ArticleEuropean radiology2019
Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 4 of them syntheses that pooled it.
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Who cites it
99 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Performance of Machine Learning in Diagnosing KRAS (Kirsten Rat Sarcoma) Mutations in Colorectal Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.BMC cancer · 2021Pooled it
- KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients.BMC medical imaging · 2023Trial
- CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.European radiology · 2022Trial
- Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.Cancer medicine · 2021Trial
- Preoperative prediction of KRAS mutation in rectal cancer using a combined T2-weighted imaging radiomics and volumetric apparent diffusion coefficient histogram model.Abdominal radiology (New York) · 2026Article
- [A bidirectional feature mapping classification model based on multi-constrained latent representation learning for differential diagnosis of pneumonia].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Deep learning feature-based model on abdominal radiography outperforms experts for early necrotizing enterocolitis diagnosis in neonates.European radiology · 2026Article
- Radiomics' Role in Predicting Distant Metastases, Recurrence and Survival Outcome in Rectal Cancer: A Systematic Review.Cancers · 2026Review
- Artificial intelligence-based method for renal function automatic assessment of each kidney using plain computed tomography (CT) scans.Physical and engineering sciences in medicine · 2026Article
- Magnetic resonance imaging differentiating benign from malignant bone and soft tissue tumors and assessing Ki-67 expression using APT and DWI tools.Frontiers in oncology · 2026Article
- Magnetic resonance imaging based radiomics for predicting pathogenetic features and survival in rectal cancer.Pathology oncology research : POR · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.European radiology · 2025Article
- Pretreatment intravoxel incoherent motion histogram metrics and clinical characteristics for prediction of perineural invasion status and survival in patients with rectal cancer.Insights into imaging · 2025Article
- The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Radiomics to predict PNI in ESCC.Abdominal radiology (New York) · 2025Article
- Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learning.Heliyon · 2025Article
39 more citing papers are in PubMed but not listed here.
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15 authors.
Funding
Abstract
objectivesTo develop and validate radiomic models in evaluating biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI).
methodsThis study consisted of 345 patients with rectal cancer who underwent MP-MRI. We focused on evaluating five postoperative confirmed characteristics: lymph node (LN) metastasis, tumor differentiation, fraction of Ki-67-positive tumor cells, human epidermal growth factor receptor 2 (HER-2), and KRAS-2 gene mutation status. Data from 197 patients were used to develop the biological characteristics evaluation models. Radiomic features were extracted from MP-MRI and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by using two feature-ranking methods (MRMR and WLCX) and three classifiers (RF, SVM, and LASSO). Multivariable logistic regression was used to build an integrated evaluation model combining radiomic signatures and clinical characteristics. The performance was evaluated using an independent validation dataset comprising 148 patients.
resultsThe MRMR and LASSO regression produced the best-performing radiomic signatures for evaluating HER-2, LN metastasis, tumor differentiation, and KRAS-2 gene status, with AUC values of 0.696 (95% CI, 0.610-0.782), 0.677 (95% CI, 0.591-0.763), 0.720 (95% CI, 0.621-0.819), and 0.651 (95% CI, 0.539-0.763), respectively. The best-performing signatures for evaluating Ki-67 produced an AUC value of 0.699 (95% CI, 0.611-0.786), and it was developed by WLCX and RF algorithm. The integrated evaluation model incorporating radiomic signature and MRI-reported LN status had improved AUC of 0.697 (95% CI, 0.612-0.781).
conclusionRadiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer. KEY POINTS: • Radiomic features were extracted from MP-MRI images of the rectal tumor. • The proposed radiomic signatures demonstrated discrimination ability in identifying the histopathological, immunohistochemical, and genetic characteristics of rectal cancer. • All MRI sequences were important and could provide complementary information in radiomic analysis.
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