Abstract 75P
Background
A parallel deep learning network framework for whole-body bone scan image analysis Whole-body bone scan image analysis in nuclear medicine is a common method assisting physicians in bone metastases detection of cancer. As the increasing need for diagnostic examinations in the huge and elderly population in China, physicians are facing a significant growth of workload but must still manage to read the diagnostic images carefully and avoid errors in interpretation. It is crucial to develop a clinical decision support tool in assisting physicians in their clinical routine.
Methods
In this study, we proposed a parallel deep learning network framework for bone-scan interpretations of the presence or absence of bone metastases. The whole-body bone scans (anterior and posterior views) of 707 patients who are suspected bone metastatic disease were studied. The physicians were asked to classify each case for the presence or absence of bone metastasis manually. Each bone scan image was automatically segmented into 26 different anatomical regions of homogeneous bones based on the skeletal frame. The corresponding 26 deep learning networks made a diagnosis by inspecting each region and searching for abnormal lesion activity simultaneously. To estimate the performance of each anatomical sub-region identification models, a ten-fold cross testing scheme was applied where the data set was divided into ten parts of equal size randomly.
Results
The sensitivity, specificity and the mean number of false lesions detected were adopted as performance indices to evaluate the proposed model. The best sensitivity and specificity of an individual network corresponding to each sub-region are 99.9 % and 97.3% respectively. The overall mean sensitivity and specificity of the parallel model are 99.2% and 71.8% respectively, as well as 2.0 false detections per patient scan image within millisecond.
Conclusions
With an extremely high sensitivity, specificity and a low false lesions detection rate, this proposed parallel deep learning network model is demonstrated as useful for detecting metastases in bone scans. Our proposed framework appears to have significant potential as a clinical decision support tool in assisting physicians in their clinical routine.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
X. Pu: Full / Part-time employment: University of Electronic Science and Technology of China; Full / Part-time employment: University of Electronic Science and Technology of China; Full / Part-time employment: University of Electronic Science and Technology of China. G. Tang: Full / Part-time employment: West China University Hospital, Sichuan University. K. Cai: Research grant / Funding (institution): School of Computer Science and Engineering, University of Electronic Science and Technology of China. Y. Huang: Research grant / Funding (institution): College of Computer Science, Sichuan University. M. Ping: Research grant / Funding (institution): School of Computer Science and Engineering, University of Electronic Science and Technology of China. Z. Peng: Research grant / Funding (institution): Big Data Research Center, University of Electronic Science and Technology of China. H. Qiu: Full / Part-time employment: School of Computer Science and Engineering, University of Electronic Science and Technology of China.
Resources from the same session
504P - A single center report for safety and efficacy of CT-707 in Chinese patients with advanced, anaplastic lymphoma kinase-rearranged non-small cell lung cancer or other tumours
Presenter: Peng Song
Session: Poster display session
Resources:
Abstract
519P - Initial results of lung cancer genomic screening project for individualized medicine in Asia: LC-SCRUM-Asia
Presenter: Chih-Hsi Kuo
Session: Poster display session
Resources:
Abstract
521P - A randomized, phase II study comparing irinotecan versus amrubicin as maintenance therapy after first-line induction therapy for extensive disease small cell lung cancer (HOT1401/NJLCG1401)
Presenter: Keisuke Baba
Session: Poster display session
Resources:
Abstract
526P - A phase II study of apatinib in patients with recurrent/metastatic esophageal squamous cell carcinoma (ESCC)
Presenter: Li Chu
Session: Poster display session
Resources:
Abstract
499P - Prevalence of uncommon epidermal growth factor receptor (EGFR) alterations detected by circulating tumour DNA (ctDNA) in non-small cell lung cancer (NSCLC) patients in Hong Kong
Presenter: Oscar Siu Hong Chan
Session: Poster display session
Resources:
Abstract
489P - Overall survival in patients with EGFRm+ NSCLC receiving sequential afatinib and osimertinib: Updated analysis of the GioTag study
Presenter: Maximilian J. Hochmair
Session: Poster display session
Resources:
Abstract
509P - Second-line treatment after first-line vinorelbine in advanced platinum unfit NSCLC patients: An exploratory analysis of randomized Tempo-Lung trial
Presenter: Andrea Camerini
Session: Poster display session
Resources:
Abstract
500P - Clinico-molecular characteristics of Chinese primary non-small cell lung cancer patients with compound EGFR mutations
Presenter: Jianchun Duan
Session: Poster display session
Resources:
Abstract
527P - A multicenter study of NRG1 fusions in Chinese non-small cell lung cancer patients and response to afatinib using next generation sequencing
Presenter: Xingliang Li
Session: Poster display session
Resources:
Abstract
481P - Updated survival outcomes of the phase II study of low starting dose of afatinib as first-line treatment in patients with EGFR mutation-positive non-small cell lung cancer (KTORG1402)
Presenter: Toshihide Yokoyama
Session: Poster display session
Resources:
Abstract