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Enhanced CT-Based Delta-Radiomics: Predicting Lymphovascular and Perineural Invasion in Rectal Cancer Preoperatively.

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Title: Enhanced CT-Based Delta-Radiomics: Predicting Lymphovascular and Perineural Invasion in Rectal Cancer Preoperatively.
Authors: Fu, Chunlong1 (AUTHOR), Yang, Zebin1 (AUTHOR), Shan, Kangfei1 (AUTHOR), Pang, Zhenzhu1 (AUTHOR), Ma, Chijun1 (AUTHOR), Xu, Jieping1 (AUTHOR), Zhu, Weidhua1 (AUTHOR), Hu, Yanqing1 (AUTHOR), Huang, Chaohui1 (AUTHOR), Sun, Jihong2,3,4 (AUTHOR) sunjihong@zju.edu.cn, Zhou, Long1 (AUTHOR) zhoulong07@hotmail.com, Zhao, Fenhua1 (AUTHOR) zhfenhua@163.com
Source: Journal of Imaging Informatics in Medicine. Apr2026, Vol. 39 Issue 2, p1242-1252. 11p.
Subjects: Preoperative period, Cancer invasiveness, Prediction models, Research funding, T-test (Statistics), Receiver operating characteristic curves, Computed tomography, Radiomics, Fisher exact test, Logistic regression analysis, Retrospective studies, Descriptive statistics, Mann Whitney U Test, Chi-squared test, Rectum tumors, Statistics, Data analysis software, Comparative studies, Contrast media, Evaluation
Geographic Terms: China
Abstract: To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1–Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193167824
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Enhanced CT-Based Delta-Radiomics: Predicting Lymphovascular and Perineural Invasion in Rectal Cancer Preoperatively.
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  Data: <searchLink fieldCode="AR" term="%22Fu%2C+Chunlong%22">Fu, Chunlong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Zebin%22">Yang, Zebin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shan%2C+Kangfei%22">Shan, Kangfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pang%2C+Zhenzhu%22">Pang, Zhenzhu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Chijun%22">Ma, Chijun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Jieping%22">Xu, Jieping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Weidhua%22">Zhu, Weidhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Yanqing%22">Hu, Yanqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Chaohui%22">Huang, Chaohui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Jihong%22">Sun, Jihong</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<i> sunjihong@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Long%22">Zhou, Long</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhoulong07@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Fenhua%22">Zhao, Fenhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhfenhua@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Informatics+in+Medicine%22">Journal of Imaging Informatics in Medicine</searchLink>. Apr2026, Vol. 39 Issue 2, p1242-1252. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Preoperative+period%22">Preoperative period</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+invasiveness%22">Cancer invasiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Radiomics%22">Radiomics</searchLink><br /><searchLink fieldCode="DE" term="%22Fisher+exact+test%22">Fisher exact test</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Retrospective+studies%22">Retrospective studies</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Mann+Whitney+U+Test%22">Mann Whitney U Test</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Rectum+tumors%22">Rectum tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Contrast+media%22">Contrast media</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation%22">Evaluation</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1–Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10278-025-01574-8
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      – Code: eng
        Text: English
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        StartPage: 1242
    Subjects:
      – SubjectFull: Preoperative period
        Type: general
      – SubjectFull: Cancer invasiveness
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      – SubjectFull: Prediction models
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      – SubjectFull: Receiver operating characteristic curves
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      – SubjectFull: Computed tomography
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      – SubjectFull: Radiomics
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      – SubjectFull: Fisher exact test
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      – SubjectFull: Logistic regression analysis
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      – SubjectFull: Mann Whitney U Test
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      – SubjectFull: Chi-squared test
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      – SubjectFull: Rectum tumors
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      – SubjectFull: Statistics
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