Activation maps; Bone scintigraphy; Deep learning; Metastatic bone disease; Radiology, Nuclear Medicine and imaging; Oncology; General Medicine; Radiological and Ultrasound Technology
Abstract :
[en] ("[en] PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans.
METHODS: We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians.
RESULTS: The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.","[en] ","")
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Ibrahim, Abdalla Khalil ; Université de Liège - ULiège > GIGA ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States ; Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Vaidyanathan, Akshayaa ; Université de Liège - ULiège > Département de pharmacie ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands. akshayaa.vaidyanathan@radiomics.bio ; Radiomics (Oncoradiomics SA), Liege, Belgium. akshayaa.vaidyanathan@radiomics.bio
Primakov, Sergey; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
Refaee, Turkey; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
Lovinfosse, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Jadoul, Alexandre ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Derwael, Céline ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Hertel, Fabian; Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Woodruff, Henry C; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
Zacho, Helle D; Department of Nuclear Medicine, Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark ; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
Hanin, François-Xavier; Department of Nuclear Medicine, Universite´CatholiqueUniversite´Catholique de Louvain, CHU-UCL-Namur, Ottignies-Louvain-la-Neuve, Belgium
Lambin, Philippe ; Université de Liège - ULiège > Département des sciences cliniques ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States
Mottaghy, Felix M; Department of Radiology and Nuclear Medicine, Columbia University Irving Medical Center, New York, United States ; Department of Nuclear Medicine and Comprehensive diagnostic centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Hustinx, Roland ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
ERC - European Research Council [BE] EU - European Union [BE] Interreg EMR - Interreg Euregio Meuse-Rhine [NL]
Funding text :
Authors acknowledge financial support from ERC advanced grant (ERCADG-2015 n° 694812 - Hypoximmuno), ERC-2020-PoC: 957565-AUTO.DISTINCT, Authors also acknowledge financial support from the European Union’s Horizon 2020 research and innovation programme under grant agreement: ImmunoSABR n° 733008, MSCA-ITN-PREDICT n° 766276, CHAIMELEON n° 952172, EuCanImage n° 952103,Interreg V-A Euregio Meuse-Rhine (EURADIOMICS n° EMR4) and Maastricht-Liege Imaging Valley grant, project no. “DEEP-NUCLE”.
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