Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism
Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal a...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-03-01
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| Series: | Open Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/med-2023-0671 |
| _version_ | 1797848643045163008 |
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| author | Liu Rong Yang Junlin Zhang Wei Li Xiaobo Shi Dai Cai Wu Zhang Yue Fan Guohua Li Chenglong Jiang Zhen |
| author_facet | Liu Rong Yang Junlin Zhang Wei Li Xiaobo Shi Dai Cai Wu Zhang Yue Fan Guohua Li Chenglong Jiang Zhen |
| author_sort | Liu Rong |
| collection | DOAJ |
| description | Our purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899–1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible. |
| first_indexed | 2024-04-09T18:31:51Z |
| format | Article |
| id | doaj.art-50c0aab18c6a4ef7acbadacfea98954f |
| institution | Directory Open Access Journal |
| issn | 2391-5463 |
| language | English |
| last_indexed | 2024-04-09T18:31:51Z |
| publishDate | 2023-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Medicine |
| spelling | doaj.art-50c0aab18c6a4ef7acbadacfea98954f2023-04-11T17:07:17ZengDe GruyterOpen Medicine2391-54632023-03-01181576410.1515/med-2023-0671Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolismLiu Rong0Yang Junlin1Zhang Wei2Li Xiaobo3Shi Dai4Cai Wu5Zhang Yue6Fan Guohua7Li Chenglong8Jiang Zhen9Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaGE Healthcare Life Science, Shanghai, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Vascular Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, ChinaOur purpose was to devise a radiomics model using preoperative computed tomography angiography (CTA) images to differentiate new from old emboli of acute lower limb arterial embolism. 57 patients (95 regions of interest; training set: n = 57; internal validation set: n = 38) with femoral popliteal acute lower limb arterial embolism confirmed by pathology and with preoperative CTA images were retrospectively analyzed. We selected the best prediction model according to the model performance tested by area under the curve (AUC) analysis across 1,000 iterations of prediction from three most common machine learning methods: support vector machine, feed-forward neural network (FNN), and random forest, through several steps of feature selection. Then, the selected best model was also validated in an external validation dataset (n = 24). The established radiomics signature had good predictive efficacy. FNN exhibited the best model performance on the training and validation groups: its AUC value was 0.960 (95% CI, 0.899–1). The accuracy of this model was 89.5%, and its sensitivity and specificity were 0.938 and 0.864, respectively. The AUC of external validation dataset was 0.793. Our radiomics model based on preoperative CTA images is valuable. The radiomics approach of preoperative CTA to differentiate new emboli from old is feasible.https://doi.org/10.1515/med-2023-0671arterial embolizationx-ray computed tomographyradiomicsthrombosis |
| spellingShingle | Liu Rong Yang Junlin Zhang Wei Li Xiaobo Shi Dai Cai Wu Zhang Yue Fan Guohua Li Chenglong Jiang Zhen Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism Open Medicine arterial embolization x-ray computed tomography radiomics thrombosis |
| title | Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| title_full | Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| title_fullStr | Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| title_full_unstemmed | Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| title_short | Radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| title_sort | radiomics model using preoperative computed tomography angiography images to differentiate new from old emboli of acute lower limb arterial embolism |
| topic | arterial embolization x-ray computed tomography radiomics thrombosis |
| url | https://doi.org/10.1515/med-2023-0671 |
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