Main Page/SlicerCommunity/2022 - Revision history

Marianna: /* Three-Dimensional Assessment of Tongue Cancer Prognosis by Preoperative MRI */

2023-07-25T16:59:25Z

Three-Dimensional Assessment of Tongue Cancer Prognosis by Preoperative MRI

Marianna at 21:06, 24 January 2023

2023-01-24T21:06:30Z

Marianna at 20:49, 24 January 2023

2023-01-24T20:49:41Z

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

2023-01-24T19:28:06Z

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

Marianna: /* A Three-Dimensionally Printed Otological Model for Cholesteatoma Mastoidectomy Training */

2023-01-24T19:26:42Z

A Three-Dimensionally Printed Otological Model for Cholesteatoma Mastoidectomy Training

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

2023-01-24T19:26:01Z

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

Marianna: /* Three-Dimensional Changes in the Mandibular Proximal Segment After Using a Surgery-First Approach in Patients With Class III Malocclusion and Facial Asymmetry */

2023-01-24T19:24:04Z

Three-Dimensional Changes in the Mandibular Proximal Segment After Using a Surgery-First Approach in Patients With Class III Malocclusion and Facial Asymmetry

Marianna: /* 2022 */

2022-11-09T00:13:01Z

2022

Marianna: /* Characteristics of Cognitive Function in Patients With Cerebellar Infarction and Its Association With Lesion Location */

2022-11-09T00:11:14Z

Characteristics of Cognitive Function in Patients With Cerebellar Infarction and Its Association With Lesion Location

Marianna: /* Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer */

2022-11-08T23:51:04Z

Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer

← Older revision		Revision as of 16:59, 25 July 2023
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	'''Abstract:''' A numerical analysis of specific absorption rate (SAR) and temperature distributions in a realistic human head model is presented in this study. The key challenge is to rise cancer temperature to an optimal temperature without heating nearby healthy tissues. The model's uniqueness is that it captures the effect of nanoparticles on both brain cancer diagnosis and treatment. A realistic human head model with a cancerous brain segmented from 2D magnetic resonance imaging (MRI) gained from an actual patient using [http://www.slicer.org '''3D Slicer'''], modeled, and simulated using CST-Microwave Studio, and illuminated by Archimedes spiral antenna. At frequencies of 2450 MHz and 915 MHz, the model simulated the absence and presence of various nanoparticles. The obtained results suggest that when using nanoparticles, it is possible to achieve sufficient energy deposition and temperature rise to therapeutic values (greater than 42 °C) in brain cancers using the proposed noninvasive hyperthermia system at 915 MHz frequency, especially for gold nanoparticles, without harming surrounding healthy tissue. Our research might pave the way for a clinical applicator prototype that can heat brain cancer.		'''Abstract:''' A numerical analysis of specific absorption rate (SAR) and temperature distributions in a realistic human head model is presented in this study. The key challenge is to rise cancer temperature to an optimal temperature without heating nearby healthy tissues. The model's uniqueness is that it captures the effect of nanoparticles on both brain cancer diagnosis and treatment. A realistic human head model with a cancerous brain segmented from 2D magnetic resonance imaging (MRI) gained from an actual patient using [http://www.slicer.org '''3D Slicer'''], modeled, and simulated using CST-Microwave Studio, and illuminated by Archimedes spiral antenna. At frequencies of 2450 MHz and 915 MHz, the model simulated the absence and presence of various nanoparticles. The obtained results suggest that when using nanoparticles, it is possible to achieve sufficient energy deposition and temperature rise to therapeutic values (greater than 42 °C) in brain cancers using the proposed noninvasive hyperthermia system at 915 MHz frequency, especially for gold nanoparticles, without harming surrounding healthy tissue. Our research might pave the way for a clinical applicator prototype that can heat brain cancer.
−	\|}
−
−	~~==Three-Dimensional Assessment of Tongue Cancer Prognosis by Preoperative MRI==~~
−
−	~~{\| width="100%"~~
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−	~~'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35426211 Oral Dis. 2022 Apr 15. PMID: 35426211]~~
−
−	~~'''Authors:''' Li C, Zhu L, Guo Y, Ji T, Ren Z.~~
−
−	~~'''Institution:''' Department of Oral and Maxillofacial Head and Neck Oncology, The Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.~~
−
−	'''Abstract:''' Purpose: To obtain the relative volume by measuring the tongue volume and the lesion volume, and further explore its relationship with the prognosis of patients, hoping to supplement the TNM staging with a new index.
−
−	~~Methods: ITK-SANP software was used to outline the patients' MRI. After MRI reconstruction and measurement, [http://www.slicer.org '''3D Slicer'''] software was used to estimate tumor volume.~~
−
−	Results: A total of 64 patients with tongue cancer who met the inclusion criteria were included in the study. The estimated tumor volume after MRI reconstruction revealed a significant and robust correlation with tumor stage (p < 0.05, Rs = 0.6207) and a substantial and medium correlation with early lymph node metastasis (p < 0.05, Rs = 0.4873).
−
−	Conclusions: We classified tongue cancer into three grades based on tumor volume (Stage I, tumors smaller than 1500 mm<sup>3</sup>; Stage II, tumors 1500-9000 mm<sup>3</sup>; and Stage III, tumors larger than 9000 mm<sup>3</sup>), and such grading could be used as a reference for tumor staging, lymph node metastasis, and patient prognosis to a certain extent.
	\|}		\|}

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 '''Abstract:''' Identification of bone erosions and quantification of erosion volume is important for rheumatoid arthritis diagnosis, and can add important information to evaluate disease progression and treatment effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) is well suited for this purpose, however analysis methods are not widely available. The purpose of this study was to develop an open-source software tool for the identification and quantification of bone erosions using images acquired by HR-pQCT. The collection of modules, Bone Analysis Modules (BAM) - Erosion, implements previously published erosion analysis techniques as modules in [http://www.slicer.org '''3D Slicer'''], an open-source image processing and visualization tool. BAM includes a module to automatically identify cortical interruptions, from which erosions are manually selected, and a hybrid module that combines morphological and level set operations to quantify the volume of bone erosions. HR-pQCT images of the second and third metacarpophalangeal (MCP) joints were acquired in patients with RA (XtremeCT, n = 14, XtremeCTII, n = 22). The number of cortical interruptions detected by BAM-Erosion agreed strongly with the previously published cortical interruption detection algorithm for both XtremeCT (r<sup>2</sup> = 0.85) and XtremeCTII (r<sup>2</sup> = 0.87). Erosion volume assessment by BAM-Erosion agreed strongly (r<sup>2</sup> = 0.95) with the Medical Image Analysis Framework. BAM-Erosion provides an open-source erosion analysis tool that produces comparable results to previously published algorithms, with improved options for visualization. The strength of the tool is that it implements multiple image processing algorithms for erosion analysis on a single, widely available, open-source platform that can accommodate future updates.
 |}
 ==A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage==
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 Conclusions: The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.
 |}

