Resection Process Map: A novel dynamic simulation system for pulmonary resection

2D/3D deformable registration for endoscopic camera images using self-supervised offline learning of intraoperative pneumothorax deformation

Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods.

Evolution of Three-Dimensional Computed Tomography Imaging in Thoracic Surgery

Radiologic reconstruction technology allows the wide use of three-dimensional (3D) computed tomography (CT) images in thoracic surgery. A minimally invasive surgery has become one of the standard therapies in thoracic surgery, and therefore, the need for preoperative and intraoperative simulations has increased. Three-dimensional CT images have been extensively used, and various types of software have been developed to reconstruct 3D-CT images for surgical simulation worldwide. Several software types have been commercialized and widely used by not only radiologists and technicians, but also thoracic surgeons. Three-dimensional CT images are helpful surgical guides; however, in almost all cases, they provide only static images, different from the intraoperative views. Lungs are soft and variable organs that can easily change shape by intraoperative inflation/deflation and surgical procedures. To address this issue, we have developed a novel software called the Resection Process Map (RPM), which creates variable virtual 3D images. Herein, we introduce the RPM and its development by tracking the history of 3D CT imaging in thoracic surgery. The RPM could help develop a real-time and accurate surgical navigation system for thoracic surgery.

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.

Application of three-dimensional technology in video-assisted thoracoscopic surgery sublobectomy

Background

Due to the widespread use of imaging techniques, the detection rate of early-stage lung cancer has increased. Video-assisted thoracoscopic surgery (VATS) sublobectomy has emerged as a prominent alternative to lobectomy, offering advantages like reduced resection range, better preservation of lung function, and enhanced postoperative quality of life. However, sublobectomy is more intricate than lobectomy, necessitating a higher level of surgical proficiency and anatomical understanding.

Methods

Three electronic databases were searched to capture relevant studies from January 2016 to March 2023, which related to the application of three-dimensional(3D) technology in VATS sublobectomy.

Results

Currently, clinical departments such as orthopedics, hepatobiliary surgery, and urology have started using 3D technology. This technology is expected to be widely used in thoracic surgery in future. Now 3D technology assists in preoperative planning, intraoperative navigation and doctor-patient communication.

Conclusion

3D technologies, instrumental in locating pulmonary nodules and identifying variations in target lung segmental vessels and bronchi, play pivotal roles in VATS sublobectomy, especially in preoperative planning, intraoperative navigation, and doctor-patient communication. The limitations of 3D technology in clinical application are analyzed, and the future direction of existing 3D technology development is prospected.

Developing a Virtual Reality Simulation System for Preoperative Planning of Robotic-Assisted Thoracic Surgery

Background. Robotic-assisted thoracic surgery (RATS) is now standard for lung cancer treatment, offering advantages over traditional methods. However, RATS's minimally invasive approach poses challenges like limited visibility and tactile feedback, affecting surgeons' navigation through com-plex anatomy. To enhance preoperative familiarization with patient-specific anatomy, we devel-oped a virtual reality (VR) surgical navigation system. Using head-mounted displays (HMDs), this system provides a comprehensive, interactive view of the patient's anatomy pre-surgery, aiming to improve preoperative simulation and intraoperative navigation. Methods. We integrated 3D data from preoperative CT scans into Perspectus VR Education software, displayed via HMDs for in-teractive 3D reconstruction of pulmonary structures. This detailed visualization aids in tailored preoperative resection simulations. During RATS, surgeons access these 3D images through Tile-ProTM multi-display for real-time guidance. Results. The VR system enabled precise visualization of pulmonary structures and lesion relations, enhancing surgical safety and accuracy. The HMDs offered true 3D interaction with patient data, facilitating surgical planning. Conclusions. VR sim-ulation with HMDs, akin to a robotic 3D viewer, offers a novel approach to developing robotic surgical skills. Integrated with routine imaging, it improves preoperative planning, safety, and accuracy of anatomical resections. This technology particularly aids in lesion identification in RATS, optimizing surgical outcomes.

Clinical utility of resected pancreatic volume ratio calculation for predicting postoperative new-onset diabetes mellitus after distal pancreatectomy-a propensity-matched analysis

Background

Limited literature is available on new-onset diabetes mellitus (NODM) after distal pancreatectomy. This study aimed to investigate the correlation between surgery-related factors and the incidence of NODM after distal pancreatectomy.

Methods

Patients were divided into the NODM-positive or NODM-negative group according to the diagnosis of NODM. After propensity score matching, the correlation between operation-related factors and the incidence of NODM was analyzed. The diagnostic threshold for predicting NODM was determined using the receiver operating characteristic (ROC) curve and the Youden index.

Results

No significant correlation was observed between the NODM incidence after distal pancreatectomy and operative blood loss, spleen preservation, surgical method (open or laparoscopy), postoperative ALB and HB (first day after surgery), and postoperative pathology. However, a significant correlation was found between the NODM incidence and the postoperative pancreatic volume or the resected pancreatic volume ratio. Resected pancreatic volume ratio was identified as a predictive risk factor for NODM. Youden index of the ROC curve was 0.548, with a cut off value of 32.05% for resected pancreatic volume ratio. The sensitivity and specificity of the cut off values were 0.952 and 0.595, respectively.

Conclusions

This study demonstrated that the volume ratio of pancreatic resection is a risk factor for the incidence of NODM after distal pancreatectomy. This can be used to predict the incidence of NODM and may have further clinical applications.

Digital Education in General Thoracic Surgery: A Narrative Review

BACKGROUND

Since advanced technologies were introduced into surgical education, a variety of new programs have been developed. However, a comprehensive review of digital education in general thoracic surgery has not been performed. This narrative review was conducted was to identify the current applications of digital education in general thoracic surgery.

METHODS

A literature search was performed using keywords related to general thoracic surgery and digital education, including e-learning and virtual simulation, up to September 2021. Studies published in English investigating the effect of digital education in general thoracic surgery were included.

RESULTS

Thirteen studies met the criteria. The settings were in undergraduate (n = 6) and postgraduate education (residency) (n = 5) and mixed audience with other disciplines (n = 2). Theoretical knowledge (n = 5), technical skills (n = 4), and both knowledge and technical skills (n = 4) were the stated educational objectives for the studies. The didactic materials were transferred to hardware, software, or online platforms and delivered with multimedia materials. Technical skills training for bronchoscopy and chest tube insertion (n = 5) were offered using virtual reality and computer-based simulations. Subjective evaluation was done in 10 studies. Although after the digital education training there was observed improvement in knowledge or skills in 8 studies, studies were not designed to test for superiority compared with controls through randomized controlled studies.

CONCLUSIONS

This review summarizes the current applications of digital education in general thoracic surgery and helps establish the needs for future studies in this field.

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy.

Computer-assisted image-based risk analysis and planning in lung surgery - a review

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

Progress in three-dimensional computed tomography reconstruction in anatomic pulmonary segmentectomy

The number of minimally invasive surgeries, such as video-assisted thoracoscopic surgery and robot-assisted thoracoscopic surgery, has increased enormously in recent years. More and more relevant studies report that anatomic pulmonary segmentectomy has the same effect as traditional lobectomy in the surgical treatment of early stage non-small cell lung cancer (diameter less than 2.0 cm). Segmentectomy requires sufficient knowledge of the location of the pulmonary nodules, as well as the anatomy of the target segments, blood vessels, and bronchi. With the rapid development of imaging technology and three-dimensional technology, three-dimensional reconstruction has been widely used in the medical field. It can effectively assess the vascular branching patterns, discover the anatomic variations of the blood vessels and bronchi, determine the location of the lesion, and clarify the division of the segments. Therefore, it is helpful for preoperative positioning, surgical planning, preoperative simulation and intraoperative navigation, and provides a reference for formulating an individualized surgical plan. It therefore plays a positive role in anatomic pulmonary segmentectomy. This study reviews the progress made in three-dimensional computed tomography reconstruction in anatomic pulmonary segmentectomy.

Improved visualization of virtual-assisted lung mapping by indocyanine green

Objectives

Virtual-assisted lung mapping (VAL-MAP) is a bronchoscopic marking method of dye application on the surface of the lungs before resecting nonpalpable nodules. However, in some cases, it can be difficult to identify the markings of VAL-MAP on computed tomography and intraoperative thoracoscopy. We developed and assessed the feasibility of indocyanine green VAL-MAP (ICG-VAL-MAP).

Methods

A historical control trial was conducted to investigate the effectiveness of ICG-VAL-MAP for marking visualization compared with that of VAL-MAP. In ICG-VAL-MAP, instead of indigo carmine, ICG and computed tomography contrast agents were used for dye marking, and near-infrared fluorescence endoscopy was employed to visualize the ICG markings. The other processes in VAL-MAP were carried out. The marking visibility was assessed in 3 grades of easy, faint, or not identifiable. We compared the visibility of the markings on computed tomography images and during thoracoscopic operations between VAL-MAP (567 markings in 147 cases) and ICG-VAL-MAP (142 markings in 63 cases).

Results

On the preoperative computed tomography images, ICG-VAL-MAP provided significantly better marking visualization than VAL-MAP (easy/faint/not identifiable = 142/0/0 vs 427/100/30; P < .0001). ICG-VAL-MAP provided significantly better intraoperative markings than VAL-MAP (easy/faint/not identifiable = 141/0/1, respectively, vs 475/50/42, respectively; P < .0001). Regarding complications, pneumothorax occurred in 8 (5.4%) cases of VAL-MAP and zero cases (0%) of ICG-VAL-MAP (P = .12); fever was observed in 7 (5.0%) cases of VAL-MAP and 2 (3.2%) cases of ICG-VAL-MAP (P = .72).

Conclusions

ICG-VAL-MAP provided significantly better visibility of markings than VAL-MAP. It might be useful in the resection of nonpalpable small lung lesions.

Deformation analysis of surface and bronchial structures in intraoperative pneumothorax using deformable mesh registration

The positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated lung deformation. Contrast-enhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. The images contain the overall shape of the lungs, including all lobes and internal bronchial structures, and were analyzed to provide a statistical deformation model that could be used as prior knowledge to predict pneumothorax. To address the difficulties of mapping pneumothorax CT images with topological changes and CT intensity shifts, we designed deformable mesh registration techniques for mixed data structures including the lobe surfaces and the bronchial centerlines. Three global-to-local registration steps were performed under the constraint that the deformation was spatially continuous and smooth, while matching visible bronchial tree structures as much as possible. The developed framework achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm, and visualized deformation fields that demonstrate per-lobe contractions and rotations with high variability between subjects. The deformation analysis results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that deformation in the deflated lung is heterogeneous.

Current trends in thoracic surgery

Thoracic surgery has evolved drastically in recent years. Although thoracic surgeons mainly deal with tumorous lesion in the lungs, mediastinum, and pleura, they also perform lung transplantation surgery in patients with end-stage lung disease. Herein, we introduce various major current topics in thoracic surgery. Minimally invasive surgical procedures include robot-assisted thoracic surgery and uniportal video-assisted thoracic surgery. Novel techniques for sublobar resection include virtual-assisted lung mapping, image-guided video-assisted thoracic surgery, and segmentectomy using indocyanine green. Three-dimensional (3D) computed tomography (CT) simulation consists of surgeon-friendly 3D-CT image analysis systems and new-generation, dynamic 3D-CT imaging systems. Updates in cadaveric lung transplantation include use of marginal donors, including donation after circulatory death, and ex vivo lung perfusion for such donors. Topics in living donor lobar lung transplantation include size matching, donor issues, and new surgical techniques. During routine clinical practice, thoracic surgeons encounter various pivotal topics related to thoracic surgery, which are described in this report.

Developing a virtual reality simulation system for preoperative planning of thoracoscopic thoracic surgery

Background

Video-assisted thoracoscopic surgery (VATS) has become a standard approach for the treatment of lung cancer. However, its minimally invasive nature limits the field of view and reduces tactile feedback. These limitations make it vital that surgeons thoroughly familiarize themselves with the patient’s anatomy preoperatively. We have developed a virtual reality (VR) surgical navigation system using head-mounted displays (HMD). The aim of this study was to investigate the potential utility of this VR simulation system in both preoperative planning and intraoperative assistance, including support during thoracoscopic sublobar resection.

Methods

Three-dimensional (3D) polygon data derived from preoperative computed tomography data was loaded into BananaVision software developed at Colorado State University and displayed on an HMD. An interactive 3D reconstruction image was created, in which all the pulmonary structures could be individually imaged. Preoperative resection simulations were performed with patient-individualized reconstructed 3D images.

Results

The 3D anatomic structure of pulmonary vessels and a clear vision into the space between the lesion and adjacent tissues were successfully appreciated during preoperative simulation. Surgeons could easily evaluate the real patient’s anatomy in preoperative simulations to improve the accuracy and safety of actual surgery. The VR software and HMD allowed surgeons to visualize and interact with real patient data in true 3D providing a unique perspective.

Conclusions

This initial experience suggests that a VR simulation with HMD facilitated preoperative simulation. Routine imaging modalities combined with VR systems could substantially improve preoperative planning and contribute to the safety and accuracy of anatomic resection.

Segmentectomy Guided by 3-Dimensional Images Reconstructed From Nonenhanced Computed Tomographic Data

Three-dimensional (3D) imaging is an essential tool for performing an accurate segmentectomy. However, data acquired from contrast-enhanced computed tomography (CT) are considered necessary when reconstructing 3D pulmonary vascular images. Therefore, 3D images are currently unavailable for patients who cannot undergo contrast-enhanced CT scans due to patient-related issues such as anaphylaxis to the contrast agent, among others. We present here our experience with atypical segmentectomies guided by 3D images reconstructed from nonenhanced CT data.