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 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 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.

scholarly journals Distant metastases and synchronous malignancies on FDG-PET/CT in patients with head and neck cancer: a retrospective study

2020 ◽  
Vol 61 (9) ◽  
pp. 1196-1204
Author(s):  
Lennart Flygare ◽  
Amal Al-Ubaedi ◽  
Wilhelm Öhman ◽  
Susanna Jakobson Mo
Keyword(s):  
Head And Neck Cancer ◽  
Salivary Gland ◽  
Head And Neck ◽  
Fdg Pet ◽  
Distant Metastases ◽  
Whole Body ◽  
Pet Ct ◽  
Synchronous Malignancies ◽  
Distant Spread ◽  
Fdg Pet Ct

Background Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) has been proven to be a good method to detect distant spread of head and neck cancer (HNC). However, most prior studies are based on Asian populations and may not be directly transferable to western populations. Purpose To investigate the frequency and distribution of distant metastases and synchronous malignancies detected by PET/CT in HNC in a northern Swedish population. Material and Methods All primary whole-body FDG-PET/CT examinations performed on the suspicion of HNC (n = 524 patients) between 1 January 2013 and 31 December 2016 at Umeå University Hospital in Sweden were retrospectively reviewed . After the exclusion of 189 examinations without evidence of primary HNC, 335 examinations were analyzed. Results Distant metastases were detected in 10 (3%) patients, all with advanced primary tumors corresponding to TNM stage 3–4, most frequently in salivary gland adenocarcinoma, where 50% of patients had distant spread. Four patients had metastases below the diaphragm, representing 20% of the salivary gland malignancies. In the remaining six patients, metastases were supraphrenic, of which all but one were identified by CT alone. Synchronous malignancies were discovered in 14 (4.2%) patients, of which five were below the diaphragm. Conclusion The overall frequency of distant spread and synchronous malignancy in primary HNC was generally low. However, the risk for distant metastases below the diaphragm was relatively higher in salivary gland adenocarcinoma, supporting whole-body FDG-PET/CT in the primary diagnostic work-up in these patients.

scholarly journals Extra-renal malignant rhabdoid tumor of head and neck region: Characteristics of tracer uptake on FDG PET/CT in tumor with rare histology

2014 ◽  
Vol 29 (3) ◽  
pp. 197
Author(s):  
Venkatesh Rangarajan ◽  
AmeyaD Puranik ◽  
Nilendu Purandare ◽  
Mukta Ramadwar ◽  
Archi Agrawal ◽  
...  
Keyword(s):  
Head And Neck ◽  
Fdg Pet ◽  
Neck Region ◽  
Tracer Uptake ◽  
Rhabdoid Tumor ◽  
Malignant Rhabdoid Tumor ◽  
Head And Neck Region ◽  
Pet Ct ◽  
Fdg Pet Ct

scholarly journals Eosinophilic Cystitis Presenting as Possible Pediatric Rhabdomyosarcoma in Conventional Imaging Including 18F-FDG-PET/CT/MRI—A Rare Case

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 672
Author(s):  
Naja Enevold Olsen ◽  
Marie Øbro Fosbøl ◽  
Jorgen Thorup ◽  
Helle Hjorth Johannesen ◽  
Lise Borgwardt
Keyword(s):  
Differential Diagnosis ◽  
Fdg Pet ◽  
Imaging Modality ◽  
Whole Body ◽  
Fdg Uptake ◽  
Eosinophilic Cystitis ◽  
Potential Biomarker ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Eosinophilic cystitis (EC) is a relatively rare, but benign inflammatory bladder disease compared to that of the malignant pediatric rhabdomyosarcoma (RMS), in which it can be mimicking on initial suspicion. The origin, symptoms and findings of both EC and RMS are still discussed and hence, lead to the challenge in distinguishing them by cystoscopy and several image modalities. We present a case in which cross-sectional imaging modalities including fluorine-18-fluro-2-deoxy-D-glucose (18F-FDG)-positron emission tomography (PET) / computed tomography (CT) / magnetic resonance imaging (MRI) (18F-FDG-PET/CT/MRI (The imaging modality 18F-FDG-PET/CT/MRI referring to two continuous scans scanned on the same 18F-FDG-tracer dose for both the whole-body 18F-FDG-PET/CT and the regional 18F-FDG-PET/MRI of the pelvis.)) raised suspicion of RMS. Hence, the final diagnosis of EC was established by repeated histopathology. It is important to have EC in mind when seeking differential diagnosis of malignant diseases like RMS in order to provide the correct treatment for the patient and highly homogenously increased 18F-FDG-uptake should raise the suspicion of EC as a differential diagnosis. Furthermore, 18F-FDG-uptake rate is suggested as a future potential biomarker for monitoring of therapeutic response in eosinophilic inflammatory diseases, thus more research on this topic is needed.

scholarly journals Brain metastases assessment by FDG-PET/CT: can it eliminate the necessity for dedicated brain imaging?

