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Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?
by
Lee, Jeong Min
, Hong, Eun Kyoung
, Park, Sae Jin
, Landolfi, Federica
, Bogveradze, Nino
, Bos, Paula
, Beets-Tan, Regina
, Bodalal, Zuhir
in
Colon
/ Colon cancer
/ Colorectal cancer
/ Confidence intervals
/ Feature extraction
/ Radiomics
/ Regression analysis
/ Risk
/ Segmentation
/ Statistical analysis
/ Tumors
2022
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Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?
by
Lee, Jeong Min
, Hong, Eun Kyoung
, Park, Sae Jin
, Landolfi, Federica
, Bogveradze, Nino
, Bos, Paula
, Beets-Tan, Regina
, Bodalal, Zuhir
in
Colon
/ Colon cancer
/ Colorectal cancer
/ Confidence intervals
/ Feature extraction
/ Radiomics
/ Regression analysis
/ Risk
/ Segmentation
/ Statistical analysis
/ Tumors
2022
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?
by
Lee, Jeong Min
, Hong, Eun Kyoung
, Park, Sae Jin
, Landolfi, Federica
, Bogveradze, Nino
, Bos, Paula
, Beets-Tan, Regina
, Bodalal, Zuhir
in
Colon
/ Colon cancer
/ Colorectal cancer
/ Confidence intervals
/ Feature extraction
/ Radiomics
/ Regression analysis
/ Risk
/ Segmentation
/ Statistical analysis
/ Tumors
2022
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Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?
Journal Article
Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist’s performance for T staging?
2022
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Overview
PurposeTo assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer.MethodsWe included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging.ResultsThe model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720–0.839 for combined model and AUC = 0.697, 95% CI = 0.538–0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621–0.777 for combined model and AUC = 0.628, 95% CI = 0.558–0.689 for CT staging only, Boot CI = 0.099).ConclusionCT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.
Publisher
Springer Nature B.V
Subject
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