Main Page/SlicerCommunity/2023

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The community that relies on 3D Slicer is large and active: (numbers below updated on December 1^st, 2023)

• 1,467,466+ downloads in the last 11 years (269,677 in 2023, 206,541 in 2022)
• over 17.900+ literature search results on Google Scholar
  • 13,400+ cancer
  • 7.290+ brain
  • 6,380+ lung
  • 3,980+ breast
  • 3,080+ prostate

• 2,147+ papers on PubMed citing the Slicer platform paper
  • Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J-C., Pujol S., Bauer C., Jennings D., Fennessy F.M., Sonka M., Buatti J., Aylward S.R., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magnetic Resonance Imaging. 2012 Nov;30(9):1323-41. PMID: 22770690. PMCID: PMC3466397.

• 39 events in open source hackathon series continuously running since 2005 with 3260 total participants
• Slicer Forum with +8,138 subscribers has approximately 275 posts every week

The following is a sample of the research performed using 3D Slicer outside of the group that develops it. in 2023

We monitor PubMed and related databases to update these lists, but if you know of other research related to the Slicer community that should be included here please email: marianna (at) bwh.harvard.edu.

2023

MRI Radiomic Features of Peritumoral Edema May Predict the Recurrence Sites of Glioblastoma Multiforme

Publication: Front Oncol. 2023 Jan 4;12:1042498.2023 Feb;280(2):919-24. PMID: 36686829 PDF Authors: Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J. Institution: Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. Abstract: Background and purpose: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D Slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.

Henri IV of France's Larynx 3D Reconstitution

Publication: Eur Arch Otorhinolaryngol. 2023 Feb;280(2):919-24. PMID: 36149490 Authors: Baudouin R, Amelot A, Laprie Y, Crevier-Buchman L, Maeda S, Huynh-Charlier I, Hans S, Charlier P. Institution: Department of Otolaryngology-Head & Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Université Paris Saclay), Montigny-le-Bretonneux, France. Abstract: Objectives: King Henri IV of France (reign from 1589 to 1610) was one of the most important kings of France. Embalmed and buried in Saint-Denis, his remains were beheaded in 1793. His head (including his larynx) survived in successive private collections until its definitive identification in 2010. The purpose of the study was to provide a morphologic study of the larynx with a 3D reconstitution. Methods: A flexible endoscopy was performed via the mouth and via the trachea. Measures of the larynx (vocal folds lengths, thickness, width, larynx height) were collected from the CT-scan by a panel of experts blind each other. The segmentation of the laryngeal anatomical components (vocal folds, cartilages) was performed using 3D Slicer. Mesh smoothing and 3D reconstitution were performed using Fusion 360®. Reconstitution was discussed between the experts. Decision was made by consensus after discussion. Results: Cricoid, thyroid, arytenoid cartilages, vocal folds and hyoid bone were identified and a computed 3D reconstitution of the larynx was made. The laryngeal 3D model appeared morphologically similar to a living subject. Measures were similar but smaller than those of a modern subject. Conclusions: The 3D reconstitution of the larynx of Henri IV of France was conducted from the CT-scan of his mummified head. This work constitutes a first valuable morphologic analysis of a larynx from an embalmed individual. This anatomical work is the first step towards the reconstruction of the voice of this historical character, which we hope to concretize with computer modeling tools in a second step.

A Three-Dimensionally Printed Otological Model for Cholesteatoma Mastoidectomy Training

Publication: Eur Arch Otorhinolaryngol. 2023 Feb;280(2):671-80. PMID: 35789285 Authors: de Souza MA, Bento RF, Lopes PT. Institution: Otolaryngology Department, University of São Paulo School of Medicine, São Paulo, Brazil. Abstract: Purpose: To relate the creation and expert validation (face and content validity) of an affordable three-dimensional (3D) printed model of temporal bones with chronic otitis media with cholesteatoma (COMC) as a simulator for mastoidectomy. Methods: We performed computed tomography (CT) of the temporal bones of a patient with COMC followed at the University of São Paulo (USP) Hospital with 3D Slicer to create a 3-D model of the affected bone using light-curing resin and silicone (cholesteatoma). The final 3-D printed images were scored by 10 otologists using a customized version of the Michigan Standard Simulation Scale Experience (MiSSES). Internal consistency and inter-rater reliability were assessed using Cronbach's α and intraclass correlations. Results: Otologists consistently scored the model positively for fidelity, educational value, reactions, and the overall model quality. Nine otologists agreed that the model was a good educational device for surgical training of COMC. All experts deemed the model ready-or nearly ready-for use. The final cost of the model, including raw materials and manufacturing, was 120 USD. Conclusions: Using 3D printing technology, we created the first anatomically accurate, low-cost, disease-reproducing 3D model of temporal bones for mastoidectomy training for cholesteatoma.

Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma

Publication: Cancers (Basel). 2023 Jan 5;15(2):365. PMID: 36672315 | PDF Authors: Tao YY, Shi Y, Gong XQ, Li L, Li ZM, Yang L, Zhang XM. Institution: Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China. Abstract: Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.

