ArticleFrontiers in oncology2021
A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.Biomedical engineering online · 2024Trial
- Regional lymph node metastasis as a risk factor for recurrence in Borrmann type II gastric cancer: particular significance of the no. 7 lymph node.Frontiers in oncology · 2026Article
- Correlation between Tomographic and Histopathological Staging in Upfront Resected Gastric Cancer: Enhancing Diagnostic Accuracy in the Era of Perioperative Therapy.Journal of gastrointestinal cancer · 2025Article
- Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma.Frontiers in oncology · 2025Article
- Fusion model combining ultrasound-based radiomics and deep transfer learning with clinical parameters for preoperative prediction of pelvic lymph node metastasis in cervical cancer.Frontiers in oncology · 2025Article
- Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study.World journal of gastrointestinal surgery · 2024Article
- Distinguishing diffuse large B-cell lymphoma from Hodgkin's lymphoma in children using an enhanced computed tomography radiomics approach.Translational pediatrics · 2024Article
- Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.BMC medical imaging · 2024Article
- Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.Frontiers in oncology · 2024Article
- Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images.Journal of hepatocellular carcinoma · 2024Article
- A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI.BMC medical imaging · 2023Article
- Artificial intelligence applications in computed tomography in gastric cancer: a narrative review.Translational cancer research · 2023Review
- Article
- Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.Frontiers in human neuroscience · 2023Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning.Frontiers in human neuroscience · 2022Article
- Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation.Frontiers in human neuroscience · 2022Article
- Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer.Frontiers in medicine · 2022Article
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15 authors.
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Abstract
objectiveThis study aims to differentiate preoperative Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL) by transfer learning radiomics nomogram (TLRN) with whole slide images of GC as source domain data. MATERIALS AND
methodsThis study retrospectively enrolled 438 patients with histopathologic diagnoses of Borrmann type IV GC and PGL. They received CT examinations from three hospitals. Quantitative transfer learning features were extracted by the proposed transfer learning radiopathomic network and used to construct transfer learning radiomics signatures (TLRS). A TLRN, which integrates TLRS, clinical factors, and CT subjective findings, was developed by multivariate logistic regression. The diagnostic TLRN performance was assessed by clinical usefulness in the independent validation set.
resultsThe TLRN was built by TLRS and a high enhanced serosa sign, which showed good agreement by the calibration curve. The TLRN performance was superior to the clinical model and TLRS. Its areas under the curve (AUC) were 0.958 (95% confidence interval [CI], 0.883-0.991), 0.867 (95% CI, 0.794-0.922), and 0.921 (95% CI, 0.860-0.960) in the internal and two external validation cohorts, respectively. Decision curve analysis (DCA) showed that the TLRN was better than any other model. TLRN has potential generalization ability, as shown in the stratification analysis.
conclusionsThe proposed TLRN based on gastric WSIs may help preoperatively differentiate PGL from Borrmann type IV GC.Borrmann type IV gastric cancer, primary gastric lymphoma, transfer learning, whole slide image, deep learning.
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