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Article ; Online: Reply to "Quality Control of Radiomics Study to Differentiate Benign and Malignant Hepatic Lesions".

Homayounieh, Fatemeh / Kalra, Mannudeep K

2021 Volume 216, Issue 3, Page(s) W13

MeSH term(s)	Diagnosis, Differential ; Humans ; Pilot Projects ; Quality Control ; ROC Curve ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-02-22
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	82076-3
ISSN	1546-3141 ; 0361-803X ; 0092-5381
ISSN (online)	1546-3141
ISSN	0361-803X ; 0092-5381
DOI	10.2214/AJR.20.24741
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Ue I Zs.57: Show issues

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bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Predictive values of AI-based triage model in suboptimal CT pulmonary angiography.

Ebrahimian, Shadi / Digumarthy, Subba R / Homayounieh, Fatemeh / Bizzo, Bernardo C / Dreyer, Keith J / Kalra, Mannudeep K

Clinical imaging

2022 Volume 86, Page(s) 25–30

Abstract: Purpose: We evaluated and compared performance of an acute pulmonary embolism (PE) triaging artificial intelligence (PE-AI) model in suboptimal and optimal CT pulmonary angiography (CTPA).: Methods: In an IRB approved, retrospective study we ... ...

Abstract	Purpose: We evaluated and compared performance of an acute pulmonary embolism (PE) triaging artificial intelligence (PE-AI) model in suboptimal and optimal CT pulmonary angiography (CTPA). Methods: In an IRB approved, retrospective study we identified 104 consecutive, suboptimal CTPA which were deemed as suboptimal for PE evaluation in radiology reports due to motion, artifacts and/or inadequate contrast enhancement. We enriched this dataset, with additional 226 optimal CTPA (over same timeframe as suboptimal CTPA) with and without PE. Two thoracic radiologists (ground truth) independently reviewed all 330 CTPA for adequacy (to assess PE down to distal segmental level), reason for suboptimal CTPA (artifacts or poor contrast enhancement), as well as for presence and location of PE. CT values (HU) were measured in the main pulmonary artery. Same attributes were assessed in 80 patients who had repeat or follow-up CTPA following suboptimal CTPA. All CTPA were processed with the PE-AI (Aidoc). Results: Among 104 suboptimal CTPA (mean age ± standard deviation 56 ± 15 years), 18/104 (17%) were misclassified as suboptimal for PE evaluation in their radiology reports but relabeled as optimal on ground truth evaluation. Of 226 optimal CTPA, 47 (21%) were reclassified as suboptimal CTPA. PEs were present in 97/330 CTPA. PE-AI had similar performance on suboptimal CTPA (sensitivity 100%; specificity 89%; AUC 0.89, 95% CI 0.80-0.98) and optimal CTPA (sensitivity 96%; specificity 92%; AUC 0.87, 95% CI 0.81-0.93). Conclusion: Suboptimal CTPA examinations do not impair the performance of PE-AI triage model; AI retains clinically meaningful sensitivity and high specificity regardless of diagnostic quality.
MeSH term(s)	Angiography ; Artificial Intelligence ; Computed Tomography Angiography ; Contrast Media ; Humans ; Pulmonary Embolism/diagnostic imaging ; Retrospective Studies ; Triage
Chemical Substances	Contrast Media
Language	English
Publishing date	2022-03-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	1028123-x
ISSN	1873-4499 ; 0899-7071
ISSN (online)	1873-4499
ISSN	0899-7071
DOI	10.1016/j.clinimag.2022.03.011
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Zs.A 1469: Show issues

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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

Anari, Pouria Yazdian / Lay, Nathan / Zahergivar, Aryan / Firouzabadi, Fatemeh Dehghani / Chaurasia, Aditi / Golagha, Mahshid / Singh, Shiva / Homayounieh, Fatemeh / Obiezu, Fiona / Harmon, Stephanie / Turkbey, Evrim / Merino, Maria / Jones, Elizabeth C / Ball, Mark W / Linehan, W Marston / Turkbey, Baris / Malayeri, Ashkan A

Abdominal radiology (New York)

2024 Volume 49, Issue 4, Page(s) 1194–1201

Abstract: Introduction: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors ...

Abstract	Introduction: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. Material and methods: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). Results: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. Conclusion: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.
MeSH term(s)	Humans ; Deep Learning ; Magnetic Resonance Imaging ; Carcinoma, Renal Cell/diagnostic imaging ; Kidney Neoplasms/diagnostic imaging ; Algorithms
Language	English
Publishing date	2024-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2839786-1
ISSN	2366-0058 ; 2366-004X
ISSN (online)	2366-0058
ISSN	2366-004X
DOI	10.1007/s00261-023-04172-w
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Zs.A 1262: Show issues

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Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Spectral segmentation and radiomic features predict carotid stenosis and ipsilateral ischemic burden from DECT angiography.

