Unsupervised machine learning model for detecting anomalous volumetric modulated arc therapy plans for lung cancer patients.

Purpose: Volumetric modulated arc therapy (VMAT) is a new treatment modality in modern radiotherapy. To ensure the quality of the radiotherapy plan, a physics plan review is routinely conducted by senior clinicians; however, this process is less efficient and less accurate. In this study, a multi-task AutoEncoder (AE) is proposed to automate anomaly detection of VMAT plans for lung cancer patients.

p38α deficiency ameliorates psoriasis development by downregulating STAT3-mediated keratinocyte proliferation and cytokine production.

Psoriasis is characterized by keratinocyte (KC) hyperproliferation and inflammatory cell infiltration, but the mechanisms remain unclear. In an imiquimod-induced mouse psoriasiform model, p38 activity is significantly elevated in KCs and p38α specific deletion in KCs ameliorates skin inflammation. p38α signaling promotes KC proliferation and psoriasis-related proinflammatory gene expression during psoriasis development.

Applying mixed-integer linear programming to the non-coplanar beam angle optimization of intensity-modulated radiotherapy for liver cancer.

Background: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach.

Application of novel CRISPR tools in brain therapy.

In recent years, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based genome editing toolkit has been widely used to modify the genome sequence of organisms. As the CRISPR toolbox continues to grow and new CRISPR-associated (Cas) proteins are discovered, its applications have expanded beyond conventional genome editing. This now encompass epigenetic editing, gene expression control, and various other functions.

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases.

Purpose: Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan.

Preliminary study of cine-MRI compression in MR-guided radiotherapy.

Background: Cine-magnetic resonance imaging (MRI) is currently used in real-time tumor tracking during magnetic resonance (MR)-guided radiotherapy. As a type of MRI specified for motion tracking, a few minutes' acquisition results in thousands of 2-dimensional (2D) images. For MR-guided radiotherapy consisting of multiple treatment fractions, the large number of cine-MRI images would be disproportionate to the tight clinical data storage available.

On-board MRI image compression using video encoder for MR-guided radiotherapy.

Background: Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated.

Anomaly detection in radiotherapy plans using deep autoencoder networks.

Purpose: Treatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement.