Deep Learning-Based Automatic Delineation of Liver Domes in kV Triggered Images for Online Breath-hold Reproducibility Verification of Liver Stereotactic Body Radiation Therapy

Stereotactic Body Radiation Therapy (SBRT) can be a precise, minimally invasive treatment method for liver cancer and liver metastases. However, the effectiveness of SBRT relies on the accurate delivery of the dose to the tumor while sparing healthy tissue. Challenges persist in ensuring breath-hold reproducibility, with current methods often requiring manual verification of liver dome positions from kV-triggered images. To address this, we propose a proof-of-principle study of a deep learning-based pipeline to automatically delineate the liver dome from kV-planar images. From 24 patients who received SBRT for liver cancer or metastasis inside liver, 711 KV-triggered images acquired for online breath-hold verification were included in the current study. We developed a pipeline comprising a trained U-Net for automatic liver dome region segmentation from the triggered images followed by extraction of the liver dome via thresholding, edge detection, and morphological operations. The performance and generalizability of the pipeline was evaluated using 2-fold cross validation. The training of the U-Net model for liver region segmentation took under 30 minutes and the automatic delineation of a liver dome for any triggered image took less than one second. The RMSE and rate of detection for Fold1 with 366 images was (6.4 +/- 1.6) mm and 91.7%, respectively. For Fold2 with 345 images, the RMSE and rate of detection was (7.7 +/- 2.3) mm and 76.3% respectively.

The Conditional Prediction Function: A Novel Technique to Control False Discovery Rate for Complex Models

In modern scientific research, the objective is often to identify which variables are associated with an outcome among a large class of potential predictors. This goal can be achieved by selecting variables in a manner that controls the the false discovery rate (FDR), the proportion of irrelevant predictors among the selections. Knockoff filtering is a cutting-edge approach to variable selection that provides FDR control. Existing knockoff statistics frequently employ linear models to assess relationships between features and the response, but the linearity assumption is often violated in real world applications. This may result in poor power to detect truly prognostic variables. We introduce a knockoff statistic based on the conditional prediction function (CPF), which can pair with state-of-art machine learning predictive models, such as deep neural networks. The CPF statistics can capture the nonlinear relationships between predictors and outcomes while also accounting for correlation between features. We illustrate the capability of the CPF statistics to provide superior power over common knockoff statistics with continuous, categorical, and survival outcomes using repeated simulations. Knockoff filtering with the CPF statistics is demonstrated using (1) a residential building dataset to select predictors for the actual sales prices and (2) the TCGA dataset to select genes that are correlated with disease staging in lung cancer patients.