Semisupervised white matter hyperintensities segmentation on MRI.

This study proposed a semisupervised loss function named level-set loss (LSLoss) for cerebral white matter hyperintensities (WMHs) segmentation on fluid-attenuated inversion recovery images. The training procedure did not require manually labeled WMH masks. Our image preprocessing steps included biased field correction, skull stripping, and white matter segmentation. With the proposed LSLoss, we trained a V-Net using the MRI images from both local and public databases. Local databases were the small vessel disease cohort (HKU-SVD, n = 360) and the multiple sclerosis cohort (HKU-MS, n = 20) from our institutional imaging center. Public databases were the Medical Image Computing Computer-assisted Intervention (MICCAI) WMH challenge database (MICCAI-WMH, n = 60) and the normal control cohort of the Alzheimer's Disease Neuroimaging Initiative database (ADNI-CN, n = 15). We achieved an overall dice similarity coefficient (DSC) of 0.81 on the HKU-SVD testing set (n = 20), DSC = 0.77 on the HKU-MS testing set (n = 5), and DSC = 0.78 on MICCAI-WMH testing set (n = 30). The segmentation results obtained by our semisupervised V-Net were comparable with the supervised methods and outperformed the unsupervised methods in the literature.

USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study.

BACKGROUND: Lung cancer is a major health problem. CT lung screening can reduce lung cancer mortality through early diagnosis by at least 20%. Screening high-risk individuals is most effective. Retrospective analyses suggest that identifying individuals for screening by accurate prediction models is more efficient than using categorical age-smoking criteria, such as the US Preventive Services Task Force (USPSTF) criteria. This study prospectively compared the effectiveness of the USPSTF2013 and PLCOm2012 model eligibility criteria. METHODS: In this prospective cohort study, participants from the International Lung Screening Trial (ILST), aged 55-80 years, who were current or former smokers (ie, had ≥30 pack-years smoking history or ≤15 quit-years since last permanently quitting), and who met USPSTF2013 criteria or a PLCOm2012 risk threshold of at least 1·51% within 6 years of screening, were recruited from nine screening sites in Canada, Australia, Hong Kong, and the UK. After enrolment, patients were assessed with the USPSTF2013 criteria and the PLCOm2012 risk model with a threshold of at least 1·70% at 6 years. Data were collected locally and centralised. Main outcomes were the comparison of lung cancer detection rates and cumulative life expectancies in patients with lung cancer between USPSTF2013 criteria and the PLCOm2012 model. In this Article, we present data from an interim analysis. To estimate the incidence of lung cancers in individuals who were USPSTF2013-negative and had PLCOm2012 of less than 1·51% at 6 years, ever-smokers in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO) who met these criteria and their lung cancer incidence were applied to the ILST sample size for the mean follow-up occurring in the ILST. This trial is registered at ClinicalTrials.gov, NCT02871856. Study enrolment is almost complete. FINDINGS: Between June 17, 2015, and Dec 29, 2020, 5819 participants from the International Lung Screening Trial (ILST) were enrolled on the basis of meeting USPSTF2013 criteria or the PLCOm2012 risk threshold of at least 1·51% at 6 years. The same number of individuals was selected for the PLCOm2012 model as for the USPSTF2013 criteria (4540 [78%] of 5819). After a mean follow-up of 2·3 years (SD 1·0), 135 lung cancers occurred in 4540 USPSTF2013-positive participants and 162 in 4540 participants included in the PLCOm2012 of at least 1·70% at 6 years group (cancer sensitivity difference 15·8%, 95% CI 10·7-22·1%; absolute odds ratio 4·00, 95% CI 1·89-9·44; p<0·0001). Compared to USPSTF2013-positive individuals, PLCOm2012-selected participants were older (mean age 65·7 years [SD 5·9] vs 63·3 years [5·7]; p<0·0001), had more comorbidities (median 2 [IQR 1-3] vs 1 [1-2]; p<0·0001), and shorter life expectancy (13·9 years [95% CI 12·8-14·9] vs 14·8 [13·6-16·0] years). Model-based difference in cumulative life expectancies for those diagnosed with lung cancer were higher in those who had PLCOm2012 risk of at least 1·70% at 6 years than individuals who were USPSTF2013-positive (2248·6 years [95% CI 2089·6-2425·9] vs 2000·7 years [1841·2-2160·3]; difference 247·9 years, p=0·015). INTERPRETATION: PLCOm2012 appears to be more efficient than the USPSTF2013 criteria for selecting individuals to enrol into lung cancer screening programmes and should be used for identifying high-risk individuals who benefit from the inclusion in these programmes. FUNDING: Terry Fox Research Institute, The UBC-VGH Hospital Foundation and the BC Cancer Foundation, the Alberta Cancer Foundation, the Australian National Health and Medical Research Council, Cancer Research UK and a consortium of funders, and the Roy Castle Lung Cancer Foundation for the UK Lung Screen Uptake Trial.

Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based Measurements and Radiomics-Based Models.

BACKGROUND: Lymph node status is vital for prognosis and treatment decisions for esophageal squamous cell carcinoma (ESCC). This study aimed to construct and evaluate an optimal radiomics-based method for a more accurate evaluation of individual regional lymph node status in ESCC and to compare it with traditional size-based measurements. METHODS: The study consecutively collected 3225 regional lymph nodes from 530 ESCC patients receiving upfront surgery from January 2011 to October 2015. Computed tomography (CT) scans for individual lymph nodes were analyzed. The study evaluated the predictive performance of machine-learning models trained on features extracted from two-dimensional (2D) and three-dimensional (3D) radiomics by different contouring methods. Robust and important radiomics features were selected, and classification models were further established and validated. RESULTS: The lymph node metastasis rate was 13.2% (427/3225). The average short-axis diameter was 6.4 mm for benign lymph nodes and 7.9 mm for metastatic lymph nodes. The division of lymph node stations into five regions according to anatomic lymph node drainage (cervical, upper mediastinal, middle mediastinal, lower mediastinal, and abdominal regions) improved the predictive performance. The 2D radiomics method showed optimal diagnostic results, with more efficient segmentation of nodal lesions. In the test set, this optimal model achieved an area under the receiver operating characteristic curve of 0.841-0.891, an accuracy of 84.2-94.7%, a sensitivity of 65.7-83.3%, and a specificity of 84.4-96.7%. CONCLUSIONS: The 2D radiomics-based models noninvasively predicted the metastatic status of an individual lymph node in ESCC and outperformed the conventional size-based measurement. The 2D radiomics-based model could be incorporated into the current clinical workflow to enable better decision-making for treatment strategies.

Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy.

OBJECTIVE: To examine the associations of intravoxel incoherent motion (IVIM) parameters with treatment response in cervical cancer following concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: Forty-five patients, median age of 58 years (range: 28-82), with pre-CCRT and post-CCRT MRI, were retrospectively analysed. The IVIM parameters pure diffusion coefficient (D) and perfusion fraction (f) were estimated using the full b-value distribution (BVD) as well as an optimised subsample BVD. Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) were used to measure observer repeatability in tumour delineation at both time points. Treatment response was determined by the response evaluation criteria in solid tumour (RECIST) 1.1 between MRI examinations. Mann-Whitney U tests were used to test for significant differences in IVIM parameters between treatment response groups. RESULTS: Pre-CCRT tumour delineation repeatability was good (DSC = 0.81) while post-CCRT delineation repeatability was moderate (DSC = 0.67). Values of D and f had good repeatability at both time points (ICC > 0.80). Pre-CCRT f estimated using the full BVD and optimised subsample BVD were found to be significantly higher in patients with partial response compared to those with stable disease or disease progression (p = 0.01 and 95% CI = -0.02-0.00 for both cases). CONCLUSION: Pre-CCRT f was associated with treatment response in cervical cancer with good observer repeatability. Similar discriminative ability was also observed in estimated pre-CCRT f from an optimised subsample BVD. KEY POINTS: • Pre-treatment tumour delineation and IVIM parameters had good observer repeatability. • Post-treatment tumour delineation was worse than at pre-treatment, but IVIM parameters retained good ICC. • Pre-treatment perfusion fraction estimated from all b-values and an optimised subsample of b-values were associated with treatment response.

Tensor Gradient L0-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19.

Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the [Formula: see text]CT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.

Cardiac magnetic resonance for asymptomatic patients with type 2 diabetes and cardiovascular high risk (CATCH): a pilot study.

BACKGROUND: Stress cardiovascular magnetic resonance (CMR) to screen for silent myocardial ischaemia in asymptomatic high risk patients with type 2 diabetes mellitus (DM) has never been performed, and its effectiveness is unknown. Our aim was to determine the feasibility of a screening programme using stress CMR by obtaining preliminary data on the prevalence of silent ischaemia caused by obstructive coronary artery disease (CAD) and quantify myocardial perfusion in asymptomatic high risk patients with type 2 diabetes. METHODS: In this prospective cohort study, we recruited 63 asymptomatic DM patients (mean age 66 years ± 4.4 years; 77.8% male); with Framingham risk score ≥ 20% from 3 sites from June 2017 to August 2018. Normal volunteers were recruited to determine normal global myocardial perfusion reserve index (MPRI). Adenosine stress CMR and global MPRI was performed and measured in all subjects. Positive stress CMR cases were referred for catheter coronary angiography (CCA) with/without fractional flow reserve (FFR) measurements. Positive CCA was defined as an FFR ≤ 0.8 or coronary narrowing ≥ 70%. Patients were followed up for major adverse cardiovascular events. Prevalence is presented as patient numbers and percentage. Mann-Whitney U test was used to compare global MPRI between patients and normal volunteers. RESULTS: 13 patients had positive stress CMR with positive CCA (20.6% of patient population), while 9 patients with positive stress CMR examinations had a negative CCA. 5 patients (7.9%) had infarcts detected of which 2 patients had no stress perfusion defects. 12 patients had coronary artery stents inserted, whilst 1 patient declined stent placement. DM patients had lower global MPRI than normal volunteers (n = 7) (1.43 ± 0.27 vs 1.83 ± 0.31 respectively; p < 0.01). After a median follow-up of 653 days, there was no death, heart failure, acute coronary syndrome hospitalisation or stroke. CONCLUSION: 20.6% of asymptomatic DM patients (with Framingham risk ≥ 20%) had silent obstructive CAD. Furthermore, asymptomatic patients have reduced global MPRI than normal volunteers. TRIAL REGISTRATION: ClinicalTrials.gov Registration Number: NCT03263728 on 28th August 2017; https://clinicaltrials.gov/ct2/show/NCT03263728.

Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients.

PURPOSE: Biomedical data frequently contain imbalance characteristics which make achieving good predictive performance with data-driven machine learning approaches a challenging task. In this study, we investigated the impact of re-sampling techniques for imbalanced datasets in PET radiomics-based prognostication model in head and neck (HNC) cancer patients. METHODS: Radiomics analysis was performed in two cohorts of patients, including 166 patients newly diagnosed with nasopharyngeal carcinoma (NPC) in our centre and 182 HNC patients from open database. Conventional PET parameters and robust radiomics features were extracted for correlation analysis of the overall survival (OS) and disease progression-free survival (DFS). We investigated a cross-combination of 10 re-sampling methods (oversampling, undersampling, and hybrid sampling) with 4 machine learning classifiers for survival prediction. Diagnostic performance was assessed in hold-out test sets. Statistical differences were analysed using Monte Carlo cross-validations by post hoc Nemenyi analysis. RESULTS: Oversampling techniques like ADASYN and SMOTE could improve prediction performance in terms of G-mean and F-measures in minority class, without significant loss of F-measures in majority class. We identified optimal PET radiomics-based prediction model of OS (AUC of 0.82, G-mean of 0.77) for our NPC cohort. Similar findings that oversampling techniques improved the prediction performance were seen when this was tested on an external dataset indicating generalisability. CONCLUSION: Our study showed a significant positive impact on the prediction performance in imbalanced datasets by applying re-sampling techniques. We have created an open-source solution for automated calculations and comparisons of multiple re-sampling techniques and machine learning classifiers for easy replication in future studies.

