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

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.

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.