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 __TOC__
 =2022=
 ==Open-Source Image Analysis Tool for the Identification and Quantification of Cortical Interruptions and Bone Erosions in High-Resolution Peripheral Quantitative Computed Tomography Images of Patients With Rheumatoid Arthritis==
@@ Line 15: / Line 28: @@
 '''Abstract:''' Identification of bone erosions and quantification of erosion volume is important for rheumatoid arthritis diagnosis, and can add important information to evaluate disease progression and treatment effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) is well suited for this purpose, however analysis methods are not widely available. The purpose of this study was to develop an open-source software tool for the identification and quantification of bone erosions using images acquired by HR-pQCT. The collection of modules, Bone Analysis Modules (BAM) - Erosion, implements previously published erosion analysis techniques as modules in [http://www.slicer.org '''3D Slicer'''], an open-source image processing and visualization tool. BAM includes a module to automatically identify cortical interruptions, from which erosions are manually selected, and a hybrid module that combines morphological and level set operations to quantify the volume of bone erosions. HR-pQCT images of the second and third metacarpophalangeal (MCP) joints were acquired in patients with RA (XtremeCT, n = 14, XtremeCTII, n = 22). The number of cortical interruptions detected by BAM-Erosion agreed strongly with the previously published cortical interruption detection algorithm for both XtremeCT (r<sup>2</sup> = 0.85) and XtremeCTII (r<sup>2</sup> = 0.87). Erosion volume assessment by BAM-Erosion agreed strongly (r<sup>2</sup> = 0.95) with the Medical Image Analysis Framework. BAM-Erosion provides an open-source erosion analysis tool that produces comparable results to previously published algorithms, with improved options for visualization. The strength of the tool is that it implements multiple image processing algorithms for erosion analysis on a single, widely available, open-source platform that can accommodate future updates.
 |}
 ==Chew on This: Oral Jaw Shape Is Not Correlated With Diet Type in Loricariid Catfishes==
@@ Line 368: / Line 419: @@
 Conclusions: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed.
 |}
 ==The Value of Whole-Lesion Histogram Analysis Based on Field‑of‑View Optimized and Constrained Undistorted Single Shot (FOCUS) DWI for Predicting Axillary Lymph Node Status in Early-Stage Breast Cancer==

← Older revision		Revision as of 19:28, 24 January 2023
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	'''Abstract:''' Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available [http://www.slicer.org '''3D Slicer'''] software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.		'''Abstract:''' Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available [http://www.slicer.org '''3D Slicer'''] software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.
−	\|}
−
−	~~==Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging==~~
−
−	~~{\| width="100%"~~
−	\|
−	~~'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35577699 Acad Radiol. 2022 May 13:S1076-6332(22)00253-7. 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, [http://www.slicer.org '''3D Slicer'''][http://www.slicer.org '''3D 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.~~
	\|}		\|}

← Older revision		Revision as of 19:26, 24 January 2023
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	Conclusion: The intra-tumoural metabolic heterogeneity of primary lesions in NB was an independent prognostic factor for EFS. The combined predictive effect of intra-tumoural metabolic heterogeneity and BMI provided prognostic survival information in NB patients.		Conclusion: The intra-tumoural metabolic heterogeneity of primary lesions in NB was an independent prognostic factor for EFS. The combined predictive effect of intra-tumoural metabolic heterogeneity and BMI provided prognostic survival information in NB patients.
−	\|}
−
−	~~==A Three-Dimensionally Printed Otological Model for Cholesteatoma Mastoidectomy Training==~~
−
−	~~{\| width="100%"~~
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−	~~'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35789285 Eur Arch Otorhinolaryngol. 2022 Jul 5. 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 [http://www.slicer.org '''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.~~
	\|}		\|}

← Older revision		Revision as of 19:26, 24 January 2023
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	Conclusion: This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.		Conclusion: This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.
−	\|}
−
−	~~==The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery==~~
−
−	~~{\| width="100%"~~
−	\|
−	~~'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36170168 Front Oncol. Neurosurgery. 2022 Sep 28. 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 [http://www.slicer.org '''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.
	\|}		\|}

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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35175981 J Craniofac Surg. 2022 Oct 1;33(7):1956-1961. PMID: 35175981] |[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9378743/pdf/scs-33-1956.pdf PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35175981 J Craniofac Surg. 2022 Oct 1;33(7):1956-1961. PMID: 35175981] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9378743/pdf/scs-33-1956.pdf PDF]
 '''Authors:''' Kim M, Jha N, Choi JH, Kim YJ, Lee U, Cevidanes L, Choi JY, Baek SH.

