximab Therapy on Mouse Lymphoma Models after 131I Prompt Emission Correction.

18F-FDG Positron Emission Tomography (PET) is used to monitor tumor response to 131I-therapy, but is confounded by prompt emissions (284, 364, 637, and 723 keV) from 131I, particularly in animal PET imaging. We propose a method for correcting this emission in 18F-FDG PET. The 131I prompt emission effect was assessed within various energy windows and various activities. We applied a single gamma correction method to a phantom and in vivo mouse model. The 131I prompt emission fraction was 12% when 300 µCi of 131I and 100 µCi of FDG were administered, and increased exponentially with escalating 131I activity for all energy windows. The difference in spill-over ratio was reduced to <5% after 131I prompt emission correction. In the mouse model, the standard uptake value (SUV) did not differ significantly between FDG PET only (gold standard) and FDG PET after 131I prompt emission-correction, whereas it was overestimated by 38% before correction. Contrast was improved by 18% after 131I prompt emission correction. We first found that count contamination on 18F-FDG follow-up scans due to 131I spilled-over count after 131I rituximab tumor targeted therapy. Our developed 131I prompt emission-correction method increased accuracy during measurement of standard uptake values on 18F-FDG PET.

Removal of computed tomography ring artifacts via radial basis function artificial neural networks.

Ring artifacts in computed tomography (CT) images are caused by the undesirable response of detector pixels, which leads to the degradation of CT images. Accordingly, it affects the image interpretation, post-processing, and quantitative analysis. In this study, a radial basis function neural network (RBFNN) was used to remove ring artifacts. The proposed method employs polar coordinate transformation. First, ring artifacts were transformed into linear artifacts by polar coordinate transformation. Then, smoothing operators were applied to locate these artifacts exactly. Subsequently, RBFNN was operated on each linear artifact. The neuron numbers of the input, hidden, and output layers of the neural network were 8, 40, and 1, respectively. Neurons in the input layer were selected according to the characteristics of the artifact itself and its relationship with the surrounding normal pixels. For the training of the neural network, a hybrid of adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) was adopted. After the corrected image was obtained using the updated neural network, the inverse coordinate transformation was implemented. The experimental data were divided into simulated ring artifacts and real ring artifacts, which were based on brain and abdomen CT images. Compared with current artifact removal methods, the proposed method removed ring artifacts more effectively and retained the maximum detail of normal tissues. In addition, for index analysis, the performance of proposed method was superior to that of the other methods.

Slice interpolation of medical images using enhanced fuzzy radial basis function neural networks.

Volume data composed of complete slice images play an indispensable role in medical diagnoses. However, system or human factors often lead to the loss of slice images. In recent years, various interpolation algorithms have been proposed to solve these problems. Although these algorithms are effective, the interpolated images have some shortcomings, such as less accurate recovery and missing details. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network to improve the performance of the interpolation method. The neural network includes an input layer (six input neurons), three hidden layers of neurons, and the output layer (one output neuron), and we propose a patch matching method to select the input variables of the neural network. Accordingly, we use two normal pending images to be interpolated as the input. Final output data is obtained by applying the trained neural network. In examining four groups of medical images, the proposed method outperforms five other methods, achieving the highest similarity image metric (ESSIM) values of 0.96, 0.95, 0.94, and 0.92 and the lowest mean squared difference (MSD) values of 35.5, 41.2, 50.9, and 47.1. In addition, for a whole MRI brain volume data experiment, the average MSD and ESSIM values of the proposed method and other methods are (41.62, 0.95) and (57.13, 0.90), respectively. The results indicate that the proposed method is superior to the other methods.

Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure.

