TY - JOUR
T1 - Deep learning for image classification in dedicated breast positron emission tomography (dbPET)
AU - Satoh, Yoko
AU - Imokawa, Tomoki
AU - Fujioka, Tomoyuki
AU - Mori, Mio
AU - Yamaga, Emi
AU - Takahashi, Kanae
AU - Takahashi, Keiko
AU - Kawase, Takahiro
AU - Kubota, Kazunori
AU - Tateishi, Ukihide
AU - Onishi, Hiroshi
N1 - Publisher Copyright:
© 2022, The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.
PY - 2022/4
Y1 - 2022/4
N2 - Objective: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images. Methods: Of the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Our DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77–89% and 79–100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907). Conclusions: Our DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.
AB - Objective: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images. Methods: Of the 1598 women who underwent dbPET examination between April 2015 and August 2020, a total of 618 breasts on 309 examinations for 284 women who were diagnosed with BC or non-BC were analyzed in this retrospective study. The Xception-based DL model was trained to predict BC or non-BC using dbPET images from 458 breasts of 109 BCs and 349 non-BCs, which consisted of mediallateral and craniocaudal maximum intensity projection images, respectively. It was tested using dbPET images from 160 breasts of 43 BC and 117 non-BC. Two expert radiologists and two radiology residents also interpreted them. Sensitivity, specificity, and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Our DL model had a sensitivity and specificity of 93% and 93%, respectively, while radiologists had a sensitivity and specificity of 77–89% and 79–100%, respectively. Diagnostic performance of our model (AUC = 0.937) tended to be superior to that of residents (AUC = 0.876 and 0.868, p = 0.073 and 0.073), although not significantly different. Moreover, no significant differences were found between the model and experts (AUC = 0.983 and 0.941, p = 0.095 and 0.907). Conclusions: Our DL model could be applied to dbPET and achieve the same diagnostic ability as that of experts.
KW - Breast cancer
KW - Dedicated breast positron emission tomography
KW - Deep learning
KW - Image classification
KW - Neural network
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U2 - 10.1007/s12149-022-01719-7
DO - 10.1007/s12149-022-01719-7
M3 - Article
C2 - 35084712
AN - SCOPUS:85123912968
SN - 0914-7187
VL - 36
SP - 401
EP - 410
JO - Annals of Nuclear Medicine
JF - Annals of Nuclear Medicine
IS - 4
ER -