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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36322589 PLoS One. 2022 Nov 2;17(11):e0277102. PMID: 36322589] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629652/pdf/pone.0277102.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36322589 PLoS One. 2022 Nov 2;17(11):e0277102. PMID: 36322589] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9629652/pdf/pone.0277102.pdf PDF]
 '''Authors:''' Black CR, Armbruster JW.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36116543 J Dent. 2022 Nov;126:104295. PMID: 36116543] | [https://www.sciencedirect.com/science/article/pii/S0300571222003505/pdfft?md5=f819d603ef8dd5aa8ba2ab73a4d811b1&pid=1-s2.0-S0300571222003505-main.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36116543 J Dent. 2022 Nov;126:104295. PMID: 36116543] | [https://www.sciencedirect.com/science/article/pii/S0300571222003505/pdfft?md5=f819d603ef8dd5aa8ba2ab73a4d811b1&pid=1-s2.0-S0300571222003505-main.pdf PDF]
 '''Authors:''' Kivovics M, Pénzes D, Moldvai J, Mijiritsky E, Németh O.
@@ Line 141: / Line 141: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36268191 Front Aging Neurosci. 2022 Oct 4;14:965022. PMID: 36268191] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577113/pdf/fnagi-14-965022.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36268191 Front Aging Neurosci. 2022 Oct 4;14:965022. PMID: 36268191] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577113/pdf/fnagi-14-965022.pdf PDF]
 '''Authors:''' Liu Q, Liu C, Zhang Y.
@@ Line 154: / Line 154: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36307532 Sci Rep. 2022 Oct 28;12(1):18191.  PMID: 36307532] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616850/pdf/41598_2022_Article_23087.pdf
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36307532 Sci Rep. 2022 Oct 28;12(1):18191.  PMID: 36307532] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9616850/pdf/41598_2022_Article_23087.pdf PDF]
 '''Authors:''' Whitta G, Liang J, Stott NS, Mirjalili SA, Battin M, Williams SA.
@@ Line 207: / Line 207: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35948490  Clin Radiol. 2022 Oct;77(10):e711-e718. PMID: 35948490]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35948490 Clin Radiol. 2022 Oct;77(10):e711-e718. PMID: 35948490]
 '''Authors:''' O'Shea A, Kilcoyne A, McDermott E, O'Grady M, McDermott S.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36229793 BMC Geriatr. 2022 Oct 13;22(1):796. PMID: 36229793] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563158/pdf/12877_2022_Article_3502.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36229793 BMC Geriatr. 2022 Oct 13;22(1):796. PMID: 36229793] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563158/pdf/12877_2022_Article_3502.pdf PDF]
 '''Authors:''' Huang CB, Hu JS, Tan K, Zhang W, Xu TH, Yang L.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36281359 PeerJ. 2022 Oct 19;10:e14127. PMID: 36281359] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587713/pdf/peerj-10-14127.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36281359 PeerJ. 2022 Oct 19;10:e14127. PMID: 36281359] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587713/pdf/peerj-10-14127.pdf PDF]
 '''Authors:''' Dong Q, Wen Q, Li N, Tong J, Li Z, Bao X, Xu J, Li D.
@@ Line 277: / Line 277: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36294805 J Pers Med. 2022 Oct 7;12(10):1666.  PMID: 36294805] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605104/pdf/jpm-12-01666.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36294805 J Pers Med. 2022 Oct 7;12(10):1666.  PMID: 36294805] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605104/pdf/jpm-12-01666.pdf PDF]
 '''Authors:''' Li J, Kang S, Du H, Wang S, Wang D, Liu M, Yang S.
@@ Line 290: / Line 290: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36248703 PeerJ. 2022 Oct 10;10:e14234. PMID: 36248703] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559056/pdf/peerj-10-14234.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36248703 PeerJ. 2022 Oct 10;10:e14234. PMID: 36248703] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559056/pdf/peerj-10-14234.pdf PDF]
