3D Slicer Software Research Articles

The anatomical variations of paranasal sinuses may be related to the formation of antrochoanal polyp by computed tomography imaging study.

There is little investigation into the connection between anatomic variations and the development of antrochoanal polyp (ACP), and the etiology of ACP remains unclear. The study aims to explore the relationship among anatomic variations, maxillary sinus volume, nasal meatus-related parameters, and the occurrence of ACP. There were 127 patients included in this retrospective cross-sectional study with unilateral ACPs hospitalized at Shandong Provincial ENT Hospital between February 2010 and February 2020. Evaluation indicators included anatomic variations, maxillary sinus volume, and nasal meatus-related parameters in 45 children and 82 adults, which were evaluated twice by 3DSlicer software. Parameters were assessed using the Kolmogorov-Smirnov test, followed by paired t-test and Chi-squared test for multiple comparisons. Significant differences were found in the accessory maxillary ostium (AMO) and maxillary sinus retention cyst between two sides (both P<0.001). Maxillary sinus volume and sex had an association of statistical significance on adults' ACP side (P=0.026) and non-antrochoanal polyp (non-ACP) side (P=0.032). The affected side's maxillary sinus volume was significantly larger than the healthy side (P<0.001). The length from the maxillary sinus orifice to the plane of the most lateral margin of the middle turbinate of the ACP side was larger than the non-ACP side in children (P=0.044). Males' length from the maxillary sinus orifice to the plane of the most lateral margin of the middle turbinate of the ACP side was considerably greater than the healthy side (P<0.001). The length from the maxillary sinus orifice to the plane of the most lateral margin of the middle turbinate (P=0.014) and the length from the inferior turbinate to the nasal septum (P=0.013) on the non-ACP side was higher than the affected side in adults. Males' length from the inferior turbinate to the nasal septum was higher on the healthy side than the affected side (P<0.001). Males had a greater maximum length from the maxillary sinus lateral wall to the nasal septum (P=0.024) and the length from the inferior turbinate to the nasal septum (P=0.003) on the non-ACP side than females. Males had a larger maximum length from the maxillary sinus lateral wall to the nasal septum on the ACP side than females (P=0.011). In our study, the occurrence of the AMO, the maxillary sinus's expanded size, and the stenosis of the associated channels around the ostiomeatal complex and common meatus are regarded as probably connected to the formation of ACPs. In addition, the anatomic variations that involve the ostiomeatal complex and may lead to a change in maxillary sinus pressure and nasal ventilation are important factors in the formation of ACPs.

Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.

Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer. We conducted a retrospective study including 223 patients diagnosed with invasive ductal breast cancer. Radiomics features were extracted from DCE-MRI using 3D-Slicer software. Two Ki-67 expression cutoff values (20% and 29%) were examined. Patients were divided into training (70%) and test (30%) sets. The Elastic Net method selected relevant features, and five machine-learning models were established. Radiomics models were created from intratumoral, peritumoral, and combined regions. Performance was assessed using ROC curves, accuracy, sensitivity, and specificity. For a Ki-67 cutoff value of 20%, the combined model exhibited the highest performance, with area under the curve (AUC) values of 0.838 (95% confidence interval (CI): 0.774-0.897) for the training set and 0.863 (95% CI: 0.764-0.949) for the test set. The AUC values for the tumor model were 0.816 (95% CI: 0.745-0.880) and 0.830 (95% CI: 0.724-0.916), and for the peritumor model were 0.790 (95% CI: 0.711-0.857) and 0.808 (95% CI: 0.682-0.910). When the Ki-67 cutoff value was set at 29%, the combined model also demonstrated superior predictive ability in both training set (AUC: 0.796; 95% CI: 0.724-0.862) and the test set (AUC: 0.823; 95% CI: 0.723-0.911). The AUC values for the tumor model were 0.785 (95% CI: 0.708-0.861) and 0.784 (95% CI: 0.663-0.882), and for the peritumor model were 0.773 (95% CI: 0.690-0.844) and 0.729 (95% CI: 0.603-0.847). Radiomics with DCE-MRI can predict Ki-67 expression in invasive ductal breast cancer. Integrating radiomics features from intratumoral and peritumoral regions yields a dependable prognostic model, facilitating pre-surgical detection and treatment decisions. This holds potential for commercial diagnostic tools.

The diagnostic value of a nomogram based on enhanced CT radiomics for differentiating between intrahepatic cholangiocarcinoma and early hepatic abscess.

This study aimed to investigate the value of a CT-enhanced scanning radiomics nomogram in distinguishing between early hepatic abscess (EHA) and intrahepatic cholangiocarcinoma (ICC) and to validate its diagnostic efficacy. Clinical and imaging data on 112 patients diagnosed with EHA and ICC who underwent double-phase CT-enhanced scanning at our hospital were collected. The contours of the lesions were delineated layer by layer across the three phases of CT scanning and enhancement using 3D Slicer software to define the region of interest (ROI). Subsequently, the contours were merged into 3D models, and radiomics features were extracted using the Radiomics plug-in. The data were randomly divided into training (n = 78) and validation (n = 34) cohorts at a 7:3 ratio, using the R programming language. Standardization was performed using the Z-score method, and LASSO regression was used to select the best λ-value for screening variables, which were then used to establish prediction models. The rad-score was calculated using the best radiomics model, and a joint model was constructed based on the rad-score and clinical scores. A nomogram was developed based on the joint model. The diagnostic efficacy of the models for distinguishing ICC and EHA was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses. Calibration curves were used to evaluate the reliability and accuracy of the nomograms, while decision curves and clinical impact curves were utilized to assess their clinical value. Compared with the ICC group, significant differences were observed in clinical data and imaging characteristics in the EHA group, including age, centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement (p < 0.05). Logistic regression analysis identified centripetal enhancement, hepatic pericardial depression sign, arterial perfusion abnormality, arterial CT value, and arteriovenous enhancement as independent influencing factors. Three, five, and four radiomics features were retained in the scanning, arterial, and venous phases, respectively. Single-phase models were constructed, with the radiomics model from the arterial phase demonstrating the best diagnostic efficacy. The rad-score was calculated using the arterial-phase radiomics model, and nomograms were drawn in conjunction with the clinical model. The nomogram based on the combined model exhibited the highest differential diagnostic efficacy between EHA and ICC (training cohort: AUC of 0.972; validation cohort: AUC of 0.868). The calibration curves indicated good agreement between the predicted and pathological results, while decision curves and clinical impact curves demonstrated higher clinical utility of the nomograms. The CT-enhanced scanning radiomics nomogram demonstrates high clinical value in distinguishing between EHA and ICC, thereby enhancing the accuracy of preoperative diagnosis.

Body composition is associated with postoperative complications in perihilar cholangiocarcinoma.

Perihilar cholangiocarcinoma (pCCA) is a malignant tumor of the hepatobiliary system which is still associated with a challenging prognosis. Postoperative complications play a crucial role in determining the overall prognosis of patients with pCCA. Changes in body composition (BC) have been shown to impact the prognosis of various types of tumors. Therefore, our study aimed to investigate the correlation between BC, postoperative complications and oncological outcome in patients with pCCA. All patients with pCCA who underwent curative-intent surgery for pCCA between 2010 and 2022 were included in this analysis. BC was assessed using preoperative computed tomography and analyzed with the assistance of a 3D Slicer software. Univariate and multivariate binary logistic regression analyses were conducted to examine the relationship between BC and clinical characteristics including various measurements of postoperative complications and Cox regressions and Kaplan-Meier analysis to evaluate oncological risk factors in the study cohort. BC was frequently altered in patients undergoing curative-intent liver resection for pCCA (n = 204) with 52.5% of the patients showing obesity, 55.9% sarcopenia, 21.6% sarcopenic obesity, 48.5% myosteatosis, and 69.1% visceral obesity. In multivariate analysis, severe postoperative complications (Clavien-Dindo ≥3b) were associated with body mass index (BMI) (Odds ratio (OR) = 2.001, p = 0.024), sarcopenia (OR = 2.145, p = 0.034), and myosteatosis (OR = 2.097, p = 0.017) as independent predictors. Furthermore, sarcopenia was associated with reduced overall survival (OS) in pCCA patients (sarcopenia vs. no-sarcopenia, 21 months vs. 32 months, p = 0.048 log rank). BC is highly associated with severe postoperative complications in patients with pCCA and shows tendency to be associated impaired overall survival. Preoperative assessment of BC and interventions to improve BC might therefore be key to improve outcome in pCCA patients undergoing surgical therapy.

A correlation study between prostate necrosis rate calculated by 3D Slicer software and clinical efficacy of prostatic artery embolization, along with an analysis of predictors of clinical success after prostatic artery embolization.

To analyze the correlation between the prostate necrosis rate at 1-month after prostatic artery embolization (PAE) and the clinical efficacy at 1-year after PAE, and to explore potential predictors of clinical success after PAE for the treatment of lower urinary tract symptoms secondary to benign prostatic hyperplasia (BPH). The prostate magnetic resonance imaging data at 1-month after PAE were imported into 3D Slicer software for calculating the prostate necrosis rate and thus analyzing the relationship between the prostate necrosis rate at 1-month after PAE and the efficacy score ratio at 1-year after PAE. The 151 patients with PAE technical success were divided into a clinical success group (n = 126) and a clinical failure group (n = 25). Independent predictors of clinical success after PAE were analyzed by multifactorial logistic regression, and the predictive performance of each factor was evaluated by applying the receiver operating characteristic curve and the area under the curve (AUC). There was a linear negative correlation between the prostate necrosis rate at 1-month after PAE and the efficacy score ratio at 1-year after surgery (P < 0.001). In the clinical success group, both the initial prostate volume (PV) and the prostate necrosis rate at 1-month after PAE were significantly higher than in the clinical failure group (P < 0.001), and acute urinary retention (AUR) and adenomatous-dominant BPH were also associated with clinical success (P < 0.05). Multifactorial logistic regression analysis revealed that larger initial PV, a higher prostate necrosis rate at 1-month after surgery, and AUR were independent predictors of clinical success after PAE. The AUC values for these three indicators and their combination were 0.720, 0.928, 0.599, and 0.951, respectively, in which the prostate necrosis rate at 1-month after PAE demonstrating a high predictive value. The higher the prostate necrosis rate at 1-month after PAE, the better the clinical efficacy at 1-year after PAE is likely to be, and the prostate necrosis rate at 1-month after PAE is expected to become a predictor of clinical success after PAE.

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model's performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91-0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8-0.84, F1-score = 0.9, PPV = 90%. Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.

Association of quantitative radiomic shape features with functional outcome after surgery for primary sporadic dorsal spinal meningiomas.

Spinal meningiomas (SM) account for 25%-46% of all primary spinal tumors and show an excellent long-term disease control in case of complete resection. Therefore, the postoperative functional outcome is of high importance. To date, reports on dorsally located SM are scarce. Moreover, the impact of radiomics shape features on the functional outcome after surgery for primary dorsal SMs has not been analyzed yet. We retrospectively performed an analysis of shape-based radiomic features in 3D slicer software and quantified the tumor volume, surface area, sphericity, surface area to volume ratio and tumor canal ratio. Subsequently, we evaluated the correlation between the radinomic parameters and the postoperative outcome according to Modified Japanese Orthopedic Association (mJOA) score. Between 2010 and 2022, we identified 24 Females and 2 Males operated on dorsal SMs in our institutional database. The most common SM localization was thoracic spine (n = 20), followed by cervical (n = 4), and lumbar (n = 2). The univariate analysis and the receiver operating characteristic (ROC) analysis showed a strong diagnostic performance of sphericity in the prediction of postoperative functional outcome based on mJOA score (AUC of 0.79, sphericity cut-of value 0.738; p = 0.01). Subsequently, the patients were divided into two groups (mJOA improved vs. mJOA stable/worsened). Patients with improved mJOA score showed significantly higher sphericity (0.79 ± 0.1 vs. 0.70 ± 1.0; p = 0.03). Finally, we divided the cohort based on sphericity (<0.738 and ≥0.738). The group with higher sphericity exhibited a significantly higher positive mJOA difference 3 months postoperatively (16.6 ± 1.4 vs. 14.8 ± 3.7; p = 0.03). In our study investigating primary sporadic dorsal SMs, we demonstrated that a higher degree of sphericity may be a positive predictor of postoperative improvement, as indicated by the mJOA score.

Impact of Altered Body Composition on Clinical and Oncological Outcomes in Intrahepatic Cholangiocarcinoma.

Intrahepatic cholangiocarcinoma is a common primary liver tumor with limited treatment options and poor prognosis. Changes in body composition (BC) have been shown to affect the prognosis of various types of tumors. Therefore, our study aimed to investigate the correlation between BC and clinical and oncological outcomes in patients with iCCA. All patients with iCCA who had surgery from 2010 to 2022 at our institution were included. We used CT scans and 3D Slicer software to assess BC and conducted logistic regressions as well as Cox regressions and Kaplan-Meier analyses to investigate associations between BC and clinical variables with focus on postoperative complications and oncological outcomes. BC was frequently altered in iCCA (n = 162), with 53.1% of the patients showing obesity, 63.2% sarcopenia, 52.8% myosteatosis, 10.1% visceral obesity, and 15.3% sarcopenic obesity. The multivariate analysis showed no meaningful association between BC and perioperative complications. Myosteatosis was associated with reduced overall survival (OS) in iCCA patients (myosteatosis vs. non-myosteatosis, 7 vs. 18 months, p = 0.016 log rank). Further, the subgroup analysis revealed a notable effect in the subset of R0-resected patients (myosteatosis vs. non-myosteatosis, 18 vs. 32 months, p = 0.025) and patients with nodal metastases (myosteatosis vs. non-myosteatosis, 7 vs. 18 months, p = 0.016). While altered BC is not associated with perioperative outcomes in iCCA, myosteatosis emerges as a prognostic factor for reduced OS in the overall and sub-populations of resected patients.

Evaluation of the accuracy of the resection template and restorations of the bone structures in the mandible area manufactured using the additive technique

The article presents the current possibilities in the field of modeling and manufacturing models of anatomical structures, surgical templates, and implants in terms of planning surgical procedures in the area of ​​the mandible. In the first stage, the 3D reconstruction of the mandible geometry using the 3D-Slicer software was presented. The next step of the process consisted in modeling resection templates and supplementing the loss of the bone structure of the mandible. The last stage consisted of manufacturing models using the additive FDM method from PC-ISO material and performing an accuracy assessment. The models' accuracy using the additive FDM technique is within the tolerance range of +/- 0.2 mm. It is sufficient to use models when planning a procedure.

Incidental Postmortem Diagnosis of Anterior Communicating Artery Aneurysm After Successful Treatment of Double Aneurysms of the Superior Cerebellar and Paraclinoid Internal Carotid Arteries

Intracranial aneurysm diagnosis uses several tools, of which digital subtraction angiography is still the gold standard. Computed tomography angiography is widely used for its specificity and sensibility. The 3D slicer software is an interesting diagnosis tool that provides high-quality 3D modeling. It facilitates diagnosis and improves aneurysm assessment. However, it is still not approved for clinical tasks. We present a case of a woman treated for a ruptured paraclinoid and superior cerebellar artery aneurysms, and who succumbed to a post-procedural rebleeding. During a research routine, we realized a computed tomography angiography-based 3D slicer modeling that detected an incidental anterior communicating artery aneurysm. This case illustrates an unusual association of two rare intracranial aneurysm locations and the usefulness of the 3D slicer modeling tool in the diagnosis of intracranial aneurysms. Through this report, we recommend using this tool worldwide for clinical tasks.

Radiology meets archaeology: digital restoration and 3D printing using CT data.

Archaeological objects are often recovered in blocks since highly porous materials and unstable and highly decayed objects cannot always be uncovered undamaged or time and resources for classic uncovering are lacking. Therefore, clinical computed tomography (CT) combined with freely available software solutions should be tested as a simple and fast method for visualizing and analyzing archaeological finds as an alternative to time-consuming restoration. As an example, a block with a shield boss was selected from a block excavation and examined by means of CT. Using the freely available software 3D-Slicer (https://www.slicer.org/), the shield boss and handle were segmented in the surrounding soil with different tools. They were then digitally reconstructed and then restored using Meshmixer (Autodesk Inc., San Francisco, CA). A 3D print was generated based on the reconstructed model of the shield boss. The individual steps of CT examination of the block recovery, segmentation, reconstruction, and 3D printing were successfully performed. Based on the restored fragments of the shield boss, it was possible to date the object and to determine the initial properties of the find non-destructively without classic restoration. Radiological imaging combined with digital reconstruction and 3D printing makes it possible to determine decisive characteristics of the archaeological find before it is uncovered and restored, which is a time-consuming process. This opens up new opportunities for cooperation between radiology and archaeology for the evaluation and analysis of archaeological finds. · The transfer of medical technology, digital image processing and 3D printing to archaeology has been demonstrated.. · The digital restoration and reconstruction of archaeological objects using CT images is possible.. · Medical imaging could make a significant contribution to the investigation and reconstruction of archaeological objects.. · Frohwerk E, Dürr A, Fiebich M et al. Radiology meets archaeology: digital restoration and 3D printing using CT data. Fortschr Röntgenstr 2024; 196: 607 - 611.

Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach

BackgroundCerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies.PurposeThis study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM.MethodsWe retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC).ResultsThe area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making.ConclusionThe combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.

Volumetric differences in temporomandibular joint components in patients with facial asymmetry: A 3D tomographic segmentation study

PurposeTo determine if there are differences in condylar volume, glenoid cavity and joint space between patients diagnosed with facial asymmetry (FA), between affected and contralateral sides and with respect to subjects without FA. MethodsCross-sectional, retrospective study comparing CT/CBCT images from patients with FA diagnosis referred to a private clinic, versus a control group, age and sex matched, of patients requiring orthodontic treatment without FA. The 3D Slicer software was used for the analysis. Bivariate analysis was performed to compare qualitative variables using Chi-square or Fisher tests, and for normal distribution quantitative variables, the t-student test was used. The level of significance was p < 0.05. ResultsThe FA group (n = 138) included: Hemimandibular Elongation (HE) (n = 64), Hemimandibular Hyperplasia (HH) (n = 12), Hybrid Form (HF) (n = 20), Asymmetric Mandibular Prognathism (AMP) (n = 25), Glenoid Cavity Asymmetry (GCA) (n = 9) and Functional Laterognathism (FL) (n = 8). The control group (n = 20) had no FA and no signs or symptoms of temporomandibular dysfunction. The right condylar and glenoid cavity volumes were significantly larger in FA cases. The volumes of the right and left joint space had significant differences only in HE patients. ConclusionCondylar volumes are higher in almost all FA subgroups compared to matched controls. The glenoid cavity volume is larger in FA patients, except for the GCA right and FL left sides. Regarding articular spaces, HE was the only entity with less volume on the contralateral side. The evaluation of structural volume by 3D-CT images helps to diagnose and differentiate FA subgroups.

Prognostic significance of perihematomal edema in basal ganglia hemorrhage after minimally invasive endoscopic evacuation.

Spontaneous basal ganglia hemorrhage is a common type of intracerebral hemorrhage (ICH) with no definitive treatment. Minimally invasive endoscopic evacuation is a promising therapeutic approach for ICH. In this study the authors examined prognostic factors associated with long-term functional dependence (modified Rankin Scale [mRS] score ≥ 4) in patients who had undergone endoscopic evacuation of basal ganglia hemorrhage. In total, 222 consecutive patients who underwent endoscopic evacuation between July 2019 and April 2022 at four neurosurgical centers were enrolled prospectively. Patients were dichotomized into functionally independent (mRS score ≤ 3) and functionally dependent (mRS score ≥ 4) groups. Hematoma and perihematomal edema (PHE) volumes were calculated using 3D Slicer software. Predictors of functional dependence were assessed using logistic regression models. Among the enrolled patients, the functional dependence rate was 45.50%. Factors independently associated with long-term functional dependence included female sex, older age (≥ 60 years), Glasgow Coma Scale score ≤ 8, larger preoperative hematoma volume (OR 1.02), and larger postoperative PHE volume (OR 1.03, 95% CI 1.01-1.05). A subsequent analysis evaluated the effect of stratified postoperative PHE volume on functional dependence. Specifically, patients with large (≥ 50 to < 75 ml) and extra-large (≥ 75 to 100 ml) postoperative PHE volumes had 4.61 (95% CI 0.99-21.53) and 6.75 (95% CI 1.20-37.85) times greater likelihood of long-term dependence, respectively, than patients with a small postoperative PHE volume (≥ 10 to < 25 ml). A large postoperative PHE volume is an independent risk factor for functional dependence among basal ganglia hemorrhage patients after endoscopic evacuation, especially with postoperative PHE volume ≥ 50 ml.

