Preoperative systemic immune-inflammation index-based nomogram for lung carcinoma following microwave ablation -a real world single center study.

Background: The preoperative inflammatory condition significantly influences the prognosis of malignancies. We aimed to investigate the potential significance of preoperative inflammatory biomarkers in forecasting the long-term results of lung carcinoma after microwave ablation (MWA). Method: This study included patients who received MWA treatment for lung carcinoma from Jan. 2012 to Dec. 2020. We collected demographic, clinical, laboratory, and outcome information. To assess the predictive capacity of inflammatory biomarkers, we utilized the area under the receiver operating characteristic curve (AUC-ROC) and assessed the predictive potential of inflammatory biomarkers in forecasting outcomes through both univariate and multivariate Cox proportional hazard analyses. Results: A total of 354 individuals underwent MWA treatment, of which 265 cases were included in this study, whose average age was 69.1 ± 9.7 years. The AUC values for the Systemic Inflammatory Response Index (SIRI) to overall survival (OS) and disease-free survival (DFS) were 0.796 and 0.716, respectively. The Cox proportional hazards model demonstrated a significant independent association between a high SIRI and a decreased overall survival (hazard ratio [HR]=2.583, P<0.001). Furthermore, a high SIRI independently correlated with a lower DFS (HR=2.391, P<0.001). We developed nomograms utilizing various independent factors to forecast the extended prognosis of patients. These nomograms exhibited AUC of 0.900, 0.849, and 0.862 for predicting 1-year, 3-year, and 5-year OS, respectively. Additionally, the AUC values for predicting 1-year, 3-year, and 5-year DFS were 0.851, 0.873, and 0.883, respectively. Conclusion: SIRI has shown promise as a valuable long-term prognostic indicator for forecasting the outcomes of lung carcinoma patients following MWA.

Chest tube removal at different gas flows in prolonged air leak: a randomized non-inferiority trial.

OBJECTIVES: To evaluate the safety and feasibility of removing drainage tubes at larger size of air leak in patients with prolonged air leak after pulmonary surgery. METHODS: Ninety-five patients who underwent pulmonary surgery with prolonged air leak in our centre were enrolled in this randomized controlled, single-centre, non-inferiority study. The drainage tube was clamped with a stable size of air leak observed over the last 6 h, which was quantified by gas flow rate using the digital drainage system. The control group (n = 48) and the study group (n = 46) had their drainage tube clamped at 0-20 ml/min and 60-80 ml/min, respectively. We continuously monitored clinical symptoms, conducted imaging and laboratory examinations, and decided whether to reopen the drainage tube. RESULTS: The reopening rate in the study group was not lower than that in the control group (2.08% vs 6.52%, P > 0.05). The absolute difference in reopening rate was 4.44% (95% confidence interval -0.038 to 0.126), with an upper limit of 12.6% below the non-inferiority margin (15%). There were significant differences in the length of stay [16.5 (13-24.75) vs 13.5 (12-19.25), P = 0.017] and the duration of drainage [12 (9.25-18.50) vs 10 (8-12.25), P = 0.007] between the control and study groups. No notable differences were observed in chest X-ray results 14 days after discharge or in the readmission rate. CONCLUSIONS: For patients with prolonged air leak, removing drainage tubes at larger size of air leak demonstrated similar safety compared to smaller size of air leak, and can shorten both length of stay and drainage duration. CLINICAL TRIAL REGISTRATION NUMBER: Name of registry: Gas flow threshold for safe removal of chest drainage in patients with alveolar-pleural fistula prolonged air leak after pulmonary surgery. Registration number: ChiCTR2200067120. URL: https://www.chictr.org.cn/.

ParaCM-PNet: A CNN-tokenized MLP combined parallel dual pyramid network for prostate and prostate cancer segmentation in MRI.

The precise prostate gland and prostate cancer (PCa) segmentations enable the fusion of magnetic resonance imaging (MRI) and ultrasound imaging (US) to guide robotic prostate biopsy systems. This precise segmentation, applied to preoperative MRI images, is crucial for accurate image registration and automatic localization of the biopsy target. Nevertheless, describing local prostate lesions in MRI remains a challenging and time-consuming task, even for experienced physicians. Therefore, this research work develops a parallel dual-pyramid network that combines convolutional neural networks (CNN) and tokenized multi-layer perceptron (MLP) for automatic segmentation of the prostate gland and clinically significant PCa (csPCa) in MRI. The proposed network consists of two stages. The first stage focuses on prostate segmentation, while the second stage uses a prior partition from a previous stage to detect the cancerous regions. Both stages share a similar network architecture, combining CNN and tokenized MLP as the feature extraction backbone to creating a pyramid-structured network for feature encoding and decoding. By employing CNN layers of different scales, the network generates scale-aware local semantic features, which are integrated into feature maps and inputted into an MLP layer from a global perspective. This facilitates the complementarity between local and global information, capturing richer semantic features. Additionally, the network incorporates an interactive hybrid attention module to enhance the perception of the target area. Experimental results demonstrate the superiority of the proposed network over other state-of-the-art image segmentation methods for segmenting the prostate gland and csPCa tissue in MRI images.

Radiofrequency ablation for stage

Pulmonary carcinoma represents the second common cancer for human race while its mortality rate ranked the first all over the world. Surgery remains the primary option for early-stage non-small cell lung cancer (NSCLC) in some surgical traditions. Nevertheless, only less than half of patients are operable subjected to the limited lung function and multiple primary/metastatic lesions. Recent improvements in minimally invasive surgical techniques have made the procedure accessible to more patients, but this percentage still does not exceed half. In recent years, radiofrequency ablation (RFA), one of the thermal ablation procedures, has gradually advanced in the treatment of lung cancer in addition to being utilized to treat breast and liver cancer. Several guidelines, including the American College of Chest Physicians (ACCP), include RFA as an option for some patients with NSCLC although the level of evidence is mostly limited to retrospective studies. In this review, we emphasize the use of the RFA technique in patients with early-stage NSCLC and provide an overview of the RFA indication population, prognosis status, and complications. Meanwhile, the advantages and disadvantages of RFA proposed in existing studies are compared with surgical treatment and radiotherapy. Due to the high rate of gene mutation and immunocompetence in NSCLC, there are considerable challenges to clinical translation of combining targeted drugs or immunotherapy with RFA that the field has only recently begun to fully appreciate.

Downregulation of AHNAK2 inhibits cell cycle of lung adenocarcinoma cells by interacting with RUVBL1.

BACKGROUND: Lung adenocarcinoma (LUAD) is the leading cause of death among cancer diseases. The tumorigenic functions of AHNAK2 in LUAD have attracted more attention in recent years, while there are few studies which have reported its high molecular weight. METHODS: The mRNA-seq data of AHNAK2 and corresponding clinical data from UCSC Xena and GEO was analyzed. LUAD cell lines were transfected with sh-NC and sh-AHNAK2, and cell proliferation, migration and invasion were then detected by in vitro experiments. We performed RNA sequencing and mass spectrometry analysis to explore the downstream mechanism and interacting proteins of AHNAK2. Finally, western blot, cell cycle analysis and CO-IP were used to confirm our assumptions regarding previous experiments. RESULTS: Our study revealed that AHNAK2 expression was significantly higher in tumors than in normal lung tissues and higher AHNAK2 expression led to a poor prognosis, especially in patients with advanced tumors. AHNAK2 suppression via shRNA reduced the LUAD cell lines proliferation, migration and invasion and induced significant changes in DNA replication, NF-kappa B signaling pathway and cell cycle. AHNAK2 knockdown also caused G1/S phase cell cycle arrest, which could be attributed to the interaction of AHNAK2 and RUVBL1. In addition, the results from gene set enrichment analysis (GSEA) and RNA sequencing suggested that AHNAK2 probably plays a part in the mitotic cell cycle. CONCLUSION: AHNAK2 promotes proliferation, migration and invasion in LUAD and regulates the cell cycle via the interaction with RUVBL1. More studies of AHNAK2 are still needed to reveal its upstream mechanism.

Effect of perioperative aspirin continuation on bleeding after pneumonectomy.

BACKGROUND: To investigate the effect of continuous oral aspirin in perioperative period on bleeding in pneumonectomy. METHODS: A total of 170 patients who underwent pneumonectomy in our hospital from March 2021 to March 2022 were selected as the study objects. All patients took oral aspirin before surgery and did not take other antiplatelet agent or anticoagulants at the same time. The continuation group included 85 cases and continued to take aspirin 100 mg/day during the perioperative period, and the interruption group included 85 cases who stopped aspirin for 7 days before surgery and 3 days after surgery, without bridging therapy. The intraoperative blood loss, operation time, conversion to thoracotomy rate, postoperative bleeding rate, blood transfusion rate, thrombotic events, postoperative drainage volume, length of hospital stay, and total hospital cost of the two groups were compared. RESULTS: There were no statistically significant differences in intraoperative blood loss, operative time, rate of conversion to open, postoperative drainage, hospital stay, and cost between the two groups (p > 0.05), and there were no reoperations due to bleeding between the two groups. CONCLUSIONS: Aspirin should be continued throughout the perioperative period in all high-risk patients requiring pneumonectomy after balancing ischemic-bleeding risks.

Construction and validation of a hypoxia-related risk signature identified EXO1 as a prognostic biomarker based on 12 genes in lung adenocarcinoma.

BACKGROUND: Increasing evidence has demonstrated the clinical importance of hypoxia and its related factors in lung adenocarcinoma (LUAD). METHODS: RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analyzed using the differentially expressed genes in hypoxia pathway by the Least Absolute Shrinkage and Selection Operator (LASSO) model. Applying gene ontology (GO) and gene set enrichment analysis (GSEA), a risk signature associated with the survival of LUAD patients was constructed between LUAD and normal tissue. RESULTS: In total, 166 hypoxia-related genes were identified. Based on the LASSO Cox regression, 12 genes were selected for the development of the risk signature. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.724. ROC curve showed better predictive ability using the nomogram (AUC = 0.811 for 5-year OS). Finally, the expressions of the 12 genes were validated in two external datasets and EXO1 was recognized as a potential biomarker in the progression of LUAD patients. CONCLUSIONS: Overall, our data suggested that hypoxia is associated with the prognosis, and EXO1 acted as a promising biomarker in LUAD.

Preoperative controlling nutritional status score (CONUT) predicts postoperative complications of patients with bronchiectasis after lung resections.

Background: The Controlled Nutritional Status (CONUT) score is a valid scoring system for assessing nutritional status and has been shown to correlate with clinical outcomes in many surgical procedures; however, no studies have reported a correlation between postoperative complications of bronchiectasis and the preoperative CONUT score. This study aimed to evaluate the value of the CONUT score in predicting postoperative complications in patients with bronchiectasis. Methods: We retrospectively analyzed patients with localized bronchiectasis who underwent lung resection at our hospital between April 2012 and November 2021. The optimal nutritional scoring system was determined by receiver operating characteristic (ROC) curves and incorporated into multivariate logistic regression. Finally, independent risk factors for postoperative complications were determined by univariate and multivariate logistic regression analyses. Results: A total of 240 patients with bronchiectasis were included, including 101 males and 139 females, with an average age of 49.83 ± 13.23 years. Postoperative complications occurred in 59 patients (24.6%). The incidence of complications, postoperative hospital stay and drainage tube indwelling time were significantly higher in the high CONUT group than in the low CONUT group. After adjusting for sex, BMI, smoking history, lung function, extent of resection, intraoperative blood loss, surgical approach and operation time, multivariate analysis showed that the CONUT score remained an independent risk factor for postoperative complications after bronchiectasis. Conclusions: The preoperative CONUT score is an independent predictor of postoperative complications in patients with localized bronchiectasis.

Surface deformation tracking in monocular laparoscopic video.

Image-guided surgery has been proven to enhance the accuracy and safety of minimally invasive surgery (MIS). Nonrigid deformation tracking of soft tissue is one of the main challenges in image-guided MIS owing to the existence of tissue deformation, homogeneous texture, smoke and instrument occlusion, etc. In this paper, we proposed a piecewise affine deformation model-based nonrigid deformation tracking method. A Markov random field based mask generation method is developed to eliminate tracking anomalies. The deformation information vanishes when the regular constraint is invalid, which further deteriorates the tracking accuracy. Atime-series deformation solidification mechanism is introduced to reduce the degradation of the deformation field of the model. For the quantitative evaluation of the proposed method, we synthesized nine laparoscopic videos mimicking instrument occlusion and tissue deformation. Quantitative tracking robustness was evaluated on the synthetic videos. Three real videos of MIS containing challenges of large-scale deformation, large-range smoke, instrument occlusion, and permanent changes in soft tissue texture were also used to evaluate the performance of the proposed method. Experimental results indicate the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness, which shows good performance in image-guided MIS.

Correlation filters tissue tracking with application to robotic minimally invasive surgery.

PURPOSE: Vision-based tissue tracking is a significant component for building efficient autonomous surgical robot system. While the methodology involves various challenges caused by occlusion, deformation and appearance changes. METHODS: We propose a novel correlation filter tissue tracking framework for minimally invasive surgery. Our model contains the innovative design of synthetic features, a bi-branch is exploited to enhance the response map. An incrementally learnt detector with the novel updating and trigger schemes is embedded to model the re-detection module for capturing the lost target. RESULTS: Promising validation has been conducted on the publicly available tracking benchmark datasets, a surgical tissue tracking dataset based on publicly available Cholec80 dataset has also been developed to focus on the application in intra-operative scenes. CONCLUSIONS: Our proposed framework meets the outstanding performance and surpasses the existing methods. The work demonstrates the feasibility to perform tissue tracking by taking advantage of the correlation filter.

Haptic virtual surgery simulation system under field programmable analogue array-based hybrid control.

In this paper, a bilateral haptic virtual surgery simulation system under a hybrid controller was studied. An analogue controller realized by a field programmable analogue array (FPAA) was paralleled in the operator robot side, which reduced the impact of controller discretisation on the system. A system stability conditions under hybrid control with multiple-operators were deduced. The stability analysis indicates that the addition of analogue derivative term widens the range of haptic controls gains that satisfy the multiple-users' stability conditions. Finally, the human's performance of a stiffness discrimination task was studied in an independently developed minimally invasive surgical (MIS) platform. The experiment results show that, human operators under the hybrid controller achieve the highest task success rates.

Real-time biomechanical modelling of the liver using LightGBM model.

BACKGROUND: Accurate and real-time biomechanical modelling of the liver is a major challenge in computer-assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations. METHOD: A liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5 N in omni-direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree-based LightGBM regression model trained with the generated data set. RESULTS: In comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774 mm, 0.0786 mm, 0.0801 mm in the mean absolute error (MAE) and 0.0591 mm, 0.0609 and 0.0622 mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33 ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91 s). CONCLUSION: These results lay a foundation for the future development of real-time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.

Lightweight Deep Neural Network for Articulated Joint Detection of Surgical Instrument in Minimally Invasive Surgical Robot.

Vision-based detection and tracking of surgical instrument are attractive because it relies purely on surgical instrument already in the operating scenario. The vision knowledge of the surgical instruments is a crucial piece of topic for surgical task understanding, autonomous robot control and human-robot collaborative surgeries to enhance surgical outcomes. In this work, a novel method has been demonstrated by developing a multitask lightweight deep neural network framework to explore surgical instrument articulated joint detection. The model has an end-to-end architecture with two branches, which share the same high-level visual features provided by a lightweight backbone while holding respective layers targeting for specific tasks. We have designed a novel subnetwork with joint detection branch and an instrument classification branch to sufficiently take advantage of the relatedness of surgical instrument presence detection and surgical instrument articulated joint detection tasks. The lightweight joint detection branch has been employed to efficiently locate the articulated joint position with simultaneously holding low computational cost. Moreover, the surgical instrument classification branch is introduced to boost the performance of joint detection. The two branches are merged to output the articulated joint location with respective instrument type. Extensive validation has been conducted to evaluate the proposed method. The results demonstrate promising performance of our proposed method. The work represents the feasibility to perform real-time surgical instrument articulated joint detection by taking advantage of the components of surgical robot system, contributing to the reference for further surgical intelligence.

Facial landmark-guided surface matching for image-to-patient registration with an RGB-D camera.

BACKGROUND: Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. METHODS: We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm. RESULTS: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. CONCLUSIONS: The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

Mix Frame Visual Servo Control Framework for Autonomous Assistive Robotic Arms.

Assistive robotic arms (ARAs) that provide care to the elderly and people with disabilities, are a significant part of Human-Robot Interaction (HRI). Presently available ARAs provide non-intuitive interfaces such as joysticks for control and thus, lacks the autonomy to perform daily activities. This study proposes that, for inducing autonomous behavior in ARAs, visual sensors integration is vital, and visual servoing in the direct Cartesian control mode is the preferred method. Generally, ARAs are designed in a configuration where its end-effector's position is defined in the fixed base frame while orientation is expressed in the end-effector frame. We denoted this configuration as 'mixed frame robotic arms'. Consequently, conventional visual servo controllers which operate in a single frame of reference are incompatible with mixed frame ARAs. Therefore, we propose a mixed-frame visual servo control framework for ARAs. Moreover, we enlightened the task space kinematics of a mixed frame ARAs, which led us to the development of a novel "mixed frame Jacobian matrix". The proposed framework was validated on a mixed frame JACO-2 7 DoF ARA using an adaptive proportional derivative controller for achieving image-based visual servoing (IBVS), which showed a significant increase of 31% in the convergence rate, outperforming conventional IBVS joint controllers, especially in the outstretched arm positions and near the base frame. Our Results determine the need for the mixed frame controller for deploying visual servo control on modern ARAs, that can inherently cater to the robotic arm's joint limits, singularities, and self-collision problems.

Investigation into Friction and Wear Characteristics of 316L Stainless-Steel Wire at High Temperature.

The damping performance of metal rubber is highly correlated with the tribological properties of the internal metal wires. In this paper, the friction and wear characteristics of 316L stainless-steel wire are investigated under different temperatures, loads, crossing angles, and working strokes. Results show that the friction coefficient increases from 0.415 to 0.635 and the wear depth increases from 34 µm to 51 µm, with the temperature rising from 20 °C to 400 °C. High temperature will soften metal materials and promote the oxidation of metal. Softened materials can be easily sheared and removed under friction action, resulting in high wear depth. However, when a continuous oxide film with high hardness is formed under higher temperature, the oxide film can work as a wear-resisting layer to prevent further wear of the wire to a certain degree. At the same temperature, the loads, crossing angles, and working strokes change the wear resistance by affecting the surface stress, debris removal efficiency, etc., and high temperature will aggravate this change. The results pave the way for the design and selection of materials for high-temperature metal rubber components.

Development and validation of tumor-to-blood based nomograms for preoperative prediction of lymph node metastasis in lung cancer.

BACKGROUND: To develop and validate tumor-to-blood based nomograms for preoperative prediction of lymph node (LN) metastasis in patients with lung cancer (LC). METHODS: A prediction model was developed in a primary cohort comprising 330 LN stations from patients with pathologically confirmed LC, these data having been gathered from January 2016 to June 2019. Tumor-to-blood variables of LNs were calculated from positron emission tomography-computed tomography (PET-CT) images of LC and the short axis diameters of LNs were measured on CT images. Tumor-to-blood variables, number of stations suspected of harboring LN metastasis according to PET, and independent clinicopathological risk factors were included in the final nomograms. After being internally validated, the nomograms were used to assess an independent validation cohort containing 101 consecutive LN stations accumulated from July 2019 to March 2020. RESULTS: Four tumor-to-blood variables (left atrium, inferior vena cava, liver, and aortic arch) and the maximum standardized uptake value (SUVmax) for LNs were found to be significantly associated with LN status (p < 0.001 for both primary and validation cohorts). Five predictive nomograms were built. Of these, one with LN SUVmax/left atrium SUVmax was found to be optimal for predicting LN status with AUC 0.830 (95% confidence interval [CI]: 0.774-0.886) in the primary cohort and AUC 0.865 (95% CI: 0.782-0.948) in the validation cohort. All models showed good discrimination, with a modest C-index, and good calibration in both primary and validation cohorts. CONCLUSIONS: We have developed tumor-to-blood based nomograms that incorporate identified clinicopathological risk factors and facilitate preoperative prediction of LN metastasis in LC patients.

Development of a transperineal prostate biopsy robot guided by MRI-TRUS image.

BACKGROUND: In the transrectal ultrasound (TRUS)-guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate region in TRUS images. However, ultrasound images with low imaging quality make doctors easy to be interfered when determining the biopsy route, which reduces the biopsy success rate. METHODS: This paper introduces the guidance method of magnetic resonance image (MRI) registration to ultrasound image and develops a 5-degrees of freedom robot for prostate biopsy guided by MRI-TRUS image. The robot uses a structure attached to the ultrasound probe to reduce the space occupied. By registering the posture relationship between MRI, TRUS image, ultrasonic probe and the robot base, the accurate localization of the suspected lesion area can be achieved with the preoperative MRIs. RESULTS: The prostate phantom biopsy based on the robotic biopsy system in this paper, the average biopsy error is 1.44 mm, and the maximum biopsy error is 2.23 mm. CONCLUSIONS: We build a robotic biopsy platform with prostate phantom, and evaluate the biopsy accuracy of MRI-TRUS guided prostate biopsy robot, the results meet clinical prostate biopsy requirements.

Deep convolutional neural network for the automated detection of Subthalamic nucleus using MER signals.

BACKGROUND: Deep brain stimulation (DBS) surgery has been extensively conducted for treating advanced Parkinson's disease (PD) patient's symptoms. DBS hinges on the localization of the subthalamic nucleus (STN) in which a permanent electrode should be accurately placed to produce electrical current. Microelectrode recording (MER) signals are routinely recorded in the procedure of DBS surgery to validate the planned trajectories. However, manual MER signals interpretation with the goal of detecting STN borders requires expertise and prone to inter-observer variability. Therefore, a computerized aided system would be beneficial to automatic detection of the dorsal and ventral borders of the STN in MER. NEW METHOD: In this study, a new deep learning model based on convolutional neural system for automatic delineation of the neurophysiological borders of the STN along the electrode trajectory was developed. COMPARISON WITH EXISTING METHODS: The proposed model does not involve any conventional standardization, feature extraction or selection steps. RESULTS: Promising results of 98.67% accuracy, 99.03% sensitivity, 98.11% specificity, 98.79% precision and 98.91% F1-score for subject based testing were achieved using the proposed convolutional neural network (CNN) model. CONCLUSIONS: This is the first study on the analysis of MER signals to detect STN using deep CNN. Traditional machine learning (ML) algorithms are often cumbersome and suffer from subjective evaluation. Though, the developed 10-layered CNN model has the capability of extracting substantial features at the convolution stage. Hence, the proposed model has the potential to deliver high performance on STN region detection which shows perspective in aiding the neurosurgeon intraoperatively.

Three-dimensional reconstruction model in the diagnosis of Morgagni's hernia.

Morgagni's hernia (MH) can be diagnosed by different utilities, but all these methods are not always 100% accurate. Three-dimensional (3D) reconstruction model could be helpful in better understanding the important anatomical structures. We report a case of MH who was once misdiagnosed as diaphragmatic eventration at the other institution and we offered laparoscopic repair according to the 3D reconstruction model. Our case highlights that 3D reconstruction model could be a useful supplementary tool in the diagnosis and preoperative assessment for patients with MH especially when it is confused in diagnosis in clinical practice.