 '''Authors:''' Vhorkate K, Banga K, Pawar AM, Mir S, Arora S, Wahjuningrum DA, Bhardwaj A, Luke AM.
@@ Line 341: / Line 341: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36230590 Cancers (Basel). 2022 Sep 25;14(19):4664. PMID: 36230590] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563411/pdf/cancers-14-04664.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36230590 Cancers (Basel). 2022 Sep 25;14(19):4664. PMID: 36230590] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9563411/pdf/cancers-14-04664.pdf PDF]
 '''Authors:''' He R, Yang X, Li T, He Y, Xie X, Chen Q, Zhang Z, Cheng T.
@@ Line 354: / Line 354: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36277144 Exp Ther Med. 2022 Sep 22;24(5):685. PMID: 36277144] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533109/pdf/etm-24-05-11621.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36277144 Exp Ther Med. 2022 Sep 22;24(5):685. PMID: 36277144] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533109/pdf/etm-24-05-11621.pdf PDF]
 '''Authors:''' Dang Y, Wang R, Qian K, Lu J, Zhang Y.
@@ Line 392: / Line 392: @@
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+|
-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36213752 Front Neurosci. 2022 Sep 21;16:1002717. PMID: 36213752] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533555/pdf/fnins-16-1002717.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36213752 Front Neurosci. 2022 Sep 21;16:1002717. PMID: 36213752] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533555/pdf/fnins-16-1002717.pdf PDF]
 '''Authors:''' Xie G, Li T, Ren Y, Wang D, Tang W, Li J, Li K.
@@ Line 432: / Line 432: @@
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+|
-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36226269 Contrast Media Mol Imaging. 2022 Sep 23;2022:3417480. PMID: 36226269] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525792/pdf/CMMI2022-3417480.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36226269 Contrast Media Mol Imaging. 2022 Sep 23;2022:3417480. PMID: 36226269] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525792/pdf/CMMI2022-3417480.pdf PDF]
 '''Authors:''' Yang Y, Wu J, Mai W, Li H.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36105833 Radiol Case Rep. 2022 Sep 6;17(11):4161-4164. PMID: 36105833] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464764/pdf/main.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36105833 Radiol Case Rep. 2022 Sep 6;17(11):4161-4164. PMID: 36105833] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464764/pdf/main.pdf PDF]
 '''Authors:''' Dalla S, Richards L, Alli A, Custer B, Rohr A.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36139620 Cancers (Basel). 2022 Sep 14;14(18):4461. PMID: 36139620]  | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497155/pdf/cancers-14-04461.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36139620 Cancers (Basel). 2022 Sep 14;14(18):4461. PMID: 36139620]  | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497155/pdf/cancers-14-04461.pdf PDF]
 '''Authors:''' Lee J, Kim SH, Kim Y, Park J, Park GE, Kang BJ.
@@ Line 530: / Line 530: @@
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36158700 Front Oncol. 2022 Sep 7;12:922185. PMID: 36158700]  | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490879/pdf/fonc-12-922185.pdf  PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36158700 Front Oncol. 2022 Sep 7;12:922185. PMID: 36158700]  | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9490879/pdf/fonc-12-922185.pdf PDF]
 '''Authors:''' Fang C, Zhang J, Li J, Shang H, Li K, Jiao T, Yin D, Li F, Cui Y, Zeng Q.
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-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35920264  Med. 2022 Aug 3. PMID: 35920264]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/35920264 Med. 2022 Aug 3. PMID: 35920264]
 '''Authors:''' Hu C, Sun J, Xu Z, Zhang Z, Zhou Q, Xu J, Chen H, Wang C, Ouyang J.