Dual-energy chest radiography (DECR) is a medical imaging technology that can improve diagnostic accuracy. This technique can decompose single-energy chest radiography (SECR) images into separate bone- and soft tissue-only images. This can, however, double the radiation exposure to the patient. To address this limitation, we developed an algorithm for the synthesis of DECR from a SECR through deep learning. To predict high resolution images, we developed a novel deep learning architecture by modifying a conventional U-net to take advantage of the high frequency-dominant information that propagates from the encoding part to the decoding part. In addition, we used the anticorrelated relationship (ACR) of DECR for improving the quality of the predicted images. For training data, 300 pairs of SECR and their corresponding DECR images were used. To test the trained model, 50 DECR images from Yonsei University Severance Hospital and 662 publicly accessible SECRs were used. To evaluate the performance of the proposed method, we compared DECR and predicted images using a structural similarity approach (SSIM). In addition, we quantitatively evaluated image quality calculating the modulation transfer function and coefficient of variation. The proposed model selectively predicted the bone- and soft tissue-only CR images from an SECR image. The strategy for improving the spatial resolution by ACR was effective. Quantitative evaluation showed that the proposed method with ACR showed relatively high SSIM (over 0.85). In addition, predicted images with the proposed ACR model achieved better image quality measures than those of U-net. In conclusion, the proposed method can obtain high-quality bone- and soft tissue-only CR images without the need for additional hardware for double x-ray exposures in clinical practice.

4D digital tomosynthesis image reconstruction using brute force-based adaptive total variation (BF-ATV) in a prototype LINAC system.

Respiratory-correlated cone-beam CT (CBCT) not only inhibits rapid scanning due to the slow speed of the LINAC head gantry rotation, but its implementation for routine patient imaging is impractical because of the high radiation dose delivered during the process. Digital tomosynthesis (DTS) is a potentially faster technique that delivers a much lower radiation dose by reducing the number of projections in a limited angular range. Unfortunately, 4D-DTS introduces strong aliasing artifacts in the reconstructed images due to the sparsely sampled projections in each respiratory phase bin. The authors hereby suggest a novel low-dose 4D-DTS image reconstruction method that achieves a compromise between the occurrence of aliasing artifacts and image smoothing using a brute force-based adaptive weighting parameter searching technique. We used a prototype LINAC system mounted with a flat-panel detector to acquire tomosynthesis projections of respiratory motion in a phantom in the anterior-posterior (AP) and lateral views. Three different 4D-DTS image reconstruction schemes that included conventional filtered back-projection (FBP), adaptive steepest descent projection onto convex sets (ASD-POCS), and the proposed brute force-based adaptive total variation (BF-ATV) were implemented in four different respiratory phase bins for both AP and lateral views. All reconstructions were accelerated using a single GPU card to reduce the computation time. To study the performance of the algorithm under various sparse conditions, we operated the prototype system in three different gantry sweep modes. The results indicate that the proposed BF-ATV method yields the largest structural similarities in the differenced image between the ground-truth dataset acquired using the slow gantry sweep mode and the sparse dataset from both moderate and fast sweep modes. In addition, the proposed method maintained the object sharpness with less streaking lines and small loss of sharpness compared to the conventional FBP and ASD-POCS methods. In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better performance due in part to its rapid scanning. Therefore, it is potentially applicable to practical 4D imaging for radiotherapy.

Investigation of shutter scan acquisition parameters in a prototype chest digital tomosynthesis system.

A shutter scan acquisition (SSA) method is proposed to reduce patient exposure dose in a chest digital tomosynthesis system. Projections obtained using the SSA constitute a combination of truncated and non-truncated projections. The truncated projections are images in which the lung field is set within a region-of-interest (ROI), and the non-truncated projections are full images in which the ROI is not set at all. We proposed a shutter weighting factor (SWF) as an acquisition parameter for SSA. We call the number of truncated projections divided by the number of non-truncated projections as SWF. We used a prototype CDT system and the LUNGMAN phantom with 8 and 10 mm lung nodules. 81 projections were obtained using SSA in five sets according to the SWFs. The image quality was quantified based on the contrast-to-noise ratio (CNR). We also calculated the figure of merit (FOM) to determine the proper acquisition parameters of the five sets. Both the CNR and FOM values of the 8 mm lung nodule in the selected ROI increased with increases of the SWF. However, the CNR value of the 10 mm lung nodule outside the ROI decreased with increases of the SWF, while the FOM value was maximized when the SWF was 3.05. We investigated the effect of the composition ratio of the truncated and non-truncated projections on the reconstructed images of the SSA based on the FOM values. In conclusion, we determined the proper SSA parameters in a prototype CDT system.

