3D Slicer Software Research Articles

The correlation study between posterior fossa crowding and classical trigeminal neuralgia

ObjectiveTo explore the correlation between posterior fossa crowding and the occurrence of classical trigeminal neuralgia (TN).MethodsA total of 60 patients diagnosed with classical TN and 60 age- and sex-matched healthy volunteers were included as a control group for a case-control study. All subjects underwent high-resolution 3D magnetic resonance imaging (MRI) examinations (including 3D-FIESTA and 3D-TOF MRA sequences). The original data were subjected to 3D reconstruction and measurement of posterior fossa volume (PFV) and hindbrain volume (HBV) using 3D-slicer software. The posterior fossa crowding index (PFCI) was calculated as HBV/PFV × 100%. Finally, data were analyzed using SPSS 22.0 statistical software.ResultsThe average PFCI in patients with TN was 85.0% ± 3.9%, compared to 82.7% ± 3.9% in the control group, with a significant statistical difference (P = 0.025). Female patients with TN had a more crowded posterior fossa than male patients (86.4% ± 3.8% vs. 83.4% ± 3.4%, P = 0.033). Multiple linear regression analysis showed that a higher PFCI was associated with being female (P = 0.022), younger age (P = − 0.003), and being a patient with TN (P = − 0.023).ConclusionPatients with PTN have a more crowded posterior fossa compared to the healthy control group. A higher PFCI is associated with being female, younger age, and being a patient with TN. Posterior fossa crowding may be a risk factor for neurovascular conflict (NVC), making it more likely to lead to the occurrence of TN.

A pilot study comparing three-dimensional models of tumor histopathology and magnetic resonance imaging

Three-dimensional (3D) modeling is often used to provide better visual understanding. This has become an everyday tool especially in medical imaging. However, modeling soft tissue histopathology in 3D is in its early stages, thus making 3D comparison between radiology and histopathology difficult. Here, we sought to create a model that combines a model based on histopathological tumor borders and a model reformatted from magnetic resonance image (MRI). The 3D models were constructed from an oral squamous cell carcinoma (OSCC) resection specimen and a preoperative MRI scan. Potential challenges were identified, and respective solutions proposed. The 3D model based on histopathological tumor borders was constructed by a method that uses a 3D table scanner and modeling software. The 3D MRI model was constructed using 3D Slicer software. The two models were fitted together by anatomical landmarks in each model. We compared the volume and dimensions between the two 3D models. As a result, we created an image fusion that presents the soft-tissue resection specimen, its histopathological findings, and the MRI findings combined in a digital 3D form. Presenting these two 3D models within the resection specimen can serve as a tool to improve the multidisciplinary discussion of patient management. To create these models, only a few additional steps to a normal protocol are required.

Application of Ultrasound Radiomics in Differentiating Benign from Malignant Breast Nodules in Women with Post-Silicone Breast Augmentation

Purpose: To evaluate the diagnostic value of ultrasound radiomics in distinguishing between benign and malignant breast nodules in women who have undergone silicone breast augmentation. Methods: A retrospective study was conducted of 99 breast nodules detected by ultrasound in 93 women who had undergone silicone breast augmentation. The ultrasound data were collected between 1 January 2006 and 1 September 2023. The nodules were allocated into a training set (n = 69) and a validation set (n = 30). Regions of interest (ROIs) were manually delineated using 3D Slicer software, and radiomic features were extracted and selected using Python programming. Eight machine learning algorithms were applied to build predictive models, and their performance was assessed using sensitivity, specificity, area under the ROC curve (AUC), accuracy, Brier score, and log loss. Model performance was further evaluated using ROC curves and calibration curves, while clinical utility was assessed via decision curve analysis (DCA). Results: The random forest model exhibited superior performance in differentiating benign from malignant nodules in the validation set, achieving sensitivity of 0.765, specificity of 0.838, and an AUC of 0.787 (95% CI: 0.561–0.960). The model’s accuracy, Brier score, and log loss were 0.796, 0.197, and 0.599, respectively. DCA suggested potential clinical utility of the model. Conclusion: Ultrasound radiomics demonstrates promising diagnostic accuracy in differentiating benign from malignant breast nodules in women with silicone breast prostheses. This approach has the potential to serve as an additional diagnostic tool for patients following silicone breast augmentation.

3DSlicer Software-Assisted Neuroendoscopic Surgery Compared with Traditional Surgery on Surgical Effects, Complications, and Safety Evaluation in Patients with Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.

This study aims to comprehensively evaluate the complications, effectiveness, and safety of neuroendoscopic surgery (NS) assisted by 3DSlicer software, compared to traditional surgery in patients with intracerebral hemorrhage (IH). We searched for case-control trials from the Chinese Biomedical Literature data (CBM) online database, Wanfang Database, EMBASE, VIP Full-text Database, China National Knowledge Infrastructure (CNKI), Science Direct, Cochrane Library, and PubMed. The studies, published since January 2010, involved NS or traditional surgery for IH patients assisted by 3DSlicer software. Data were independently retrieved by two researchers, and the risk of bias in each study was evaluated using the Cochrane Handbook 5.3 standard. A total of 591 patients from seven controlled clinical studies were included. Fixed-effect model analysis revealed a significantly higher treatment effectiveness rate in the study group (SG) (P < 0.05). Random-effects model (REM) analysis indicated that the operation time in the SG was significantly shorter (P < 0.05). Furthermore, the SG group experienced significantly shorter hospitalization time (P < 0.05). NIHSS scores in the SG were notably lower (P < 0.05). Fixed-effect model analysis also showed that the incidence of postoperative complications in the SG was significantly lower (P < 0.05). Neuroendoscopic surgery assisted by 3DSlicer software is more effective in treating IH, enhancing prognosis, improving neurological function, and reducing complication rates. This approach appears to be a promising candidate for clinical adoption.

A study on the correlation between MRI-assessed breast volume asymmetry and patient satisfaction after immediate breast reconstruction in breast cancer patients

Immediate breast reconstruction provides breast cancer patients with a valuable opportunity to restore breast shape. However, post-reconstruction breast asymmetry remains a common issue that affects patient satisfaction. This study aims to quantify breast asymmetry after surgery using magnetic resonance imaging (MRI) and assess its impact on both breast satisfaction and overall outcome satisfaction, offering scientific evidence to guide improvements in preoperative evaluation. We retrospectively collected MRI images and demographic characteristics from 98 patients who underwent unilateral immediate breast reconstruction. All patients underwent breast MRI scans post-reconstruction, and the MRI images were converted into 3D models using 3D Slicer software. The implant volumes were measured and compared with the actual implant volumes inserted during surgery to assess the accuracy of the MRI measurements. Breast symmetry after surgery was quantified using MRI-measured breast volumes, and patient satisfaction was analyzed through BREAST-Q questionnaires to explore the relationship between breast symmetry, post-reconstruction breast satisfaction, and overall outcome satisfaction. The implant volumes measured in the 3D reconstructed MRI images exhibited high concordance with the volumes recorded during surgery (r = 0.980, p < 0.001). Breast asymmetry values were significantly higher in the high BMI group compared to the low BMI group (P = 0.013), while the asymmetry ratio was significantly lower in the high weight group compared to the low weight group (P = 0.006). The asymmetry ratio showed a weak correlation with breast satisfaction (r = − 0.341, P = 0.002) and outcome satisfaction (r = − 0.371, P = 0.004). When grouped by low asymmetry (asymmetry > 10%), there was no significant difference in breast satisfaction (P = 0.054) or outcome satisfaction scores (P = 0.23) between the groups. However, in cases of high asymmetry (asymmetry ratio > 40%), breast satisfaction (P = 0.003) and outcome satisfaction scores (P = 0.005) were significantly lower in the asymmetry group compared to the symmetry group. Breast volume asymmetry after immediate breast reconstruction is associated with patient satisfaction, with a significant decrease in satisfaction when asymmetry exceeds 40%. This suggests that preoperative evaluation should focus on breast symmetry to improve post-reconstruction patient satisfaction and surgical outcomes.

The effectiveness of CT-MRI image fusion technology in the preoperative assessment of middle ear cholesteatoma

Objective: To evaluate the effectiveness of CT-MRI image fusion technology in the preoperative assessment of middle ear cholesteatoma. Methods: The clinical data of patients with middle ear cholesteatoma who underwent surgery at Guangdong Provincial People's Hospital from September 2018 to January 2020 were retrospectively analyzed. Preoperative CT and MRI of the patients were automatically fused using the open-source software 3D Slicer, followed by 3D reconstruction of the fused inner ear structures. The degree of overlap was assessed and scored to evaluate the image fusion quality, with manual adjustments made for cases with poor fusion. The value of CT-MRI image fusion technology in the preoperative assessment of middle ear cholesteatoma was then analyzed. Results: A total of 27 patients (12 males and 15 females) aged (40.0±16.2) years were included. The fusion effect was satisfactory (score≥3) in 21 cases and poor (score<3) in 6 cases. After manual adjustments, all 6 cases achieved a satisfactory fusion effect (score≥3). The area under the receiver operating characteristic curve (AUC) values for diagnosing the extent of invasion of middle ear cholesteatoma in the retrotympanic and mastoid regions using CT, MRI, and CT-MRI fused images were 0.621 (95%CI: 0.404-0.838), 0.803 (95%CI: 0.638-0.969), and 0.841 (95%CI: 0.693-0.990), respectively, with the sensitivity of 94.1%, 70.6% and 88.2%, the specificity of 30.0%, 90.0% and 80.0%, and the accuracy of 70.4%, 77.8% and 85.2%, respectively. Conclusions: The 3D reconstruction of inner ear structures can be used to assess and refine the fusion quality of CT-MRI images. This CT-MRI image fusion technology is valuable for the preoperative localization assessment of middle ear cholesteatoma.

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques

IntroductionGadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.Methods107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score.ResultsThe sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%.ConclusionThe performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

Diagnostic performance of ClinCheck, Dolphin Imaging, and 3D Slicer software for Bolton discrepancy analysis.

To evaluate the diagnostic performance of ClinCheck, Dolphin Imaging orthodontic software, and 3D Slicer for the analysis of Bolton discrepancy (BD). Fifty-five pairs of early-stage digital models of patients treated with Invisalign were printed to measure the BD by manual method with a digital caliper (gold standard). The discrepancy values calculated by ClinCheck were obtained. In addition, the sample STL files were measured using Dolphin Imaging and 3D Slicer software to obtain BD values. To assess reliability, precision, and accuracy of the methods, intraclass correlation coefficients (ICCs), Dahlberg's formula, paired t-tests, and the Bland-Altman method were used, respectively. Repeated-measures analysis of variance with Bonferroni post hoc test was used to assess the difference between groups. The three methods showed reliable measurements (ICC ≥ 0.7), with the values of anterior Bolton slightly higher than overall Bolton. Measurements for the anterior Bolton showed higher precision (Dahlberg's formula 0.65, 0.70, and 0.55) than those for the overall Bolton. For anterior Bolton, only the measurements obtained by ClinCheck and Dolphin Imaging were accurate (P > .05, no proportion bias), while for overall Bolton, all groups had a significant difference. The Bland-Altman plots demonstrated no consistency for anterior Bolton measurements when 3D Slicer was used and for the overall Bolton. ClinCheck and Dolphin Imaging showed accuracy to quantify anterior BD. For the overall Bolton measurements, ClinCheck showed a statistical difference from the manual assessment but without relevant clinical significance.

Evaluation of hyoid bone position, shape, area, volume, and tongue volume.

Our study aims to determine the position and types of the hyoid bone and to evaluate the morphometry of the hyoid bone and tongue according to sex. Our study included cervical Computed Tomography (CT) images of 200 individuals (100 females, 100 males) between the ages of 18 and 84. Using the 3D Slicer software package, hyoid bone position, shape, area, volume and tongue volume measurements were made on these images. In our study, the position of the hyoid bone was found to be proportionally at the C3 vertebral level in both sexes. The most common hyoid bone was type B, while the least common was type V. Type B rate was statistically higher in females, and type H rate was higher in males (p < 0.05). Participants with type H had statistically higher bone volumes and areas than those with type B (p < 0.05). Differences between bone volumes and areas of other bone types were not statistically significant (p > 0.05). Average hyoid bone volume (females 1575.9mm3; males 2609.6mm3), hyoid bone area (females 1519.8mm2; males 2406.4mm2), tongue volume (females 66,659.5mm3; males 83,085.5mm3) were measured in females and males. A statistically weak negative correlation was found between the participants' ages and hyoid bone areas (rho = -0.162; p = 0.022). A statistically moderate positive correlation was found between tongue volume and hyoid bone volume/area (respectively p < 0.001; rho1 = 0.658, rho2 = 0.546). Hyoid bone volume, hyoid bone area and tongue volume were higher in males than females. As tongue volume increased, hyoid bone volume and hyoid bone area increased. The connection between tongue volume and hyoid bone volume will contribute to dentomaxillary development and surgical procedures, and bone morphology will also be important in different disciplines.

Modelling of Human Cerebral Blood Vessels for Improved Surgical Training: Image Processing and 3D Printing

Human cerebral blood vessels are highly intricate and significantly contribute to brain function support. In the surgical process of these vessels, the neurosurgeons will basically employ magnetic resonance imaging (MRI) as an imaging media to understand the location of the disorder, the anatomical position of vessels, and a guide in the surgical process. However, the usage of MRI data remains a challenge for surgeons in understanding anatomical structures in greater detail, as well as the limitations of training in handling difficult cases. This study aims to provide further technology, combining three-dimensional (3D) image models and 3D printing to accommodate the lack of visualization and pre-operative simulation using MRI data. First, the MRI data would be exported to a software 3D slicer that has the ability to process images with a threshold method to segment the required body parts and generate 3D models. Then, the 3D model of blood vessels would be imprinted using the SLA method to provide the complex anatomical structures of blood vessels. The results from both 3D image modeling and 3D printing have been validated and have dimensions similar to those of the MRI data, indicating that this work is highly accurate. This work significantly helps the surgeons to have a better plan for the surgery steps, identify potential issues before the procedure begins, and develop more precise approaches.

Maxillary protraction anchored on miniplates versus miniscrews: three-dimensional dentoskeletal comparison.

This retrospective study aimed to compare the three-dimensional (3D) outcomes of the novel miniscrew-anchored maxillary protraction (MAMP) therapy and the bone-anchored maxillary protraction (BAMP) therapy. The sample comprised growing patients with skeletal Class III malocclusion treated with two skeletal anchored maxillary protraction protocols. The MAMP group comprised 22 patients (9 female, 13 male; 10.9 ± 0.9 years of age at baseline) treated with Class III elastics anchored on a hybrid hyrax expander in the maxilla and two mandibular miniscrews distally to the permanent canines. The BAMP group comprised 24 patients (14 female, 10 male; 11.6 ± 1.1 years of age at baseline) treated with Class III elastic anchored in two titanium miniplates in the infra-zygomatic crest and two miniplates in the mesial of the mandibular permanent canines. Three-dimensional displacements were measured in the pre- and post-treatment cone-beam computed tomography scans superimposed on the cranial base using the Slicer Automated Dental Tools module of 3D Slicer software (www.slicer.org). Mean differences (MD) between groups and 95% confidence interval (CI) were obtained for all variables. Intergroup comparison was performed using the Analysis of Covariance (P < .05). Both groups showed improvements after treatment. The MAMP group showed a smaller anterior (MD: -1.09mm; 95% CI, -2.07 to -0.56) and 3D (MD: -1.27mm; 95% CI, -2.16 to -0.74) displacements of the maxilla after treatment when compared with BAMP. Both groups showed negligible and similar anteroposterior changes in the mandible (MD: 0.33mm; 95% CI, -2.15 to 1.34). A greater increase in the nasal cavity width (MD of 2.36; 95% CI, 1.97-3.05) was observed in the MAMP group when compared with BAMP. The absence of an untreated control group to assess the possible growth impact in these findings is a limitation of this study. Both BAMP and MAMP therapies showed adequate 3D outcomes after treatment. However, BAMP therapy produced a greater maxillary advancement with treatment, while MAMP therapy showed greater transversal increases in the nasal cavity.

CFD Analysis of the Choledynamic Flow Characteristics of a Patient with Gallbladder Carcinoma

Gallbladder carcinoma (GBC) is a rare malignancy with a low detection rate due to non-specific symptoms, complicating early diagnosis and treatment. This study aims to improve pre-operative and post-operative surgical analysis using patient-specific CFD analysis. CT scan images of a male patient with GBC were segmented using 3D Slicer software, and the biliary tree geometry was imported into ANSYS Workbench for fluid and solid meshing. The mechanical properties of the biliary tree and the rheological properties of bile were determined, with an LRN k-ω model used for simulating the gallbladder refilling stage. Results indicate that unhealthy bile flows slower than healthy bile, suggesting a link between obstructed bile flow and tumors or stones. Tumor-induced blockages cause pressure accumulation in the gallbladder neck, unlike the minimum pressure in healthy cases. These findings highlight the complex interplay between bile rheology, tumor development, and pressure dynamics, suggesting the model’s potential use in surgical planning.

An Application of Machine-Learning-Oriented Radiomics Model in Clear Cell Renal Cell Carcinoma (ccRCC) Early Diagnosis.

Aims/Background Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of kidney cancer, where early diagnosis is crucial for improving prognosis and treatment outcomes. Radiomics, which utilizes machine learning techniques, presents a promising approach in medical imaging for the early detection and characterization of such conditions. This study aims to explore the clinical utility of a machine-learning-based radiomics model in the early diagnosis of ccRCC. Methods Case data and abdominal computed tomography (CT) tumour images of patients with ccRCC were obtained from The Cancer Imaging Archive (TCIA) database. The dataset included 31 cases in the training set (19 males and 12 females, with an average age of 58.1 years) and 13 cases in the validation set (8 males and 5 females, with an average age of 69.6 years). The volume of interest (VOI) was manually delineated, slice by slice, along the tumour's edge in cross-sectional images of ccRCC. Radiomics features were extracted from each region of interest (ROI) using the "PyRadiomics" plug-in in 3D Slicer software (version 5.1.0, Massachusetts Institute of Technology and Brigham and Women's Hospital, Boston, MA, USA). Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, followed by 10-fold cross-validation. The selected radiomics features were then used to construct prediction models based on two different supervised machine learning algorithms: logistic regression and random forest. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curves and calibration curves. Finally, clinical data were integrated with the radiomics features to enhance the prediction model. Results A total of 44 radiomics features were ultimately selected to establish the prediction model based on the training set results. Among the two machine learning models, the logistic regression model demonstrated superior diagnostic performance. An evaluation of model establishment, considering both individual radiomics features (DifferenceVariance, JointEnergy.1, JointEntropy.2, MeanAbsoluteDeviation.7, SmallAreaHighGrayLevelEmphasis.7) and clinical data, indicated that the logistic regression model was stable and exhibited strong diagnostic performance, good calibration, and clinical applicability in patients with ccRCC. When clinical data were combined with radiomics features in the model, the area under the curve (AUC) reached 0.969, with an optimal threshold of -2.290, and sensitivity and specificity values of 89.3% and 95.2%, respectively. The calibration curve also confirmed that the logistic regression model had high calibration accuracy and greater clinical application value. Conclusion This machine-learning-based radiomics prediction model demonstrated significant value in the early diagnosis of clear cell renal cell carcinoma (ccRCC).

Radiomic and Clinical Model in the Prognostic Evaluation of Adenoid Cystic Carcinoma of the Head and Neck.

Adenoid Cystic Carcinoma (AdCC) is a rare malignant salivary gland tumor, with high rates of recurrence and distant metastasis. This study aims to stratify patients Relapse-Free Survival (RFS) using a combined model of clinical and radiomic features from preoperative MRI. This retrospective study included patients with primary AdCC who underwent surgery and adjuvant radiotherapy. Segmentations were manually performed by two head and neck radiologists. Radiomic features were extracted using the 3D Slicer software. Descriptive statistics was performed. A Survival Random Forest model was employed to select which radiological feature predict RFS. Cox proportional hazards models were constructed using clinical, radiological variables or both. Synthetic data augmentation was applied to address the small sample size and improve model robustness. Models were validated on real data and compared using the C-index and Prediction Error Curves (PEC). Three Cox models were developed: one with clinical features (C-index = 0.67), one with radiomic features (C-index = 0.68), and one combining both (C-index = 0.77). The combined clinical-radiomic model had the highest predictive accuracy and outperformed models based on clinical or radiomic features. The combined model also exhibited the lowest mean Brier score in PEC analysis, indicating better predictive performance. This study demonstrate that a combined radiomic-clinical model can predict RFS in AdCC patients. This model may provide clinicians a valuable tool in patient's management and may aid in personalized treatment planning.

Automated pipeline for linear and volumetric assessment of facial swelling after third molar surgery

BackgroundExtraction of mandibular third molars (M3Ms) is a routine procedure in oral and maxillofacial surgery, often associated with postoperative symptoms like pain, facial swelling, and trismus. This study aimed to introduce a standardized and automated protocol for swelling analysis following M3M surgery, presenting results regarding clinical conditions immediately and one-week after surgery.MethodsIn a prospective study, 35 patients were enrolled (mean age: 24.4 ± 5.8 years) for removal of 54 M3Ms. Facial swelling was evaluated through 3D facial scans before surgery (T0), at three days (T1), and seven days (T2) post-surgery. The open-source software 3DSlicer facilitated automated analysis, including data anonymization, orientation, surface registration, qualitative comparisons, linear measurements, and volumetric quantification. Pairwise superimposition of facial models enabled qualitative, vectorial, and quantitative assessments, comparing initial conditions with swelling development at T1 and T2. Additionally, changes between T1 and T2 were also evaluated. Secondary outcomes encompassed clinical evaluations of pain, trismus (maximum mouth opening), and surgery time. Statistical analysis involved the paired Student t-test to assess longitudinal changes and analysis of variance to evaluate outcome variables concerning difficulty scores. Linear regression models correlated primary outcome variables with secondary study variables (α < 0.05).ResultsLongitudinal analysis demonstrated significant but variable facial swelling, pain, and trismus at T1, followed by improvement at T2 (p < 0.001). Linear and volumetric differences correlated positively with surgery time (p < 0.05). A direct proportionality between linear and volume differences was observed, higher values at T1 correlated with higher values at T2 (p < 0.05).ConclusionsAn innovative digital workflow for precise quantification of postoperative facial changes was implemented, incorporating volumetric measurements that surpass linear assessments. Clinical conditions demonstrated a direct correlation with surgery time, deteriorating immediately and improving one-week after surgery.

Determination of the Surgical Landmarks for the Anterior and Middle Cranial Fossa in Dry Skulls With the Photography System, Cadavers and 3-Dimensional Computed Tomography

The present paper was designed to analyze the dimensions of such important bony structures and surgical landmarks, which are used by many clinicians in many surgical interventions, in dry skull, cadaver, and healthy subjects on computed tomography (CT) images, and to determine whether there is a significant difference between these methods, and to obtain reference values from 3 different methods. Eight cadavers and 16 dry skulls and 100 three-dimensional (3D) CT images were studied. Necessary permissions for the study were obtained from Ethics Comittee. The 16 parameters were measured with an electronic digital caliper accurate 0.01 mm (LCD Digital Vernier Dial Microcaliper (INCA, DCLA-0605, 0.6–150 mm, USA). Also, the images obtained were transferred to the 3D Slicer (version 5.6.2) software program. Eight cadavers and 16 dry skulls of Turkish adults were unknown age and sex, whereas the mean age of females and males on CT images were 31.63±11.23 and 33.70±13.34 years, respectively. All values of the surgical landmarks for the anterior and middle cranial fossa obtained from cadavers, dry skulls, and 3D CT subjects (except length of lesser wing, anterior clinoid lengths for 2 sides, and width for right side) were statistically significant between 3 groups (P&lt;0.05). This paper was conducted for the morphometric analysis of the specific regions of the anterior cranial fossa (ACF), and middle cranial fossa (MCF), which are used in neurosurgical procedures This detailed anatomic and radiologic reference values will be an extremely important source in the planning of both clinical and surgical approaches for neurosurgeon, anatomist, and radiologists.

INNV-02. BRAINMETS: A CLINICIAN-FRIENDLY SOFTWARE TO MANUALLY SEGMENT BRAIN METASTASES AND DETECT PRIMARY TUMOR ORIGIN USING DEEP LEARNING

Abstract Estimates suggest that around 20%-40% of cancer patients are affected by brain metastases. A subset of these patients first present with central nervous system manifestations with an unknown primary. The issue then arises regarding which region of the body should be imaged, if not the entire body, exposing the patient to more radiation. To address this issue, streamline workflows, and save precious time, we developed a software clinicians can use to identify primary tumor locus. “BrainMets” is a user-friendly software that handles a DICOM input, allows the user to segment the region(s) of interest manually, and uses a deep learning model to predict the most likely tumor origin. The model is a mixed-effects neural network pre-trained on radiomic features from open-source data by Ocaña-Tienda et al. (2023) using 637 high-resolution imaging studies from 10 different medical centers with an accuracy of 90.5%, precision of 92.7%, recall of 87.8%, and F1 score of 90.2%. The software leverages the open-source software 3D-Slicer to help segment regions of interest, extract radiomic features, and make predictions. It is built using Flask, a Python package for backend development. The frontend user interface is created using HTML/CSS and Bootstrap. The “BrainMets” software thus strives to enhance diagnostic workflow significantly, reduce appointment times for scans, and improve patient experience. Future enhancements include integrating more AI models, improving healthcare IT system compatibility, and updating datasets to boost predictive accuracy and generalizability. This tool sets a new standard in personalized medicine, optimizing treatment strategies for cancer patients with brain metastases. As adoption increases, it could substantially improve patient outcomes and reduce healthcare costs associated with misdiagnosis and overtreatment. Further research and development will focus on expanding its applicability across various types of cancers.

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Enhanced Preoperative Planning for Intracranial Aneurysms Through Multimodal Image Fusion of Silent/Time-of-Magnetic Resonance Angiography and Computed Tomography Using 3DSlicer: A Comparative Efficacy Analysis With Computed Tomography Angiography.

This study evaluates the effectiveness of multimodal image fusion (MIF) using silent and time-of-flight (TOF) magnetic resonance angiography (MRA) and computed tomography (CT) for preoperative planning in patients with intracranial aneurysms who have contraindications to contrast media. A retrospective study included 40 patients with intracranial aneurysms, diagnosed using three-dimensional computed tomography angiography (CTA). These patients underwent both Silent and TOF MRA scans, followed by a CTA scan. The multi-image fusion (MIF) technique, applied using 3DSlicer software, integrated the silent/TOF-MRA with CT images for preoperative assessment. This study compared the image quality, aneurysm detection sensitivity, and anatomic accuracy of the MIF images with those of three-dimensional CTA. Silent-MRA-CT fusion images demonstrated higher sensitivity (95.5%) and lower false negative rates (4.5%) compared with TOF-MRA-CT. Furthermore, silent-MRA-CT fusion images outperformed TOF-MRA-CT in terms of signal homogeneity, venous signal interference suppression, and aneurysm visibility (all P < 0.05). The interclass correlation coefficient and kappa values for aneurysm morphology and shape indicated superior measurement consistency and shape concordance of silent-MRA-CT with CTA compared with TOF-MRA-CT (all P < 0.01). This study supports the use of silent/TOF-MRA-CT fusion imaging as a reliable alternative to CTA, noting that silent-MRA-CT closely mirrors CTA. Contrast-free MRA-CT fusion images have the potential to be used for preoperative planning in patients with intracranial aneurysms who have contraindications to contrast.

Digital assessment of soft and hard tissue alterations after anterior maxillary tooth extraction

Objectives: This study aimed to assess dimensional alterations in soft and hard tissues 12 weeks post-extraction using novel computerized methods. Secondary objectives included exploring potential correlations between soft tissue changes and underlying bone variations, as well as investigating relationships between soft and hard tissue alterations and various contributing factors. Methods: For all patients, preoperative and postoperative CBCT scans and intra-oral scans were obtained. Soft and hard tissue dimensions before extraction and at 12 weeks post-extraction were measured using 3D-slicer software. Both DICOM and STL files were superimposed, and measurements were taken at 2mm and 4mm from the facial crestal level. Results: At twelve weeks, soft tissue thickness increased by 73.5% at 2mm and 51.2% at 4mm from the facial crestal bone. In contrast, there was a 42.5% and 33% bone resorption at 2mm and 4mm, respectively. Furthermore, the study found that smokers had a higher rate of soft tissue thickening and hard tissue resorption compared to non-smokers. The female group exhibited greater soft tissue thickening and bone resorption than the male group. Finally, the central incisor showed the highest bone loss rate, followed by the canine and the lateral incisor. Conclusions: Three months following tooth extraction, a physiological thickening of the facial soft tissue occurs concurrently with significant bone loss beneath it. Clinical Significance: This article’s novel digital assessment methods for evaluating soft and hard tissue alterations offer the potential to optimize treatment planning, enhancing patient outcomes and minimizing complications. By providing a comprehensive understanding of post-extraction tissue changes, it empowers clinicians to tailor treatments for individual patients, improving predictability and personalized care.