43 results on '"Song, Jiangdian"'
Search Results
2. PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma
3. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC
4. Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures
5. A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images
6. Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study
7. LWMA-Net: Light-weighted morphology attention learning for human embryo grading
8. A deep learning-based system for survival benefit prediction of tyrosine kinase inhibitors and immune checkpoint inhibitors in stage IV non-small cell lung cancer patients: A multicenter, prognostic study
9. Assessing the reproducibility, stability, and biological interpretability of multimodal computed tomography image features for prognosis in advanced non‐small cell lung cancer
10. End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT
11. Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve
12. Radiomics in Medical Imaging—Detection, Extraction and Segmentation
13. Development and validation of a prognostic index for efficacy evaluation and prognosis of first-line chemotherapy in stage III–IV lung squamous cell carcinoma
14. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
15. Public Code from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy
16. Data from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy
17. Supplementary Appendix, Supplementary References, Supplementary Figures 1-5, Supplementary Tables 1-4 from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy
18. Correction to: End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT
19. Correction to: Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures
20. Selection Between Liver Resection Versus Transarterial Chemoembolization in Hepatocellular Carcinoma: A Multicenter Study
21. Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma
22. Automatic Lung Nodule Segmentation and Intra-Nodular Heterogeneity Image Generation
23. Trends in the application of deep learning networks in medical image analysis: Evolution between 2012 and 2020
24. Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study
25. Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma
26. An Opportunity to Reduce Disparities in Lung Cancer Screening
27. Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images
28. Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features
29. Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant–Positive Non–Small Cell Lung Cancer
30. Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures
31. Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies
32. A review of original articles published in the emerging field of radiomics
33. Radiomics for the prediction of EGFR mutation subtypes in non‐small cell lung cancer
34. A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy
35. Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer
36. 2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer
37. Development and validation of a radiomics nomogram for progression-free survival prediction in stage IV EGFR-mutant non-small cell lung cancer
38. Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis
39. Prediction of malignant and benign of lung tumor using a quantitative radiomic method
40. Association between tumor heterogeneity and progression-free survival in non-small cell lung cancer patients with EGFR mutations undergoing tyrosine kinase inhibitors therapy
41. Association between tumor heterogeneity and overall survival in patients with non-small cell lung cancer
42. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach.
43. Development and validation of a radiomics nomogram for progression-free survival prediction in stage IV EGFR-mutant non-small cell lung cancer
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