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With advances in both medical imaging and computer programming, two-dimensional axial images can be processed into other reformatted views (sagittal and coronal) and three-dimensional (3D) virtual models that represent a patients' own anatomy. This processed digital information can be analyzed in detail by orthopedic surgeons to perform patient-specific orthopedic procedures. The use of 3D printing is rising and has become more prevalent in medical applications over the last decade as surgeons and researchers are increasingly utilizing the technology's flexibility in manufacturing objects. 3D printing is a type of manufacturing process in which materials such as plastic or metal are deposited in layers to create a 3D object from a digital model. This additive manufacturing method has the advantage of fabricating objects with complex freeform geometry, which is impossible using traditional subtractive manufacturing methods. Specifically in surgical applications, the 3D printing techniques can not only generate models that give a better understanding of the complex anatomy and pathology of the patients and aid in education and surgical training, but can also produce patient-specific surgical guides or even custom implants that are tailor-made to the surgical requirements. As the clinical workflow of the 3D printing technology continues to evolve, orthopedic surgeons should embrace the latest knowledge of the technology and incorporate it into their clinical practice for patient-specific orthopedic applications. This paper is written to help orthopedic surgeons stay up-to-date on the emerging 3D technology, starting from the acquisition of clinical imaging to 3D printing for patient-specific applications in orthopedics. It 1) presents the necessary steps to prepare the medical images that are required for 3D printing, 2) reviews the current applications of 3D printing in patient-specific orthopedic procedures, 3) discusses the potential advantages and limitations of 3D-printed custom orthopedic implants, and 4) suggests the directions for future development. The 3D printing technology has been reported to be beneficial in patient-specific orthopedics, such as in the creation of anatomic models for surgical planning, education and surgical training, patient-specific instruments, and 3D-printed custom implants. Besides being anatomically conformed to a patient's surgical requirement, 3D-printed implants can be fabricated with scaffold lattices that may facilitate osteointegration and reduce implant stiffness. However, limitations including high cost of the implants, the lead time in manufacturing, and lack of intraoperative flexibility need to be addressed. New biomimetic materials have been investigated for use in 3D printing. To increase utilization of 3D printing technology in orthopedics, an all-in-one computer platform should be developed for easy planning and seamless communications among different care providers. Further studies are needed to investigate the real clinical efficacy of 3D printings in orthopedic applications.

The purpose of this meta‐analysis was to identify if patient‐specific instrumentation (PSI) could increase the accuracy of the correction in high tibial osteotomy (HTO) and to explore the assessment indices and the necessity of using a PSI in HTO. A systematic search was carried out using online databases. A total of 466 patients were included in 11 papers that matched the inclusion criteria. To evaluate the accuracy of PSI‐assisted HTO, the weight bearing line ratio (WBL%), hip‐knee‐ankle angle (HKA), mechanical medial proximal tibial angle (mMPTA), and posterior tibial slope angle (PTSA) were measured preoperatively and postoperatively and compared to the designed target values. Statistical analysis was performed after strict data extraction with Review Manager (version 5.4). Significant differences were detected in WBL% (MD = −36.41; 95% CI: −42.30 to −30.53; p < 0.00001), HKA (MD = −9.95; 95% CI: –11.65 to –8.25; p < 0.00001), and mMPTA (MD = –8.40; 95% CI:−10.27 to −6.53; p < 0.00001) but not in PTSA (MD = 0.34; 95% CI: −0.59 to 1.27; p = 0.47) between preoperative and postoperative measurements. There was no significant difference between the designed target values and the postoperative correction values of HKA (MD = 0.14; 95% CI: −0.19 to 0.47; p = 0.41) or mMPTA (MD = 0.11; 95% CI −0.34 to 0.55; p = 0.64). The data show that 3D‐based planning of PSI for HTO is both accurate and safe. WBL%, HKA, and mMPTA were the optimal evaluation indicators of coronal plane correction. Sagittal correction is best evaluated by the PTSA. The present study reports that PSI is accurate but not necessary in typical HTO.

Mechanistic modeling of signaling pathways mediating patient‐specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large‐scale perturbation data. Here, we present an approach that couples ex vivo high‐throughput screenings of cancer biopsies using microfluidics with logic‐based modeling to generate patient‐specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K‐Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine. Synopsis Patient‐specific signaling models are built from microfludic‐based perturbation screenings on cells from tumour biopsies and pathway knowledge. Combination therapies predicted by the models are validated experimentally. Microfluidic‐based high‐throughput perturbation screenings performed ex vivo on pancreatic cancer patients’ resections and on cell lines provide functional insights into signaling processes. Training a generic network built from prior knowledge of the underlying signaling pathways to this data provides patient specific logic models of the pathways. The models can be used to uncover mechanistic differences among patients, and to simulate the effect of novel perturbations. Simulation of drug combinations generated personalized candidate therapies that were validated experimentally. Graphical Abstract Patient‐specific signaling models are built from microfludic‐based perturbation screenings on cells from tumour biopsies and pathway knowledge. Combination therapies predicted by the models are validated experimentally.

