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Article ; Online: Pre-operative spine tumour embolization: Clinical outcomes and effect of embolization completeness.

Omid-Fard, Nima / Salameh, Jean-Paul / McInnes, Matthew Df / Fisher, Charles G / Heran, Manraj Ks

Journal of medical imaging and radiation oncology

2024

Abstract: Introduction: To assess the association between the impact of the completeness of pre-operative spine tumour embolisation and clinical outcomes, including estimated blood loss (EBL), neurological status and complications.: Methods: Retrospective ... ...

Abstract	Introduction: To assess the association between the impact of the completeness of pre-operative spine tumour embolisation and clinical outcomes, including estimated blood loss (EBL), neurological status and complications. Methods: Retrospective chart review of all preoperative spine tumour embolisation procedures performed over 11 years by a single operator (2007-2018) at Vancouver General Hospital on 44 consecutive patients (mean age 57; 77% males) with 46 embolisation procedures, of which surgery was done en bloc in 26 cases and intralesional in the remaining 20. A multivariable negative binomial regression model was fit to examine the association between EBL and surgery type, tumour characteristics, embolisation completeness and operative duration. Results: Among intralesional surgeries, complete versus incomplete embolisation was associated with reduced blood loss (772 vs 1428 mL, P < 0.01). There was no statistically significant difference in neurological outcomes or complications between groups. Highly vascular tumours correlated with greater blood loss than their less vascular counterparts, but tumour location did not have a statistically significant effect. Conclusion: This study provides evidence in support of our hypothesis that complete as opposed to incomplete tumour embolisation correlates with reduced blood loss in intralesional surgeries. Randomised control trials with larger samples are necessary to confirm this benefit and to ascertain other potential clinical benefits.
Language	English
Publishing date	2024-04-02
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2389687-5
ISSN	1754-9485 ; 1440-1673 ; 1754-9477 ; 0004-8461
ISSN (online)	1754-9485 ; 1440-1673
ISSN	1754-9477 ; 0004-8461
DOI	10.1111/1754-9485.13650
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Article ; Online: Screening with Breast Cancer Mammography: Re-Evaluation of Current Evidence.

Salameh, Jean-Paul / Kashif Al-Ghita, Mohammed / McInnes, Matthew D F / Seely, Jean M

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

2023 Volume 74, Issue 3, Page(s) 596–599

MeSH term(s)	Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Early Detection of Cancer ; Mammography ; Mass Screening
Language	English
Publishing date	2023-01-02
Publishing country	United States
Document type	Letter
ZDB-ID	418190-6
ISSN	1488-2361 ; 0846-5371 ; 0008-2902
ISSN (online)	1488-2361
ISSN	0846-5371 ; 0008-2902
DOI	10.1177/08465371221148134
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Article ; Online: Chest CT Findings in Marijuana Smokers.

Murtha, Luke / Sathiadoss, Paul / Salameh, Jean-Paul / Mcinnes, Matthew D F / Revah, Giselle

Radiology

2022 Volume 307, Issue 1, Page(s) e212611

Abstract: Background Global consumption of marijuana is increasing, but there is a paucity of evidence concerning associated lung imaging findings. Purpose To use chest CT to investigate the effects of marijuana smoking in the lung. Materials and Methods This ... ...

Abstract	Background Global consumption of marijuana is increasing, but there is a paucity of evidence concerning associated lung imaging findings. Purpose To use chest CT to investigate the effects of marijuana smoking in the lung. Materials and Methods This retrospective case-control study evaluated results of chest CT examinations (from October 2005 to July 2020) in marijuana smokers, nonsmoker control patients, and tobacco-only smokers. We compared rates of emphysema, airway changes, gynecomastia, and coronary artery calcification. Age- and sex-matched subgroups were created for comparison with tobacco-only smokers older than 50 years. Results were analyzed using χ
MeSH term(s)	Humans ; Male ; Middle Aged ; Cannabis ; Retrospective Studies ; Case-Control Studies ; Smokers ; Gynecomastia ; Pulmonary Emphysema ; Bronchiectasis ; Tomography, X-Ray Computed ; Emphysema
Language	English
Publishing date	2022-11-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.212611
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Article ; Online: Individual Participant Data Meta-Analyses for Diagnostic Accuracy Research: Challenges and Lessons Learned from the LI-RADS IPD Group.

Costa, Andreu F / McInnes, Matthew D F / van der Pol, Christian B / Lam, Eric / Dawit, Haben / Salameh, Jean-Paul / Levis, Brooke / Bashir, Mustafa R

Radiology. Imaging cancer

2024 Volume 6, Issue 3, Page(s) e240015

MeSH term(s)	Humans ; Carcinoma, Hepatocellular ; Liver Neoplasms ; Data Interpretation, Statistical ; Research Subjects
Language	English
Publishing date	2024-03-28
Publishing country	United States
Document type	Editorial
ISSN	2638-616X
ISSN (online)	2638-616X
DOI	10.1148/rycan.240015
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Article ; Online: Impact of Reference Standard on CT, MRI, and Contrast-enhanced US LI-RADS Diagnosis of Hepatocellular Carcinoma: A Meta-Analysis.

van der Pol, Christian B / McInnes, Matthew D F / Salameh, Jean-Paul / Chernyak, Victoria / Tang, An / Bashir, Mustafa R

Radiology

2022 Volume 303, Issue 3, Page(s) 544–545

Abstract: See also the editorial by Ronot in this issue. ...

Abstract	See also the editorial by Ronot in this issue.
MeSH term(s)	Carcinoma, Hepatocellular/diagnostic imaging ; Contrast Media ; Humans ; Liver Neoplasms/diagnostic imaging ; Magnetic Resonance Imaging ; Reference Standards ; Retrospective Studies ; Sensitivity and Specificity ; Tomography, X-Ray Computed
Chemical Substances	Contrast Media
Language	English
Publishing date	2022-03-01
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.212340
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Article ; Online: Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.

Hu, Ricky / Chen, Ishita / Peoples, Jacob / Salameh, Jean-Paul / Gönen, Mithat / Romesser, Paul B / Simpson, Amber L / Reyngold, Marsha

Physics and imaging in radiation oncology

2022 Volume 24, Page(s) 36–42

Abstract: Background and purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of ... ...

Abstract	Background and purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62-0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93-2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05-6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56-0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.
Language	English
Publishing date	2022-09-13
Publishing country	Netherlands
Document type	Journal Article
ISSN	2405-6316
ISSN (online)	2405-6316
DOI	10.1016/j.phro.2022.09.004
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Article ; Online: Conversion Strategy for LI-RADS Category 5 Observations across Versions 2014, 2017, and 2018.

Goins, Stacy M / Adamo, Robert G / Lam, Eric / Costa, Andreu F / van der Pol, Christian B / Salameh, Jean-Paul / Dawit, Haben / McInnes, Matthew D F / Bashir, Mustafa R

Radiology

2023 Volume 307, Issue 4, Page(s) e222971

MeSH term(s)	Humans ; Carcinoma, Hepatocellular ; Liver Neoplasms ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Retrospective Studies ; Contrast Media ; Sensitivity and Specificity
Chemical Substances	Contrast Media
Language	English
Publishing date	2023-05-02
Publishing country	United States
Document type	Editorial ; Research Support, Non-U.S. Gov't
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.222971
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Article ; Online: Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy

Ricky Hu / Ishita Chen / Jacob Peoples / Jean-Paul Salameh / Mithat Gönen / Paul B. Romesser / Amber L. Simpson / Marsha Reyngold

Physics and Imaging in Radiation Oncology, Vol 24, Iss , Pp 36-

2022 Volume 42

Abstract: Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of ... ...

Abstract	Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62–0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93–2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05–6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56–0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.
Keywords	Radiomics ; Artificial intelligence ; Machine learning ; Computer vision ; Survival analysis ; Medical physics. Medical radiology. Nuclear medicine ; R895-920 ; Neoplasms. Tumors. Oncology. Including cancer and carcinogens ; RC254-282
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Impact of Virtual Monoenergetic Imaging on Visualization of the Cervical Spinal Canal.

McComiskey, David / Erdenebold, Undrakh-Erdene / McInnes, Matthew D F / Salameh, Jean-Paul / Chatelain, Robert / Torres, Carlos / Chakraborty, Santanu / Zakhari, Nader

Journal of computer assisted tomography

2022 Volume 47, Issue 1, Page(s) 160–164

Abstract: Rationale and objectives: Our purpose is to explore the role of dual-energy computed tomography (DECT) and virtual monoenergetic energy levels in reducing shoulder artifact to improve visualization of the cervical spinal canal.: Materials and methods!# ...

