Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study.

Rationale And Objectives: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

Materials And Methods: Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard.

Using CT features of cystic airspace to predict lung adenocarcinoma invasiveness.

Background: Lung adenocarcinoma associated with cystic airspace (LACA) was once considered an uncommon manifestation of lung adenocarcinoma (LUAD), and understandings of it are limited; however, it is being observed more frequently in clinical practice. This study sought to assess the prevalence of LACA, and compare the high-resolution computed tomography (HRCT) features of LACA in patients with varying degrees of invasiveness.

Methods: This study retrospectively reviewed the HRCT scans of 1,525 patients with LUAD ≤3 cm in diameter at the Shanghai Chest Hospital between January 2016 and May 2016.

Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer.

Objectives: EGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy.

Materials And Methods: We retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of gene testing.

A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC.

Purpose: This study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).

Methods: This study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.

Objectives: To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods: From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated.

Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules.

Controversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules.

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

Background: Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is also subject to the qualification of radiologist. We aim at a clinically applicable automatic system for rib fracture detection and segmentation from CT scans.

Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.

Objectives: To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods: This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule.

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma.

Up to 50% of Asian patients with NSCLC have gene mutations, indicating that selecting eligible patients for -TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict mutation status and subtypes. We included 637 patients with lung adenocarcinomas, who performed the mutations analysis in the current study.

Impacts of grazing exclusion on productivity partitioning along regional plant diversity and climatic gradients in Tibetan alpine grasslands.

The biodiversity-productivity relationship is critical for better predicting ecosystem responses to climate change and human disturbance. However, it remains unclear about the effects of climate change, land use shifts, plant diversity, and their interactions on productivity partitioning above- and below-ground components in alpine grasslands on the Tibetan Plateau. To answer this question, we conducted field surveys at 33 grazed vs.

Responses of ecosystem CO 2 fluxes to short-term experimental warming and nitrogen enrichment in an Alpine meadow, northern Tibet Plateau.

Over the past decades, the Tibetan Plateau has experienced pronounced warming, yet the extent to which warming will affect alpine ecosystems depends on how warming interacts with other influential global change factors, such as nitrogen (N) deposition. A long-term warming and N manipulation experiment was established to investigate the interactive effects of warming and N deposition on alpine meadow. Open-top chambers were used to simulate warming.

Changes in individual plant traits and biomass allocation in alpine meadow with elevation variation on the Qinghai-Tibetan Plateau.

Plant traits and individual plant biomass allocation of 57 perennial herbaceous species, belonging to three common functional groups (forbs, grasses and sedges) at subalpine (3700 m ASL), alpine (4300 m ASL) and subnival (≥5000 m ASL) sites were examined to test the hypothesis that at high altitudes, plants reduce the proportion of aboveground parts and allocate more biomass to belowground parts, especially storage organs, as altitude increases, so as to geminate and resist environmental stress. However, results indicate that some divergence in biomass allocation exists among organs. With increasing altitude, the mean fractions of total biomass allocated to aboveground parts decreased.