Enhancing supercapacitor performance of Ni-Co-Mn metal-organic frameworks by compositing it with polyaniline and reduced graphene oxide.

Metal-organic frameworks (MOFs) are one of the most sought-after materials in the domain of supercapacitors and can be tailored to accommodate diverse compositions, making them amenable to facile functionalization. However, their intrinsic specific capacitance as well as energy density is minimal, which hinders their usage for advanced energy storage applications. Therefore, herein, we have prepared six electrodes, i.e., Ni-Co-Mn MOFs, polyaniline (PANI), and reduced graphene oxide (rGO) along with their novel nanocomposites, i.e., C1, C2, and C3, comprising MOFs : PANI : rGO in a mass ratio of 100 : 1 : 0.5, 100 : 1 : 1, and 100 : 1 : 10, respectively. The polyaniline conducting polymer and rGO enabled efficient electron transport, enhanced charge storage processes, substantial surface area facilitating higher loading of active materials, promoting electrochemical reactions, and ultimately enhanced nanocomposite system performance. As a result, scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques confirmed the successful synthesis and revealed distinct morphological features of the materials. Following electrochemical testing, it was observed that composition C2 exhibited the highest performance, demonstrating a groundbreaking specific capacitance of 1007 F g-1 at 1 A g-1. The device showed a good energy density of 25.11 W h kg-1 and a power density of 860 W kg-1. Remarkably, the device demonstrated a capacity retention of 115% after 1500 cycles, which is a clear indication of the wettability factor, according to the literature. The power law indicated b-values in a range of 0.58-0.64, verifying the hybrid-type behavior of supercapacitors.

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques are largely black-box in nature, limiting explainability. We compared CPH, RSF and DL to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients receiving radiotherapy using pre-treatment covariables. We employed explainable techniques to provide insights into the contribution of each covariable on OS prediction. MATERIALS AND METHODS: The dataset contained 471 stage I-IV NSCLC patients treated with radiotherapy. We built CPH, RSF and DL OS prediction models using several baseline covariable combinations. 10-fold Monte-Carlo cross-validation was employed with a split of 70%:10%:20% for training, validation and testing, respectively. We primarily evaluated performance using the concordance index (C-index) and integrated Brier score (IBS). Local interpretable model-agnostic explanation (LIME) values, adapted for use in survival analysis, were computed for each model. RESULTS: The DL method exhibited a significantly improved C-index of 0.670 compared to the CPH and a significantly improved IBS of 0.121 compared to the CPH and RSF approaches. LIME values suggested that, for the DL method, the three most important covariables in OS prediction were stage, administration of chemotherapy and oesophageal mean radiation dose. CONCLUSION: We show that, using pre-treatment covariables, a DL approach demonstrates superior performance over CPH and RSF for OS prediction and use explainable techniques to provide transparency and interpretability.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF.

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent 3He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI. Six healthy volunteers underwent 129Xe DW-MRI at baseline only. Moreover, 3He DW-MRI was indirectly co-registered to CT via spatially aligned 3He ventilation and structural 1H MRI. A voxel-wise comparison of the overlapping 3He apparent diffusion coefficient (ADC) and mean acinar dimension (LmD) maps with CALIPER CT patterns was performed at baseline and after 1 year. The abnormal lung percentage classified with the LmD value, based on a healthy volunteer 129Xe LmD, and CALIPER was compared with a Bland-Altman analysis. The largest DW-MRI metrics were found in the regions classified as honeycombing, and longitudinal DW-MRI changes were observed in the baseline-classified reticular changes and ground-glass opacities regions. A mean bias of -15.3% (95% interval -56.8% to 26.2%) towards CALIPER was observed for the abnormal lung percentage. This suggests DW-MRI may detect microstructural changes in areas of the lung that are determined visibly and quantitatively normal by CT.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.

Changes in the prevalence of diabetes, prediabetes and associated risk factors in rural Baluchistan; a secondary analysis from repeated surveys (2002-2017).

