Title:Development and Validation of an Algorithm Model for Predicting Heat Sink
Effects during Pulmonary Thermal Ablation
Volume: 20
Author(s): Peng Du, Zenan Chen, Chang Yuwen, Liangliang Meng, Wei Feng, Zongyuan Ge, Xiao Zhang and Yueyong Xiao*
Affiliation:
- Department of Radiology, Chinese PLA General Hospital- First Medical Center, Beijing, China
Keywords:
Machine learning, Lung tumor, CT, Segmentation method, Pulmonary thermal ablation, Algorithm model.
Abstract:
Aims:
The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors
in the formulation and adjustment of surgical protocols.
Background:
The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to
predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time.
Objective:
To construct a segmentation model based on a convolutional neural network that can automatically identify and segment pulmonary nodules and
vascular structure and measure the distance between the nodule and vascular.
Methods:
First, the classical Faster RCNN model was used as the nodule detection network. After obtaining the bounding box of pulmonary nodules, the
VSPP-NET model was used to segment nodules in the bounding box. The distance from the nodule to the vasculature was measured after the
surrounding vasculature was segmented by the VSPP-NET model. The lung CT images of 392 patients with pulmonary nodules were used as the
training data for the algorithm. 68 cases were used as algorithm validation data, 29 as nodule algorithm test data, and 80 as vascular algorithm test
data. We compared the heat sink effect of 29 cases of data with the results of the algorithm model and expert segmentation and compared the
difference between the two results.
Results:
In pulmonary CT image vasculature segmentation, the recall and precision of the algorithm model reached >0.88 and >0.78, respectively. The
average time for automatic segmentation of each image model is 29 seconds, and the average time for manual segmentation is 158 seconds. The
output image of the model shows that the results of nodule segmentation and nodule distance measurement are satisfactory. In terms of heat sink
effect prediction, the positive rate of the algorithm group was 28.3%, and that of the expert group was 32.1%, with no significant difference
between the two groups (p=0.687).
Conclusion:
The algorithm model developed in this study shows good performance in predicting the heat sink effect during pulmonary thermal ablation. It can
improve the speed and accuracy of nodule and vessel segmentation, save ablation planning time, reduce the interference of human factors, and
provide more reference information for surgeons to make ablation plans to improve the ablation effect.