Pub Date : 2025-04-02DOI: 10.1109/TRPMS.2025.3552178
{"title":">Member Get-a-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2025.3552178","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552178","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"529-529"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1109/TRPMS.2025.3552176
{"title":"IEEE DataPort","authors":"","doi":"10.1109/TRPMS.2025.3552176","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552176","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"528-528"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1109/TRPMS.2025.3552150
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2025.3552150","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552150","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947672","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1109/TRPMS.2025.3552148
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2025.3552148","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552148","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in deep-learning-based methods have shown great potential in improving low-dose CT image quality. Meanwhile, these methods are constructed based on a large, centralized, and diverse CT dataset from multiple institutions that is difficult to collect and share due to the high-cost acquisition and data privacy regulations. Previously developed federated learning (FL)-based methods enable collaborative and decentralized training without exchanging local data to preserve data privacy. In this work, we focus on analyzing the robustness of FL-based methods against dataset shifts (i.e., the datasets among multiple institutions are from different scanners, different protocols, or different sampling conditions). The results show that the FL-based CT reconstruction methods are sensitive to domain shifts, which can be attributed to the data heterogeneity among multiple institutions. Based on these findings, we propose a unified CT reconstruction method that leverages high-quality metadata (e.g., low-dose images and their corresponding normal-dose counterparts) stored on the cloud server to address the challenge of multi-institutional domain shifts. For simplicity, we refer to the proposed method as FM-iRadonMAP, representing federated metadata learning (FMDL) with a personalized condition-modulated iRadonMAP (CM-iRadonMAP). Specifically, the FM-iRadonMAP consists of two modules, i.e., CM-iRadonMAP and FMDL. CM-iRadonMAP introduces the knowledge of client-specific sampling conditions, i.e., imaging geometries and scan protocols, into iRadonMAP reconstruction network at each client to modulate the reconstruction effectively. FMDL trains a supervised meta model using high-quality metadata in an additional round and then adaptively unifies the network parameters of the meta model with those of the local models from all clients for broadcasting, addressing the issue of data heterogeneity. A large-scale multi-institutional CT dataset is used to validate and evaluate the reconstruction performance of the FM-iRadonMAP. The experimental results demonstrate the feasibility of the FM-iRadonMAP for multi-institutional CT reconstruction with severe data heterogeneity.
{"title":"Toward Unified CT Reconstruction: Federated Metadata Learning With Personalized Condition-Modulated iRadonMAP","authors":"Hao Wang;Mingqiang Li;Shixuan Chen;Mingqiang Meng;Ji He;Jianhua Ma;Dong Zeng","doi":"10.1109/TRPMS.2025.3574209","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3574209","url":null,"abstract":"Recent advances in deep-learning-based methods have shown great potential in improving low-dose CT image quality. Meanwhile, these methods are constructed based on a large, centralized, and diverse CT dataset from multiple institutions that is difficult to collect and share due to the high-cost acquisition and data privacy regulations. Previously developed federated learning (FL)-based methods enable collaborative and decentralized training without exchanging local data to preserve data privacy. In this work, we focus on analyzing the robustness of FL-based methods against dataset shifts (i.e., the datasets among multiple institutions are from different scanners, different protocols, or different sampling conditions). The results show that the FL-based CT reconstruction methods are sensitive to domain shifts, which can be attributed to the data heterogeneity among multiple institutions. Based on these findings, we propose a unified CT reconstruction method that leverages high-quality metadata (e.g., low-dose images and their corresponding normal-dose counterparts) stored on the cloud server to address the challenge of multi-institutional domain shifts. For simplicity, we refer to the proposed method as FM-iRadonMAP, representing federated metadata learning (FMDL) with a personalized condition-modulated iRadonMAP (CM-iRadonMAP). Specifically, the FM-iRadonMAP consists of two modules, i.e., CM-iRadonMAP and FMDL. CM-iRadonMAP introduces the knowledge of client-specific sampling conditions, i.e., imaging geometries and scan protocols, into iRadonMAP reconstruction network at each client to modulate the reconstruction effectively. FMDL trains a supervised meta model using high-quality metadata in an additional round and then adaptively unifies the network parameters of the meta model with those of the local models from all clients for broadcasting, addressing the issue of data heterogeneity. A large-scale multi-institutional CT dataset is used to validate and evaluate the reconstruction performance of the FM-iRadonMAP. The experimental results demonstrate the feasibility of the FM-iRadonMAP for multi-institutional CT reconstruction with severe data heterogeneity.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"169-180"},"PeriodicalIF":3.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11017337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-27DOI: 10.1109/TRPMS.2025.3574259
F. M. El-Hossary;Gamal Badr;Fakhr El-din M. Lashein;A. E. Metawa;Mohamed Khalaf;Sahar M. Gebril
Cold atmospheric plasma (CAP) in the air has been applied to improve the rate of diabetic wound healing in streptozotocin (STZ)-induced diabetic mice. The mice were classified into control and diabetic. Two experimental diabetic groups were treated with CAP; one was treated with CAP once per day for 10 s and the other for 20 s for 15 consecutive days. The rate of wound healing in diabetic mice treated for 10 s was higher than that for 20 s and the control. Histological and immunohistochemistry analyses of control and plasma-treated for 10 s revealed epidermal and dermal reformation and improvement of inflammation earlier than both the diabetic and the treated diabetic for 20 s. Concomitantly, tumor necrosis factor-alpha (TNF$alpha $ ) and connective tissue growth factor (CTGF) were relatively similar in the control and the 10 s-treated groups compared with the diabetic one. Our study confirmed the efficacy of CAP as a plasma therapeutic medical strategy in improving diabetic wound healing via controlling the diabetic-associated inflammatory response.
{"title":"Improving the Healing Rate of Diabetic Wounds by Applying Dielectric Barrier Discharge: An Applied Study in Male Mice","authors":"F. M. El-Hossary;Gamal Badr;Fakhr El-din M. Lashein;A. E. Metawa;Mohamed Khalaf;Sahar M. Gebril","doi":"10.1109/TRPMS.2025.3574259","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3574259","url":null,"abstract":"Cold atmospheric plasma (CAP) in the air has been applied to improve the rate of diabetic wound healing in streptozotocin (STZ)-induced diabetic mice. The mice were classified into control and diabetic. Two experimental diabetic groups were treated with CAP; one was treated with CAP once per day for 10 s and the other for 20 s for 15 consecutive days. The rate of wound healing in diabetic mice treated for 10 s was higher than that for 20 s and the control. Histological and immunohistochemistry analyses of control and plasma-treated for 10 s revealed epidermal and dermal reformation and improvement of inflammation earlier than both the diabetic and the treated diabetic for 20 s. Concomitantly, tumor necrosis factor-alpha (TNF<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>) and connective tissue growth factor (CTGF) were relatively similar in the control and the 10 s-treated groups compared with the diabetic one. Our study confirmed the efficacy of CAP as a plasma therapeutic medical strategy in improving diabetic wound healing via controlling the diabetic-associated inflammatory response.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 3","pages":"460-471"},"PeriodicalIF":3.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147352533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Four-dimensionalcone-beam computed tomography (4-D CBCT) provides respiration-resolved images and facilitates image-guided radiation therapy. However, the ability to reveal respiratory motion comes at the cost of image artifacts. As raw projection data are sorted into multiple respiratory phases, the reconstructed 4-D CBCT images are covered by severe streak artifacts. Although several deep learning-based methods have been proposed to address this issue, most algorithms formulate it as a 2-D image enhancement task, neglecting the dynamic nature of 4-D CBCT. In this article, we first identify the origin and appearance of streak artifacts in 4-D CBCT images. We find that streak artifacts exhibit a unique “rotational motion” along with the patient’s respiration, distinguishable from diaphragm-driven respiratory motion in 4-D space. Therefore, we introduce RSTAR4D-Net, a 4-D model that performs rotational streak artifact reduction by exploring the dynamic prior of 4-D CBCT images. Specifically, we overcome the computational and training difficulties of a 4-D neural network. The specially designed model decomposes the 4-D convolutions into multiple lower-dimensional operations and thus efficiently processes a whole 4-D image. Additionally, a Tetris training strategy is proposed to effectively train the model using limited 4-D data. Extensive experiments substantiate the superior performance of RSTAR4D-Net compared to existing methods.
