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
437P - Correlation between bio-impedance analysis and abdominal CT scan to diagnose decreased muscle mass in adult cancer patients
Presenter: Andree Kurniawan
Session: Poster display session
Resources:
Abstract
438P - Evaluating mitochondrial biomarkers between fatigue subclasses identified using latent class analysis in early-stage breast cancer patients
Presenter: Yi Long Toh
Session: Poster display session
Resources:
Abstract
440P - Accuracy of risk scoring system to determine delayed chemotherapy induced nausea and vomiting (CINV) in cancer patients
Presenter: Jada Harika
Session: Poster display session
Resources:
Abstract
441P - A pilot cross-sectional study on incidence of liver toxicity in cancer patients on western anti-cancer drug therapy with or without concurrent Chinese herbal medicine
Presenter: Tsz Him So
Session: Poster display session
Resources:
Abstract
442P - Relationship between QOL and support elderly patients with permanent colostomies
Presenter: Yukiko Orii
Session: Poster display session
Resources:
Abstract
443P - The effectiveness of individual nutritional counselling for patients with advanced cancer undergoing chemotherapy: A preliminary study
Presenter: Saori Koshimoto
Session: Poster display session
Resources:
Abstract
444P - The prophylactic effect of 0.1% fluorometholone eye drops on eye disorders caused by high-dose cytarabine
Presenter: Takayuki Tsuchiya
Session: Poster display session
Resources:
Abstract
445P - Safety and feasibility of extending flushing interval every 3 months for maintenance of TICVPS in CRC patients after completion of curative intended treatments
Presenter: Sang Bo Oh
Session: Poster display session
Resources:
Abstract
446P - Accuracy of risk scoring system to determine chemotherapy induced nausea and vomiting (CINV) in cancer patients receiving first cycle chemotherapy
Presenter: Jada Harika
Session: Poster display session
Resources:
Abstract
447P - Hypomagnesaemia: An unnoticed problem in lung cancer patients treated with concurrent chemoradiation
Presenter: Sharif Ahmed
Session: Poster display session
Resources:
Abstract