MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients (117 views) (PDF public 31 views)
Eur J Of Radioloy, 2022 Feb; N/D: 1-6.
Export to BibTeX Export to EndNote
Paper type: Journal Article,
Impact factor: 2.687, 5-year impact factor: 0
Url: Not available.
Keywords: Not available.
Affiliations:
*** IBB - CNR ***
Purpose: To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based risk
stratification in patients with endometrial cancer (EC).
Method: From two institutions, 133 patients (Institution1 = 104 and Institution2 = 29) with EC and pre-operative
MRI were retrospectively enrolled and divided in two a low-risk and a high-risk group according to EC stage and
grade. T2-weighted (T2w) images were three-dimensionally annotated to obtain volumes of interest of the entire
tumor. A PyRadiomics based and previously validated pipeline was used to extract radiomics features and
perform feature selection. In particular, feature stability, variance and pairwise correlation were analyzed. Then,
the least absolute shrinkage and selection operator technique and recursive feature elimination were used to
obtain the final feature set. The performance of a Support Vector Machine (SVM) algorithm was assessed on the
dataset from Institution 1 via 2-fold cross-validation. Then, the model was trained on the entire Institution 1
dataset and tested on the external test set from Institution 2.
Results: In total, 1197 radiomics features were extracted. After the exclusion of unstable, low variance and
intercorrelated features least absolute shrinkage and selection operator and recursive feature elimination identified
4 features that were used to build the predictive ML model. It obtained an accuracy of 0.71 and 0.72 in the
train and test sets respectively.
Conclusions: Whole-lesion T2w-derived radiomics showed encouraging results and good generalizability for the
identification of low-risk EC patients.
References:
Not available.
Eur J Of Radioloy, 2022 Feb; N/D: 1-6.
Export to BibTeX Export to EndNote
Paper type: Journal Article,
Impact factor: 2.687, 5-year impact factor: 0
Url: Not available.
Keywords: Not available.
Affiliations:
*** IBB - CNR ***
Purpose: To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based risk
stratification in patients with endometrial cancer (EC).
Method: From two institutions, 133 patients (Institution1 = 104 and Institution2 = 29) with EC and pre-operative
MRI were retrospectively enrolled and divided in two a low-risk and a high-risk group according to EC stage and
grade. T2-weighted (T2w) images were three-dimensionally annotated to obtain volumes of interest of the entire
tumor. A PyRadiomics based and previously validated pipeline was used to extract radiomics features and
perform feature selection. In particular, feature stability, variance and pairwise correlation were analyzed. Then,
the least absolute shrinkage and selection operator technique and recursive feature elimination were used to
obtain the final feature set. The performance of a Support Vector Machine (SVM) algorithm was assessed on the
dataset from Institution 1 via 2-fold cross-validation. Then, the model was trained on the entire Institution 1
dataset and tested on the external test set from Institution 2.
Results: In total, 1197 radiomics features were extracted. After the exclusion of unstable, low variance and
intercorrelated features least absolute shrinkage and selection operator and recursive feature elimination identified
4 features that were used to build the predictive ML model. It obtained an accuracy of 0.71 and 0.72 in the
train and test sets respectively.
Conclusions: Whole-lesion T2w-derived radiomics showed encouraging results and good generalizability for the
identification of low-risk EC patients.
References:
Not available.
MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients
Not available.
Not available.
MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients
| ▼ Clinical Oncology |
MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients
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View Export to BibTeX Export to EndNote
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View Export to BibTeX Export to EndNote
Petretta M, Acampa W, Cuocolo A
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J Nucl Cardiol (ISSN: 1071-3581, 1532-6551, 1532-6551electronic), 2014 Dec; 21(6): 1144-1147.
Impact Factor: 2.942
View Export to BibTeX Export to EndNote
Acampa W, Cantoni V, Green R, Cuocolo R, Petretta MP, Orlandi M, Petretta M
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Impact Factor: 0
View Export to BibTeX Export to EndNote
Maffei E, Midiri M, Russo V, Rengo M, Tedeschi C, Spagnolo P, Seitun S, Francone M, Guaricci AI, Carrabba N, Malagò R, Cuocolo A, Arcadi T, Catalano OA, Cademartiri F
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Impact Factor: 1.368
View Export to BibTeX Export to EndNote
Petretta M, Acampa W, Fiumara G, Cuocolo A
* Cardiovascular risk stratification in diabetic patients (526 views)
Clin Transl Imaging (ISSN: 2281-5872, 2281-5872print, 2281-7565online), 2013; 1(5): 325-339.
Impact Factor: 0
View Export to BibTeX Export to EndNote
Petretta M, Cuocolo A
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Eur J Nucl Med (ISSN: 1619-7089, 1619-7070, 0340-6997), 2012 Dec; 39(12): 1959-1969.
Impact Factor: 5.114
View Export to BibTeX Export to EndNote
Perrone-Filardi P, Musella F, Savarese G, Cecere M, Marciano C, Scala O, Rengo G, Dellegrottaglie S, Cuocolo A, Leosco D
* Coronary computed tomography: current role and future perspectives for cardiovascular risk stratification (943 views)
Eur Heart J-Card Img (ISSN: 2047-2412, 2047-2404, 2047-2412electronic), 2012 Jun; 13(6): 453-458.
Impact Factor: 3.669
View Export to BibTeX Export to EndNote
Cuocolo A, Concilio C, Acampa W, Ferro A, Evangelista L, Daniele S, Petretta M
* Cardiovascular risk stratification of diabetic patients (709 views)
Minerva Endocrinol (ISSN: 0391-1977, 1827-1634electronic, 0391-1977linking), 2009 Sep; 34(3): 205-221.
Impact Factor: 1.464
View Export to BibTeX Export to EndNote
Evangelista L, Acampa W, Ricci F, Daniele S, Luongo L, De Falco T, De Marino M, Petretta MP, Nappi G, Cuocolo A
* Stress Cardiac Single-photon Emission Computed Tomographic Imaging After Coronary Artery Bypass Surgery for Risk Stratification and Estimation of Time to Cardiac Events (503 views)
Eur J Nucl Med (ISSN: 1619-7070, 1619-7089, 0340-6997), 2008 Oct; 35: N/D-N/D.
Impact Factor: 4.532
View Export to BibTeX Export to EndNote
Acampa W, Petretta M, Evangelista L, Nappi G, Luongo L, Petretta MP, Cuocolo A
* Stress cardiac single-photon emission computed tomographic imaging late after coronary artery bypass surgery for risk stratification and estimation of time to cardiac events (395 views)
J Thorac Cardiovasc Surg (ISSN: 0022-5223, 1097-685x), 2008 Jul; 136(1): 46-51.
Impact Factor: 3.037
View Export to BibTeX Export to EndNote
Acampa W, Petretta M, Cuocolo A
* Nuclear medicine procedures in cardiovascular diseases: An evidence based approach (587 views)
Q J Nucl Med (ISSN: 1124-3937, 1125-0135), 2002 Dec; 46(4): 323-330.
Impact Factor: 1.903
View Export to BibTeX Export to EndNote
23 Records (22 excluding Abstracts).
Total impact factor: 58.095 (53.563 excluding Abstracts).
Total 5 year impact factor: 46.975 (42.35 excluding Abstracts).
Total impact factor: 58.095 (53.563 excluding Abstracts).
Total 5 year impact factor: 46.975 (42.35 excluding Abstracts).
117 views. Last view on Saturday 30 November 2024, 23:20:47
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