Abstract	Rationale and objectives: Our purpose is to explore the role of dual-energy computed tomography (DECT) and virtual monoenergetic energy levels in reducing shoulder artifact to improve visualization of the cervical spinal canal. Materials and methods: A retrospective review of 171 consecutive DECT scans of the neck (95 male, 65 female; mean age, 60.9 years, ranging from 18 to 88 years; with 11 excluded because of nondiagnostic image quality) during an 8-month period was performed with postprocessing of monoenergetic images at 50, 70, 100, and 140 keV. Subjective comparisons and objective image noise between the monoenergetic images and standard computed tomography (CT) were analyzed by 1-way analysis of variance to determine the optimal DECT energy level with the highest image quality. Results: Subjectively, 100-keV DECT best visualizes the spinal canal relative to standard CT, 50 and 70 keV ( P < 0.01), and was superior to 140 keV for reader 1 ( P < 0.01). Objectively, 100 keV demonstrated less noise relative to 50 keV (72.02; P < 0.01). There was no difference in noise between 100 keV and 70 keV, or between 100 keV and standard CT, which also demonstrated lower noise relative to 50-, 70-, and 140-keV levels (91.53, P < 0.01; 29.84, P < 0.01; and 22.66, P < 0.03). Conclusion: Dual-energy CT at 100 keV may be the preferred DECT monoenergetic level for soft tissue assessment. Increasing energy level is associated with reduction in shoulder artifact, with no difference in noise between 100 keV and standard CT, although 100-keV images may be subjectively better.
MeSH term(s)	Humans ; Male ; Female ; Middle Aged ; Radiography, Dual-Energy Scanned Projection/methods ; Tomography, X-Ray Computed/methods ; Neck ; Retrospective Studies ; Spinal Canal/diagnostic imaging ; Signal-To-Noise Ratio ; Radiographic Image Interpretation, Computer-Assisted/methods
Language	English
Publishing date	2022-09-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	80392-3
ISSN	1532-3145 ; 0363-8715
ISSN (online)	1532-3145
ISSN	0363-8715
DOI	10.1097/RCT.0000000000001383
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Article ; Online: Diagnostic Accuracy of Dual-Energy CT for Evaluation of Renal Masses: Systematic Review and Meta-Analysis.

Salameh, Jean-Paul / McInnes, Matthew D F / McGrath, Trevor A / Salameh, Gilles / Schieda, Nicola

AJR. American journal of roentgenology

2019 Volume 212, Issue 4, Page(s) W100–W105

Abstract: Objective: The purpose of this study is to determine the diagnostic accuracy of dual-energy CT (DECT) using quantitative iodine concentration in patients with renal masses using histopathologic analysis or follow-up imaging as the reference standard. ... ...

Abstract	Objective: The purpose of this study is to determine the diagnostic accuracy of dual-energy CT (DECT) using quantitative iodine concentration in patients with renal masses using histopathologic analysis or follow-up imaging as the reference standard. The secondary objective is to compare the accuracy of DECT (using iodine concentration) to that of conventional CT (using Hounsfield unit measurements). Materials and methods: We searched the MEDLINE, Embase, and Cochrane Central Register of Controlled Trials databases for studies evaluating the accuracy of DECT for renal mass characterization (1947-2018). To be included, studies had to evaluate quantitative iodine concentrations in human patients with indeterminate renal masses. Risk of bias and applicability were assessed using quality assessment of diagnostic accuracy studies-2. A bivariate random-effects model was used to determine pooled sensitivity and specificity. Variability was assessed by subgroup analyses (DECT technique and risk of bias) and metaregression using test type and threshold applied as covariates. Results: Of 201 studies identified, five were included (367 patients). Pooled sensitivity and specificity for DECT were 96.6% (95% CI, 85.9-99.3%) and 95.1% (95% CI, 90.7-97.5%), respectively. Metaregression evaluating the influence of the test type (DECT vs conventional CT) did not identify differences in accuracy (p = 0.06). No differences in accuracy based on risk of bias or DECT technique were identified. Limitations include the small number of studies, most of which were at risk of bias. Conclusion: DECT with iodine quantification shows sensitivity and specificity greater than 95% for evaluation of renal masses and may be an alternative to conventional CT for assessment of renal masses. Larger scale trials are needed to corroborate our findings.
MeSH term(s)	Contrast Media ; Diagnosis, Differential ; Humans ; Iodine ; Kidney Neoplasms/diagnostic imaging ; Radiography, Dual-Energy Scanned Projection ; Sensitivity and Specificity ; Tomography, X-Ray Computed
Chemical Substances	Contrast Media ; Iodine (9679TC07X4)
Language	English
Publishing date	2019-02-04
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Systematic Review
ZDB-ID	82076-3
ISSN	1546-3141 ; 0361-803X ; 0092-5381
ISSN (online)	1546-3141
ISSN	0361-803X ; 0092-5381
DOI	10.2214/AJR.18.20527
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