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Abstract Background Inferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals. Methods All IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved. Results The usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively. Conclusion 3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time.

Abstract Background Three-dimensional (3D) printing technology has impacted many clinical applications across medicine. However, 3D printing for Minimally Invasive Direct Coronary Artery Bypass (MIDCAB) has not yet been reported in the peer-reviewed literature. The current observational cohort study aimed to evaluate the impact of half scaled (50% scale) 3D printed (3DP) anatomic models in the pre-procedural planning of MIDCAB. Methods Retrospective analysis included 12 patients who underwent MIDCAB using 50% scale 3D printing between March and July 2020 (10 males, 2 females). Distances measured from CT scans and 3DP anatomic models were correlated with Operating Room (OR) measurements. The measurements were compared statistically using Tukey’s test. The correspondence between the predicted (3DP & CT) and observed best InterCostal Space (ICS) in the OR was recorded. Likert surveys from the 3D printing registry were provided to the surgeon to assess the utility of the model. The OR time saved by planning the procedure using 3DP anatomic models was estimated subjectively by the cardiothoracic surgeon. Results All 12 patients were successfully grafted. The 3DP model predicted the optimal ICS in all cases (100%). The distances measured on the 3DP model corresponded well to the distances measured in the OR. The measurements were significantly different between the CT and 3DP (p

Abstract Background The use of medical 3D printing (focusing on anatomical modeling) has continued to grow since the Radiological Society of North America’s (RSNA) 3D Printing Special Interest Group (3DPSIG) released its initial guideline and appropriateness rating document in 2018. The 3DPSIG formed a focused writing group to provide updated appropriateness ratings for 3D printing anatomical models across a variety of congenital heart disease. Evidence-based- (where available) and expert-consensus-driven appropriateness ratings are provided for twenty-eight congenital heart lesion categories. Methods A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with pediatric congenital heart disease indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. Results Evidence-based recommendations for when 3D printing is appropriate are provided for pediatric congenital heart lesions. Recommendations are provided in accordance with strength of evidence of publications corresponding to each cardiac clinical scenario combined with expert opinion from members of the 3DPSIG. Conclusions This consensus appropriateness ratings document, created by the members of the RSNA 3DPSIG, provides a reference for clinical standards of 3D printing for pediatric congenital heart disease clinical scenarios.

Abstract Background Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications. Methods A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. Results Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG. Conclusion This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions.

Poly(lactic acid) (PLA) is widely used in the field of medicine due to its biocompatibility, versatility, and cost-effectiveness. Three-dimensional (3D) printing or the systematic deposition of PLA in layers has enabled the fabrication of customized scaffolds for various biomedical and clinical applications. In tissue engineering and regenerative medicine, 3D-printed PLA has been mostly used to generate bone tissue scaffolds, typically in combination with different polymers and ceramics. PLA’s versatility has also allowed the development of drug-eluting constructs for the controlled release of various agents, such as antibiotics, antivirals, anti-hypertensives, chemotherapeutics, hormones, and vitamins. Additionally, 3D-printed PLA has recently been used to develop diagnostic electrodes, prostheses, orthoses, surgical instruments, and radiotherapy devices. PLA has provided a cost-effective, accessible, and safer means of improving patient care through surgical and dosimetry guides, as well as enhancing medical education through training models and simulators. Overall, the widespread use of 3D-printed PLA in biomedical and clinical settings is expected to persistently stimulate biomedical innovation and revolutionize patient care and healthcare delivery.

Abstract The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Abstract Medical 3D printing is a form of manufacturing that benefits patient care, particularly when the 3D printed part is patient-specific and either enables or facilitates an intervention for a specific condition. Most of the patient-specific medical 3D printing begins with volume based medical images of the patient. Several digital manipulations are typically performed to prescribe a final anatomic representation that is then 3D printed. Among these are image segmentation where a volume of interest such as an organ or a set of tissues is digitally extracted from the volumetric imaging data. Image segmentation requires medical expertise, training, software, and effort. The theme of image segmentation has a broad intersection with medical 3D printing. The purpose of this editorial is to highlight different points of that intersection in a recent thematic series within 3D Printing in Medicine.

