Nuclear Medicine Communications最新文献_第10页

Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma. 下牙龈鳞状细胞癌中骨髓侵犯对 18F-FDG PET 纹理分析机器学习的评估

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-05-01 Epub Date: 2024-02-19 DOI: 10.1097/MNM.0000000000001826

Yasuhiro Fukushima, Keisuke Suzuki, Mai Kim, Wenchao Gu, Satoshi Yokoo, Yoshito Tsushima

{"title":"Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma.","authors":"Yasuhiro Fukushima, Keisuke Suzuki, Mai Kim, Wenchao Gu, Satoshi Yokoo, Yoshito Tsushima","doi":"10.1097/MNM.0000000000001826","DOIUrl":"10.1097/MNM.0000000000001826","url":null,"abstract":"Objectives: Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning.Methods: A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed.Results: From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83.Conclusion: Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":" ","pages":"406-411"},"PeriodicalIF":1.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning diagnosis of active Juvenile Idiopathic Arthritis on blood pool [ 99M Tc] Tc-MDP scintigraphy images. 根据血池 [99MTc] Tc-MDP 闪烁扫描图像对活动性幼年特发性关节炎进行机器学习诊断。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-05-01 Epub Date: 2024-02-05 DOI: 10.1097/MNM.0000000000001822

Hossein Kian Ara, Nafiseh Alemohammad, Zeinab Paymani, Marzieh Ebrahimi

{"title":"Machine learning diagnosis of active Juvenile Idiopathic Arthritis on blood pool [ 99M Tc] Tc-MDP scintigraphy images.","authors":"Hossein Kian Ara, Nafiseh Alemohammad, Zeinab Paymani, Marzieh Ebrahimi","doi":"10.1097/MNM.0000000000001822","DOIUrl":"10.1097/MNM.0000000000001822","url":null,"abstract":"Purpose: Neural network has widely been applied for medical classifications and disease diagnosis. This study employs deep learning to best discriminate Juvenile Idiopathic Arthritis (JIA), a pediatric chronic joint inflammatory disease, from healthy joints by exploring blood pool images of 2phase [ 99m Tc] Tc-MDP bone scintigraphy.Methods: Self-deigned multi-input Convolutional Neural Network (CNN) in addition to three available pre-trained models including VGG16, ResNet50 and Xception are applied on 1304 blood pool images of 326 healthy and known JIA children and adolescents (aged 1-16).Results: The self-designed model ROC analysis shows diagnostic efficiency with Area Under the Curve (AUC) 0.82 and 0.86 for knee and ankle joints, respectively. Among the three pertained models, VGG16 ROC analysis reveals AUC 0.76 and 0.81 for knee and ankle images, respectively.Conclusion: The self-designed model shows best performance on blood pool scintigraph diagnosis of patients with JIA. VGG16 was the most efficient model rather to other pre-trained networks. This study can pave the way of artificial intelligence (AI) application in nuclear medicine for the diagnosis of pediatric inflammatory disease.","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":" ","pages":"355-361"},"PeriodicalIF":1.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139681340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adjacent object distorts the organ-of-interest in filtered back-projection tomographic image reconstruction: 'ramp filter' or 'reconstruction' artifact revisited. 滤波后投影断层图像重建中邻近物体对感兴趣器官的扭曲："斜坡滤波 "或 "重建 "伪影再探。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-05-01 Epub Date: 2024-03-11 DOI: 10.1097/MNM.0000000000001830

Mohsen Qutbi

{"title":"Adjacent object distorts the organ-of-interest in filtered back-projection tomographic image reconstruction: 'ramp filter' or 'reconstruction' artifact revisited.","authors":"Mohsen Qutbi","doi":"10.1097/MNM.0000000000001830","DOIUrl":"10.1097/MNM.0000000000001830","url":null,"abstract":"Objective: To simulate the artifact caused by an adjacent object on organ-of-interest during filtered back-projection (FBP) tomographic reconstruction (the so-called \"ramp filter\" artifact) and to demonstrate the extent to which an organ-of-interest is influenced by such adjacent hot spot or attenuating object.Methods and materials: Two simulations are conducted with two simplified phantoms: \"hot spot\" and \"cardiac.\" First one is used to visualize effect of hot spot on its periphery. Second one is used to evaluate effect of nearby object (liver) on left ventricle (LV) as organ-of-interest. To generate sinograms, forward projection process is done with and without modeling radiation attenuation. FBP using windowed ramp filter is done. All slices are analyzed by plotting intensity profiles.Results: In tomographic slices, there is a hypo-intense halo around presumed edge of object when compared to background intensity, more noticeable in phantoms with less blurring. Even with ramp filter applied, no halo is visible in FBP without attenuation for cardiac phantom. In contrast, in slices with considering attenuation, zones with different levels of count suppression on both sides of object are visualized instead. The most prominent one is between liver and LV in simulation with higher-attenuating object and higher activity.Conclusion: A single hot spot with sufficient amount of blurring does not distort its surroundings. Hot spots and attenuating objects near organ-of-interest, however, distort myocardial perfusion imaging. Artifactual defects are thus only created when attenuation is modeled during FBP, producing zones of count suppression between organ-of-interest and nearby object or hot spot.","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":" ","pages":"362-371"},"PeriodicalIF":1.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140094406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can 18F FDG PET/CT metabolic parameters be used to noninvasively differentiate between different histopathological subtypes and Fuhrman grades of renal cell cancer? 18F FDG PET/CT 代谢参数能否用于无创区分不同组织病理学亚型和 Fuhrman 等级的肾细胞癌？

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-30 DOI: 10.1097/mnm.0000000000001844

Yash Jain, Archi Agrawal, Amit Joshi, Santosh Menon, Gagan Prakash, Vedang Murthy, Nilendu Purandare, Sneha Shah, Ameya Puranik, Sayak Choudhury, Varun Shukla, Indraja Dev, Kumar Prabhash, Vanita Noronha, Venkatesh Rangarajan

引用次数: 0

Comparison of 68Ga-FAPI-04 PET/CT with 18F-FDG PET/CT for diagnosis and staging of gastric and colorectal cancer. 68Ga-FAPI-04 PET/CT 与 18F-FDG PET/CT 在胃癌和结直肠癌诊断和分期方面的比较。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-29 DOI: 10.1097/mnm.0000000000001845

Bin Wang, Xinming Zhao, Yunuan Liu, Zhaoqi Zhang, Xiaoshan Chen, Fenglian Jing, Xiaolin Chen, Yu Hua, Jianqiang Zhao

引用次数: 0

Union is strength: the combination of radiomics features and 3D-deep learning in a sole model increases diagnostic accuracy in demented patients: a whole brain 18FDG PET-CT analysis. 联合就是力量：在单一模型中结合放射组学特征和三维深度学习可提高痴呆症患者的诊断准确性：全脑 18FDG PET-CT 分析。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-18 DOI: 10.1097/mnm.0000000000001853

Alberto Bestetti, Barbara Zangheri, Sara Vincenzina Gabanelli, Vincenzo Parini, Carla Fornara

引用次数: 0

Relationship between transient ischemic dilatation and changes in heart rate during gated SPECT acquisition in a low-risk population without perfusion defects. 在无灌注缺损的低风险人群中进行选通 SPECT 采集时，一过性缺血扩张与心率变化之间的关系。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-15 DOI: 10.1097/mnm.0000000000001852

Fernando Mut, María P Gaudiano, Miguel Kapitán

引用次数: 0

Noninvasive measurement of tracer extraction efficiency in tissue, illustrated with Tc-99m-MAG3. 以 Tc-99m-MAG3 为例，无创测量组织中的示踪剂提取效率。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-15 DOI: 10.1097/mnm.0000000000001837

Sarah Wicks, Danielle Levart, Lauren Conway, Neil Heraghty, A Michael Peters

{"title":"Noninvasive measurement of tracer extraction efficiency in tissue, illustrated with Tc-99m-MAG3.","authors":"Sarah Wicks, Danielle Levart, Lauren Conway, Neil Heraghty, A Michael Peters","doi":"10.1097/mnm.0000000000001837","DOIUrl":"https://doi.org/10.1097/mnm.0000000000001837","url":null,"abstract":"The aim of this study is to develop a noninvasive technique for measuring tissue tracer extraction efficiency (E) and illustrate it for Tc-99m-mercaptoacetyltriglycine (MAG3) and kidney. E was measured in 10 patients with normal MAG3 renography. E is the ratio of tissue clearance-to-blood flow (Ki/F). For single-photon tracers, attenuation constants are unknown, so Ki and F cannot be separately measured. However, by deriving attenuation-uncorrected Ki' and F' from the same regions of interests (ROIs), these constants cancel out, giving E. Using a lung ROI for blood activity, F was measured from first-pass and Ki' from Gjedde-Patlak-Rutland (GPR) analysis up to 130 s. Because of interference from right ventricle, a left ventricular ROI (LV) is unsuitable for F' but was used in GPR analysis, making an adjustment for the ratio of respective blood pool signals arising from lung and LV ROIs in early frames (60-90 s). A lung ROI underestimates F' by 4% at normal LV function. Chest wall interstitial activity (I), which does not affect F', amounted to 53 and 30% of the lung and LV signals at 20 min, and 12 and 6% at 130 s, resulting in underestimations of Ki of 4 and 2%, respectively. Ignoring these opposing errors, E based on lung ROI for left and right kidneys was 43.5 (SD 8)% and 47.3 (9)%, and based on LV ROI for GPR analysis was 44.5 (10.9)% and 48.3 (10.6)%. E can be measured by combining blood flow from first-pass with clearance from GPR analysis, and has potential value both clinically and in clinical research.","PeriodicalId":19708,"journal":{"name":"Nuclear Medicine Communications","volume":"221 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140611565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of iodine contrast media on gamma camera-based GFR and factors affecting the difference between serum creatinine-based estimated GFR and Gate's GFR. 碘造影剂对伽马相机估算的 GFR 的影响，以及影响血清肌酐估算的 GFR 与盖特 GFR 之间差异的因素。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-15 DOI: 10.1097/mnm.0000000000001848

Shiro Ishii, Shigeyasu Sugawara, Yumi Tanaka, Natsumi Kawamoto, Junko Hara, Ryo Yamakuni, Hiroki Suenaga, Kenji Fukushima, Hiroshi Ito

引用次数: 0

18F-FDG PET imaging for treatment response assessment and management guidance in patients with skull base osteomyelitis. 18F-FDG PET 成像用于颅底骨髓炎患者的治疗反应评估和管理指导。

IF 1.5 4区医学

Nuclear Medicine Communications Pub Date : 2024-04-15 DOI: 10.1097/mnm.0000000000001847

Awiral Saxena, Padma Subramanyam, Manjit Sarma, Bhagirath Bhad, Renjitha Bhaskaran, Shanmuga Sundaram Palaniswamy

引用次数: 0