Association Between Emphysema and Other Pulmonary Computed Tomography Patterns in COVID-19 Pneumonia

Publication: BMC Neurol. J Med Virol. 2023 Jan;95(1):e28293. PMID: 36358023 PDF Authors: Han K, Wang J, Zou Y, Zhang Y, Zhou L, Yin Y. Institution: Department of Cardiothoracic Vascular Surgery, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China. Abstract: To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software, 3D Slicer to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.

Evaluation of the Prognosis of Acute Subdural Hematoma According to the Density Differences Between Gray and White Matter

Publication: Front Neurol. 2023 Jan 6;13:1024018. PMID: 36686517 | PDF Authors: Li Z, Feng Y, Wang P, Han S, Zhang K, Zhang C, Lu S, Lv C, Zhu F, Bie L. Institution: Department of Neurosurgery of the First Clinical Hospital, Jilin University, Changchun, China. Abstract: Objective: Acute subdural hematoma (ASDH) is a common neurological emergency, and its appearance on head-computed tomographic (CT) imaging helps guide clinical treatment. To provide a basis for clinical decision-making, we analyzed that the density difference between the gray and white matter of the CT image is associated with the prognosis of patients with ASDH. Methods: We analyzed the data of 194 patients who had ASDH as a result of closed traumatic brain injury (TBI) between 2018 and 2021. The patients were subdivided into surgical and non-surgical groups, and the non-surgical group was further subdivided into "diffused [hematoma]" and "non-diffused" groups. The control group's CT scans were normal. The 3D Slicer software was used to quantitatively analyze the density of gray and white matter depicted in the CT images. Results: Imaging evaluation showed that the median difference in density between the gray and white matter on the injured side was 4.12 HU (IQR, 3.91-4.22 HU; p < 0.001) and on the non-injured side was 4.07 HU (IQR, 3.90-4.19 HU; p < 0.001), and the hematoma needs to be surgically removed. The median density difference value of the gray and white matter on the injured side was 3.74 HU (IQR, 3.53-4.01 HU; p < 0.001) and on the non-injured side was 3.71 HU (IQR, 3.69-3.73 HU; p < 0.001), and the hematoma could diffuse in a short time. Conclusion: Quantitative analysis of the density differences in the gray and white matter of the CT images can be used to evaluate the clinical prognosis of patients with ASDH.

The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery

Publication: Front Oncol. Neurosurgery. 2023 Jan 1;92(1):179-85. PMID: 36170168 Authors: DiRisio AC, Feng R, Shuman WH, Platt S, Price G, Dullea JT, Gilja S, D'Andrea MR, Delman BN, Bederson JB, Shrivastava RK. Institution: Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Abstract: Background: The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery. Objective: To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas. Methods: Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined. Results: One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort (P > .05, area under the curve 0.46). Conclusion: Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.

Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging

Publication: Acad Radiol. 2023 Jan;30(1):40-6. PMID: 35577699 Authors: Shang H, Li J, Jiao T, Fang C, Li K, Yin D, Zeng Q. Institution: Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China. Abstract: Rationale and objectives: To explore the feasibility of differentiating three predominant metastatic tumor types using lung computed tomography (CT) radiomics features based on supervised machine learning. Materials and methods: This retrospective analysis included 252 lung metastases (LM) (from 78 patients), which were divided into the training (n = 176) and test (n = 76) cohort randomly. The metastases originated from colorectal cancer (n = 97), breast cancer (n = 87), and renal carcinoma (n = 68). An additional 77 LM (from 35 patients) were used for external validation. All radiomics features were extracted from lung CT using an open-source software, 3D Slicer3D Slicer. The least absolute shrinkage and selection operator (LASSO) method selected the optimal radiomics features to build the model. Random forest and support vector machine (SVM) were selected to build three-class and two-class models. The performance of the classification model was evaluated with the area under the receiver operating characteristic curve (AUC) by two strategies: one-versus-rest and one-versus-one. Results: Eight hundred and fifty-one quantitative radiomics features were extracted from lung CT. By LASSO, 23 optimal features were extracted in three-class, and 25, 29, and 35 features in two-class for differentiating every two of three LM (colorectal cancer vs. renal carcinoma, colorectal cancer vs. breast cancer, and breast cancer vs. renal carcinoma, respectively). The AUCs of the three-class model were 0.83 for colorectal cancer, 0.79 for breast cancer, and 0.91 for renal carcinoma in the test cohort. In the external validation cohort, the AUCs were 0.77, 0.83, and 0.81, respectively. Swarmplot shows the distribution of radiomics features among three different LM types. In the two-class model, high accuracy and AUC were obtained by SVM. The AUC of discriminating colorectal cancer LM from renal carcinoma LM was 0.84, and breast cancer LM from colorectal cancer LM and renal carcinoma LM were 0.80 and 0.94, respectively. The AUCs were 0.77, 0.78, and 0.84 in the external validation cohort. Conclusion: Quantitative radiomics features based on Lung CT exhibited good discriminative performance in LM of primary colorectal cancer, breast cancer, and renal carcinoma.

MRI Radiomic Features of Peritumoral Edema May Predict the Recurrence Sites of Glioblastoma Multiforme

Publication: Front Oncol. 2023 Jan 4;12:1042498. PMID: 36686829 Authors: Long H, Zhang P, Bi Y, Yang C, Wu M, He D, Huang S, Yang K, Qi S, Wang J. Institution: Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. Abstract: Background and purpose: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D Slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.