ArticleBMC cancer2023
Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 25 citations in OpenAlex.
- Deep learning model based on MRI-derived microvascular network simulation parameters for noninvasive assessment of lymphovascular invasion in rectal cancer patients.Journal of gastrointestinal oncology · 2026Article
- Microstructural mapping of time-dependent diffusion MRI for predicting lymphovascular invasion in rectal cancer: a proof-of-concept investigation.Insights into imaging · 2026Article
- Prediction of lymphovascular invasion in rectal cancer based on multimodal magnetic resonance imaging radiomics model.World journal of gastrointestinal surgery · 2026Article
- An interpretable machine learning model based on CT imaging for predicting lymphovascular invasion and survival in bladder urothelial carcinoma: a multicenter study.BMC medical imaging · 2025Article
- Integrative multi-region MRI radiomics and clinical nomogram for preoperative lymphovascular invasion prediction in rectal cancer: a multicenter validation.BMC medical imaging · 2025Article
- Preoperative stratification of Ki-67 proliferation index in intrahepatic mass-forming cholangiocarcinoma via tumor-halo radiomic phenotyping on biparametric MRI: a multicenter diagnostic model.BMC medical imaging · 2025Article
- Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI.Abdominal radiology (New York) · 2025Review
- Noninvasive prediction of lymphovascular invasion in rectal cancer without lymph node metastasis using a SHAP-interpretable combined model integrating MRI radiomics features and clinical immune-inflammatory biomarkers: a bicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Interpretable deep learning model and nomogram for predicting pathological grading of PNETs based on endoscopic ultrasound.BMC medical informatics and decision making · 2025Article
- Improving radiologists' diagnostic accuracy for lymphovascular invasion in colorectal cancer: insights from a multicenter CT-based study.Abdominal radiology (New York) · 2025Article
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer.Abdominal radiology (New York) · 2025Article
- Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.Insights into imaging · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Development and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer.Quantitative imaging in medicine and surgery · 2025Article
- Preoperative Imaging Assessment of Lymphovascular Invasion in Breast Cancer: Current Evidence, Technical Advances, and Future Directions.Breast cancer (Dove Medical Press) · 2025Review
- Multi-parametric MRI radiomics for predicting response to neoadjuvant therapy in patients with locally advanced rectal cancer.Japanese journal of radiology · 2024Article
- Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning radiomics analyses for distinguishing insulinomas from non-functional pancreatic neuroendocrine tumors.Frontiers in endocrinology · 2024Article
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5 authors at 3 institutions in 1 country.
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Abstract
backgroundPreoperative assessment of lymphovascular invasion(LVI) of rectal cancer has very important clinical significance. However, accurate preoperative imaging evaluation of LVI is highly challenging because the resolution of MRI is still limited. Relatively few studies have focused on prediction of LVI of rectal cancer with the tool of radiomics, especially in patients with negative statue of MRI-based extramural vascular invasion (mrEMVI).The purpose of this study was to explore the preoperative predictive value of biparametric MRI-based radiomics features for LVI of rectal cancer in patients with the negative statue of mrEMVI.
methodsThe data of 146 cases of rectal adenocarcinoma confirmed by postoperative pathology were retrospectively collected. In the cases, 38 had positive status of LVI. All patients were examined by MRI before the operation. The biparametric MRI protocols included T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). We used whole-volume three-dimensional method and two feature selection methods, minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO), to extract and select the features. Logistics regression was used to construct models. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the diagnostic performance of the radiomics based on T2WI and DWI and the combined models.
resultsRadiomics models based on T2WI and DWI had good predictive performance for LVI of rectal cancer in both the training cohort and the validation cohort. The AUCs of the T2WI model were 0.87 and 0.87, and the AUCs of the DWI model were 0.94 and 0.92. The combined model was better than the T2WI model, with AUCs of 0.97 and 0.95. The predictive performance of the DWI model was comparable to that of the combined model.
conclusionsThe radiomics model based on biparametric MRI, especially DWI, had good predictive value for LVI of rectal cancer. This model has the potential to facilitate the clinical recognition of LVI in rectal cancer preoperatively.
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