Outcomes of Fluoroscopy-Guided Percutaneous High Ligation of the Great Saphenous Vein Combined With Foam Sclerotherapy for Symptomatic Great Saphenous Veins

PurposeTo evaluate the effectiveness and safety of fluoroscopy-guided percutaneous high ligation (FPHL) combined with fluoroscopy-guided foam sclerotherapy (FGFS) to treat varicose veins of the great saphenous veins (GSVs).Materials and MethodsThis was a retrospective study of 113 patients (mean age, 62.1 ± 10.8 years; 60 men) with varicose veins of the GSVs (133 limbs) that were treated with FPHL combined with FGFS between April 1 and October 31, 2019. Demographic and clinical data were collected from these patients before the FPHL procedure, after which FGFS was performed. The preterminal GSV was ligated percutaneously by a percutaneously-positioned polypropylene ligature under fluoroscopic guidance. The outcome of ligation was confirmed by venography. Then, foam sclerotherapy was performed under fluoroscopy. At 1-year follow-up, GSV occlusion was evaluated by ultrasound. The venous clinical severity scores (VCSSs) were compared between the preoperative and 1-year follow-up periods.ResultsThe technical success rate was 100% (133 limbs). Complete 12-month follow-up was available for 112 limbs (84.2%) and 103 of these limbs (92.0%) remained occluded during this period. The VCSS improved from 4.71 ± 2.15 to 0.74 ± 0.60 (V = 6328, P < .001). During follow-up, there were 16 limbs with thrombophlebitis and 38 limbs with saphenous junction pain; these events were alleviated within 2 weeks of the procedure. There was no deep venous thrombosis or other severe adverse events.ConclusionsFPHL combined with FGFS to treat varicose veins in the GSVs achieved an occlusion rate of 92% and improved the clinical symptoms within 1 year; this minimally-invasive procedure was safe and effective.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Utilization of and Outcomes Associated with Intravascular Ultrasound during Deep Venous Stent Placement among Medicare Beneficiaries

PurposeTo evaluate temporal trends, practice variation, and associated outcomes with the use of intravascular ultrasound (US) during deep venous stent placement among Medicare beneficiaries.Materials and MethodsAll lower extremity deep venous stent placement procedures performed between January 1, 2017, and December 31, 2019 among Medicare beneficiaries were included. Temporal trends in intravascular US use were stratified by procedural setting and physician specialty. The primary outcome was a composite of 12-month all-cause mortality, all-cause hospitalization, or repeat target vessel intervention. The secondary outcome was a composite of 12-month stent thrombosis, embolization, or restenosis.ResultsAmong the 20,984 deep venous interventions performed during the study period, 15,184 (72.4%) utilized intravascular US. Moderate growth in intravascular US use was observed during the study period in all clinical settings. There was a variation in the use of intravascular US among all operators (median, 77.3% of cases; interquartile range, 20.0%–99.2%). In weighted analyses, intravascular US use during deep venous stent placement was associated with a lower risk of both the primary (adjusted hazard ratio, 0.72; 95% confidence interval [CI], 0.69–0.76; P < .001) and secondary (adjusted hazard ratio, 0.32; 95% CI, 0.27–0.39; P < .001) composite end points.ConclusionsIntravascular US is frequently used during deep venous stent placement among Medicare beneficiaries, with further increase in use from 2017 to 2019. The utilization of intravascular US as part of a procedural strategy was associated with a lower cumulative incidence of adverse outcomes after the procedure, including venous stent thrombosis and embolization.     

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net–based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.