{"title":"RSTAR4D: Rotational Streak Artifact Reduction in 4-D CBCT Using Separable 4-D Convolutions","authors":"Ziheng Deng;Hua Chen;Yongzheng Zhou;Haibo Hu;Zhiyong Xu;Tianling Lyu;Yan Xi;Yang Chen;Jiayuan Sun;Jun Zhao","doi":"10.1109/TRPMS.2025.3553866","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553866","url":null,"abstract":"Four-dimensionalcone-beam computed tomography (4-D CBCT) provides respiration-resolved images and facilitates image-guided radiation therapy. However, the ability to reveal respiratory motion comes at the cost of image artifacts. As raw projection data are sorted into multiple respiratory phases, the reconstructed 4-D CBCT images are covered by severe streak artifacts. Although several deep learning-based methods have been proposed to address this issue, most algorithms formulate it as a 2-D image enhancement task, neglecting the dynamic nature of 4-D CBCT. In this article, we first identify the origin and appearance of streak artifacts in 4-D CBCT images. We find that streak artifacts exhibit a unique “rotational motion” along with the patient’s respiration, distinguishable from diaphragm-driven respiratory motion in 4-D space. Therefore, we introduce RSTAR4D-Net, a 4-D model that performs rotational streak artifact reduction by exploring the dynamic prior of 4-D CBCT images. Specifically, we overcome the computational and training difficulties of a 4-D neural network. The specially designed model decomposes the 4-D convolutions into multiple lower-dimensional operations and thus efficiently processes a whole 4-D image. Additionally, a Tetris training strategy is proposed to effectively train the model using limited 4-D data. Extensive experiments substantiate the superior performance of RSTAR4D-Net compared to existing methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1094-1106"},"PeriodicalIF":3.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-22DOI: 10.1109/TRPMS.2025.3572863
P. Shali;N. Caz;J. Van den Bosch;R. Ghobeira;S. Aliakbarshirazi;M. Narimisa;R. Morent;E. Wolfs;N. De Geyter
Cancer remains a leading cause of mortality, emphasizing the need for innovative therapies. Plasma-treated liquids, containing reactive oxygen and nitrogen species, have demonstrated therapeutic potential. This study investigates the physicochemical properties and anti-cancer efficacy of phosphate-buffered saline (PBS) treated using a novel liquid-submerged plasma jet, which enhances interactions between plasma species and the liquid for a more uniform treatment. Operational parameters, including voltage, gas flow, and treatment time, were optimized concurrently. Notably, the submerged configuration produced significantly higher H2O2 concentrations in PBS (up to $2000~mu $ M) compared to the above-liquid plasma set-ups reported in literature. However, ${NO}{2}^{-}$ concentrations remained low (6–$18~mu $ M). Voltage variations influenced H2O2 production but had a minimal effect on ${NO}{2}^{-}$ , while gas flow rates did not impact their concentrations. PBS maintained a stable pH, demonstrating its effective buffering capacity. Stability tests showed H2O2 remained stable at $21~^{circ }$ C, slightly increased at $4~^{circ }$ C, and decreased at $37~^{circ }$ C; nitrites were stable below $21~^{circ }$ C but slightly decreased at $37~^{circ }$ C. Plasma-treated PBS selectively reduced oral squamous cell carcinoma (OSCC) cell viability while sparing healthy keratinocytes (HaCaT), with H2O2 identified as the primary anti-cancer agent. These findings suggest that PBS plasma-treated using a new liquid-submerged set-up shows potential as selective OSCC therapy.
