Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning

Zhou, Zhiguo; Chen, Liyuan; Sher, David J.; Zhang, Qiongwen; Shah, Jennifer; Pham, Nhat-Long; Jiang, Steve B; Wang, Jing

doi:10.1109/embc.2018.8513070

Cited by 37 publications

(30 citation statements)

References 24 publications

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“…The computational complexity becomes enormous when CNN directly learns with 3D images [21][22][23][24][25]. Although we employed MIP images in the current study, an alternative approach may be to provide each slice to CNN.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…In patient-based prediction, the accuracy was higher than that in image-based prediction by an ensemble effect. This approach takes The computational complexity becomes enormous when CNN directly learns with 3D images [21][22][23][24][25].…”

Section: Discussionmentioning

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.

show abstract

“…This approach takes advantage of MIP images generated from various angles. More specifically, we applied The computational complexity becomes enormous when CNN directly learns with 3D images [21][22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

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

Kawauchi

Furuya

Hirata

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning

Cited by 37 publications

References 24 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

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

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