Impact of segmentation methods on pathological grade prediction in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics / 中华核医学与分子影像杂志
Chinese Journal of Nuclear Medicine and Molecular Imaging
;
(6): 454-459, 2021.
Article
in Chinese
| WPRIM
| ID: wpr-910785
ABSTRACT
Objective:
To investigate the segmentation methods of pancreatic ductal adenocarcinoma (PDAC) tumor regions in 18F-fluorodeoxyglucose (FDG) PET/CT images, as well as their impact on radiomic features-based pathological grade prediction.Methods:
A total of 72 patients (46 males, 26 females, age range 25-87 years) with pathologically confirmed PDAC and a preoperative 18F-FDG PET/CT scan in Peking Union Medical College Hospital between September 2010 and January 2016 were enrolled retrospectively. The cohort of patients was classified as well differentiated group and non-well differentiated group based on the pathological grade of PDAC, and patients were divided into training set and validation set in the ratio of 3∶1 randomly. Two physicians performed manual contours in the tumor region (referred as region of interest (ROI)_M1 and ROI_M2) and semi-automatic ROIs based on standardized uptake value (SUV) gradient edge search (referred as ROI_G) and 40% threshold applied to the maximum SUV (SUV max; referred as ROI_S) were drawn. The four types of segmentation results were compared in terms of volume and Dice similarity coefficient (DSC). Shape, first-order, and texture features were extracted from PET/CT original and preprocessed images, and the interclass correlation coefficient (ICC) was used to assess each feature′s consistency across all segmentations. Kruskal-Wallis rank sum test, independent-sample t test or z test were used to analyze the data. The area under the receiver operating characteristic curve was used to assess model accuracy, and cross validation was used to assess generalization ability.Results:
There were 55 patients in the training set (14 well differentiated cases and 41 non-well differentiated cases) and 17 patients in the validation set (4 well differentiated cases and 13 non-well differentiated cases). A total of 44 selected features were predictive of the pathological grade of PDAC among 20 feature groups. There was significant difference among the volumes of ROI_M1, ROI_M2, ROI_G and ROI_S (10.29(4.01, 19.43), 9.34(4.26, 17.27), 11.86(5.52, 19.74) and 15.08(9.62, 27.44) cm 3; H=18.641, P<0.05). The degree of contour coincidence and feature consistency between ROI_M1 and ROI_M2 were both higher (DSC=0.86 (0.76, 0.90), ICC=0.86 (0.74, 0.94)). Compared to manual contours, the degree of contour coincidence and feature consistency of ROI_G (DSC 0.86(0.75, 0.91), 0.91(0.85, 0.96); ICC 0.87(0.72, 0.94), 0.94(0.88, 0.98)) were better. There was no statistically significant difference in model accuracy or generalization ability between ROI_M1 and ROI_G ( z=1.052, t=0.712, both P>0.05). The accuracy of ROI_M2 was better than ROI_G ( z=3.031, P=0.002), but the generalization ability of ROI_M2 was insufficient ( t=3.086, P=0.012).Conclusions:
Although the manual contour prediction models are highly accurate, their performance are unstable. Semi-automatic contouring based on gradient can achieve comparable accuracy to manual contouring, and the model′s generalization ability is stronger.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Prognostic study
Language:
Chinese
Journal:
Chinese Journal of Nuclear Medicine and Molecular Imaging
Year:
2021
Type:
Article
Similar
MEDLINE
...
LILACS
LIS