Nuclear medicine practice in Japan: a report of the eighth nationwide survey in 2017

Nishiyama, Yoshihiro; Kinuya, Seigo; Kato, Takashi; Kayano, Daiki; Sato, Shuhei; Tashiro, Masato; Tatsumi, Mitsuaki; Hashimoto, Teisuke; Baba, Shingo; Hirata, Kenji; Yoshimura, Mana; Yoneyama, Hiroto

doi:10.1007/s12149-019-01382-5

Cited by 29 publications

(20 citation statements)

References 17 publications

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“…PET/CT scanners are becoming widespread because of their usefulness, whereas the number of FDG PET/CT examinations has also increased. In Japan, the number of institutes that have installed a PET/CT scanner has increased by 177 (212 to 389) from 2007 to 2017, with examinations increasing 72% from 414,300 to 711,800 [3]. In the current clinical practice, FDG PET/CT images require interpretation by specialists in nuclear medicine.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

et al. 2020

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Background: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal. Methods: This retrospective study investigated 3485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region). Results: There were 1280 (37%), 1450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4, 99.4, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively. Conclusion: The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.

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Section: Introductionmentioning

confidence: 99%

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

et al. 2020

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“…In Japan, the number of institutes that have installed a PET/CT scanner has increased by 177 (212 to 389) from 2007 to 2017, with examinations increasing 72% from 414,300 to 711,800. [3] In the current clinical practice, FDG PET/CT images require interpretation by specialists in nuclear medicine. As the physicians' burden of interpreting images increases, the risk of oversight or misdiagnosis also increases.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

Kawauchi

Furuya

Hirata

et al. 2020

Preprint

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Background: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal. Methods: This retrospective study investigated 3,485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region). Results: There were 1,280 (37%), 1,450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4%, 99.4%, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively. Conclusion: The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.

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“…42.1 radiologists per million population were employed, and thus 8137 CT/MRI examinations were performed per radiologist [11]. As for nuclear medicine, 0.6 million FDG PET studies were performed in Japan in 2017 [12]. A signi cant number of these studies require radiologist(s) to interpret the images and write the reports.…”

Section: Introductionmentioning

confidence: 99%

SUVmax of FDG PET-CT Can Be Used As An Identifier of Lesion for AI To Interpret Radiology Reports

Hirata

Manabe

Magota

et al. 2020

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Background Radiology reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily radiology reports of FDG PET-CT. If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret radiology reports. We aimed to clarify whether the lesion can be localized using SUVmax written in radiology reports.Methods The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax=3.14 was given, the voxels with 3.135≤SUVmax<3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6, IQR = 5.2). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax<2), 79.5% (2≤SUVmax<5), and 97.8% (5≤SUVmax) of lesions.Conclusions SUVmax of FDG PET-CT can be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax<2 were difficult to identify. The proposed method may have potential to make use of radiology reports retrospectively for constructing training datasets for AI.

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Nuclear medicine practice in Japan: a report of the eighth nationwide survey in 2017

Cited by 29 publications

References 17 publications

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

A convolutional neural network-based system to classify patients using FDG PET/CT examinations

SUVmax of FDG PET-CT Can Be Used As An Identifier of Lesion for AI To Interpret Radiology Reports

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