Magnetic resonance cholangiogram patterns and clinical profiles of ketamine-related cholangiopathy in drug users.

BACKGROUND & AIMS: Recreational ketamine use has emerged as an important health and social issue worldwide. Although ketamine is associated with biliary tract damage, the clinical and radiological profiles of ketamine-related cholangiopathy have not been well described. METHODS: Chinese individuals who had used ketamine recreationally at least twice per month for six months in the previous two years via a territory-wide community network of charitable organizations tackling substance abuse were recruited. Magnetic resonance cholangiography (MRC) was performed, and the findings were interpreted independently by two radiologists, with the findings analysed in association with clinical characteristics. RESULTS: Among the 343 ketamine users referred, 257 (74.9%) were recruited. The mean age and ketamine exposure duration were 28.7 (±5.8) and 10.5 (±3.7) years, respectively. A total of 159 (61.9%) had biliary tract anomalies on MRC, categorized as diffuse extrahepatic dilatation (n = 73), fusiform extrahepatic dilatation (n = 64), and intrahepatic ductal changes (n = 22) with no extrahepatic involvement. Serum alkaline phosphatase (ALP) level (odds ratio [OR] 1.007; 95% CI 1.002-1.102), lack of concomitant recreational drug use (OR 1.99; 95% CI 1.11-3.58), and prior emergency attendance for urinary symptoms (OR 1.95; 95% CI 1.03-3.70) had high predictive values for biliary anomalies on MRC. Among sole ketamine users, ALP level had an AUC of 0.800 in predicting biliary anomalies, with an optimal level of ≥113 U/L having a positive predictive value of 85.4%. Cholangiographic anomalies were reversible after ketamine abstinence, whereas decompensated cirrhosis and death were possible after prolonged exposure. CONCLUSIONS: We have identified distinctive MRC patterns in a large cohort of ketamine users. ALP level and lack of concomitant drug use predicted biliary anomalies, which were reversible after abstinence. The study findings may aid public health efforts in combating the growing epidemic of ketamine abuse. LAY SUMMARY: Recreational inhalation of ketamine is currently an important substance abuse issue worldwide, and can result in anomalies of the biliary system as demonstrated by magnetic resonance imaging. Although prolonged exposure may lead to further clinical deterioration, such biliary system anomalies might be reversible after ketamine abstinence. Clinical trial number: NCT02165488.

Sinew acupuncture for knee osteoarthritis: study protocol for a randomized sham-controlled trial.

BACKGROUND: Sinew acupuncture is a new modality of acupuncture in which needles are inserted into acupoints, ashi points or spasm points of sinew and muscles along the meridian sinew pathway. A previous observational study revealed that sinew acupuncture has immediate analgesic effects on various soft tissue injuries, including knee injuries. However, no rigorous trials have been conducted. This study aims to examine whether sinew acupuncture can safely relieve pain and symptoms of knee osteoarthritis (KOA) and improve patients' functional movement and quality of life. METHODS/DESIGN: A randomized, sham-controlled, patient- and assessor-blinded trial will be conducted to compare the efficacy of sinew acupuncture and sham acupuncture. Subjects will be assessed by the physician and acupuncturists. A sample of eighty-six eligible subjects will be randomized into either the sinew acupuncture group or the sham acupuncture group. The intervention will be performed in the Hong Kong Tuberculosis Association Chinese Medicine Clinic cum Training Centre of the University of Hong Kong by acupuncturists with over 3 years of acupuncture experience. Subjects will receive 10 sessions of interventions for 4 weeks, followed by a 6-week follow-up. The visual analogue scale (VAS) score at week 4 will be the primary outcome. The Western Ontario and McMasters University Osteoarthritis Index (WOMAC), Timed Up & Go Test (TUG), 8-step Stair Climb Test (SCT) and the 36-Item Short Form Survey (SF-36) will be secondary outcomes. DISCUSSION: Sinew acupuncture is a potential alternative non-pharmacological therapy for KOA. This rigorous trial will expand our knowledge of whether sinew acupuncture reduces pain intensity and improves symptoms, functional movements, and quality of life of KOA patients. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov (Identifier: NCT03099317) in March 2017.

Intra-patient comparison of reduced-dose model-based iterative reconstruction with standard-dose adaptive statistical iterative reconstruction in the CT diagnosis and follow-up of urolithiasis.

OBJECTIVES: To evaluate the accuracy of reduced-dose CT scans reconstructed using a new generation of model-based iterative reconstruction (MBIR) in the imaging of urinary tract stone disease, compared with a standard-dose CT using 30% adaptive statistical iterative reconstruction. METHODS: This single-institution prospective study recruited 125 patients presenting either with acute renal colic or for follow-up of known urinary tract stones. They underwent two immediately consecutive scans, one at standard dose settings and one at the lowest dose (highest noise index) the scanner would allow. The reduced-dose scans were reconstructed using both ASIR 30% and MBIR algorithms and reviewed independently by two radiologists. Objective and subjective image quality measures as well as diagnostic data were obtained. RESULTS: The reduced-dose MBIR scan was 100% concordant with the reference standard for the assessment of ureteric stones. It was extremely accurate at identifying calculi of 3 mm and above. The algorithm allowed a dose reduction of 58% without any loss of scan quality. CONCLUSIONS: A reduced-dose CT scan using MBIR is accurate in acute imaging for renal colic symptoms and for urolithiasis follow-up and allows a significant reduction in dose. KEY POINTS: • MBIR allows reduced CT dose with similar diagnostic accuracy • MBIR outperforms ASIR when used for the reconstruction of reduced-dose scans • MBIR can be used to accurately assess stones 3 mm and above.

Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank.

The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.

Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study.

Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease worldwide, yet detection has remained largely based on surrogate serum biomarkers, elastography or biopsy. In this study, we used a total of 2959 participants from the UK biobank cohort and established the association of dual-energy X-ray absorptiometry (DXA)-derived body composition parameters and leveraged machine learning models to predict NAFLD. Hepatic steatosis reference was based on MRI-PDFF which has been extensively validated previously. We found several significant associations with traditional measurements such as abdominal obesity, as defined by waist-to-hip ratio (OR = 2.50 (male), 3.35 (female)), android-gynoid ratio (OR = 3.35 (male), 6.39 (female)) and waist circumference (OR = 1.79 (male), 3.80 (female)) with hepatic steatosis. Similarly, A Body Shape Index (Quantile 4 OR = 1.89 (male), 5.81 (female)), and for fat mass index, both overweight (OR = 6.93 (male), 2.83 (female)) and obese (OR = 14.12 (male), 5.32 (female)) categories were likewise significantly associated with hepatic steatosis. DXA parameters were shown to be highly associated such as visceral adipose tissue mass (OR = 8.37 (male), 19.03 (female)), trunk fat mass (OR = 8.64 (male), 25.69 (female)) and android fat mass (OR = 7.93 (male), 21.77 (female)) with NAFLD. We trained machine learning classifiers with logistic regression and two histogram-based gradient boosting ensembles for the prediction of hepatic steatosis using traditional body composition indices and DXA parameters which achieved reasonable performance (AUC = 0.83-0.87). Based on SHapley Additive exPlanations (SHAP) analysis, DXA parameters that had the largest contribution to the classifiers were the features predicted with significant association with NAFLD. Overall, this study underscores the potential utility of DXA as a practical and potentially opportunistic method for the screening of hepatic steatosis.

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions.

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammograms, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.

Radiologic extranodal extension for nodal staging in nasopharyngeal carcinoma.

PURPOSE: Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N8th) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort. METHODS & MATERIALS: Pre-treatment MRI of 750 patients from the internal cohort were retrospectively reviewed. Predictive values of six modified nodal staging systems that incorporated four patterns of ENE and two patterns of CNN to the current stage N3 for disease-free survival (DFS) were compared with that of N8th using multivariate cox-regression and concordance statistics in the internal cohort. Performance of stage N3 for predicting disease recurrence was calculated. An external cohort of 179 patients was used to validate the findings. RESULTS: Incorporation of advanced ENE, which infiltrates into adjacent muscle/skin/salivary glands outperformed the other five modifications for predicting outcomes (p < 0.01) and achieved a significantly higher c-index for 5-year DFS (0.69 vs 0.72) (p < 0.01) when compared with that of N8th staging system. By adding advanced ENE to the current N3 increased the sensitivity for predicting disease recurrence from 22.4 % to 47.1 %. The finding was validated in the external cohort (5-year DFS 0.65 vs. 0.72, p < 0.01; sensitivity of stage N3 increased from 14.0 % to 41.9 % for disease recurrence). CONCLUSION: Results from two centre cohorts confirmed that the radiological advanced ENE should be considered as a criterion for stage N3 disease in NPC.

Stereotactic Body Radiotherapy and Liver Transplant for Liver Cancer: A Nonrandomized Controlled Trial.

Importance: Whether stereotactic body radiotherapy (SBRT) as a bridge to liver transplant for hepatocellular carcinoma (HCC) is effective and safe is still unknown. Objective: To investigate the feasibility of SBRT before deceased donor liver transplant (DDLT) for previously untreated unresectable HCC. Design, Setting, and Participants: In this phase 2 nonrandomized controlled trial conducted between June 1, 2015, and October 18, 2019, 32 eligible patients within UCSF (University of California, San Francisco) criteria underwent dual-tracer (18F-fluorodeoxyglucose and 11C-acetate [ACC]) positron emission tomography with computed tomography (PET-CT) and magnetic resonance imaging (MRI) with gadoxetate followed by SBRT of 35 to 50 Gy in 5 fractions, and the same imaging afterward while awaiting DDLT. Statistical analysis was performed on an intention-to-treat basis between October 1 and 31, 2023. Intervention: Patients received SBRT followed by DDLT when matched deceased donor grafts were available. Main Outcomes and Measures: Coprimary end points were progression-free survival (PFS) and objective response rates (ORRs) by the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1), modified RECIST (mRECIST), and PET Response Criteria in Solid Tumors (PERCIST). Secondary end points were local control rate, overall survival (OS), and safety. Results: A total of 32 patients (median age, 59 years [IQR, 54-63 years]; 22 men [68.8%]) with 56 lesions received SBRT. After a median follow-up of 74.6 months (IQR, 40.1-102.9 months), the median PFS was 17.6 months (95% CI, 6.6-28.6 months), and the median OS was 60.5 months (95% CI, 29.7-91.2 months). The 5-year PFS was 39.9% (95% CI, 19.9%-59.9%), and the 5-year OS was 51.3% (95% CI, 31.7%-70.9%). In terms of number of patients, ORRs were 62.5% ([n = 20] 95% CI, 54.2%-68.7%) by RECIST 1.1, 71.9% ([n = 23] 95% CI, 63.7%-79.0%) by mRECIST, and 78.1% ([n = 25] 95% CI, 73.2%-86.7%) by PERCIST. In terms of number of lesions, ORRs were 75.0% ([n = 42] 95% CI, 61.6%-80.8%) by RECIST 1.1, 83.9% ([n = 47] 95% CI, 74.7%-90.6%) by mRECIST, and 87.5% ([n = 49] 95% CI, 81.3%-98.6%) by PERCIST. Twenty patients with 36 lesions received DDLT, of whom 15 patients (75.0%) with 21 lesions (58.3%) exhibited pathologic complete response. Multivariable analyses revealed that pretreatment metabolic tumor volume (MTV) based on ACC (hazard ratio [HR], 1.06 [95% CI, 1.01-1.10]; P = .01) and complete metabolic response (CMR) by PERCIST (HR, 0.31 [95% CI, 0.10-0.96]; P = .04) were associated with PFS, while pretreatment MTV based on ACC (HR, 1.07 [95% CI, 1.03-1.16]; P = .01), total lesion activity based on ACC (HR, 1.01 [95% CI, 1.00-1.02]; P = .02), and CMR by PERCIST (HR, 0.21 [95% CI, 0.07-0.73]; P = .01) were associated with OS. Toxic effects associated with SBRT were reported for 9 patients (28.1%), with 1 grade 3 event. Conclusions and Relevance: This phase 2 nonrandomized controlled trial demonstrated promising survival and safety outcomes of SBRT before DDLT for unresectable HCC. Future randomized clinical trials are warranted.

Image quality assessment of standard- and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms.

OBJECTIVE: The purpose of this article is to compare image quality between filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR) at standard dose and two preselected low-dose scans. SUBJECTS AND METHODS: Thirty patients (16 men and 14 women; mean age, 67 years) were prospectively recruited. Patients underwent three scans (one standard-dose scan and two low-dose scans at noise indexes [NIs] of 33, 60, and 70, respectively). All three scans were reconstructed with FBP, ASIR, and MBIR. Objective and subjective image qualities were compared. Dose-length products and effective doses for each scans were recorded. Mean image noise and attenuation values were compared between different reconstruction algorithms using repeated-measures analysis of variance and paired Student t tests. The interobserver variation between the two radiologists for subjective image quality and lesion assessment was estimated by using weighted kappa statistics. RESULTS: Objective image analysis supports significant noise reduction with low-dose scans using the MBIR technique (p < 0.05). There was no significant change in mean CT numbers between different reconstructions (p > 0.05). Subjective analysis reveals no significant difference between image quality and diagnostic confidence between low-dose MBIR scans compared with standard-dose scans reconstructed using ASIR (p > 0.05). Average effective doses were 3.7, 1.2, and 0.9 mSv for standard scans at NIs of 33, 60, and 70, respectively. CONCLUSION: MBIR shows superior noise reduction and improved image quality. Substantial dose reduction can be achieved by increasing the NI parameters as tested in this study without affecting image quality and diagnostic confidence.

Comprehensive musculoskeletal sonographic evaluation of the hand and wrist.

Sonography is widely used for evaluation of hand and wrist lesions. The easy accessibility, cost-effectiveness, and good diagnostic accuracy of sonography coupled with the numerous benefits of real-time imaging make it desirable. The aim of this article is to describe the typical sonographic appearances of lesions in the hand and wrist that are encountered frequently in routine clinical practice, such as inflammatory arthropathies, tumors, traumatic injuries, foreign bodies, and nerve entrapment syndromes. Relevant anatomy, scanning methods, and recent developments in musculoskeletal sonography are also discussed.

Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT.

Background: In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment. Aims: Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare. Methods: We designed the study in two parts. First, we performed an intra-individual comparison of the 4558 participants from the UK Biobank cohorts with matching data of MRI abdominal body composition, DXA with VAT estimation, and BIA. Second, we evaluated 174 CT scans from the publicly available dataset to assess the correlation of the commonly used single-slice technique compared to three-dimensional VAT volume. Results: Across the UK Biobank cohort, the DXA-derived VAT measurement correlated better (R2 0.94, p<0.0001) than BIA (R2 0.49, p<0.0001) with reference three-dimensional volume on MRI. However, DXA-derived VAT correlation was worse for participants with a BMI of < 20 (R2 = 0.62, p=0.0013). A commonly used single slice method on CT demonstrated a modest correlation (R2 between 0.51 - 0.64), with best values at L3- and L4 (R2 L3 = 0.63, p<0.0001; L4 = 0.64, p<0.0001) compared to reference three-dimensional volume. Combining multiple slices yielded a better correlation, with a strong correlation when L2-L3 levels were combined (R2 = 0.92, p<0.0001). Conclusion: When deployed at scale, DXA-derived VAT volume measurement shows excellent correlation with three-dimensional volume on MRI based on the UK Biobank cohort. Whereas a single slice CT technique demonstrated moderate correlation with three-dimensional volume on CT, with a stronger correlation achieved when multiple levels were combined.