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling.

Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival. We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software. The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (>670 mm3) of infiltration had a worse prognosis compared to those with a lower volume. Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Artificial neural network application to characterize patients with Alzheimer ’s disease and Mild Cognitive Impairment from Cognitive Normal.

AbstractBackgroundThis study aimed to build an artificial neural network model to differentiate patients with various diagnosis, including Alzheimer’s disease (AD), mild cognitive impairment (MCI) from cognitive normal (CN) using structural MRI images.MethodsMRI T1 image of 580 participants (97 AD, 291 MCI and 192 CN) were downloaded from ADNI, which is a publicly available database. The MPRAGE T1W MRI were pre‐processed by SPM software, including re‐orientation, segmentation and normalized to the whole brain mask template. 3D slicer software with pyradiomics extension were employed to extract the 107 radiomics features within the brain. Also, 45 brain regional volumes were segmented and calculated using Freesurfer. The AD, MCI and CN groups were divided into a training group and a validation group based on 70:30 ratio. Ten‐fold cross validation were performed to minimize the chance of overfitting. Three feed forward neural network model were built using the neural network toolbox of MATLAB, with one model using 107 radiomics alone (R model), one with 45 brain regional volumes (V Model) and the last one with both radiomics and volumes features (R+V model).ResultsThe receiver operating characteristics (ROC) curves showed that the V model and the R+V model achieved excellent accuracy, with 96.6% and 94.8% respectively. The area under the curve (AUC) was 99.6% and 97.9% respectively.ConclusionThe neural network model can accurately differentiate AD, MCI from HC. The application is practical and can be fully automatic, which can be used for dementia screening.

Penggunaan Software 3Dd Slicer pada Pemodelan dan Visualisasi Volumetrik Meningioma

Meningioma is a tumor that is often found in Indonesia. Treatment for meningomenia itself can be through surgery, radiotherapy, or a combination of both. The decision to carry out radiotherapy itself must go through a planning stage using a treatment planning system (TPS) which requires accurate 3D volumetric imaging. On the other hand, 3D slicer itself is 3D imaging software that is commonly used in the field of radiology. Therefore, this study aims to find out how to use 3D slicer software in modeling and volumetric visualization of meningioma disease. This study uses MRI data from meningioma patients which are processed for volumetric segmentation with a 3D slicer. Based on the results obtained, it can be concluded that the 3D slicer software is effective for modeling and visualizing volumetric brain tumor disease. In this case the visualization &amp; volumetrics look clear and relatively accurate, namely a volume of 16,8402 cm3 with an accuracy of 98.5%.

MRI-based radiomics features for the non-invasive prediction of FIGO stage in cervical carcinoma: A multi-center study

PurposeTo develop and validate a model based on MRI radiomics modals for predicting surgical high FIGO(IB3 and ≥ IIA2) and low FIGO(IB1, IB2, and IIA1) stages in patients with cervical carcinoma (CC). MethodsA total of 296 early-stage patients with CC (preoperative FIGO stages IB-IIA) confirmed by surgery and pathology were included in this retrospective study from two institutions For each patient,we extracted radiomics features from spectral attenuated inversion-recovery T2-weighted (SPAIR-T2W) and contrast-enhanced T1-weighted (CE-T1W) images.Manual segmentation was performed using the 3D Slicer software, while radiomics features were extracted, screened using the R software. A 2-stage feature extraction strategy involving univariate analysis and the Least Absolute Shrinkage Selection Operator technique was performed. A support vector machine-based model was eventually constructed. Predictive accuracy of the training and validation datasets was assessed in terms of area under the ROC curve (AUC). ResultsA total of 1130 features were extracted from SPAIR-T2WI and CET1WI images respectively, in which 8 and 7 features significantly were associated with FIGO staging. AUCs of the SPAIR-T2W and CE-T1W models were were 0.803 and 0.790, respectively, in the internal validation group. In the external validation group, the AUCs were 0.767 and 0.749, respectively, which increased to 0.771 in the combined model. ConclusionOur study demonstrated the feasibility of radiomics features from SPAIR-T2W and CE-T1W images for the prediction of surgical FIGO stage in CC. Our proposed model thereby carries the potential as a non-invasive tool for the staging and treatment planning of this disease. Advances in knowledgeA radiomics model provide a non-invasive and objective method for the detection of FIGO staging in patients with cervical cancer before surgery, thus providing a reference for the selection of treatment options for patients.

Enhancing skeletal stability and Class III correction through active orthodontist engagement in virtual surgical planning: A voxel-based 3-dimensional analysis

Skeletal stability after bimaxillary surgical correction of Class III malocclusion was investigated through a qualitative and quantitative analysis of the maxilla and the distal and proximal mandibular segments using a 3-dimensional voxel-based superimposition among virtual surgical predictions performed by the orthodontist in close communication with the maxillofacial surgeon and 12-18 months postoperative outcomes. A comprehensive secondary data analysis was conducted on deidentified preoperative (1 month before surgery [T1]) and 12-18 months postoperative (midterm [T2]) cone-beam computed tomography scans, along with virtual surgical planning (VSP) data obtained by Dolphin Imaging software. The sample for the study consisted of 17 patients (mean age, 24.8 ± 3.5 years). Using 3D Slicer software, automated tools based on deep-learning approaches were used for cone-beam computed tomography orientation, registration, bone segmentation, and landmark identification. Colormaps were generated for qualitative analysis, whereas linear and angular differences between the planned (T1-VSP) and observed (T1-T2) outcomes were calculated for quantitative assessments. Statistical analysis was conducted with a significance level of α= 0.05. The midterm surgical outcomes revealed a slight but significantly less maxillary advancement compared with the planned position (mean difference, 1.84 ± 1.50 mm; P= 0.004). The repositioning of the mandibular distal segment was stable, with insignificant differences in linear (T1-VSP, 1.01 ± 3.66 mm; T1-T2, 0.32 ± 4.17 mm) and angular (T1-VSP, 1.53° ± 1.60°; T1-T2, 1.54° ± 1.50°) displacements (P>0.05). The proximal segments exhibited lateral displacement within 1.5° for both the mandibular right and left ramus at T1-VSP and T1-T2 (P>0.05). The analysis of fully digital planned and surgically repositioned maxilla and mandible revealed excellent precision. In the midterm surgical outcomes of maxillary advancement, a minor deviation from the planned anterior movement was observed.

Application of 3D visualization technology based on hematoma edge key points setting for emergency hypertensive cerebral hemorrhage surgery in primary hospitals

ObjectiveThis study aims to explore the application potential of 3D visualization technology based in emergency hypertensive cerebral hemorrhage surgery in primary hospitals. The specific goal is to use 3DSlicer software to perform 3D reconstruction and body surface projection on patients with hypertensive cerebral hemorrhage, provide accurate hematoma location information, help surgeons determine the specific location of hematoma on the body surface, and reduce the expansion of surgical incisions. Methods3D reconstruction technology based on 3DSlicer software was employed to process CT images of patients with cerebral hemorrhage. By segmenting and reconstructing the images, a 3D model of the hematoma was generated and projected onto the patient's body surface. Utilizing the functionalities of 3DSlicer software in conjunction with the surgeon's anatomical knowledge, accurate hematoma positioning on the body surface was achieved. Results23 patients were enrolled in this study, and underwent successful surgical evacuation. The implementation of 3D visualization technology using 3DSlicer software is expected to provide precise hematoma localization information for emergency hypertensive intracerebral hemorrhage surgery in primary hospitals. This approach will enable surgeons to accurately determine the appropriate surgical incision, thereby minimizing unnecessary trauma and improving the overall success rate of surgery. ConclusionThis study demonstrates the potential application of 3D visualization technology based on 3DSlicer software in emergency hypertensive cerebral hemorrhage surgery within primary hospitals. By utilizing 3DSlicer software for hematoma localization, accurate information support can be provided to assist surgeons in managing patients with hypertensive cerebral hemorrhage.