← Older revision		Revision as of 00:11, 9 November 2022
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	'''Abstract:''' This study was performed to evaluate condylar position and angulation after asymmetric mandibular setback between a conventional (CA) and surgery-first approach (SFA) using three-dimensional analysis. The condylar positions of 30 patients with skeletal Class III malocclusion and facial asymmetry who underwent 1-jaw (sagittal split ramus osteotomy) or 2-jaw orthognathic surgery (Le Fort I osteotomy and sagittal split ramus osteotomy) with CA (n = 18) or SFA (n = 12) from 2 university hospitals were studied. The three-dimensional assessment of condylar changes was performed using computed tomography images at the initial time point (T0) and at least 6 months after surgery (T1). Segmentation of condyles and cranial base assessment from cone-beam computed tomography images were performed using ITK-SNAP software (version 3.4.0). Condylar position and angulation changes were calculated using [http://www.slicer.org '''3D Slicer'''] v. 4.10.2 software, and statistical analysis was performed. No significant translational or rotational condylar changes were observed between the deviated and nondeviated sides in each group or between the CA and SFA groups except yaw (P = 0.014). Linear mixed-model analysis and multivariate analysis showed no significant difference between the CA and SFA groups. Surgery-first approach might not be associated with more harmful effects on the condylar position and angulation changes as compared with CA.		'''Abstract:''' This study was performed to evaluate condylar position and angulation after asymmetric mandibular setback between a conventional (CA) and surgery-first approach (SFA) using three-dimensional analysis. The condylar positions of 30 patients with skeletal Class III malocclusion and facial asymmetry who underwent 1-jaw (sagittal split ramus osteotomy) or 2-jaw orthognathic surgery (Le Fort I osteotomy and sagittal split ramus osteotomy) with CA (n = 18) or SFA (n = 12) from 2 university hospitals were studied. The three-dimensional assessment of condylar changes was performed using computed tomography images at the initial time point (T0) and at least 6 months after surgery (T1). Segmentation of condyles and cranial base assessment from cone-beam computed tomography images were performed using ITK-SNAP software (version 3.4.0). Condylar position and angulation changes were calculated using [http://www.slicer.org '''3D Slicer'''] v. 4.10.2 software, and statistical analysis was performed. No significant translational or rotational condylar changes were observed between the deviated and nondeviated sides in each group or between the CA and SFA groups except yaw (P = 0.014). Linear mixed-model analysis and multivariate analysis showed no significant difference between the CA and SFA groups. Surgery-first approach might not be associated with more harmful effects on the condylar position and angulation changes as compared with CA.
		+	\|}
		+
		+	==A Novel Stereotactic Aspiration Technique for Intracerebral Hemorrhage==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/36270590 World Neurosurg. 2022 Oct 18:S1878-8750(22)01467-X. PMID: 36270590]
		+
		+	'''Authors:''' Xu HZ, Guo J, Wang C, Liu X, Song ZQ, Chen RF, Qiu B, Wang Q, Huang Y.
		+
		+	'''Institution:''' Departments of Neurosurgery, Civil Aviation General Hospital, Beijing, China.
		+
		+	'''Abstract:''' Background: Minimally invasive surgery (MIS) is effective and recommended for the treatment of intracerebral hemorrhage (ICH), whereas neurosurgeons in grass-root hospitals in undeveloped countries are lacking in effective and precise MIS techniques. Hereby, we present the technique of CTA-based 3D-printed navigation mold-guided stereotactic aspiration and demonstrate its clinical application using a hard needle for a series of patients.
		+
		+	Methods: We give a detailed description of the procedure and technique used in the novel stereotactic aspiration and report on its clinical outcome in our center. Moreover, we compared the volume of hematoma measured by three different methods: ABC/2 formula, manual segmentation with Osirix and manual segmentation with [http://www.slicer.org '''3D Slicer'''].
		+
		+	Results: The surgery was completed safely within an average operation time of 15.11 minutes, achieving the goal of < 15ml residual clot volume or >70% clot removal in all patients. No intracranial rebleeding or infection was observed postoperatively. 61.11% (11/18) of patients achieved mRS< 3 after 6 months' rehabilitation. There was overall better agreement of hematoma measurement using segmentation with [http://www.slicer.org '''3D Slicer'''] rather than the ABC/2 measurement, when compared with hematoma measurement using segmentation with Osirix.
		+
		+	Conclusions: We present a new method of stereotactic aspiration, which benefits patients in this study with good percent clot removal, few surgery-related complications, and a favorable prognosis. And manual segmentation with [http://www.slicer.org '''3D Slicer'''] could be used to provide neurosurgeon with solid information about hematoma volume. This cheap and convenient technique may provide a novel choice for grass-root hospitals in undeveloped countries, but it needs to be assessed in multicenter prospective clinical trials.
	\|}		\|}

@@ Line 507: / Line 507: @@
 |}
-==Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer==
+==Clinical-Radiomics Nomogram for Identifying HER2 Status in Patients With Breast Cancer: A Multicenter Study==
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