scholarly journals A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET‐CT scans

2020 ◽  
Vol 47 (3) ◽  
pp. 1058-1066
Author(s):  
Xiaofan Xiong ◽  
Timothy J. Linhardt ◽  
Weiren Liu ◽  
Brian J. Smith ◽  
Wenqing Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fdg Pet ◽  
Tracer Uptake ◽  
Ct Scans ◽  
Automated Measurement ◽  
Network Approach ◽  
Neural Network Approach ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans

2012 ◽  
Vol 39 (6Part1) ◽  
pp. 3112-3123 ◽  
Author(s):  
Christian Bauer ◽  
Shanhui Sun ◽  
Wenqing Sun ◽  
Justin Otis ◽  
Audrey Wallace ◽  
...  
Keyword(s):  
Aortic Arch ◽  
Fdg Pet ◽  
Ct Scans ◽  
Automated Measurement ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Full Body

scholarly journals ­­A convolutional neural network-based system to detect malignant findings in FDG PET/CT examinations

2020 ◽  
Author(s):  
Keisuke Kawauchi ◽  
Sho Furuya ◽  
Kenji Hirata ◽  
Chietsugu Katoh ◽  
Osamu Manabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Head And Neck ◽  
Fdg Pet ◽  
Imaging Modality ◽  
Whole Body ◽  
Pelvic Region ◽  
Pet Ct ◽  
Medicine Physician ◽  
Fdg Pet Ct

Abstract 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 region-based analysis of the 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 patient-based analysis, the 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.

scholarly journals Prevalence of incidental thyroid malignancy on routine 18F-fluorodeoxyglucose PET-CT in a large teaching hospital

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Shea Roddy ◽  
Thomas Biggans ◽  
Ahmad K. Raofi ◽  
Avinash Kanodia ◽  
Thiru Sudarshan ◽  
...  
Keyword(s):  
Teaching Hospital ◽  
Fdg Pet ◽  
Tracer Uptake ◽  
Ct Scans ◽  
Study Group ◽  
Thyroid Malignancy ◽  
Focal Lesions ◽  
Number Of Patients ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Purpose To quantify incidental thyroid pathology including malignancy on routine 18F-FDG PET-CT scans To compare standardised uptake values (SUVmax) in thyroid malignancy subtypes Methods and materials This is a retrospective study of all 18F-FDG PET-CT scans (n = 6179) performed in a teaching hospital between June 2010 and May 2019. RIS database search of reports for the word “thyroid” was performed. Studies with evidence of thyroid uptake were included. Patient age and gender, primary indication for PET scan (malignant or non-malignant), thyroid result on PET (diffuse or focal tracer uptake, SUVmax), ultrasound and FNAC results were recorded. Results Incidental abnormal thyroid tracer uptake as a proportion of all 18F-FDG PET-CT scans was 4.37% (n = 270). Out of region patients (n = 87) whose records could not be obtained were excluded leaving a study group of n = 183. Ninety-four in this group had focal uptake, and 89 had diffuse uptake. Fifty-five patients in the focal group had undergone further investigations. Of these, 30 were thought to be benign on USS alone, and 25 patients underwent USS/FNAC. Thirteen (24%) malignancies were identified (5 papillary, 6 follicular, 1 poorly differentiated thyroid cancer, 1 metastatic malignancy). Mean SUVmax for papillary carcinoma was noted to be 8.2 g/ml, and follicular carcinoma was 12.6 g/ml. Conclusion Incidental abnormal thyroid 18F-FDG PET-CT uptake in PET-CT scans of 4.37% is in keeping with the known limited literature. Rather similar number of patients was noted in the focal and diffuse tracer uptake categories in the final study group. Around quarter of the focal lesions were identified to be malignant, implying focal lesions should always be further investigated.

scholarly journals ­­A convolutional neural network-based system to detect malignant findings in FDG PET/CT examinations

2019 ◽  
Author(s):  
Keisuke Kawauchi ◽  
Sho Furuya ◽  
Kenji Hirata ◽  
Chietsugu Katoh ◽  
Osamu Manabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Head And Neck ◽  
Fdg Pet ◽  
Imaging Modality ◽  
Neck Region ◽  
Whole Body ◽  
Pet Ct ◽  
Medicine Physician ◽  
Fdg Pet Ct

Abstract 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. This network was trained with PET images. Five-fold cross-validations were carried out to estimate the classification performance. In addition, we examined whether the CNN could determine the location of the malignant uptake, be it in the head-and-neck region, chest, abdomen, or pelvic region.Results There were 1,280 (37%), 1,450 (42%) and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In patient-based analysis, the CNN predicted benign and malignant images with 99.4% and 99.4% accuracy, respectively. Furthermore, 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 would be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.

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

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Keisuke Kawauchi ◽  
Sho Furuya ◽  
Kenji Hirata ◽  
Chietsugu Katoh ◽  
Osamu Manabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fdg Pet ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiaonan Shao ◽  
Rong Niu ◽  
Xiaoliang Shao ◽  
Jianxiong Gao ◽  
Yunmei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fdg Pet ◽  
Training Data ◽  
Benign Lesions ◽  
Training Set ◽  
Invasive Adenocarcinoma ◽  
Pet Ct ◽  
3D Cnn ◽  
Fdg Pet Ct

Abstract Purpose This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

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

2020 ◽  
Author(s):  
Keisuke Kawauchi ◽  
Sho Furuya ◽  
Kenji Hirata ◽  
Chietsugu Katoh ◽  
Osamu Manabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Head And Neck ◽  
Fdg Pet ◽  
Imaging Modality ◽  
Whole Body ◽  
Pelvic Region ◽  
Pet Ct ◽  
Medicine Physician ◽  
Fdg Pet Ct

Abstract 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.

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

2020 ◽  
Author(s):  
Keisuke Kawauchi ◽  
Sho Furuya ◽  
Kenji Hirata ◽  
Chietsugu Katoh ◽  
Osamu Manabe ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Head And Neck ◽  
Fdg Pet ◽  
Imaging Modality ◽  
Whole Body ◽  
Pelvic Region ◽  
Pet Ct ◽  
Medicine Physician ◽  
Fdg Pet Ct

Abstract 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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