To observe trends of diabetes and its associated risk factors from health surveys 2002-2017 in rural areas of Baluchistan-Pakistan and a secondary analysis based on community based health surveys of Baluchistan conducted between 2001-02, 2009-10, and 2016-17. A total of 4250 participants were included in this combined analysis, 2515 from 2001-2002, 1377 from 2009-2010 and 358 from 2016-2017 survey year. In each survey, detailed information of baseline parameters were noted on a predesigned questionnaire. Fasting plasma glucose (FPG) was used for diagnosis of diabetes for comparative purposes in this analysis. Cardiovascular (CVD) risk factors including hypertension, obesity, dyslipidaemia, tobacco use, alcohol consumption, and physical activity were compared. Most subjects were aged 30-50 years and males were found higher in 2016-17 compared to 2001-02 and 2009-10. Pronounced increases in BMI, waist circumference, blood pressure, and family history of diabetes were observed in 2016-17. Diabetes prevalence was 4.2 (3.4-4.9), 7.8 (6.6-9.2) and 31.9 (26.9-37.4), whilst pre-diabetes was 1.7 (1.3-2.2), 3.6 (2.8-4.6) and 10.7 (7.6-14.9) in years 2001-02, 2009-10, and 2016-17, respectively. Among those aged 20-39years, prevalence of diabetes was stable from 2001-10 yet increased considerably between the ages of 30-39years in 2016-17. Throughout the observed period, rapid increases were observed in hypertension, obesity, and dyslipidaemia, however, addiction to tobacco use and alcohol intake decreased. Adjusted odd ratios showed age, marital status, education, hypertension, and family history of diabetes as associated risk factors for glycaemic dysregulation. The rural Baluchistan population is confronted with increasing trends of early onset diabetes due to highly associated CVD risk factors, especially central obesity and dyslipidaemia, raising a major public health challenge.

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE: To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS: In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS: We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION: The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. PURPOSE: Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center. STUDY TYPE: Retrospective. POPULATION: A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI. ASSESSMENT: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. STATISTICAL TESTS: Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. RESULTS: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data. DATA CONCLUSION: The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

BACKGROUND: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1 H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives. PURPOSE: We hypothesized that a DL-based dual-channel approach, leveraging both 1 H-MRI and Xenon-129-MRI (129 Xe-MRI), can generate LCEs more accurately than single-channel alternatives. STUDY TYPE: Retrospective. POPULATION: A total of 480 corresponding 1 H-MRI and 129 Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8-71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6-83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets. FIELD STRENGTH/SEQUENCE: 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1 H-MRI and 3D steady-state free-precession 129 Xe-MRI. ASSESSMENT: We developed a multimodal DL approach, integrating 129 Xe-MRI and 1 H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs. STATISTICAL TESTS: Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland-Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant. RESULTS: The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867-0.978), 1.68 mm (37.0-0.778), and 0.066 (0.246-0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). DATA CONCLUSION: Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Towards survival prediction of cancer patients using medical images.

Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients. Recently, state-of-the-art medical imaging and analysis techniques have paved the way for survival prediction of cancer patients by understanding key features extracted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanned images with the help of image processing and machine learning techniques. However, survival prediction is a challenging task due to the complexity in benchmarking of image features, feature selection methods, and machine learning models. In this article, we evaluate the performance of 156 visual features from radiomic and hand-crafted feature classes, six feature selection methods, and 10 machine learning models to benchmark their performance. In addition, MRI scanned Brain Tumor Segmentation (BraTS) and CT scanned non-small cell lung cancer (NSCLC) datasets are used to train classification and regression models. Our results highlight that logistic regression outperforms for the classification with 66 and 54% accuracy for BraTS and NSCLC datasets, respectively. Moreover, our analysis of best-performing features shows that age is a common and significant feature for survival prediction. Also, gray level and shape-based features play a vital role in regression. We believe that the study can be helpful for oncologists, radiologists, and medical imaging researchers to understand and automate the procedure of decision-making and prognosis of cancer patients.

Glycemic status and general characteristics among individuals with undiagnosed diabetes; findings from second National Diabetes Survey of Pakistan 2016-2017 (NDSP 08).

BACKGROUND AND AIM: To observe the glycemic status among individuals with undiagnosed diabetes in urban and rural areas of all four provinces of Pakistan. METHODS: The findings of this study are obtained from the second National Diabetes Survey of Pakistan (NDSP) 2016-2017, a nationwide epidemiological survey. Out of 12,486 individuals approached, 10,834 participants agreed to be included in the second NDSP. An oral glucose tolerance test was used to assess undiagnosed diabetes individuals according to World Health Organization criteria. RESULTS: The overall weighted prevalence of undiagnosed diabetes was 7.1% (2.4% in urban and 4.7% in rural areas). Based on isolated fasting plasma glucose or 2-h post 75 gm glucose load or combining both, the weighted prevalence of undiagnosed diabetes was 43.5%, 17.6%, and 38.9%, respectively. Out of the 43.5% of individuals, 15.3% had fasting plasma glucose higher than 250 mg/dl at the time of presentation. Among all the individuals, 23.7% were found to be of less than 40 years of age, 29.2% had a positive family history of diabetes, 80.8% were abdominal obese, 53.8% were hypertensive, and 98% were dyslipidemic. In comparison to the elder group, poor glycemic control of ≥10% HbA1c was observed in most of the younger age groups (p < 0.05). CONCLUSION: A significant number of people remain undiagnosed in this part of the world. The glycemic status along with the other associated risk factors at the time of presentation is alarming. Thus, for the early detection of diabetes, awareness and education in the community are crucial. Hence, long-term complications of diabetes can be prevented.

Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI.

Respiratory diseases are leading causes of mortality and morbidity worldwide. Pulmonary imaging is an essential component of the diagnosis, treatment planning, monitoring, and treatment assessment of respiratory diseases. Insights into numerous pulmonary pathologies can be gleaned from functional lung MRI techniques. These include hyperpolarized gas ventilation MRI, which enables visualization and quantification of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. Here, we evaluate several 3D convolutional neural networks to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 759 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 341 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans using a 3D nn-UNet outperformed other DL methods, achieving a mean ± SD Dice coefficient of 0.963 ± 0.018, average boundary Hausdorff distance of 1.505 ± 0.969 mm, Hausdorff 95th percentile of 5.754 ± 6.621 mm and relative error of 0.075 ± 0.039. Moreover, limited differences in performance were observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). In addition, we observed very strong correlation and agreement between DL and expert segmentations, with Pearson correlation of 0.99 (p < 0.0001) and Bland-Altman bias of - 0.8%. The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and artefacts. This approach is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing.

NDSP-10: The Cardiometabolic Risk Profile with Various Degrees of Dysglycemia in Younger and Older Adults: Findings from the Second National Diabetes Survey of Pakistan 2016-2017.

Background: To assess and compare the cardiometabolic risk profile of the Pakistani population between younger (<45 years) and older adults (≥45 years). Methods: This is a substudy of a nationally representative community-based epidemiological second National Diabetes Survey of Pakistan (NDSP) 2016-2017. Out of 12,486 individuals approached, 10,834 participants agreed to be included (response rate 87%). Cardiovascular risk factors assessed were obesity, central obesity, hypertension, diabetes, hyperlipidemia, and tobacco use. Participants were stratified into young adults (<45 years) and older adults (≥45 years) and was subcategorized into four groups that is, diagnosed diabetes mellitus (DM), undiagnosed DM, prediabetes, and without diabetes. Results: Around 14.3% were prediabetes, 8.8% were undiagnosed DM, and 24% were diagnosed diabetes. Most participants in dysglycemic (prediabetes, undiagnosed DM, and diagnosed DM) groups were females and were from rural regions. Cardiovascular disease (CVD) risk factors were more noted in dysglycemic states, compared with without diabetes. Overall CVD risk profile was worse in males in both age categories, although the accumulation of four or five CVD risk factors at one point was more predominant in females in either age group. Conclusion: Overall, the preponderance of CVD risk factors, such as overweight, obesity, central obesity, tobacco use, ex-tobacco uses, and dyslipidemia, were substantially more prevalent in dysglycemia groups of young adults (<45 years) compared with older adults (≥45 years). Early and targeted intervention in young may prevent poor CVD outcomes as they age.

Deep learning in structural and functional lung image analysis.

The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow.

Palbociclib in combination with aromatase inhibitors in patients ≥ 75 years with oestrogen receptor-positive, human epidermal growth factor receptor 2 negative advanced breast cancer: A real-world multicentre UK study.

BACKGROUND: Breast cancer incidence increases with age and real-world data is essential to guide prescribing practices in the older population. The aim of this study was to collect large scale real-world data on tolerability and efficacy of palbociclib + AI in the first line treatment of ER+/HER2-advanced breast cancer in those aged ≥75 years. METHODS: 14 cancer centres participated in this national UK retrospective study. Patients aged ≥75 years treated with palbociclib + AI in the first line setting were identified. Data included baseline demographics, disease characteristics, toxicities, dose reductions and delays, treatment response and survival data. Multivariable Cox regression was used to assess independent predictors of PFS, OS and toxicities. RESULTS: 276 patients met the eligibility criteria. The incidence of febrile neutropenia was low (2.2%). The clinical benefit rate was 87%. 50.7% of patients had dose reductions and 59.3% had dose delays. The 12- and 24- month PFS rates were 75.9% and 64.9%, respectively. The 12- and 24- month OS rates were 85.1% and 74.0%, respectively. Multivariable analysis identified PS, Age-adjusted Charlson Comorbidity Index (ACCI) and number of metastatic sites to be independent predictors of PFS. Dose reductions and delays were not associated with adverse survival outcomes. Baseline ACCI was an independent predictor of development and severity of neutropenia. CONCLUSION: Palbociclib is an effective therapy in the real-world older population and is well-tolerated with low levels of clinically significant toxicities. The use of geriatric and frailty assessments can help guide decision making in these patients.

Pancreatic cystic neoplasms and post-inflammatory cysts: interobserver agreement and diagnostic performance of MRI with MRCP.

PURPOSE: We aimed to answer several clinically relevant questions; (1) the interobserver agreement, (2) diagnostic performance of MRI with MRCP for (a) branch duct intraductal papillary mucinous neoplasms (BD-IPMN), mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN), (b) distinguishing mucinous (BD-IPMN and MCN) from non-mucinous cysts, and (c) distinguishing three pancreatic cystic neoplasms (PCN) from post-inflammatory cysts (PIC). METHODS: A retrospective analysis was performed at a tertiary referral center for pancreatic diseases on 71 patients including 44 PCNs and 27 PICs. All PCNs were confirmed by surgical pathology to be 17 BD-IPMNs, 13 MCNs, and 14 SCNs. Main duct and mixed type IPMNs were excluded. Two experienced abdominal radiologists blindly reviewed all the images. RESULTS: Sensitivity of two radiologists for BD-IPMN, MCN and SCN was 88-94%, 62-69% and 57-64%, specificity of 67-78%, 67-78% and 67-78%, and accuracy of 77-82%, 65-75% and 63-73%, respectively. There was 80% sensitivity, 63-73% specificity, 70-76% accuracy for distinguishing mucinous from non-mucinous neoplasms, and 73-75% sensitivity, 67-78% specificity, 70-76% accuracy for distinguishing all PCNs from PICs. There was moderate-to-substantial interobserver agreement (Cohen's kappa: 0.65). CONCLUSION: Two experienced abdominal radiologists had moderate-to-high sensitivity, specificity, and accuracy for BD-IPMN, MCN, and SCN. The interobserver agreement was moderate-to-substantial. MRI with MRCP can help workup of incidental pancreatic cysts by distinguishing PCNs from PICs, and premalignant mucinous neoplasms from cysts with no malignant potential.

NDSP 09: Risk Assessment of Pakistani Individual for Diabetes (RAPID) - Findings from Second National Diabetes Survey of Pakistan (NDSP) 2016-2017.

OBJECTIVE: To elucidate the effectiveness of Risk Assessment of Pakistani individuals with diabetes (RAPID) tool in epidemiological and population-based second National Diabetes Survey of Pakistan (NDSP) 2016-2017 for identifying risk of developing type 2 diabetes. METHODOLOGY: This observational study was a sub-analysis of the second National Diabetes Survey of Pakistan (NDSP) 2016-2017 conducted from February 2016 to August 2017 in all four provinces of Pakistan. Ethical approval was obtained from National Bioethics Committee Pakistan. RAPID score, a validated and published scoring scale to assess risk of diabetes, originally developed from community-based surveys was used. The risk score is assessed by parameters namely: age, waist circumference, and positive family history of diabetes. Subjects with score greater ≥4 were considered at risk of diabetes. RESULTS: A total of 4904 individuals were assessed (2205 males and 2699 females). Mean age of participants was 41.8±14.2 years. Positive family history of diabetes was seen in 1379 (28.1%) people. According to RAPID score 1268 (25.9%) individuals scored ≥4 and were at risk of diabetes. OGTT status of people at risk of diabetes according to RAPID score showed that 18.1% people with diabetes and 29.2% were prediabetic. Whereas, OGTT status of people not at risk of diabetes showed that only 7.6% people with diabetes, 20% were prediabetic. CONCLUSION: A simple diabetes risk score can be used for identification of high-risk individuals for diabetes so that timely intervention can be implemented. Community-based awareness programs are needed to educate people regarding healthy lifestyle in order to reduce risk of diabetes.

