Publications by authors named "Hongzhuo Qi"

Background: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognostic factor. However, there is still a lack of practice in combining fatty tissue to discriminate benign and malignant pulmonary nodules.

Purpose: This study proposes a deep learning (DL) approach to explore the potential predictive value of dual imaging markers, including intrathoracic fat (ITF), in patients with pulmonary nodules.

View Article and Find Full Text PDF

Background: Correctly distinguishing between benign and malignant pulmonary nodules can avoid unnecessary invasive procedures. This study aimed to construct a deep learning radiomics clinical nomogram (DLRCN) for predicting malignancy of pulmonary nodules.

Methods: One thousand and ninety-eight patients with 6-30 mm pulmonary nodules who received histopathologic diagnosis at 3 centers were included and divided into a primary cohort (PC), an internal test cohort (I-T), and two external test cohorts (E-T1, E-T2).

View Article and Find Full Text PDF
Article Synopsis
  • - The study aimed to create a deep learning model to differentiate between benign and malignant pulmonary nodules using CT images and mediastinal fat analysis.
  • - Patients were split into various groups for training and testing the model, which combined features from both nodules and surrounding fat to enhance predictive accuracy.
  • - Results showed that including mediastinal fat improved the model's performance significantly, making it a valuable tool for better diagnosis and patient care in lung health.
View Article and Find Full Text PDF