Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma
Purpose: To predict distant metastasis (DM) in patients with borderline resectable pancreatic carcinoma using delta-radiomics features calculated from contrast-enhanced computed tomography (CECT) and non-CECT images. Methods and Materials: Among 250 patients who underwent radiation therapy at our in...
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Elsevier
2025-01-01
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| Series: | Advances in Radiation Oncology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S245210942400232X |
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| author | Takanori Adachi, PhD Mitsuhiro Nakamura, PhD Takahiro Iwai, PhD Michio Yoshimura, MD, PhD Takashi Mizowaki, MD, PhD |
| author_facet | Takanori Adachi, PhD Mitsuhiro Nakamura, PhD Takahiro Iwai, PhD Michio Yoshimura, MD, PhD Takashi Mizowaki, MD, PhD |
| author_sort | Takanori Adachi, PhD |
| collection | DOAJ |
| description | Purpose: To predict distant metastasis (DM) in patients with borderline resectable pancreatic carcinoma using delta-radiomics features calculated from contrast-enhanced computed tomography (CECT) and non-CECT images. Methods and Materials: Among 250 patients who underwent radiation therapy at our institution between February 2013 and December 2021, 67 patients were deemed eligible. A total of 11 clinical features and 3906 radiomics features were incorporated. Radiomics features were extracted from CECT and non-CECT images, and the differences between these features were calculated, resulting in delta-radiomics features. The patients were randomly divided into the training (70%) and test (30%) data sets for model development and validation. Predictive models were developed with clinical features (clinical model), radiomics features (radiomics model), and a combination of the abovementioned features (hybrid model) using Fine-Gray regression (FG) and random survival forest (RSF). Optimal hyperparameters were determined using stratified 5-fold cross-validation. Subsequently, the developed models were applied to the remaining test data sets, and the patients were divided into high- or low-risk groups based on their risk scores. Prognostic power was assessed using the concordance index, with 95% CIs obtained through 2000 bootstrapping iterations. Statistical significance between the above groups was assessed using Gray's test. Results: At a median follow-up period of 23.8 months, 47 (70.1%) patients developed DM. The concordance indices of the FG-based clinical, radiomics, and hybrid models were 0.548, 0.603, and 0.623, respectively, in the test data set, whereas those of the RSF-based models were 0.598, 0.680, and 0.727, respectively. The RSF-based model, including delta-radiomics features, significantly divided the cumulative incidence curves into two risk groups (P < .05). The feature map of the gray-level size-zone matrix showed that the difference in feature values between CECT and non-CECT images correlated with the incidence of DM. Conclusions: Delta-radiomics features obtained from CECT and non-CECT images using RSF successfully predict the incidence of DM in patients with borderline resectable pancreatic carcinoma. |
| format | Article |
| id | doaj-art-d6cfba93cffd417d988d8a59c7af9697 |
| institution | Kabale University |
| issn | 2452-1094 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Advances in Radiation Oncology |
| spelling | doaj-art-d6cfba93cffd417d988d8a59c7af96972024-11-30T07:13:21ZengElsevierAdvances in Radiation Oncology2452-10942025-01-01101101669Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic CarcinomaTakanori Adachi, PhD0Mitsuhiro Nakamura, PhD1Takahiro Iwai, PhD2Michio Yoshimura, MD, PhD3Takashi Mizowaki, MD, PhD4Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Shogoin, Sakyo-ku, Kyoto, JapanDepartment of Radiation Oncology and Image-Applied Therapy, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan; Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan; Corresponding author: Mitsuhiro Nakamura, PhD.Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Shogoin, Sakyo-ku, Kyoto, JapanDepartment of Radiation Oncology and Image-Applied Therapy, Kyoto University, Shogoin, Sakyo-ku, Kyoto, JapanDepartment of Radiation Oncology and Image-Applied Therapy, Kyoto University, Shogoin, Sakyo-ku, Kyoto, JapanPurpose: To predict distant metastasis (DM) in patients with borderline resectable pancreatic carcinoma using delta-radiomics features calculated from contrast-enhanced computed tomography (CECT) and non-CECT images. Methods and Materials: Among 250 patients who underwent radiation therapy at our institution between February 2013 and December 2021, 67 patients were deemed eligible. A total of 11 clinical features and 3906 radiomics features were incorporated. Radiomics features were extracted from CECT and non-CECT images, and the differences between these features were calculated, resulting in delta-radiomics features. The patients were randomly divided into the training (70%) and test (30%) data sets for model development and validation. Predictive models were developed with clinical features (clinical model), radiomics features (radiomics model), and a combination of the abovementioned features (hybrid model) using Fine-Gray regression (FG) and random survival forest (RSF). Optimal hyperparameters were determined using stratified 5-fold cross-validation. Subsequently, the developed models were applied to the remaining test data sets, and the patients were divided into high- or low-risk groups based on their risk scores. Prognostic power was assessed using the concordance index, with 95% CIs obtained through 2000 bootstrapping iterations. Statistical significance between the above groups was assessed using Gray's test. Results: At a median follow-up period of 23.8 months, 47 (70.1%) patients developed DM. The concordance indices of the FG-based clinical, radiomics, and hybrid models were 0.548, 0.603, and 0.623, respectively, in the test data set, whereas those of the RSF-based models were 0.598, 0.680, and 0.727, respectively. The RSF-based model, including delta-radiomics features, significantly divided the cumulative incidence curves into two risk groups (P < .05). The feature map of the gray-level size-zone matrix showed that the difference in feature values between CECT and non-CECT images correlated with the incidence of DM. Conclusions: Delta-radiomics features obtained from CECT and non-CECT images using RSF successfully predict the incidence of DM in patients with borderline resectable pancreatic carcinoma.http://www.sciencedirect.com/science/article/pii/S245210942400232X |
| spellingShingle | Takanori Adachi, PhD Mitsuhiro Nakamura, PhD Takahiro Iwai, PhD Michio Yoshimura, MD, PhD Takashi Mizowaki, MD, PhD Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma Advances in Radiation Oncology |
| title | Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma |
| title_full | Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma |
| title_fullStr | Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma |
| title_full_unstemmed | Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma |
| title_short | Delta-Radiomics Approach Using Contrast-Enhanced and Noncontrast-Enhanced Computed Tomography Images for Predicting Distant Metastasis in Patients With Borderline Resectable Pancreatic Carcinoma |
| title_sort | delta radiomics approach using contrast enhanced and noncontrast enhanced computed tomography images for predicting distant metastasis in patients with borderline resectable pancreatic carcinoma |
| url | http://www.sciencedirect.com/science/article/pii/S245210942400232X |
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