Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model

Abstract

Objectives: The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, density, and location impact the ability to successfully detect and track a lung lesion in 2D orthogonal X-ray images. The standard workflow to identify successful candidates for lung tumor tracking is called Lung Optimized Treatment (LOT) simulation, and involves multiple steps from CT acquisition to the execution of the simulation plan on CyberKnife. The aim of the study is to develop a deep learning classification model to predict which patients can be successfully treated with lung tumor tracking, thus circumventing the LOT simulation process.

Methods: Target tracking is achieved by matching orthogonal X-ray images with a library of digital radiographs reconstructed from the simulation CT scan (DRRs). We developed a deep learning model to create a binary classification of lung lesions as being trackable or untrackable based on tumor template DRR extracted from the CyberKnife system, and tested five different network architectures. The study included a total of 271 images (230 trackable, 41 untrackable) from 129 patients with one or multiple lung lesions. Eighty percent of the images were used for training, 10% for validation, and the remaining 10% for testing.

Results: For all five convolutional neural networks, the binary classification accuracy reached 100% after training, both in the validation and the test set, without any false classifications.

Conclusions: A deep learning model can distinguish features of trackable and untrackable lesions in DRR images, and can predict successful candidates for fiducial-free lung tumor tracking.

Keywords: 4DCT; CyberKnife; LOT simulation; SBRT; accuray; artificial intelligence; binary classification; deep learning; digitally reconstructed radiograph (DRR); lung cancer; lung tumors; motion modeling; radiation therapy; respiratory motion; respiratory motion modeling; transfer learning.

FIGURE 1

Example of a lesion that can be tracked with two‐views lung tumor tracking: (a) axial, coronal, and sagittal CT slices showing the lesion; (b) full content DRR; (c) tumor template DRR.

FIGURE 2

Treatment images for the same lesion displayed in Figure 1: (a) DRR; (b) DRR overlay with 135‐kV X‐ray image.

FIGURE 3

Enhanced DRR image of a trackable lung tumor.

FIGURE 4

Enhanced DRR image of a nontrackable lung tumor.

FIGURE 5

The training progress and the final classification accuracy for the Inception‐ResNet‐v2 deep‐learning network, which results in 100% validation accuracy in determining the feasibility to track the tumor.

FIGURE 6

Features extracted from the strongest activation channel in the middle layer of the Inception‐ResNet‐v2 network for a trackable lung tumor. Three full arrows are pointing to several bright white disks throughout the image, located at the corners of a blurry squared shape at the center of the image. These features are associated with a trackable lung tumor.

FIGURE 7

Features extracted from the strongest activation channel in the middle layer of the Inception‐ResNet‐v2 network for a nontrackable lung tumor. One full arrow is pointing to a bright white disk on the top left corner of the image, whereas three dashed arrows are pointing to dark black disks, which are located at the corners of a blurry squared shape at the center of the image. These features are associated with a nontrackable lung tumor.