Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study.

Objectives: To build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS).

Methods: In total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance.

The signal intensity characteristics of normal bone marrow in diffusion weighted imaging at various menstrual status women.

Purpose: Diffuse hyperintensities of the bone marrow in whole-body diffusion-weighted (DW) imaging (DWI) have been encountered more frequently in females aged 21-50 compared to elder females or men. Therefore, we aimed to visually evaluate DWI among pre-, peri- and postmenopausal women and to verify whether it correlates also quantitatively with hormonal status.

Method: The prospective study was approved by our institutional review board and informed consent was obtained in a total of 70 healthy premenopausal, perimenopausal, and postmenopausal women aged 40-58 years from February 2017 to October 2017.

Characterizing the Blood Oxygen Level-Dependent Fluctuations in Musculoskeletal Tumours Using Functional Magnetic Resonance Imaging.

This study characterized the blood oxygen level-dependent (BOLD) fluctuations in benign and malignant musculoskeletal tumours via power spectrum analyses in pre-established low-frequency bands. BOLD MRI and T1-weighted imaging (T1WI) were collected for 52 patients with musculoskeletal tumours. Three ROIs were drawn on the T1WI image in the tumours' central regions, peripheral regions and neighbouring tissue.