Ebrahimian, Shadi / Homayounieh, Fatemeh / Singh, Ramandeep / Primak, Andrew / Kalra, Mannudeep K / Romero, Javier M

Diagnostic and interventional radiology (Ankara, Turkey)

2022 Volume 28, Issue 3, Page(s) 264–274

Abstract: PURPOSE The purpose of this study is to compare spectral segmentation, spectral radiomic, and single- energy radiomic features in the assessment of internal and common carotid artery (ICA/CCA) stenosis and prediction of surgical outcome. METHODS Our ... ...

Abstract	PURPOSE The purpose of this study is to compare spectral segmentation, spectral radiomic, and single- energy radiomic features in the assessment of internal and common carotid artery (ICA/CCA) stenosis and prediction of surgical outcome. METHODS Our ethical committee-approved, Health Insurance Portability and Accountability Act (HIPAA)- compliant study included 85 patients (mean age, 73 ± 10 years; male : female, 56 : 29) who under- went contrast-enhanced, dual-source dual-energy CT angiography (DECTA) (Siemens Definition Flash) of the neck for assessing ICA/CCA stenosis. Patients with a prior surgical or interventional treatment of carotid stenosis were excluded. Two radiologists graded the severity of carotid ste- nosis on DECTA images as mild (<50% luminal narrowing), moderate (50%-69%), and severe (>70%) stenosis. Thin-section, low- and high-kV DICOM images from the arterial phase acquisi- tion were processed with a dual-energy CT prototype (DTA, eXamine, Siemens Healthineers) to generate spectral segmentation and radiomic features over regions of interest along the entire length (volume) and separately at a single-section with maximum stenosis. Multiple logistic regressions and area under the receiver operating characteristic curve (AUC) were used for data analysis. RESULTS Among 85 patients, 22 ICA/CCAs had normal luminal dimensions and 148 ICA/CCAs had luminal stenosis (mild stenosis: 51, moderate: 38, severe: 59). For differentiating non-severe and severe ICA/CCA stenosis, radiomic features (volume: AUC=0.94, 95% CI 0.88-0.96; section: AUC=0.92, 95% CI 0.86-0.93) were significantly better than spectral segmentation features (volume: AUC = 0.86, 95% CI 0.74-0.87; section: AUC = 0.68, 95% CI 0.66-0.78) (P < .001). Spectral radiomic features predicted revascularization procedure (AUC = 0.77) and the presence of ipsilateral intra- cranial ischemic changes (AUC = 0.76). CONCLUSION Spectral segmentation and radiomic features from DECTA can differentiate patients with differ- ent luminal ICA/CCA stenosis grades.
MeSH term(s)	Aged ; Aged, 80 and over ; Angiography ; Carotid Artery, Internal ; Carotid Stenosis/diagnostic imaging ; Carotid Stenosis/surgery ; Constriction, Pathologic ; Female ; Humans ; Male ; Middle Aged ; ROC Curve
Language	English
Publishing date	2022-06-23
Publishing country	Turkey
Document type	Journal Article
ZDB-ID	2184145-7
ISSN	1305-3612 ; 1305-3612
ISSN (online)	1305-3612
ISSN	1305-3612
DOI	10.5152/dir.2022.20842
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Article ; Online: Correlation of Radiomics with Treatment Response in Liver Metastases.

Mostafavi, Leila / Homayounieh, Fatemeh / Lades, Felix / Primak, Andrew / Muse, Victorine / Harris, Gordon J / Kalra, Mannudeep K / Digumarthy, Subba R

Academic radiology

2023

Abstract: Rationale and objectives: To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at ...

Abstract	Rationale and objectives: To assess differences in radiomics derived from semi-automatic segmentation of liver metastases for stable disease (SD), partial response (PR), and progressive disease (PD) based on RECIST1.1 and to assess if radiomics alone at baseline can predict response. Materials and methods: Our IRB-approved study included 203 women (mean age 54 ± 11 years) with metastatic liver disease from breast cancer. All patients underwent contrast abdomen-pelvis CT in the portal venous phase at two points: baseline (pre-treatment) and follow-up (between 3 and 12 months following treatment). Patients were subcategorized into three subgroups based on RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors version 1.1): 66 with SD, 69 with PR, and 68 with PD on follow-up CT. The deidentified baseline and follow-up CT images were exported to the radiomics prototype. The prototype enabled semi-automatic segmentation of the target liver lesions for the extraction of first and high order radiomics. Statistical analyses with logistic regression and random forest classifiers were performed to differentiate SD from PD and PR. Results: There was no significant difference between the radiomics on the baseline and follow-up CT images of patients with SD (area under the curve (AUC): 0.3). Random forest classifier differentiated patients with PR with an AUC of 0.845. The most relevant feature was the large dependence emphasis's high and low pass wavelet filter (derived gray level dependence matrix features). Random forest classifier differentiated PD with an AUC of 0.731, with the most relevant feature being the surface-to-volume ratio. There was no difference in radiomics among the three groups at baseline; therefore, a response could not be predicted. Conclusion: Radiomics of liver metastases with semi-automatic segmentation demonstrate differences between SD from PR and PD. Summary statement: Semiautomatic segmentation and radiomics of metastatic liver disease demonstrate differences in SD from the PR and progressive metastatic on the baseline and follow-up CT. Despite substantial variations in the scanners, acquisition, and reconstruction parameters, radiomics had an AUC of 0.84-0.89 for differentiating stable hepatic metastases from decreasing and increasing metastatic disease.
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	1355509-1
ISSN	1878-4046 ; 1076-6332
ISSN (online)	1878-4046
ISSN	1076-6332
DOI	10.1016/j.acra.2023.11.007
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Zs.A 4594: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

Zahergivar, Aryan / Yazdian Anari, Pouria / Mendhiratta, Neil / Lay, Nathan / Singh, Shiva / Dehghani Firouzabadi, Fatemeh / Chaurasia, Aditi / Golagha, Mahshid / Homayounieh, Fatemeh / Gautam, Rabindra / Harmon, Stephanie / Turkbey, Evrim / Merino, Maria / Jones, Elizabeth C / Ball, Mark W / Turkbey, Baris / Linehan, W Marston / Malayeri, Ashkan A

Journal of magnetic resonance imaging : JMRI

2024

Abstract: Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC).: Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of ... ...

Abstract	Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. Study type: Retrospective analysis of a prospectively maintained cohort. Population: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. Field strength and sequences: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. Assessment: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. Statistical tests: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. Results: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. Data conclusion: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. Level of evidence: 1 TECHNICAL EFFICACY: Stage 2.
Language	English
Publishing date	2024-02-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	1146614-5
ISSN	1522-2586 ; 1053-1807
ISSN (online)	1522-2586
ISSN	1053-1807
DOI	10.1002/jmri.29222
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Zs.A 3648: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 236: Show issues

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Article ; Online: Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.

Antony, Maria B / Anari, Pouria Y / Gopal, Nikhil / Chaurasia, Aditi / Firouzabadi, Fatemeh Dehghani / Homayounieh, Fatemeh / Kozel, Zach / Gautam, Rabindra / Gurram, Sandeep / Linehan, W Marston / Turkbey, Evrim B / Malayeri, Ashkan A / Ball, Mark W

European urology open science

2023 Volume 57, Page(s) 66–73

Abstract: Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal ... ...

Abstract	Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design setting and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.
Language	English
Publishing date	2023-09-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	3040546-4
ISSN	2666-1683 ; 2058-4881
ISSN (online)	2666-1683
ISSN	2058-4881
DOI	10.1016/j.euros.2023.08.010
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Article ; Online: CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis.

Dehghani Firouzabadi, Fatemeh / Gopal, Nikhil / Hasani, Amir / Homayounieh, Fatemeh / Li, Xiaobai / Jones, Elizabeth C / Yazdian Anari, Pouria / Turkbey, Evrim / Malayeri, Ashkan A

PloS one

2023 Volume 18, Issue 7, Page(s) e0287299

Abstract: Purpose: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic ... ...

Abstract	Purpose: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. Methods: We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). Results: Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. Conclusion: This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.
MeSH term(s)	Humans ; Carcinoma, Renal Cell/pathology ; Angiomyolipoma/diagnostic imaging ; Angiomyolipoma/pathology ; Retrospective Studies ; Diagnosis, Differential ; Kidney Neoplasms/diagnostic imaging ; Kidney Neoplasms/pathology ; Tomography, X-Ray Computed/methods ; Sensitivity and Specificity ; Leukemia, Myeloid, Acute/diagnosis
Language	English
Publishing date	2023-07-27
Publishing country	United States
Document type	Meta-Analysis ; Systematic Review ; Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0287299
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Article ; Online: Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT.

Homayounieh, Fatemeh / Yan, Pingkun / Digumarthy, Subba R / Kruger, Uwe / Wang, Ge / Kalra, Mannudeep K

Academic radiology

2021 Volume 28, Issue 7, Page(s) 972–979

Abstract: Rationale and objectives: We aimed to assess relationship between single-click, whole heart radiomics from low-dose computed tomography (LDCT) for lung cancer screening with coronary artery calcification and stenosis.: Materials and methods: The ... ...

Abstract	Rationale and objectives: We aimed to assess relationship between single-click, whole heart radiomics from low-dose computed tomography (LDCT) for lung cancer screening with coronary artery calcification and stenosis. Materials and methods: The institutional review board-approved, retrospective study included all 106 patients (68 men, 38 women, mean age 64 ± 7 years) who underwent both LDCT for lung cancer screening and had calcium scoring and coronary computed tomography angiography in our institution. We recorded the clinical variables including patients' demographics, smoking history, family history, and lipid profiles. Coronary calcium scores and grading of coronary stenosis were recorded from the radiology information system. We calculated the multiethnic scores for atherosclerosis risk scores to obtain 10-year coronary heart disease (MESA 10-Y CHD) risk of cardiovascular disease for all patients. Deidentified LDCT exams were exported to a Radiomics prototype for automatic heart segmentation, and derivation of radiomics. Data were analyzed using multiple logistic regression and kernel Fisher discriminant analyses. Results: Whole heart radiomics were better than the clinical variables for differentiating subjects with different Agatston scores (≤400 and >400) (area under the curve [AUC] 0.92 vs 0.69). Prediction of coronary stenosis and MESA 10-Y CHD risk was better on whole heart radiomics (AUC:0.86-0.87) than with clinical variables (AUC:0.69-0.79). Addition of clinical variables or visual assessment of coronary calcification from LDCT to whole heart radiomics resulted in a modest change in the AUC. Conclusion: Single-click, whole heart radiomics obtained from LDCT for lung cancer screening can differentiate patients with different Agatston and MESA risk scores for cardiovascular diseases.
MeSH term(s)	Aged ; Constriction, Pathologic ; Coronary Angiography ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/epidemiology ; Coronary Stenosis/diagnostic imaging ; Coronary Stenosis/epidemiology ; Coronary Vessels ; Early Detection of Cancer ; Female ; Humans ; Lung Neoplasms ; Male ; Middle Aged ; Retrospective Studies ; Tomography, X-Ray Computed ; Vascular Calcification/diagnostic imaging ; Vascular Calcification/epidemiology
Language	English
Publishing date	2021-07-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1355509-1
ISSN	1878-4046 ; 1076-6332
ISSN (online)	1878-4046
ISSN	1076-6332
DOI	10.1016/j.acra.2020.09.021
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Zs.A 4594: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management.

Chaurasia, Aditi / Singh, Shiva / Homayounieh, Fatemeh / Gopal, Nikhil / Jones, Elizabeth C / Linehan, W Marston / Shyn, Paul B / Ball, Mark W / Malayeri, Ashkan A

Radiographics : a review publication of the Radiological Society of North America, Inc

2023 Volume 43, Issue 7, Page(s) e220196

Abstract: The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care ... ...

Abstract	The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care for treating many localized renal masses. Although uncommon, complications resulting from PN can range from asymptomatic and mild to symptomatic and life-threatening. These complications include vascular injuries such as hematoma, pseudoaneurysm, arteriovenous fistula, and/or renal ischemia; injury to the collecting system causing urinary leak; infection; and tumor recurrence. The incidence of complications after any nephron-sparing surgery depends on many factors, such as the proximity of the tumor to blood vessels or the collecting system, the skill or experience of the surgeon, and patient-specific factors. More recently, image-guided percutaneous renal ablation has emerged as a safe and effective treatment option for small renal tumors, with comparable oncologic outcomes to those of PN and a low incidence of major complications. Radiologists must be familiar with the imaging findings encountered after these surgical and image-guided procedures, especially those indicative of complications. The authors review cross-sectional imaging characteristics of complications after PN and image-guided thermal ablation of kidney tumors and highlight the respective management strategies, ranging from clinical observation to interventions such as angioembolization or repeat surgery.
MeSH term(s)	Humans ; Neoplasm Recurrence, Local ; Kidney Neoplasms/diagnostic imaging ; Kidney Neoplasms/surgery ; Nephrons/diagnostic imaging ; Kidney ; Carcinoma, Renal Cell/diagnostic imaging ; Carcinoma, Renal Cell/surgery
Language	English
Publishing date	2023-06-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	603172-9
ISSN	1527-1323 ; 0271-5333
ISSN (online)	1527-1323
ISSN	0271-5333
DOI	10.1148/rg.220196
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Zs.A 1751: Show issues

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bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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