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Mamdouh A. Zidan ◽  
Radwa S. Hassan ◽  
Khaled I. El-Noueam ◽  
Yasser M. Zakaria
Keyword(s):  
Brain Metastases ◽  
Fdg Pet ◽  
Diagnostic Performance ◽  
Imaging Modality ◽  
False Negative ◽  
Whole Body ◽  
Cross Sectional ◽  
Primary Tumors ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Background Brain metastases (BM) are the most common intracranial tumors in adults outnumbering all other intracranial neoplasms. Positron emission tomography combined with computed tomography (PET/CT) is a widely used imaging modality in oncology with a unique combination of cross-sectional anatomic information provided by CT and the metabolic information provided by PET using the [18F]-2-fluoro-2-deoxy-d-glucose (FDG) as a tracer. The aim of the study is to assess the role and diagnostic performance of brain-included whole-body PET/CT in detection and evaluation of BM and when further imaging is considered necessary. The study was conducted over a period of 12 months on 420 patients suffering from extra-cranial malignancies utilizing brain-included whole-body PET/CT. Results Thirty patients with 71 brain lesions were detected, 18 patients (60%) had BM of unknown origin while 12 patients (40%) presented with known primary tumors. After brain-included whole-body FDG-PET/CT examination, the unknown primaries turned out to be bronchogenic carcinoma in 10 patients (33.3%), renal cell carcinoma in 2 patients (6.7%), and lymphoma in 2 patients (6.7%), yet the primary tumors remained unknown in 4 patients (13.3%). In 61 lesions (85.9%), the max SUV ranged from 0.2- < 10, while in 10 lesions (14.1%) the max SUV ranged from 10 to 20. Hypometabolic lesions were reported in 41 (57.7%) lesions, hypermetabolic in 3 lesions (4.2%), whereas 27 lesions (38.0%) showed similar FDG uptake to the corresponding contralateral brain matter. PET/CT overall sensitivity, specificity, positive and negative predictive, and accuracy values were 78.1, 92.6, 83.3, 90, and 88% respectively. Conclusion Brain-included whole-body FDG-PET/CT provides valuable complementary information in the evaluation of patients with suspected BM. However, the diagnostic performance of brain PET-CT carries the possibility of false-negative results with consequent false sense of security. The clinicians should learn about the possible pitfalls of PET/CT interpretation to direct patients with persistent neurological symptoms or high suspicion for BM for further dedicated CNS imaging.

scholarly journals A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET

2020 ◽  
Author(s):  
Pavel Nikulin ◽  
Frank Hofheinz ◽  
Jens Maus ◽  
Yimin Li ◽  
Rebecca Bütof ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Glucose Consumption ◽  
Whole Body ◽  
Automated Method ◽  
Arterial Blood ◽  
Pet Ct ◽  
Blood Suv

Abstract Purpose The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor’s glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload, which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spillover from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean ± SD) = (– 0.5 ± 2.2)% with a 95% confidence interval of [− 5.1,3.8]% and a total range of [− 10.0, 12.0]%. For four test cases, the derived ROIs were unusable (< 1 ml). Conclusion CNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting.

scholarly journals Just another “Clever Hans”? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer

2021 ◽  
Author(s):  
Manuel Weber ◽  
David Kersting ◽  
Lale Umutlu ◽  
Michael Schäfers ◽  
Christoph Rischpler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Overall Survival ◽  
Fdg Pet ◽  
Tumor Volume ◽  
Whole Body ◽  
The Neural Network ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Background Manual quantification of the metabolic tumor volume (MTV) from whole-body 18F-FDG PET/CT is time consuming and therefore usually not applied in clinical routine. It has been shown that neural networks might assist nuclear medicine physicians in such quantification tasks. However, little is known if such neural networks have to be designed for a specific type of cancer or whether they can be applied to various cancers. Therefore, the aim of this study was to evaluate the accuracy of a neural network in a cancer that was not used for its training. Methods Fifty consecutive breast cancer patients that underwent 18F-FDG PET/CT were included in this retrospective analysis. The PET-Assisted Reporting System (PARS) prototype that uses a neural network trained on lymphoma and lung cancer 18F-FDG PET/CT data had to detect pathological foci and determine their anatomical location. Consensus reads of two nuclear medicine physicians together with follow-up data served as diagnostic reference standard; 1072 18F-FDG avid foci were manually segmented. The accuracy of the neural network was evaluated with regard to lesion detection, anatomical position determination, and total tumor volume quantification. Results If PERCIST measurable foci were regarded, the neural network displayed high per patient sensitivity and specificity in detecting suspicious 18F-FDG foci (92%; CI = 79–97% and 98%; CI = 94–99%). If all FDG-avid foci were regarded, the sensitivity degraded (39%; CI = 30–50%). The localization accuracy was high for body part (98%; CI = 95–99%), region (88%; CI = 84–90%), and subregion (79%; CI = 74–84%). There was a high correlation of AI derived and manually segmented MTV (R2 = 0.91; p < 0.001). AI-derived whole-body MTV (HR = 1.275; CI = 1.208–1.713; p < 0.001) was a significant prognosticator for overall survival. AI-derived lymph node MTV (HR = 1.190; CI = 1.022–1.384; p = 0.025) and liver MTV (HR = 1.149; CI = 1.001–1.318; p = 0.048) were predictive for overall survival in a multivariate analysis. Conclusion Although trained on lymphoma and lung cancer, PARS showed good accuracy in the detection of PERCIST measurable lesions. Therefore, the neural network seems not prone to the clever Hans effect. However, the network has poor accuracy if all manually segmented lesions were used as reference standard. Both the whole body and organ-wise MTV were significant prognosticators of overall survival in advanced breast cancer.

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