Using Radiomics to Detect Subtle Architecture Changes of Cartilage and Subchondral Bone in Chronic Lateral Ankle Instability Patients Based on MRI PD-FS Images.

Rationale And Objectives: To use radiomics to detect the subtle changes of cartilage and subchondral bone in chronic lateral ankle instability (CLAI) patients based on MRI PD-FS images.

Materials And Methods: A total of 215 CLAI patients and 186 healthy controls were included and randomly split into a training set (n=281, patients/controls=151/130) and an independent test set (n=120, patients/controls=64/56). They underwent ankle MRI examinations.

Self-supervised learning for multi-center magnetic resonance imaging harmonization without traveling phantoms.

With the progress of artificial intelligence (AI) in magnetic resonance imaging (MRI), large-scale multi-center MRI datasets have a great influence on diagnosis accuracy and model performance. However, multi-center images are highly variable due to the variety of scanners or scanning parameters in use, which has a negative effect on the generality of AI-based diagnosis models. To address this problem, we propose a self-supervised harmonization (SSH) method.

Hydrogel modification of 3D printing hybrid tracheal scaffold to construct an orthotopic transplantation.

Objective: To evaluate the biological properties of modified 3D printing scaffold (PTS) and applied the hybrid graft for transplantation.

Methods: PTS was prepared via 3D printing and modified by Pluronic F-127. Biocompatibility of the scaffold was examined to ascertain its benefit in attachment and proliferation of bone marrow mesenchymal stem cells (BMSCs).

Radiomics Feature Analysis of Cartilage and Subchondral Bone in Differentiating Knees Predisposed to Posttraumatic Osteoarthritis after Anterior Cruciate Ligament Reconstruction from Healthy Knees.

Objectives: To introduce a new implementation of radiomics analysis for cartilage and subchondral bone of the knee and to compare the performance of the proposed models to classic T2 relaxation time in distinguishing knees predisposed to posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR) and healthy controls.

Methods: 114 patients following ACLR after at least 2 years and 43 healthy controls were reviewed and allocated to training ( = 110) and testing ( = 47) cohorts. Radiomics models are built for cartilage and subchondral bone regions of different compartments: lateral femur (LF), lateral tibia (LT), medial femur (MF), and medial tibia (MT) and combined models of four compartments on T2 mapping images.

Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers.

Purpose: To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively.

Materials And Methods: This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers.

A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer.

Background: Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinical-radiomics nomogram for the individual preoperative prediction of LNM in CRC patients.

Methods: We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC.