An Automatic Deep Learning-Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study.

Purpose: Most radiomic studies use the features extracted from the manually drawn tumor contours for classification or survival prediction. However, large interobserver segmentation variations lead to inconsistent features and hence introduce more challenges in constructing robust prediction models. Here, we proposed an automatic workflow for glioblastoma (GBM) survival prediction based on multimodal magnetic resonance (MR) images.

Geometric validation of MV topograms for patient localization on TomoTherapy.

Our goal was to geometrically validate the use of mega-voltage orthogonal scout images (MV topograms) as a fast and low-dose alternative to mega-voltage computed tomography (MVCT) for daily patient localization on the TomoTherapy system. To achieve this, anthropomorphic head and pelvis phantoms were imaged on a 16-slice kilo-voltage computed tomography (kVCT) scanner to synthesize kilo-voltage digitally reconstructed topograms (kV-DRT) in the Tomotherapy detector geometry. MV topograms were generated for couch speeds of 1-4 cm s(-1) in 1 cm s(-1) increments with static gantry angles in the anterior-posterior and left-lateral directions.

Tomotherapy improves local control and changes failure patterns in locally advanced malignant pleural mesothelioma.

Purpose: The purpose of the study was to determine whether intensity modulated radiation therapy delivered via helical tomotherapy improves local control (LC) after pleurectomy/decortication (P/D) for malignant pleural mesothelioma compared with 3-dimensional conformal radiation therapy (3D-CRT).

Methods And Materials: Forty-five consecutive patients were treated with adjuvant radiation to 45 Gy in 1.8 Gy fractions after P/D between 2006 and 2014; 23 received 3D-CRT, and 22 received tomotherapy.

Denoised and texture enhanced MVCT to improve soft tissue conspicuity.

Purpose: MVCT images have been used in TomoTherapy treatment to align patients based on bony anatomies but its usefulness for soft tissue registration, delineation, and adaptive radiation therapy is limited due to insignificant photoelectric interaction components and the presence of noise resulting from low detector quantum efficiency of megavoltage x-rays. Algebraic reconstruction with sparsity regularizers as well as local denoising methods has not significantly improved the soft tissue conspicuity. The authors aim to utilize a nonlocal means denoising method and texture enhancement to recover the soft tissue information in MVCT (DeTECT).