Wen-Bo Zhang

To evaluate the feasibility and effectiveness of mixed reality combined with surgical navigation for resection of oral and maxillofacial tumors. Patients w

Objective To evaluate the feasibility and effectiveness of mixed reality combined with surgical navigation for resection of oral and maxillofacial tumors. Methods Patients with tumors involving maxilla and mandible in the oral and maxillofacial region who underwent surgical treatment at Peking University School and hospital of Stomatology between January 2020 and June 2023 were enrolled in this study. The experimental group underwent image data processing using mixed reality–surgical navigation technology, and tumor localization and resection were performed under its guidance. The control group received assistance from digital surgical technology for three-dimensional reconstruction and computer-aided design during image processing, with tumor resection also completed under navigation guidance. Basic patient characteristics were recorded for both groups. Using a normal vector-based osteotomy plane selection method, osteotomy planes were selected from both the preoperative virtual design and the postoperative CT reconstruction model via digital software for chromatographic analysis. The deviations in osteotomy planes between the experimental and control groups were compared. Statistical analyses were conducted using independent samples t-tests, with a significance level set at P < 0.05. Results A total of 38 patients were included, with 19 assigned to the experimental group and 19 to the control group. The osteotomy deviation was significantly smaller in the experimental group than in the control group (1.84 mm vs. 3.07 mm, P < 0.001). Chromatographic analysis revealed that the proportion of osteotomy points deviating by less than 3 mm was 74.84% in the experimental group and 64.81% in the control group. When analyzed by subcategory—maxillary versus mandibular and benign versus malignant tumors—the experimental group consistently exhibited smaller deviations than the control group. No significant differences in osteotomy accuracy were observed between maxillary and mandibular tumors, or between benign and malignant tumors, within either group ( P > 0.05). During an average follow-up of 27 months in the experimental group, five patients experienced complications, including facial paralysis, skin numbness, and titanium mesh exposure; no tumor exposure or distant metastasis occurred. In the control group, with an average follow-up of 18.8 months, two patients developed complications such as infection and titanium mesh exposure, and three patients experienced tumor recurrence. Conclusion The clinical application of mixed reality combined with surgical navigation is feasible in the resection of oral and maxillofacial tumors. Compared to conventional navigation systems, this technology enables more precise localization of osteotomy planes and enhances the accuracy of tumor resection.

The limitations of traditional, autologous bone grafts, such as the scarcity of donor material and the risks of secondary surgical trauma, have spurred the development of alternatives for the repair of large bone defects. Bionic bone scaffolds fabricated via fused deposition modeling (FDM)—a three-dimensional (3D) printing technique—are considered promising. While gyroid-structured scaffolds mimic the complex micro-architecture of cancellous bone, their application in FDM 3D printing remains understudied. Furthermore, no consensus has been reached on the ideal pore size for gyroid scaffolds, which is influenced by the infill density. In this study, we fabricated five groups of polycaprolactone/hydroxyapatite (PCL/HA) scaffolds with different infill densities (40%, 45%, 50%, 55%, and 60%) using a solvent-free filament preparation method. Scanning electron microscopy (SEM) observation showed that all scaffolds exhibit an interconnected porous structure. The scaffold with the 55% infill density, featuring a pore size of 465 ± 63 μm, demonstrated optimal hydrophilicity and mechanical properties comparable to natural cancellous bone. In addition, this scaffold supported cellular bridging within its pores and showed the highest alkaline phosphatase (ALP) activity and calcium salt deposition. Our findings offer novel insights into the design of gyroid-like scaffolds and their fabrication via FDM, paving the way for potential clinical applications.

Objective This cohort study aimed to preliminarily explore the effect of midfacial tumor resection procedure with assistance of virtual planning based on CT/MRI multimodal image fusion. Methods For the experimental cohort, patients with midfacial tumors receiving treatment from February 2019 to March 2021 were enrolled. Virtual planning was completed preoperatively based on CT/MRI multimodal image fusion. Tumor resection was performed under virtual planning-based surgical navigation, and intraoperative frozen sections were taken to determine resection margin status. For the control cohort, patients with midfacial tumors treated by the same surgical team were enrolled according to the same criteria. Patients underwent surgery assisted by virtual planning based on single-modality CT images. Resection margin and survival status were compared between groups during follow-up. Results Altogether twenty-nine patients were enrolled. The resection margin status was significantly different between groups at the per-margin level (experimental group: 100% [66/66] negative; control group: 90.9% negative [70/77]; P = 0.014). During follow-up period, two patients in experimental cohort and seven patients in control cohort confirmed local recurrence. Conclusions Patients with midfacial tumor who underwent virtual planning based on CT/MRI multimodal image fusion were more likely to have tumor-free resection margins. The use of the image fusion procedure may improve treatment outcomes in such patients.

Objectives In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). Materials and methods CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Results A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908–0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%). Conclusion A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC. Clinical relevance The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

Background Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary. Methods A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool. Results The Alzheimer’s Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models. Conclusion The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.