Relationship between diabetic retinopathy and diabetic nephropathy; A longitudinal follow-up study from a tertiary care unit of Karachi, Pakistan.

OBJECTIVE: To determine the development and progression of diabetic retinopathy in subjects with diabetic nephropathy. METHODOLOGY: This retrospective longitudinal follow up study was conducted in outpatient department of Baqai Institute of Diabetology and Endocrinology (BIDE), a tertiary care diabetes unit of Karachi Pakistan, from January 2005 to December 2016. Type 2 diabetic subjects with newly diagnosed diabetic nephropathy (DN) and sex-age matched controls were identified from the electronic database of the institute, Health Management System (HMS). Subjects with type 1 diabetes, gestational diabetes and subjects with diabetic retinopathy (DR) at the baseline of both DN and non-DN group were excluded from the study. Statistical analyses were conducted by using SPSS version 20. RESULT: Out of 3056 type 2 diabetic subjects, 2389 were with DN and 667 were without DN. The incidence of retinopathy was found to be 21.7 per 1000 person years. The incidence rate ratio (IRR) of 2.57 (1.92-3.43) showed that retinopathy was significantly higher in subjects with DN as compared to subjects without DN. Kaplan-Meier survival plot confirmed that subjects with DN had a worse diabetic retinopathy-free survival than subjects without DN. CONCLUSION: Diabetic nephropathy is an independent risk factor for the development and progression of diabetic retinopathy.

Glycated hemoglobin (HbA1c) as diagnostic criteria for diabetes: the optimal cut-off points values for the Pakistani population; a study from second National Diabetes Survey of Pakistan (NDSP) 2016-2017.

AIM: Glycated hemoglobin (HbA1c) cut-off values as diagnostic tool in diabetes and prediabetes with its concordance to oral glucose tolerance test (OGTT) in Pakistani population. METHODOLOGY: Data for this substudy was obtained from second National Diabetes Survey of Pakistan (NDSP) 2016-2017. With this survey, 10 834 individuals were recruited and after excluding known subjects with diabetes, 6836 participants fulfilled inclusion criteria for this study. Demographic, anthropometric and biochemical parameters were obtained. OGTT was used as standard diagnostic tool to screen population and HbA1c for optimal cut-off values. Participants were categorized into normal glucose tolerance (NGT), newly diagnosed diabetes (NDD) and prediabetes. RESULTS: Out of 6836 participants, 4690 (68.6%) had NGT, 1333 (19.5%) had prediabetes and 813 (11.9%) had NDD by OGTT criteria with median (IQR) age of 40 (31-50) years. Optimal HbA1c cut-off point for identification of diabetes and prediabetes was observed as 5.7% ((AUC (95% CI)=0.776 (0.757 to 0.795), p<0.0001)) and 5.1% ((AUC (95% CI)=0.607 (0.590 to 0.624), p<0.0001)), respectively. However, out of 68.6% NGT subjects identified through OGTT, 24.1% and 9.3% participants were found to have prediabetes and NDD, respectively by using HbA1c criteria. By using both OGTT and HbA1c criteria, only 7.9% and 7.3% were observed as prediabetes and diabetes, respectively. CONCLUSION: Findings from second NDSP demonstrated disagreement between findings of OGTT and HbA1c as diagnostic tool for Pakistani population. As compared with international guidelines, HbA1c threshold for prediabetes and NDD were lower in this part of world. HbA1c as diagnostic tool might require ethnic or regional-based modification in cut-off points, validated by relevant community-based epidemiological surveys.

Radical accelerated radiotherapy for non-small cell lung cancer (NSCLC): A 5-year retrospective review of two dose fractionation schedules.

BACKGROUND AND PURPOSE: Numerous fractionation regimes are used for inoperable NSCLC patients not suitable for stereotactic ablative radiotherapy. Continuous hyperfractionated accelerated radiotherapy (CHART, 54 Gy, 36 fractions over 12 days) and hypofractionated accelerated radiotherapy (55 Gy, 20 fractions over 4 weeks) are recommended UK schedules. In this single-centre retrospective analysis, we compare both fractionation schemes for patients treated at our institution from 2010 to 15. MATERIALS AND METHODS: Clinical demographic, tumour and survival data were collected alongside radiotherapy dosimetric data from the Varian Eclipse Scripting application programming interface. Differences were assessed using independent samples t-tests. Multivariate survival analysis was performed using Cox regression. RESULTS: We identified 563 eligible patients; 43% received CHART and 57% hypofractionated radiotherapy. Median age was 71 years, 56% were male, 95% PET staged with 53% WHO performance status 0-1. 30%, 14%, 50% and 6% were stage I, II, III and IV, respectively. 38% of patients underwent induction chemotherapy. 99% completed their prescribed radiotherapy treatment. Overall response rate was 50% with a 6.5% 90-day mortality rate. Median disease-free survival was 19 months, 50% recurred locally. Median overall survival was 22.5 months with 48% alive at 2 years. Multivariate analysis identified histology, stage, performance status, chemotherapy and radiotherapy response as independent predictors of survival; no significant differences between radiotherapy regimes were observed. CONCLUSION: In our centre, CHART and hypofractionated accelerated radiotherapy produce similar outcomes. Dose escalation studies are in progress to develop these schedules to match outcomes reported in concurrent chemo-radiation studies.

Imaging of regional ventilation: Is CT ventilation imaging the answer? A systematic review of the validation data.

Computed Tomography Ventilation Imaging (CTVI) is an experimental imaging modality that derives regional lung function information from non-contrast respiratory-correlated CT datasets. Despite CTVI being extensively studied in cross-modality imaging comparisons, there is a lack of consensus on the state of its clinical validation in humans. This systematic review evaluates the CTVI clinical validation studies to date, highlights their common strengths and weaknesses and makes recommendations. We performed a PUBMED and EMBASE search of all English language papers on CTVI between 2000 and 2018. The results of these searches were filtered in accordance to a set of eligibility criteria and analysed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. One hundred and forty-four records were identified, and 66 full text records were reviewed. After detailed assessment, twenty-three full text papers met the selection criteria and were included in the final review. This included thirteen prospective studies, with 579 human subjects. Studies used diverse methodologies, with a large amount of heterogeneity between different studies in terms of the reference ventilation imaging modality (e.g. nuclear medicine, hyperpolarised gas MRI), imaging parameters, DIR algorithm(s) used, and ventilation metric(s) applied. The most common ventilation metrics used deformable image registration to evaluate the exhale-to-inhale motion field Jacobian determinant (DIR-Jac) or changes in air volume content based on Hounsfield Units (DIR-HU). The strength of correlation between CTVI and the reference ventilation imaging modalities was moderate to strong when evaluated at the lobar or global level, with the average ±â€¯S.D. (number of studies) linear regression correlation coefficients were 0.73 ±â€¯0.25 (n = 6) and 0.86 ±â€¯0.11 (n = 12) for DIR-Jac and DIR-HU respectively, and the SPC were 0.45 ±â€¯0.31 (n = 6) and 0.41 ±â€¯0.11 (n = 5) for DIR-Jac and DIR-HU respectively. We concluded that it is difficult to make a broad statement about the validity of CTVI due to the diverse methods used in the validation literature. Typically, CTVI appears to show reasonable cross-modality correlations at the lobar/whole lung level but poor correlations at the voxel level. Since CTVI is seeing new implementations in prospective trials, it is clear that refinement and standardization of the clinical validation methodologies are required. CTVI appears to be of relevance in radiotherapy planning, particularly in patients whose main pulmonary impairment is not a gas exchange problem but alternative imaging approaches may need to be considered in patients with other pulmonary diseases (i.e. restrictive or gas exchange problems).