Đang chuẩn bị liên kết để tải về tài liệu:
CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction

Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ

The value of the CT features and quantitative analysis of lung subsolid nodules (SSNs) in the prediction of the pathological grading of lung adenocarcinoma is discussed. | CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction

TÀI LIỆU LIÊN QUAN

Clinical and imaging features on CT-Scaner of Aspergilloma on thoracic plombage procedure

Using CT radiomic features based on machine learning models to subtype adrenal adenoma

CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction

Imaging features and prognostic value of 18F-FDG PET/CT detection of soft-tissue metastasis from lung cancer: A retrospective study

What is the difference in morphologic features of the lumbar vertebrae between Caucasian and Taiwanese subjects? A CT-based study: Implications of pedicle screw placement via Roy-Camille or Weinstein method

Prediction of serosal invasion in gastric cancer: Development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images

Correlation between AI-based CT organ features and normal lung dose in adjuvant radiotherapy following breast-conserving surgery: A multicenter prospective study

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy

The role of clinical characteristics and pulmonary function testing in predicting risk of pneumothorax by CT-guided percutaneous core needle biopsy of the lung

A deep learning approach to identify missing is-a relations in SNOMED CT