Prognostic impact of mutations in T1-4N0M0 lung adenocarcinoma: analyses focus on imaging and pathological features.

Background: With the development of tyrosine kinase inhibitor (TKI) treatment, the prognosis of advanced lung adenocarcinoma (LUAD) patients with epidermal growth factor receptor () mutations has been continuously improving. This study aims to propose the utilization of pathological characteristics and imaging features to evaluate the impact of gene mutations on the prognosis of T1-4N0M0 LUAD.

Methods: Among the cases diagnosed with LUAD between April 2015 and April 2016, 438 patients with T1-4N0M0 LUAD were included, and the clinical characteristics were collected.

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients.

Background: Gastric cancer (GC) is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide. The disease poses a serious public health problem in China, ranking fifth for incidence and third for mortality. Knowledge of the invasive depth of the tumor is vital to treatment decisions.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

Objectives: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma.

Methods: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model).

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

Objectives: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

Methods: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists.