ArticleEuropean radiology2020
Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.
Article in European radiology, 2020. 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
- Review
- Review
- Validation of multiple deep learning models for colorectal tumor differentiation with endoscopic ultrasound images: a dual-center study.Journal of gastrointestinal oncology · 2025Article
- Ultrasound-based radiogenomics: status, applications, and future direction.Ultrasonography (Seoul, Korea) · 2025Article
- Comparative performance of multiple ensemble learning models for preoperative prediction of tumor deposits in rectal cancer based on MR imaging.Scientific reports · 2025Article
- An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis.Radiological physics and technology · 2024Review
- T2WI-based MRI radiomics for the prediction of preoperative extranodal extension and prognosis in resectable rectal cancer.Insights into imaging · 2024Article
- Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features.Insights into imaging · 2023Article
- Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.European radiology · 2023Article
- MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study.European radiology · 2023Article
- The Crying Need for a Better Response Assessment in Rectal Cancer.Current treatment options in oncology · 2023Review
- Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer.Abdominal radiology (New York) · 2023Article
- Magnetic resonance imaging radiomics modeling predicts tumor deposits and prognosis in stage T3 lymph node positive rectal cancer.Abdominal radiology (New York) · 2023Article
- Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.Frontiers in oncology · 2023Article
- Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review.Frontiers in oncology · 2023Review
- Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.Abdominal radiology (New York) · 2022Article
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer.World journal of gastroenterology · 2022Article
- Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017.BMC medical imaging · 2022Article
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14 authors.
Funding
Abstract
objectiveTo develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.
methodsFrom December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set.
resultsThe training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384).
conclusionsUS radiomics may be a potential model to accurately predict TDs before therapy. KEY POINTS: • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.
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