Bone Metastasis Detection in the Chest and Pelvis from a Whole-Body Bone Scan Using Deep Learning and a Small Dataset

Da-Chuan Cheng,Kuo-Yang Yen,Te-Chun Hsieh,Chia-Hung Kao,Chia-Chuan Liu

doi:10.3390/electronics10101201

Abstract

The aim of this study was to establish an early diagnostic system for the identification of the bone metastasis of prostate cancer in whole-body bone scan images by using a deep convolutional neural network (D-CNN). The developed system exhibited satisfactory performance for a small dataset containing 205 cases, 100 of which were of bone metastasis. The sensitivity and precision for bone metastasis detection and classification in the chest were 0.82 ± 0.08 and 0.70 ± 0.11, respectively. The sensitivity and specificity for bone metastasis classification in the pelvis were 0.87 ± 0.12 and 0.81 ± 0.11, respectively. We propose the use of hard example mining for increasing the sensitivity and precision of the chest D-CNN. The developed system has the potential to provide a prediagnostic report for physicians’ final decisions.

Highlights

According to a report published in 2018 by the National Health Insurance Research Database of Taiwan, prostate cancer (PC) is the seventh highest ranking cause of cancerrelated deaths among Taiwanese men [1]
PC has a high degree of osteotropism [2] because the possibility of metastases is relatively high; PC has a slower progression than many other cancers
One of the current diagnostic media used in clinics for bone metastasis diagnosis is the whole-body bone scan (WBBS), with the vein injection of the Tc-99m MDP tracer

Summary

Introduction

According to a report published in 2018 by the National Health Insurance Research Database of Taiwan, prostate cancer (PC) is the seventh highest ranking cause of cancerrelated deaths among Taiwanese men [1]. The aim of this research was to develop an automated system for helping physicians to detect bone metastasis in the early stage. We propose two neural network (NN)-based systems: (1) an D-CNN-based deep learning technique that can identify bone metastases in the pelvis as early as possible, and (2) a faster region-based convolutional NN (R-CNN) that can identify metastasis spots on the ribs or spinal cord, if any, in the WBBS. Both these systems aim to help a physician in the early detection of small metastasis

Objectives

Methods

Results

Discussion

Conclusion