Background Sarcomas are associated with a relatively high local recurrence rate of around 30 % in the pelvis. Inadequate surgical margins are the most important reason. However, obtaining adequate margins is particularly difficult in this anatomically demanding region. Recently, three-dimensional (3-D) planning, printed models, and patient-specific instruments (PSI) with cutting blocks have been introduced to improve the precision during surgical tumor resection. This case series illustrates these modern 3-D tools in pelvic tumor surgery. Methods The first consecutive patients with 3-D-planned tumor resection around the pelvis were included in this retrospective study at a University Hospital in 2015. Detailed information about the clinical presentation, imaging techniques, preoperative planning, intraoperative surgical procedures, and postoperative evaluation is provided for each case. The primary outcome was tumor-free resection margins as assessed by a postoperative computed tomography (CT) scan of the specimen. The secondary outcomes were precision of preoperative planning and complications. Results Four patients with pelvic sarcomas were included in this study. The mean follow-up was 7.8 (range, 6.0–9.0) months. The combined use of preoperative planning with 3-D techniques, 3-D-printed models, and PSI for osteotomies led to higher precision (maximal (max) error of 0.4 centimeters (cm)) than conventional 3-D planning and freehand osteotomies (max error of 2.8 cm). Tumor-free margins were obtained where measurable ( n  = 3; margins were not assessable in a patient with curettage). Two insufficiency fractures were noted postoperatively. Conclusions Three-dimensional planning as well as the intraoperative use of 3-D-printed models and PSI are valuable for complex sarcoma resection at the pelvis. Three-dimensionally printed models of the patient anatomy may help visualization and precision. PSI with cutting blocks help perform very precise osteotomies for adequate resection margins.

Percutaneous Coronary Intervention (PCI) is currently the most conventional and effective method for clinically treating cardiovascular diseases such as atherosclerosis. Stent implantation, as one of the ways of PCI in the treatment of coronary artery diseases, has become a hot spot in scientific research with more and more patients suffering from cardiovascular diseases. However, vascular stent implanted into vessels of patients often causes complications such as In-Stent Restenosis (ISR). The vascular stent is one of the sophisticated medical devices, a reasonable structure of stent can effectively reduce the complications. In this paper, we introduce the evolution, performance evaluation standards, delivery and deployment, and manufacturing methods of vascular stents. Based on a large number of literature pieces, this paper focuses on designing structures of vascular stents in terms of “bridge (or link)” type, representative volume unit (RVE)/representative unit cell (RUC), and patient-specific stent. Finally, this paper gives an outlook on the future development of designing vascular stents.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric‐arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method for QA. This approach allows the verification of the accuracy of the dose calculation, data transfer, and the delivery system. However, patient‐specific QA procedures are expensive and require significant time and effort by the physicists. Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated. Various ML and DL models have shown promising prediction accuracy and a high potential as time‐efficient virtual QA tool. In this paper, we review the ML and DL based models that were developed for patient specific IMRT and VMAT QA outcome predictions from algorithmic and clinical applicability perspectives. We focus on comparing the algorithms, the dataset sizes, the input parameters and features, the QA outcome prediction approaches, the validation, the performance, the clinical applicability, and the potential clinical impact. In addition, we discuss the present challenges as well as the future directions in the implementation of these models. To the best of our knowledge, this is the first review on the application of ML and DL based models in IMRT/VMAT QA predictions.

Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid–structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm2 orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients’ risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.

During the minimally-invasive liver surgery, only the partial surface view of the liver is usually provided to the surgeon via the laparoscopic camera. Therefore, it is necessary to estimate the actual position of the internal structures such as tumors and vessels from the pre-operative images. Nevertheless, such task can be highly challenging since during the intervention, the abdominal organs undergo important deformations due to the pneumoperitoneum, respiratory and cardiac motion and the interaction with the surgical tools. Therefore, a reliable automatic system for intra-operative guidance requires fast and reliable registration of the pre- and intra-operative data. In this paper we present a complete pipeline for the registration of pre-operative patient-specific image data to the sparse and incomplete intra-operative data. While the intra-operative data is represented by a point cloud extracted from the stereo-endoscopic images, the pre-operative data is used to reconstruct a biomechanical model which is necessary for accurate estimation of the position of the internal structures, considering the actual deformations. This model takes into account the patient-specific liver anatomy composed of parenchyma, vascularization and capsule, and is enriched with anatomical boundary conditions transferred from an atlas. The registration process employs the iterative closest point technique together with a penalty-based method. We perform a quantitative assessment based on the evaluation of the target registration error on synthetic data as well as a qualitative assessment on real patient data. We demonstrate that the proposed registration method provides good results in terms of both accuracy and robustness w.r.t. the quality of the intra-operative data.

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.