The Easy Dysphagia Symptom Questionnaire (EDSQ): a new dysphagia screening questionnaire for the older adults.

PURPOSE: The early detection of dysphagia, a common clinical issue among older adults, is important. However, healthcare professionals sometimes experience difficulties in applying the current screening tools to older adults. Therefore, we developed the Easy Dysphagia Symptom Questionnaire (EDSQ), a simplified tool for the older adults, and investigated its reliability and validity. METHODS: The 12-item "yes/no" EDSQ assesses dysphagia symptoms, with a total score being the sum of all "yes" responses. Each item was determined by consensus of three physiatrists after reviewing the previous dysphagia questionnaires. Participants were aged 65 years or older who either complained of or were suspected by a physician of facing swallowing disturbance. They completed the EDSQ, modified water swallow test (MWST), and videofluoroscopic swallowing study. The EDSQ's internal consistency was assessed. Correlations between the EDSQ total score and the MWST, American Speech-Language-Hearing Association's National Outcome Measurement System (ASHA NOMS) swallowing scale, and videofluoroscopic dysphagia scale (VDS) were analyzed. RESULTS: The sample comprised 51 participants (29 male, 56.9%; mean age 76.7 ± 6.6 years). Mean EDSQ total score was 4.33 ± 3.03 (range 0-12). Regarding the EDSQ's reliability, the Cronbach's α coefficient was 0.785. The EDSQ total score correlated with the MWST (r = - 0.468, p = 0.001), ASHA NOMS swallowing scale (r = - 0.635, p < 0.001), and VDS (r = 0.449, p = 0.001). The receiver-operating characteristic analysis revealed an optimal cut-off score of ≥ 5, with a sensitivity of 90.9% and a specificity of 67.5%. CONCLUSIONS: The EDSQ showed acceptable reliability and validity, indicating its applicability to older adults as a simple screening tool for safe swallowing.

Extracellular vesicle-based EGFR genotyping in bronchoalveolar lavage fluid from treatment-naive non-small cell lung cancer patients.

BACKGROUND: Extracellular vesicles (EV) have been proven to contain double-stranded DNA reflecting the mutational status of the parental tumor cells in non-small cell lung cancer (NSCLC), which can be translated into clinically useful EV-based liquid biopsy for Epidermal growth factor receptor (EGFR) genotyping using bronchoalveolar lavage fluid (BALF) obtained from tumor site. METHODS: Patients subjected for an initial lung cancer work-up underwent bronchoscopy and BALF was obtained from tumor site. After isolating EVs from BALF by ultracentrifugation, EV-derived DNA (EV DNA) was extracted for subsequent EGFR genotyping performed through peptide nucleic acid (PNA)-mediated Real-Time PCR. The sensitivity, specificity, and concordance rate of BALF EV-based EGFR genotyping were calculated in comparison to tissue genotyping. RESULTS: The average sensitivity and specificity of BALF EV-based EGFR genotyping were 76% and 87%, respectively, while the sensitivity significantly increased as the stage progressed. Especially, in stage IV, BALF EV-based EGFR typing identified all tissue-proven EGFR mutant cases (n=31) and detected 6 additional mutant cases. The concordance rate was 79% in stage I, 100% in stage II, 74% in stage III, and 92% in stage IV. As TNM stage advanced, especially in the presence of metastasis, concordance rate significantly increased (P<0.05). CONCLUSIONS: The use of BALF for the collection of EV DNA in lung cancer patients resulted in a highly accurate diagnosis. The establishment of a fast and reliable method to identify target genes using EV DNA illustrated that it can overcome the problems of low sensitivity and instability in using cell-free DNA (cfDNA).