ArticleScientific reports2021
Preoperative prediction of perineural invasion with multi-modality radiomics in rectal cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The accuracy of radiomics in diagnosing tumor deposits and perineural invasion in rectal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Radiomics model based on multi-sequence MR images for predicting preoperative immunoscore in rectal cancer.La Radiologia medica · 2022Trial
- Editorial for "MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer".Journal of magnetic resonance imaging : JMRI · 2026Article
- Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.Insights into imaging · 2026Article
- Development and validation of a predictive and prognostic model of perineural invasion in colorectal cancer.World journal of surgical oncology · 2026Article
- MRI-based habitat, intra-, and peritumoral machine learning model for perineural invasion prediction in rectal cancer.Abdominal radiology (New York) · 2026Article
- Multimodal analysis of whole slide images in colorectal cancer.NPJ digital medicine · 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 radiomics and deep learning based on PET/CT in predicting perineural nerve invasion in rectal cancer.Abdominal radiology (New York) · 2025Article
- Radiomics to predict PNI in ESCC.Abdominal radiology (New York) · 2025Article
- Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Amide proton transfer-weighted MRI for assessing rectal adenocarcinoma T-staging and perineural invasion: a prospective study.European radiology · 2025Article
- Application of cellular microstructural diffusion MRI (cell size imaging) in rectal lesions: a preliminary study.Frontiers in oncology · 2025Article
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
- Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer.Quantitative imaging in medicine and surgery · 2024Article
- Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer.European journal of radiology open · 2024Article
- Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study.World journal of gastroenterology · 2024Article
- The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study.Insights into imaging · 2024Article
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
- The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study.Frontiers in oncology · 2023Article
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6 authors.
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Abstract
Perineural invasion (PNI) as a grossly underreported independent risk predictor in rectal cancer is hard to identify preoperatively. We aim to predict PNI status in rectal cancer using multi-modality radiomics. In total, 396 radiomics features were extracted from T2-weighted images (T2WIs), diffusion-weighted images (DWIs), and portal venous phase of contrast-enhanced CT (CE-CT) respectively of 94 consecutive patients with histologically confirmed rectal cancer. T2WI score, DWI score, and CT score were calculated via the radiomics features selection and optimization. Discrimination, calibration, and clinical benefit ability were used to evaluate the performance of the radiomics scores in both training and testing datasets. CT score and T2WI score were independent risk predictors [CT score, OR (95% CI) = 4.218 (1.070-16.620); T2WI score, OR (95% CI) = 105.721 (3.091-3615.790)]. The concise score which combined CT score and T2WI score, showed the best performance [training dataset, AUC (95% CI) = 0.906 (0.833-0.979); testing dataset, AUC (95% CI) = 0.884 (0.761-1.000)] and good calibration (P > 0.05 in the Hosmer-Lemeshow test for the training and testing datasets). Decision curve analysis showed that the multi-modality radiomics nomogram had a higher clinical net benefit. The multi-modality radiomics score could be used to preoperatively assess PNI status in rectal cancer.
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