Automatic diagnosis and assessment of bone metastases on bone scans based on deep learning
10.3760/cma.j.cn321828-20200906-00337
- VernacularTitle:基于深度学习的核素骨显像中骨转移瘤的诊断及评估
- Author:
Simin LIU
1
;
Ming FENG
;
Haidong CAI
;
Ming SUN
;
Yin WANG
;
Zhongwei LYU
;
Dan LI
Author Information
1. 同济大学附属第十人民医院核医学科，上海 200072
- Keywords:
Neoplasm metastasis;
Skeleton;
Radionuclide imaging;
Technetium Tc 99m medronate;
Neural networks (computer)
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2022;42(1):22-26
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an approach for the automatic diagnosis of bone metastasis and to design a parameter of quantitative evaluation for tumor burden on bone scans based on deep learning technology.Methods:A total of 621 cases (389 males, 232 females, age: 12-93 years) of bone scan images from the Department of Nuclear Medicine in Tenth People′s Hospital of Tongji University from March 2018 to July 2019 were retrospectively analyzed. Images were divided into bone metastasis group and non-bone metastasis group. Eighty percent of the cases were randomly extracted from both groups as the training set, and the rest of cases were used as the test set. A deep residual convolutional neural network ResNet34 was used to construct the classification model and the segmentation model. The sensitivity, specificity and accuracy were calculated and the performance differences of the classification model in different age groups (15 cases of <50 years, 75 cases of ≥50 and <70 years, 33 cases of ≥70 years) were analyzed. The regions of metastatic bone lesions were automatically segmented by the segmentation model. The Dice coefficient was used to evaluate the effect of the segmentation model and the manual labeled results. Finally, the bone scans tumor burden index (BSTBI) was calculated to assess the tumor burden of bone metastases.Results:There were 280 cases with bone metastases and 341 cases with non-bone metastases, including 498 in training set and 123 in test set. The classification model could accurately identify bone metastases, with the sensitivity, specificity and accuracy of 92.59%(50/54), 85.51%(59/69) and 88.62%(109/123), respectively, and it performed best in the <50 years group (sensitivity, 2/2; specificity, 12/13; accuracy, 14/15). The specificity in the ≥70 years group (8/12) was the lowest. The Dice coefficient of bone metastatic area and bladder area were 0.739 and 0.925 in the segmentation model, which performed similarly in the three age groups. Preliminary results showed that the value of BSTBI increased with the increase of the number of bone metastatic lesions and the degree of 99Tc m-MDP uptake. The machine learning model in this study took (0.48±0.07) s for the entire analysis process from input to the final BSTBI calculation. Conclusions:The deep learning based on automatic diagnosis framework for bone metastases can automatically and accurately identify segment bone metastases and calculate tumor burden. It provides a new way for the interpretation of bone scans. The proposed BSTBI may be used as a quantitative evaluation indicator in the future to assess the tumor burden of bone metastases based on bone scans.