{"title":"Investigation of the Physicochemical Properties and Selective Anti-Cancer Efficacy of In-Plasma Treated PBS Using an Exclusive Liquid-Submerged Plasma Jet","authors":"P. Shali;N. Caz;J. Van den Bosch;R. Ghobeira;S. Aliakbarshirazi;M. Narimisa;R. Morent;E. Wolfs;N. De Geyter","doi":"10.1109/TRPMS.2025.3572863","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3572863","url":null,"abstract":"Cancer remains a leading cause of mortality, emphasizing the need for innovative therapies. Plasma-treated liquids, containing reactive oxygen and nitrogen species, have demonstrated therapeutic potential. This study investigates the physicochemical properties and anti-cancer efficacy of phosphate-buffered saline (PBS) treated using a novel liquid-submerged plasma jet, which enhances interactions between plasma species and the liquid for a more uniform treatment. Operational parameters, including voltage, gas flow, and treatment time, were optimized concurrently. Notably, the submerged configuration produced significantly higher H2O2 concentrations in PBS (up to <inline-formula> <tex-math>$2000~mu $ </tex-math></inline-formula>M) compared to the above-liquid plasma set-ups reported in literature. However, <inline-formula> <tex-math>${NO}{2}^{-}$ </tex-math></inline-formula> concentrations remained low (6–<inline-formula> <tex-math>$18~mu $ </tex-math></inline-formula>M). Voltage variations influenced H2O2 production but had a minimal effect on <inline-formula> <tex-math>${NO}{2}^{-}$ </tex-math></inline-formula>, while gas flow rates did not impact their concentrations. PBS maintained a stable pH, demonstrating its effective buffering capacity. Stability tests showed H2O2 remained stable at <inline-formula> <tex-math>$21~^{circ }$ </tex-math></inline-formula>C, slightly increased at <inline-formula> <tex-math>$4~^{circ }$ </tex-math></inline-formula>C, and decreased at <inline-formula> <tex-math>$37~^{circ }$ </tex-math></inline-formula>C; nitrites were stable below <inline-formula> <tex-math>$21~^{circ }$ </tex-math></inline-formula>C but slightly decreased at <inline-formula> <tex-math>$37~^{circ }$ </tex-math></inline-formula>C. Plasma-treated PBS selectively reduced oral squamous cell carcinoma (OSCC) cell viability while sparing healthy keratinocytes (HaCaT), with H2O2 identified as the primary anti-cancer agent. These findings suggest that PBS plasma-treated using a new liquid-submerged set-up shows potential as selective OSCC therapy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"317-332"},"PeriodicalIF":3.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-20DOI: 10.1109/TRPMS.2025.3553409
M. Mehdi Khalighi;Christina B. Young;Matthew G. Spangler-Bickell;Timothy W. Deller;Floris Jansen;Dawn Holley;Hillary Vossler;Moss Y. Zhao;Feliks Kogan;Gary Steinberg;Elizabeth Mormino;Michael Moseley;Greg Zaharchuk
The current spatial resolution of positron emission tomography (PET) images is 3–4 mm for whole body PET/MR. Anatomical MR images with higher resolution and superior image quality have been used in PET reconstruction to improve the image quality and spatial resolution; however, mismatches between MR priors and actual tracer distribution can hinder accuracy. A novel PET reconstruction with MR priors, magnetic resonance-guided block sequential regularized expectation maximum (MRgBSREM), that is robust to mismatches between anatomical priors and true activity distribution is proposed. This method is evaluated in diverse clinical settings using various tracers: 18F-florbetaben (FBB) in 373 subjects from a dementia study, 18F-FDG in a patient with chronic ischemic stroke, 18F-NaF in a knee study, and 15O-water in a patient with Moyamoya disease. Reconstruction using MRgBSREM visually improved both spatial resolution and image quality in all studies. In the 18FBB study, it mitigated white-matter spill-in into gray-matter as well as gray-matter spill over to the adjacent tissues, potentially leading to more accurate measurement of FBB uptake in the gray-matter. Visual assessment suggests that the proposed PET reconstruction enhances spatial resolution, which may contribute to improved diagnostic accuracy, while it displays robustness to mismatches between MR priors and true activity distribution.
{"title":"A Novel Method in PET Image Reconstruction Using MRI Anatomical Priors","authors":"M. Mehdi Khalighi;Christina B. Young;Matthew G. Spangler-Bickell;Timothy W. Deller;Floris Jansen;Dawn Holley;Hillary Vossler;Moss Y. Zhao;Feliks Kogan;Gary Steinberg;Elizabeth Mormino;Michael Moseley;Greg Zaharchuk","doi":"10.1109/TRPMS.2025.3553409","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553409","url":null,"abstract":"The current spatial resolution of positron emission tomography (PET) images is 3–4 mm for whole body PET/MR. Anatomical MR images with higher resolution and superior image quality have been used in PET reconstruction to improve the image quality and spatial resolution; however, mismatches between MR priors and actual tracer distribution can hinder accuracy. A novel PET reconstruction with MR priors, magnetic resonance-guided block sequential regularized expectation maximum (MRgBSREM), that is robust to mismatches between anatomical priors and true activity distribution is proposed. This method is evaluated in diverse clinical settings using various tracers: 18F-florbetaben (FBB) in 373 subjects from a dementia study, 18F-FDG in a patient with chronic ischemic stroke, 18F-NaF in a knee study, and 15O-water in a patient with Moyamoya disease. Reconstruction using MRgBSREM visually improved both spatial resolution and image quality in all studies. In the 18FBB study, it mitigated white-matter spill-in into gray-matter as well as gray-matter spill over to the adjacent tissues, potentially leading to more accurate measurement of FBB uptake in the gray-matter. Visual assessment suggests that the proposed PET reconstruction enhances spatial resolution, which may contribute to improved diagnostic accuracy, while it displays robustness to mismatches between MR priors and true activity distribution.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1074-1082"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although atmospheric pressure plasma (APP) treatment has exhibited promising antitumor efficacy across various cancer types, no studies have analyzed the effects of APP on pituitary adenoma (PA). In this study, APP generation and treatment conditions were optimized and investigated. Four pituitary tumor cell lines (GH3, AtT-20, GT1-1, and MMQ) were used to assess the inhibitory effect of APP treatment and were compared with two glioblastoma (GBM) cell lines (U87MG and LN229) and a neuronal cell line (SH-SY5Y). Results showed that the APP treatment has a better inhibitory effect on pituitary tumor cells with minimal neurotoxicity. The best inhibitory effect was observed in GH3, which had an IC50 value of only 32.33 s. APP treatment elevated both intra- and extra-cellular reactive oxygen/nitrogen species (ROS/RNS) in GH3 cells, which induced significantly GH3 cell apoptosis. Noninvasive micro-test technology (NMT) experiment revealed substantial ${mathrm { Ca}}^{2+}$ influx following APP treatment in GH3 cells. Moreover, validation on primary pituitary tumor cells from patients corroborated these findings. Overall, our results highlight that APP treatment exerts substantial antitumor effects on PA cells compared to GBM cell lines, suggesting its potential as a complementary therapy in clinical neurosurgical treatment of PA.
{"title":"Inhibitory Effect of Atmospheric Pressure Plasma on GH3 Pituitary Adenoma Cell Line and Primary Pituitary Tumor Cells From Patients","authors":"Qiuyue Fang;Yixiao Liu;Yanan Xing;Xi Zhang;Yuqing Liu;Yuxuan Liu;Zhiyan Sun;Yuqi Guo;Yulou Liu;Gaosheng He;Lixin Xu;Xiaojin Xu;Jiting Ouyang;Chuzhong Li;Xu Yan;Zilan Xiong","doi":"10.1109/TRPMS.2025.3552789","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552789","url":null,"abstract":"Although atmospheric pressure plasma (APP) treatment has exhibited promising antitumor efficacy across various cancer types, no studies have analyzed the effects of APP on pituitary adenoma (PA). In this study, APP generation and treatment conditions were optimized and investigated. Four pituitary tumor cell lines (GH3, AtT-20, GT1-1, and MMQ) were used to assess the inhibitory effect of APP treatment and were compared with two glioblastoma (GBM) cell lines (U87MG and LN229) and a neuronal cell line (SH-SY5Y). Results showed that the APP treatment has a better inhibitory effect on pituitary tumor cells with minimal neurotoxicity. The best inhibitory effect was observed in GH3, which had an IC50 value of only 32.33 s. APP treatment elevated both intra- and extra-cellular reactive oxygen/nitrogen species (ROS/RNS) in GH3 cells, which induced significantly GH3 cell apoptosis. Noninvasive micro-test technology (NMT) experiment revealed substantial <inline-formula> <tex-math>${mathrm { Ca}}^{2+}$ </tex-math></inline-formula> influx following APP treatment in GH3 cells. Moreover, validation on primary pituitary tumor cells from patients corroborated these findings. Overall, our results highlight that APP treatment exerts substantial antitumor effects on PA cells compared to GBM cell lines, suggesting its potential as a complementary therapy in clinical neurosurgical treatment of PA.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1135-1146"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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