Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
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Summary
A deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance is presented and could be a potential tool to select patients for adjuvant imatinib therapy.
- Type
- article
- Published
- 2018-12-23
- Cited by
- 45
- References
- 43
- Access
- Open access
- OpenAlex
- https://openalex.org/W2905979019
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:58647424
Keywords
Nomogram, Medicine, Oncology, Internal medicine, Residual neural network
References
- Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
- SMALL GASTROINTESTINAL STROMAL TUMOR OF THE STOMACH SHOWING RAPID GROWTH AND EARLY METASTASIS TO THE LIVER
- Physician Estimations of the Risk of Gastrointestinal Stromal Tumor Recurrence--Not Accurate Enough?: More Education May Be Needed.
- Physician Underestimation of the Risk of Gastrointestinal Stromal Tumor Recurrence After Resection.
- Risk stratification of patients diagnosed with gastrointestinal stromal tumor.
- Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited*
- Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts.
- Gastrointestinal stromal tumors: pathology and prognosis at different sites.
- How to build and interpret a nomogram for cancer prognosis.
- Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers
- Gastrointestinal stromal tumour.
- Gradient-based learning applied to document recognition
- Radiomics: Extracting more information from medical images using advanced feature analysis
- X-Tile
- An Improved Technique for Mitosis Counting
- Development and validation of a prognostic nomogram for recurrence-free survival after complete surgical resection of localised primary gastrointestinal stromal tumour: a retrospective analysis.
- Radiomics: Images Are More than Pictures, They Are Data
- Deep Residual Learning for Image Recognition
- Natural History of Imatinib-naive GISTs: A Retrospective Analysis of 929 Cases With Long-term Follow-up and Development of a Survival Nomogram Based on Mitotic Index and Size as Continuous Variables
- Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Cited by
- Are left ventricular muscle area and radiation attenuation associated with overall survival in advanced pancreatic cancer patients treated with chemotherapy?
- Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort
- Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results.
- Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging
- New advances in radiomics of gastrointestinal stromal tumors
- Prognostic Value of Fibrinogen and Lymphocyte Count in Intermediate and High Risk Gastrointestinal Stromal Tumors
- Reply to Collins et al.
- Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis
- A Nomogram to Predict the 28-day Mortality of Critically Ill Patients With Acute Kidney Injury and Treated With Continuous Renal Replacement Therapy.
- Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging
- Artificial intelligence in outcomes research: a systematic scoping review
- Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients
- Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images
- Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI
- Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study.
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases
- Machine learning in gastrointestinal surgery
- Expression of the HOXA gene family and its relationship to prognosis and immune infiltrates in cervical cancer
- A Study on the Intelligent Translation Model for English Incorporating Neural Network Migration Learning
- MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data
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