Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Journal: Scientific reports
Published Date:

Abstract

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of Tc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis. This AI model demonstrated considerable diagnostic performance, the areas under the curve (AUC) of receiver operating characteristic (ROC) was 0.988 for breast cancer, 0.955 for prostate cancer, 0.957 for lung cancer, and 0.971 for other cancers. Applying this AI model to a new dataset of 400 BS cases, it represented comparable performance to that of human physicians individually classifying bone metastasis. Further AI-consulted interpretation also improved human diagnostic sensitivity and accuracy. In total, this AI model performed a valuable benefit for nuclear medicine physicians in timely and accurate evaluation of cancer bone metastasis.

Authors

  • Zhen Zhao
  • Yong Pi
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, PR China.
  • Lisha Jiang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Yongzhao Xiang
    Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610041, PR China.
  • Jianan Wei
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China.
  • Pei Yang
    Department of Bone and Joint Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Wenjie Zhang
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Xiao Zhong
    Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, People's Republic of China.
  • Ke Zhou
    School of Statistics at University of International Business and Economics, Beijing, China. Electronic address: 02417@uibe.edu.cn.
  • Yuhao Li
    Institute of Bismuth Science, University of Shanghai for Science and Technology Shanghai 200093 P. R. China ouyangrz@usst.edu.cn.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Zhang Yi
  • Huawei Cai
    Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610041, PR China. Electronic address: hw.cai@yahoo.com.