Three-dimensional (3D) printing, also known as additive manufacturing, has revolutionized the production of physical 3D objects by transforming computer-aided design models into layered structures, eliminating the need for traditional molding or machining techniques. In recent years, hydrogels have emerged as an ideal 3D printing feedstock material for the fabrication of hydrated constructs that replicate the extracellular matrix found in endogenous tissues. Hydrogels have seen significant advancements since their first use as contact lenses in the biomedical field. These advancements have led to the development of complex 3D-printed structures that include a wide variety of organic and inorganic materials, cells, and bioactive substances. The most commonly used 3D printing techniques to fabricate hydrogel scaffolds are material extrusion, material jetting, and vat photopolymerization, but novel methods that can enhance the resolution and structural complexity of printed constructs have also emerged. The biomedical applications of hydrogels can be broadly classified into four categories—tissue engineering and regenerative medicine, 3D cell culture and disease modeling, drug screening and toxicity testing, and novel devices and drug delivery systems. Despite the recent advancements in their biomedical applications, a number of challenges still need to be addressed to maximize the use of hydrogels for 3D printing. These challenges include improving resolution and structural complexity, optimizing cell viability and function, improving cost efficiency and accessibility, and addressing ethical and regulatory concerns for clinical translation.

Medical 3D printing is a complex, highly interdisciplinary, and revolutionary technology that is positively transforming the care of patients. The technology is being increasingly adopted at the Point of Care (PoC) as a consequence of the strong value offered to medical practitioners. One of the key technologies within the medical 3D printing portfolio enabling this transition is desktop inverted Vat Photopolymerization (VP) owing to its accessibility, high quality, and versatility of materials. Several reports in the peer-reviewed literature have detailed the medical impact of 3D printing technologies as a whole. This review focuses on the multitude of clinical applications of desktop inverted VP 3D printing which have grown substantially in the last decade. The principles, advantages, and challenges of this technology are reviewed from a medical standpoint. This review serves as a primer for the continually growing exciting applications of desktop-inverted VP 3D printing in healthcare.

Abstract Background 3D printing of anatomical models requires multi-factorial decision making for optimal model manufacturing. Due to the complex nature of the printing process, there are frequently multiple potentialities based on the desired end goal. The task of identifying the most optimal combination of print control variables is inherently subjective and rests on sound operator intuition. This study investigates the effect of orientation, layer and support settings on print time and material usage. This study also presents a quantitative optimization framework to jointly optimize print time and material usage as a function of those settings for multi-pathological anatomical models. Methods Seven anatomical models representing different anatomical regions (cardiovascular, abdominal, neurological and maxillofacial) were selected for this study. A reference cube was also included in the simulations. Using PreForm print preparation software the print time and material usage was simulated for each model across 4 orientations, 2 layer heights, 2 support densities and 2 support tip sizes. A 90–10 weighted optimization was performed to identify the 5 most optimal treatment combinations that resulted in the lowest print time (90% weight) and material usage (10% weight) for each model. Results The 0.1 mm layer height was uniformly the most optimal setting across all models. Layer height had the largest effect on print time. Orientation had a complex effect on both print time and material usage in certain models. The support density and the support tip size settings were found to have a relatively minor effect on both print time and material usage. Hollow models had a larger support volume fraction compared to solid models. Conclusions The quantitative optimization framework identified the 5 most optimal treatment combinations for each model using a 90–10 weighting for print time and material usage. The presented optimization framework could be adapted based on the individual circumstance of each 3D printing lab and/or to potentially incorporate additional response variables of interest.

Abstract Background Fill density is a critical parameter affecting the functional performance of 3D printed porous constructs in the biomedical and pharmaceutical domain. Numerous studies have reported the impact of fill density on the mechanical properties, diffusion characteristics and content release rates of constructs. However, due to the way in which slicing toolpath calculations are performed, there is substantial deviation between the measured and slicing fill density for relatively small sized constructs printed at low fill densities (high porosities). The purpose of the current study was to investigate this discrepancy using a combination of mathematical modeling and experimental validation. Methods The open source slicer Slic3r was used to 3D print 20 mm × 20 mm × 5 mm constructs at three identified slicing fill density values, 9.58%, 20.36% and 32.33% (exact values entered into software), in triplicates. A mathematical model was proposed to accurately predict fill density, and the measured fill density was compared to both the predicted as well as the slicing fill density. The model was further validated at two additional slicing fill densities of 15% and 40%. The total material within the construct was analyzed from the perspective of material extruded within the beads as well as the bead to bead interconnects using the predictive model. Results The slicing fill density deviated substantially from measured fill density at low fill densities with absolute errors larger than 26% in certain instances. The proposed model was able to predict fill density to within 5% of the measured fill density in all cases. The average absolute error between predicted vs. measured fill density was 3.5%, whereas that between slicing vs. measured fill density was 13%. The material extruded in the beads varied from 86.5% to 95.9%, whereas that extruded in the interconnects varied from 13.5% to 4.1%. Conclusions The proposed model and approach was able to predict fill density to a reasonable degree of accuracy. Findings from the study could prove useful in applications where controlling construct fill density in relatively small sized constructs is important for achieving targeted levels of functional criteria such as mechanical strength, weight loss and content release rate.

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

Abstract Background There is a growing body of evidence to support tears as a non-traditional biological fluid in clinical laboratory testing. In addition to the simplicity of tear fluid processing, the ability to access key cancer biomarkers in high concentrations quickly and inexpensively is significantly enhanced. Tear fluid is a dynamic environment rich in both proteomic and genomic information, making it an ideal medium for exploring the potential for biological testing modalities. Methods All protocols involving human subjects were reviewed and approved by the University of Arkansas IRB committee (13-11-289) prior to sample collection. Study enrollment was open to women ages 18 and over from October 30, 2017-June 19, 2019 at The Breast Center, Fayetteville, AR and Bentonville, AR. Convenience sampling was used and samples were age/sex matched, with enrollment open to individuals at any point of the breast health continuum of care. Tear samples were collected using the Schirmer strip method from 847 women. Concentration of selected tear proteins were evaluated using standard sandwich ELISA techniques and the resulting data, combined with demographic and clinical covariates, was analyzed using logistic regression analysis to build a model for classification of samples. Results Logistic regression analysis produced three models, which were then evaluated on cases and controls at two diagnostic thresholds and resulted in sensitivity ranging from 52 to 90% and specificity from 31 to 79%. Sensitivity and specificity variation is dependent on the model being evaluated as well as the selected diagnostic threshold providing avenues for assay optimization. Conclusions and relevance The work presented here builds on previous studies focused on biomarker identification in tear samples. Here we show successful early classification of samples using two proteins and minimal clinical covariates.

The changing expression levels of ocular proteins in response to systemic disease has been well established in literature. In this study, we examined the ocular proteome to identify protein biomarkers with altered expression levels in women diagnosed with breast cancer. Tear samples were collected from 273 participants using Schirmer strip collection methods. Following protein elution, proteome wide trypsin digestion with Liquid chromatography/tandem mass spectrometry (LC-MS/MS) was used to identify potential protein biomarkers with altered expression levels in breast cancer patients. Selected biomarkers were further validated by enzyme linked immunosorbent assay (ELISA). A total of 102 individual tear samples (51 breast cancer, 51 control) were analyzed by LC-MS/MS which identified 301 proteins. Spectral intensities between the groups were compared and 14 significant proteins (p-value

The need to easily isolate small molecular weight proteins and genomic fragments has prompted a search for an alternative biofluid to blood that has traversed sweat, urine, saliva, and even breath. In this study, both the genomic and proteomic profiles of tears and blood are evaluated to determine the similarity and differences between the two biofluids. Both fluids were tested utilizing microarray panels for identifying proteins as well as isolation of microRNA for sequencing. As anticipated, most (118) of the proteins detected in plasma were also detected in the tear samples, with tear samples also showing 34 unique proteins that were not found in the plasma. Over 400 microRNAs were isolated in both samples with 250 microRNA fragments commonly expressed in both tears and blood. This preliminary analysis, along with simplicity of collection and processing, lends credence to further investigate tears as an alternative biofluid to blood.