Dihydroartemisinin enhances the radiosensitivity of breast cancer by targeting ferroptosis signaling pathway through hsa_circ_0001610.

Objective: This study aimed to elucidate how DHA enhances the radiosensitivity of BC and to explain its potential mechanisms of action.

Methods: The circular structure of hsa_circ_0001610 was confirmed by Sanger sequencing, RNase R treatment, RT-PCR analysis using gDNA or cDNA. Cellular localization of hsa_circ_0001610 and microRNA-139-5p (miR-139-5p) was detected by fluorescence in situ hybridization.

Clinical and imaging features of 112 patients with irregular and flat bone osteosarcoma.

Background: This study evaluated the clinical characteristics and imaging findings of 112 patients with irregular and flat bone osteosarcoma (IFBO).

Methods: The age, gender, location, tumor size, density and signal intensity, osteoid matrix, periosteal reaction, and histological subtypes were analyzed for 112 patients with IFBO.

Results: A total of 112 patients with IFBO, including 64 males and 48 females, with a mean age of 34.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

Objectives: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes.

Methods: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set.

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

Objectives: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion.

Methods: In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data.

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

Objectives: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors.

Materials And Methods: In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience.

CT and MRI features of calvarium and skull base osteosarcoma (CSBO).

Objective: This study aims to assess the CT and MRI features of calvarium and skull base osteosarcoma (CSBO).

Methods: The CT and MRI features and pathological characteristics of 12 cases of pathologically confirmed CSBO were analyzed retrospectively.

Results: 12 patients (age range 9-67 years; 3 male, 9 female) were included in the study.