Patch-based generative adversarial neural network models for head and neck MR-only planning.

Purpose: To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic computed tomography (sCT) generation for magnetic resonance (MR)-only treatment planning in head and neck (HN) cancer patients.

Materials And Methods: Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per-epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters.

Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm.

Purpose: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART).

Methods: We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course.

Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.

Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD). For these features to be clinically useful, they should be robust to tumor size variations and not correlated with the normal lung volume of interest, i.e.

Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer.

We proposed a framework to detect and quantify local tumor morphological changes due to chemo-radiotherapy (CRT) using a Jacobian map and to extract quantitative radiomic features from the Jacobian map to predict the pathologic tumor response in locally advanced esophageal cancer patients. In 20 patients who underwent CRT, a multi-resolution BSpline deformable registration was performed to register the follow-up (post-CRT) CT to the baseline CT image. The Jacobian map (J) was computed as the determinant of the gradient of the deformation vector field.

Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

Purpose: To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer.

Methods: We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI).