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Sun G, Zang Y, Ding H, Chen Y, Groothof D, Gong H, Lou Z, Meng R, Chen Z, Furnee E, Xiang J, Zhang W. Comparison of anal function and quality of life after conformal sphincter preservation operation and intersphincteric resection of very low rectal cancer: a multicenter, retrospective, case-control analysis. Tech Coloproctol 2023; 27:1275-1287. [PMID: 37248369 PMCID: PMC10638180 DOI: 10.1007/s10151-023-02819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 05/02/2023] [Indexed: 05/31/2023]
Abstract
PURPOSE Conformal sphincter preservation operation (CSPO) is a sphincter preservation operation for very low rectal cancers. Compared to intersphincteric resection (ISR), CSPO retains more dentate line and distal rectal wall, and also avoids damaging the nerves in the intersphincteric space. This study aimed to compare the postoperative anal function and quality of life between the CSPO and ISR. METHOD Patients with low rectal cancer undergoing CSPO (n = 117) and ISR (n = 66) were included from Changhai and Huashan Hospital, respectively, between 2011 and 2020. A visual analog scale (range 0-10) was utilized to evaluate satisfaction with anal function and quality of life. The anal function was evaluated with Wexner scores and low anterior resection syndrome (LARS) score. Quality of life was evaluated with the EORTC QLQ-C30 and QLQ-CR38. RESULTS The CSPO group had more male patients (65.8% vs. 50%, p = 0.042), more preoperative chemoradiotherapy (33.3% vs. 10.6%, p < 0.001), lower tumor position (3.45 ± 1.13 vs. 4.24 ± 0.86 cm, p < 0.001), and more postoperative chemotherapy (65% vs. 13.6%, p < 0.001) compared to the ISR group. In addition, CSPO patients had shorter postoperative stay (6.63 ± 2.53 vs. 7.85 ± 4.73 days, p = 0.003) and comparable stoma reversal rates within 1 year after surgery (92.16% vs. 96.97%, p = 0.318). Multivariable analysis showed that CSPO significantly contributed to higher satisfaction with anal function (beta = 1.752, 95% CI 0.776-2.728) and with quality of life (beta = 1.219, 95% CI 0.374-2.064), but not to Wexner, LARS score, or EORTC QLQ-C30 and QLQ-CR38. CONCLUSION CSPO improved the satisfaction with anal function and quality of life but utilized more preoperative chemoradiotherapy. CSPO may be an alternative choice for patients with very low rectal cancers in better physical health and with higher requirements for anal function and quality of life.
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Du B, Zhang W, Shao X, An J, Ma H, Zhao X, Xu L, An D, Tian Y, Dong Y, Niu H. "Triple-low" radiation dose bronchial artery CT angiography before bronchial artery embolisation: a feasibility study. Clin Radiol 2023; 78:e1017-e1022. [PMID: 37813755 DOI: 10.1016/j.crad.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
AIM To explore the feasibility of a "triple-low" dose (low tube voltage, low tube current, and low contrast agent volume) bronchial artery computed tomography (CT) angiography (CTA) to replace routine dose bronchial artery CTA before bronchial artery embolisation (BAE). MATERIALS AND METHODS CTA was obtained from 60 patients with body mass index (BMI) < 30 kg/m2 using a 256 multi-section iCT system, and they were divided into two groups: (1) group A: 100 kVp, 100 mAs, 50 ml contrast medium (CM); (2) group B: 120 kVp, automatic tube current modulation (ACTM), 80 ml CM. CT attenuation of the thoracic aorta, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and subjective image quality scores and traceability scores assessed. The effective radiation dose was calculated. RESULTS The radiation dose was reduced by 79.7% in group A compared to group B (p<0.05). The CT attenuation of the thoracic aorta was increased by approximately 13% in group A compared to group B (p<0.05). Higher image noise, lower SNR, and CNR were obtained in group A compared to group B (all p<0.05). Both subjective image quality scores and traceability scores did not differ between groups A and B (both p>0.05). CONCLUSION It is feasible to use the "triple-low" dose CTA protocol for patients with a body mass index (BMI) < 30 kg/m2. The radiation dose was reduced by 79.7%, and the dose of contrast medium was reduced by 37.5% to ensure the diagnostic value.
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Zhang W, Ma X, Yu S, Zhang X, Mu Y, Li Y, Xiao Q, Ji M. Occupational stress, respect, and the need for psychological counselling in Chinese nurses: a nationwide cross-sectional study. Public Health 2023; 225:72-78. [PMID: 37922589 DOI: 10.1016/j.puhe.2023.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/11/2023] [Accepted: 09/06/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVES This study aimed to explore occupational stress, perceived respect, and the need for psychological counselling among nurses in China. STUDY DESIGN This was a nationwide cross-sectional study. METHODS Chinese nurses from 311 cities were randomly selected through a simple random sampling method. Occupational stress, perceived respect, and psychological counselling need were assessed using an online questionnaire validated by experts. The underlying associated factors were analysed using multiple logistic regression analyses. RESULTS We collected and analysed 51,406 valid online questionnaires. Family factors and low income were the most commonly cited sources of occupational stress, and 91.9% and 80.0% of nurses, respectively, perceived that individuals in society and patients did not give adequate respect. Furthermore, 75.5% and 79.7%, respectively, believed they were not respected by clinical managers and doctors. As a result, 64.7% nurses believed they had a moderate or high need for psychological counselling. However, 80.7% indicated that receiving adequate respect could decrease the need for stress-related psychological counselling. Indeed, multiple logistic regression analyses showed that lower respect perceived by nurses was associated with higher need for psychological counselling, particularly regarding criticism that nurses perceived from nursing managers (a little: odds ratio [OR], 1.597; 95% confidence interval [CI], 1.176-2.170; P = 0.003; moderately: OR, 1.433; 95% CI, 1.180-1.741; P < 0.001) and the difficulty of receiving respect from patients and their families (a little: OR, 1.389; 95% CI, 1.044-1.850; P = 0.024). CONCLUSIONS Nurses in China perceive high levels of occupational stress and low levels of respect and often seek psychological counselling.
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Meng L, Yu Q, Zhao X, Chen L, Wang Y, Zhang W, Chen H, Chen Y. Purtscher-like retinopathy in systemic lupus erythematosus: clinical features, risk factors and prognosis. QJM 2023; 116:923-932. [PMID: 37665730 DOI: 10.1093/qjmed/hcad204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Purtscher-like retinopathy (PLR) is a rare ocular manifestation in systemic lupus erythematosus (SLE) with poor prognosis, but its clear risk factors and treatment consensus are still lacking. AIM To investigate the clinical features, risk factors and prognosis of PLR in SLE patients. DESIGN AND METHODS A retrospective analysis was conducted on SLE patients with PLR admitted at Peking Union Medical College Hospital from 2013 to 2022. Clinical data, including demographic characteristics, lupus-related features, laboratory findings and ophthalmologic examinations, were collected and analyzed. The prognosis was evaluated based on best-corrected visual acuity and ophthalmologic outcomes. RESULTS Seventeen SLE patients (32 eyes) diagnosed with PLR were included, along with a random selection of 100 SLE patients without retinopathy and 100 with retinal microvasculopathy as controls. Patients with PLR had a significantly younger age, a higher proportion of hemolytic anemia, a shorter duration of SLE, a higher SLE disease activity index-2000 (SLEDAI-2K) score, higher erythrocyte sedimentation rate (ESR) values and lower hemoglobin (HGB) values than the group without retinopathy (P < 0.05). They also had a significantly higher SLEDAI-2K score, higher ESR values and higher white blood cell values (P < 0.05) than the Microvasculopathy group. The majority of eyes (22/26, 84.62%) achieved stabilization at the last follow-up, with different therapeutic strategies, while a few (4/26, 15.38%) experienced complications or progression. CONCLUSION This is the largest reported case series of PLR in SLE, which was associated with higher disease activity and poor visual prognosis. It was also associated with younger age, shorter SLE duration, concomitant hemolytic anemia, lower HGB and higher ESR value. Early recognition and prompt treatment are crucial for improving visual outcomes.
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Cap JGB, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Sánchez MCDLB, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gao T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Elayavalli RK, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Aguilar MAR, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen D, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Tyler J, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang J, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Hyperon Polarization along the Beam Direction Relative to the Second and Third Harmonic Event Planes in Isobar Collisions at sqrt[s_{NN}]=200 GeV. PHYSICAL REVIEW LETTERS 2023; 131:202301. [PMID: 38039468 DOI: 10.1103/physrevlett.131.202301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/07/2023] [Accepted: 10/03/2023] [Indexed: 12/03/2023]
Abstract
The polarization of Λ and Λ[over ¯] hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at sqrt[s_{NN}]=200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild p_{T} dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagrees with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and p_{T} dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.
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Li SL, Hou LK, Zhang LP, Huang Y, Zhang W, Wu CY. [Analysis on features of intraoperative pathological diagnosis of bronchial adenoma by frozen section investigation]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2023; 52:1151-1153. [PMID: 37899322 DOI: 10.3760/cma.j.cn112151-20230228-00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
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Yang Y, Liu Y, Xu R, Jiao Y, Hao J, Sun YE, Gu XP, Zhang W. [The predictive values of platelet mitochondrial mass and quantity during the perioperative period in elderly patients on the occurrence of postoperative delirium]. ZHONGHUA YI XUE ZA ZHI 2023; 103:3258-3262. [PMID: 37926568 DOI: 10.3760/cma.j.cn112137-20230627-01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Objective: To investigate the changes of platelet mitochondrial mass and quantity during perioperative period in elderly patients, and assess their predictive values on the occurrence of postoperative delirium (POD). Methods: In this prospective study, 162 elderly patients scheduled for abdominal surgery under general anesthesia were enrolled from November 2021 to January 2022 in Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School. Among them, 20 patients [10 males, 10 females, aged (71.4±6.8) years] developed POD within 3 days after surgery (POD group), and another 20 patients[12 males, 8 females, aged (67.7±5.3) years] who did not develope POD were selected as controls (control group) using propensity score matching method. Blood samples were collected preoperatively, at the end of surgery and on the first postoperative day. Platelets were extracted and mitochondrial mass was detected with flow cytometry. Transmission electron microscopy was used to determine mitochondrial quantity. The receiver operating characteristic (ROC) curve was drawn to analyze the value of mitochondrial mass and quantity in predicting the occurrence of POD. Results: The mean fluorescence intensities of platelet mitochondrial mass were 193±46, 236±61, 264±53 preoperatively, at the end of surgery and on the first postoperative day in the POD group, respectively. The corresponding values were 209±61, 191±67 and 201±56 in the control group. The platelet mitochondrial mass of patients in the POD group was significantly increased on the first postoperative day compared to preoperative levels (P<0.001). In contrast, there was no significant difference in the control group (P=0.410). Patients in the POD group had higher platelet mitochondrial mass than patients in the control group on the first postoperative day(P=0.002). Meanwhile, platelets from patients in the POD group showed significantly higher number of mitochondria than platelets from patients in the control group [3 (2, 4) vs 2 (1, 2), P<0.001]. According to the ROC curve of platelet on the first postoperative day, at a mitochondrial mass cut-off value of>275.35, the sensitivity, specificity and area under the ROC curve to detect the occurrence of POD were 55%, 90% and 0.800 (95%CI: 0.666-0.934, P<0.001). At a mitochondrial quantity cut-off value of>2, the sensitivity, specificity and area under the ROC curve to detect the occurrence of POD were 53%, 78% and 0.680 (95%CI: 0.584-0.776, P<0.001). Conclusions: Patients who developed POD show higher platelet mitochondrial mass after surgery compared to preoperative levels. The mitochondrial mass of platelets on the first postoperative day has good predictive value on the occurrence of POD.
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Shu K, Cai C, Chen W, Ding J, Guo Z, Wei Y, Zhang W. Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma. Sci Rep 2023; 13:19151. [PMID: 37932413 PMCID: PMC10628222 DOI: 10.1038/s41598-023-46669-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/03/2023] [Indexed: 11/08/2023] Open
Abstract
Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.
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Basu S, Shukron O, Hall D, Parutto P, Ponjavic A, Shah D, Boucher W, Lando D, Zhang W, Reynolds N, Sober LH, Jartseva A, Ragheb R, Ma X, Cramard J, Floyd R, Balmer J, Drury TA, Carr AR, Needham LM, Aubert A, Communie G, Gor K, Steindel M, Morey L, Blanco E, Bartke T, Di Croce L, Berger I, Schaffitzel C, Lee SF, Stevens TJ, Klenerman D, Hendrich BD, Holcman D, Laue ED. Live-cell three-dimensional single-molecule tracking reveals modulation of enhancer dynamics by NuRD. Nat Struct Mol Biol 2023; 30:1628-1639. [PMID: 37770717 PMCID: PMC10643137 DOI: 10.1038/s41594-023-01095-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 08/14/2023] [Indexed: 09/30/2023]
Abstract
To understand how the nucleosome remodeling and deacetylase (NuRD) complex regulates enhancers and enhancer-promoter interactions, we have developed an approach to segment and extract key biophysical parameters from live-cell three-dimensional single-molecule trajectories. Unexpectedly, this has revealed that NuRD binds to chromatin for minutes, decompacts chromatin structure and increases enhancer dynamics. We also uncovered a rare fast-diffusing state of enhancers and found that NuRD restricts the time spent in this state. Hi-C and Cut&Run experiments revealed that NuRD modulates enhancer-promoter interactions in active chromatin, allowing them to contact each other over longer distances. Furthermore, NuRD leads to a marked redistribution of CTCF and, in particular, cohesin. We propose that NuRD promotes a decondensed chromatin environment, where enhancers and promoters can contact each other over longer distances, and where the resetting of enhancer-promoter interactions brought about by the fast decondensed chromatin motions is reduced, leading to more stable, long-lived enhancer-promoter relationships.
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Guo Q, Hu S, Wang S, Su L, Zhang W, Xu J, Wei Y. Comparative analysis of methodologies for predicting overall survival in patients with non-small cell lung cancer based on the number and rate of resected positive lymph nodes: A study based on the SEER database for 2010 through 2019. THE CLINICAL RESPIRATORY JOURNAL 2023; 17:1145-1157. [PMID: 37723579 PMCID: PMC10632082 DOI: 10.1111/crj.13699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/18/2023] [Accepted: 08/28/2023] [Indexed: 09/20/2023]
Abstract
BACKGROUND Lymph node (LN) metastasis is crucial in non-small cell lung cancer (NSCLC) prognosis and treatment, but the TNM system lacks LN quantity consideration. Our goal is to investigate the role of positive LNs (nPLN) and positive LN rate (LNR) in overall survival (OS) and assess whether they offer higher value in prognostic assessment of NSCLC than N-stage. METHODS Patients were stratified into four subgroups using X-Tile software. Statistical analysis was conducted using the Kaplan-Meier method, univariate analysis, and multivariate Cox regression analysis. Model performance was evaluated using the Harrell consistency index (C-index), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The prognostic performance of the nodal classification was validated using overall survival as the endpoint. RESULTS The survival curves demonstrate distinct disparities between each nPLN and LNR category. A pronounced trend toward deteriorating overall survival from N-PLN 1 to N-PLN 2+ was observed across all tumor size categories. However, the differences between each LNR category were only significant for tumors ≤3 cm and 5-7 cm. Notably, both nPLN and LNR classifications displayed a higher C-index, lower AIC, and lower BIC compared with the N staging. Furthermore, the LNR classification provided superior prognostic stratification when compared with the nPLN classification. CONCLUSIONS Our results demonstrate that nPLN and LNR classifications may offer improved prognostic performance compared with the current N classification for LN-positive NSCLC patients. Nonetheless, more studies are needed to assess the feasibility of incorporating these classifications into the next TNM staging system.
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Lai C, Sun R, Zhang W, Peng Y. Gastrointestinal: A case of IgG4-related disease involving intestinal tract and orbital cavity. J Gastroenterol Hepatol 2023; 38:1865. [PMID: 37340618 DOI: 10.1111/jgh.16254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 05/18/2023] [Accepted: 05/25/2023] [Indexed: 06/22/2023]
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Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Del Valle AE, Hussain PS, Jeitler M, Krammer N, Lechner L, Liko D, Mikulec I, Paulitsch P, Pitters FM, Schieck J, Schöfbeck R, Schwarz D, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Kello T, Sfar HR, Van Mechelen P, Bols ES, D’Hondt J, De Moor A, Delcourt M, El Faham H, Lowette S, Moortgat S, Morton A, Müller D, Sahasransu AR, Tavernier S, Van Doninck W, Vannerom D, Clerbaux B, De Lentdecker G, Favart L, Jaramillo J, Lee K, Mahdavikhorrami M, Makarenko I, Malara A, Paredes S, Pétré L, Postiau N, Starling E, Thomas L, Bemden MV, Vander Velde C, Vanlaer P, Dobur D, Knolle J, Lambrecht L, Mestdach G, Niedziela M, Rendón C, Roskas C, Samalan A, Skovpen K, Tytgat M, Van Den Bossche N, Vermassen B, Wezenbeek L, Benecke A, Bruno G, Bury F, Caputo C, David P, Delaere C, Donertas IS, Giammanco A, Jaffel K, Jain S, Lemaitre V, Mondal K, Prisciandaro J, Taliercio A, Tran TT, Vischia P, Wertz S, Alves GA, Coelho E, Hensel C, Moraes A, Teles PR, Júnior WLA, Alves Gallo Pereira M, Barroso Ferreira Filho M, Malbouisson HB, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, Sousa VDS, De Souza SF, Martins J, Herrera CM, Amarilo KM, Mundim L, Nogima H, Santoro A, Do Amaral SMS, Sznajder A, Thiel M, Da Silva De Araujo FT, Pereira AV, Bernardes CA, Calligaris L, Gregores EM, Mercadante PG, Novaes SF, Padula SS, Fernandez Perez Tomei TR, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Rodozov M, Shopova M, Sultanov G, Dimitrov A, Ivanov T, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Cheng T, Javaid T, Mittal M, Yuan L, Ahmad M, Bauer G, Hu Z, Lezki S, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Milosevic V, Monti F, Sharma R, Tao J, Thomas-Wilsker J, Wang J, Zhang H, Zhao J, Agapitos A, An Y, Ban Y, Chen C, Levin A, Li C, Li Q, Lyu X, Mao Y, Qian SJ, Sun X, Wang D, Xiao J, Yang H, Li J, Lu M, You Z, Gao X, 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Herwig TC, Hirschauer J, Horyn L, Jayatilaka B, Jindariani S, Johnson M, Joshi U, Klijnsma T, Klima B, Kwok KHM, Lammel S, Lincoln D, Lipton R, Liu T, Madrid C, Maeshima K, Mantilla C, Mason D, McBride P, Merkel P, Mrenna S, Nahn S, Ngadiuba J, Papadimitriou V, Pastika N, Pedro K, Pena C, Ravera F, Hall AR, Ristori L, Sexton-Kennedy E, Smith N, Soha A, Spiegel L, Strait J, Taylor L, Tkaczyk S, Tran NV, Uplegger L, Vaandering EW, Weber HA, Zoi I, Avery P, Bourilkov D, Cadamuro L, Cherepanov V, Field RD, Guerrero D, Kim M, Koenig E, Konigsberg J, Korytov A, Lo KH, Matchev K, Menendez N, Mitselmakher G, Madhu AM, Rawal N, Rosenzweig D, Rosenzweig S, Shi K, Wang J, Wu Z, Adams T, Askew A, Habibullah R, Hagopian V, Khurana R, Kolberg T, Martinez G, Prosper H, Schiber C, Viazlo O, Yohay R, Zhang J, Baarmand MM, Butalla S, Elkafrawy T, Hohlmann M, Verma RK, Noonan D, Rahmani M, Yumiceva F, Adams MR, Gonzalez HB, Cavanaugh R, Lemos DS, Dittmer S, Evdokimov O, Gerber CE, Hofman DJ, Merrit AH, Mills C, Oh G, Roy T, Rudrabhatla S, Tonjes MB, Varelas N, Wang X, Ye Z, Yoo J, Alhusseini M, Dilsiz K, Emediato L, Gandrajula RP, Karaman G, Köseyan OK, Merlo JP, Mestvirishvili A, Nachtman J, Neogi O, Ogul H, Onel Y, Penzo A, Snyder C, Tiras E, Amram O, Blumenfeld B, Corcodilos L, Davis J, Gritsan AV, Kang L, Kyriacou S, Maksimovic P, Roskes J, Sekhar S, Swartz M, Vámi TÁ, Abreu A, Alcerro LFA, Anguiano J, Baringer P, Bean A, Flowers Z, Isidori T, Khalil S, King J, Krintiras G, Lazarovits M, Le Mahieu C, Lindsey C, Marquez J, Minafra N, Murray M, Nickel M, Rogan C, Royon C, Salvatico R, Sanders S, Schmitz E, Smith C, Wang Q, Warner Z, Williams J, Wilson G, Allmond B, Duric S, Gurunadha RG, Ivanov A, Kaadze K, Kim D, Maravin Y, Mitchell T, Modak A, Nam K, Natoli J, Roy D, Rebassoo F, Wright D, Adams E, Baden A, Baron O, Belloni A, Bethani A, Eno SC, Hadley NJ, Jabeen S, Kellogg RG, Koeth T, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Seidel M, Wang L, Wong K, 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Author Correction: A portrait of the Higgs boson by the CMS experiment ten years after the discovery. Nature 2023; 623:E4. [PMID: 37853130 PMCID: PMC10620073 DOI: 10.1038/s41586-023-06164-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
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Xiang Z, Feng N, Wu B, Zhang X, Zhang W. The influence of different sequences of vessel ligation on long-term survival during video-assisted thoracoscopic lobectomy for non-small cell lung cancer: A matched cohort study. Medicine (Baltimore) 2023; 102:e35619. [PMID: 37904443 PMCID: PMC10615535 DOI: 10.1097/md.0000000000035619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 09/21/2023] [Indexed: 11/01/2023] Open
Abstract
In lobectomy of patients with lung cancer, the principle of operation is to cut off the pulmonary vein first, but it is often not taken seriously in clinical practice. We conducted this research to compare the impact of different sequences of pulmonary vessel ligation on the long-term survival of patients. This cohort study included 1239 patients treated surgically with video-assisted thoracoscopic lobectomy from January 2015 to December 2019 at The Second Affiliated Hospital of Nanchang University. Survival and perioperative indicators were compared between a Vein-first group (VF) and an artery-first group. After matching, 364 patients were included in each group for analysis. VF was associated with better overall survival (hazard ratio: 1.96 [1.4~2.74], P < .0001) and disease-free survival (hazard ratio: 1.65 [1.22~2.24], P = .0011). Meanwhile, the survival advantage of VF was achieved in almost all subgroups, particularly in the pathological tumor node metastasis stage I-II group and squamous cell carcinoma group. We obtained no significant differences in perioperative indications (operation time, hospital stay, etc) between VF and artery-first group. With better overall survival and disease-free survival, especially for pathological stage I-II squamous cell carcinoma, vein-first ligation should be strictly observed in lobectomy for patients with non-small cell lung cancer.
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Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Del Valle AE, Hussain PS, Jeitler M, Krammer N, Lechner L, Liko D, Mikulec I, Paulitsch P, Pitters FM, Schieck J, Schöfbeck R, Schwarz D, Sonawane M, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Kello T, Sfar HR, Van Mechelen P, Bols ES, D’Hondt J, De Moor A, Delcourt M, El Faham H, Lowette S, Moortgat S, Morton A, Müller D, Sahasransu AR, Tavernier S, Van Doninck W, Vannerom D, Clerbaux B, De Lentdecker G, Favart L, Hohov D, Jaramillo J, Lee K, Mahdavikhorrami M, Makarenko I, Malara A, Paredes S, Pétré L, Postiau N, Thomas L, Vanden Bemden M, Vander Velde C, Vanlaer P, Dobur D, Knolle J, Lambrecht L, Mestdach G, Rendón C, Samalan A, Skovpen K, Tytgat M, Van Den Bossche N, Vermassen B, Wezenbeek L, Benecke A, Bruno G, Bury F, Caputo C, David P, Delaere C, Donertas IS, Giammanco A, Jaffel K, Jain S, Lemaitre V, Mondal K, Taliercio A, Tran TT, Vischia P, Wertz S, Alves GA, Coelho E, Hensel C, Moraes A, Teles PR, Júnior WLA, Pereira MAG, Filho MBF, Malbouisson HB, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, Dos Santos Sousa V, De Souza SF, Martins J, Herrera CM, Amarilo KM, Mundim L, Nogima H, Santoro A, Do Amaral SMS, Sznajder A, Thiel M, Pereira AV, Bernardes CA, Calligaris L, Tomei TRFP, Gregores EM, Mercadante PG, Novaes SF, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Rodozov M, Shopova M, Sultanov G, Dimitrov A, Ivanov T, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Thakur S, Cheng T, Javaid T, Mittal M, Yuan L, Ahmad M, Bauer G, Hu Z, Lezki S, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Milosevic V, Monti F, Sharma R, Tao J, Thomas-Wilsker J, Wang J, Zhang H, Zhao J, Agapitos A, An Y, Ban Y, Levin A, Li C, Li Q, Lyu X, Mao Y, Qian SJ, Sun X, Wang D, Xiao J, Yang H, Lu M, You Z, Lu N, Gao X, Leggat D, Okawa H, Zhang Y, Lin Z, Lu C, Xiao M, Avila C, Trujillo DAB, 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Dremin I, Dubinin M, Dudko L, Epshteyn V, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Kardapoltsev L, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Konstantinov D, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Litomin A, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oskin A, Ovtin I, Palichik V, Perelygin V, Perfilov M, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toms M, Toropin A, Uvarov L, Uzunian A, Vlasov E, Volkov P, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Measurement of the top quark mass using a profile likelihood approach with the lepton + jets final states in proton-proton collisions at s=13TeV. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2023; 83:963. [PMID: 37906635 PMCID: PMC10600315 DOI: 10.1140/epjc/s10052-023-12050-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/16/2023] [Indexed: 11/02/2023]
Abstract
The mass of the top quark is measured in 36.3fb - 1 of LHC proton-proton collision data collected with the CMS detector at s = 13 Te V . The measurement uses a sample of top quark pair candidate events containing one isolated electron or muon and at least four jets in the final state. For each event, the mass is reconstructed from a kinematic fit of the decay products to a top quark pair hypothesis. A profile likelihood method is applied using up to four observables per event to extract the top quark mass. The top quark mass is measured to be 171.77 ± 0.37 Ge V . This approach significantly improves the precision over previous measurements.
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Grants
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Ministry of Education and Research
- Estonian Research Council via PRG780, PRG803, and PRG445
- European Regional Development Fund
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Lithuanian Academy of Sciences
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- JINR, Dubna
- Ministry of Education and Science of the Russian Federation
- Federal Agency of Atomic Energy of the Russian Federation
- Russian Academy of Sciences
- Russian Foundation for Basic Research
- National Research Center “Kurchatov Institute”
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Thailand Center of Excellence in Physics
- Institute for the Promotion of Teaching Science and Technology of Thailand
- Special Task Force for Activating Research
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Individual
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, “Excellence of Science - EOS” - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, “Excellence of Science - EOS” - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Ministry of Science and Higher Education, project no. 0723-2020-0041 and FSWW-2020-0008
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand)
- CUAASC
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- Kyungpook National University, Daegu
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino
- Institute for Nuclear Research (INR) of the Russian Academy of Sciences, Troitsk
- Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ’Kurchatov Institute’, Moscow
- Joint Institute for Nuclear Research, Dubna
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin - Madison, Madison
- Vanderbilt University, Nashville
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Liu F, Zhang W, Xie WG, Chen L, Zhang WD, Zhou JX, Li Z. [Effects of miniature free groin perforator flaps in repairing small wounds on hands]. ZHONGHUA SHAO SHANG YU CHUANG MIAN XIU FU ZA ZHI 2023; 39:933-938. [PMID: 37899558 DOI: 10.3760/cma.j.cn501225-20230701-00244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/31/2023]
Abstract
Objective: To investigate the effects of miniature free groin perforator flaps in repairing small wounds on hands. Methods: The retrospective observational study was conducted. Fifteen patients with 16 small wounds on hands were admitted to Tongren Hospital of Wuhan University & Wuhan Third Hospital from July 2020 to October 2022, including 12 males and 3 females, aged 19 to 56 years. The size of skin and soft tissue defect was 2.0 cm×1.5 cm to 6.0 cm×3.0 cm after debridement. According to size and shape of the wounds, 13 single-lobe perforator flaps and 2 bilobed perforator flaps were designed in the groin region, with the flap size of 4.5 cm×2.5 cm to 7.5 cm×3.5 cm. According to the condition of the recipient area, the arteries and veins at the pedicle of the flap were anastomosed to the arteries and veins of the recipient area respectively. The wounds in the donor area of the flap was closed by layered and tension-reducing suture. The thickness of the flap was measured during operation. The survival of the flap was observed, and the complications in the donor and recipient areas were recorded after operation. The appearance and texture of the flap were observed during follow-up. At the last follow-up, the sensory recovery of the affected hand was evaluated, the function of the affected hand was evaluated according to the trial standard of the upper limb partial function evaluation of the Hand Surgery Society of the Chinese Medical Association, the scar in the donor and recipient areas were observed, and the satisfaction of patients for the curative effects was inquired. Results: The thickness of the flap was ranged from 0.3 to 1.0 cm, with an average thickness of 0.6 cm. After operation, 11 single-lobe flaps and 2 bilobed flaps survived well; in the left 2 single-lobe flaps, one flap had venous crisis but returned to normal after removing stitches to reduce tension and bloodletting of flaps, while the other one flap had a little necrosis on tip but healed after dressing change. No complications occurred in donor and recipient areas. During follow-up of 8 to 35 months after operation, the flaps had good elasticity and soft texture; 8 flaps were slightly bloated and were trimmed 3 to 8 months after operation, while the appearances of the other flaps were good. At the last follow-up, all flaps recovered protective feeling; the function of the affected hand was evaluated as excellent in 10 cases, good in 4 cases, and fair in 1 case; only linear scar remained in the donor and recipient areas; the patients were satisfied with the appearance and function recovery of the affected hand. Conclusions: The miniature free groin perforator flaps in repairing small wounds on hands have the advantages of high survival rate of flaps, hidden flap donor area, little damage, few complications, good repair effect, etc., showing clinical application value. It is recommended for repairing non-functional wounds on hands.
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Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Escalante Del Valle A, Hussain PS, Jeitler M, Krammer N, Lechner L, Liko D, Mikulec I, Paulitsch P, Schieck J, Schöfbeck R, Schwarz D, Sonawane M, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Kello T, Rejeb Sfar H, Van Mechelen P, Bols ES, D’Hondt J, De Moor A, Delcourt M, Faham HE, Lowette S, Morton A, Müller D, Sahasransu AR, Tavernier S, Van Doninck W, Van Putte S, Vannerom D, Clerbaux B, Dansana S, De Lentdecker G, Favart L, Hohov D, Jaramillo J, Lee K, Mahdavikhorrami M, Makarenko I, Malara A, Paredes S, Pétré L, Postiau N, Thomas L, Vanden Bemden M, Vander Velde C, Vanlaer P, Dobur D, Knolle J, Lambrecht L, Mestdach G, Rendón C, Samalan A, Skovpen K, Tytgat M, Van Den Bossche N, Vermassen B, Wezenbeek L, Benecke A, Bruno G, Bury F, Caputo C, David P, Delaere C, Donertas IS, Giammanco A, Jaffel K, Jain S, Lemaitre V, Mondal K, Taliercio A, Tran TT, Vischia P, Wertz S, Alves GA, Coelho E, Hensel C, Moraes A, Rebello Teles P, Aldá Júnior WL, Alves Gallo Pereira M, Barroso Ferreira Filho M, Brandao Malbouisson H, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, Dos Santos Sousa V, Fonseca De Souza S, Martins J, Mora Herrera C, Mota Amarilo K, Mundim L, Nogima H, Santoro A, Silva Do Amaral SM, Sznajder A, Thiel M, Vilela Pereira A, Bernardes CA, Calligaris L, Fernandez Perez Tomei TR, Gregores EM, Mercadante PG, Novaes SF, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Rodozov M, Shopova M, Sultanov G, Dimitrov A, Ivanov T, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Thakur S, Cheng T, Javaid T, Mittal M, Yuan L, Ahmad M, Bauer G, Hu Z, Lezki S, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Milosevic V, Monti F, Sharma R, Tao J, Thomas-Wilsker J, Wang J, Zhang H, Zhao J, Agapitos A, An Y, Ban Y, Levin A, Li C, Li Q, Lyu X, Mao Y, Qian SJ, Sun X, Wang D, Xiao J, Yang H, Lu M, You Z, Lu N, Gao X, Leggat D, Okawa H, Zhang Y, Lin Z, Lu C, Xiao M, Avila C, Barbosa Trujillo DA, Cabrera A, Florez C, Fraga J, Mejia Guisao J, Ramirez F, Rodriguez M, Ruiz Alvarez JD, Giljanovic D, Godinovic N, Lelas D, Puljak I, Antunovic Z, Kovac M, Sculac T, Brigljevic V, Chitroda BK, Ferencek D, Mishra S, Roguljic M, Starodumov A, Susa T, Attikis A, Christoforou K, Konstantinou S, Mousa J, Nicolaou C, Ptochos F, Razis PA, Rykaczewski H, Saka H, Stepennov A, Finger M, Finger Jr. M, Kveton A, Ayala E, Carrera Jarrin E, Abdelalim AA, Salama E, Abdullah Al-Mashad M, Mahmoud MA, Bhowmik S, Dewanjee RK, Ehataht K, Kadastik M, Lange T, Nandan S, Nielsen C, Pata J, Raidal M, Tani L, Veelken C, Eerola P, Kirschenmann H, Osterberg K, Voutilainen M, Bharthuar S, Brücken E, Garcia F, Havukainen J, Kim MS, Kinnunen R, Lampén T, Lassila-Perini K, Lehti S, Lindén T, Lotti M, Martikainen L, Myllymäki M, Rantanen MM, Siikonen H, Tuominen E, Tuominiemi J, Luukka P, Petrow H, Tuuva T, Amendola C, Besancon M, Couderc F, Dejardin M, Denegri D, Faure JL, Ferri F, Ganjour S, Gras P, Hamel de Monchenault G, Lohezic V, Malcles J, Rander J, Rosowsky A, Sahin M, Savoy-Navarro A, Simkina P, Titov M, Baldenegro Barrera C, Beaudette F, Buchot Perraguin A, Busson P, Cappati A, Charlot C, Damas F, Davignon O, Diab B, Falmagne G, Fontana Santos Alves BA, Ghosh S, Granier de Cassagnac R, Hakimi A, Harikrishnan B, Liu G, Motta J, Nguyen M, Ochando C, Portales L, Salerno R, Sarkar U, Sauvan JB, Sirois Y, Tarabini A, Vernazza E, Zabi A, Zghiche A, Agram JL, Andrea J, Apparu D, Bloch D, Bourgatte G, Brom JM, Chabert EC, Collard C, Darej D, Goerlach U, Grimault C, Le Bihan AC, Van Hove P, Beauceron S, Blancon B, Boudoul G, Carle A, Chanon N, Choi J, Contardo D, Depasse P, Dozen C, El Mamouni H, Fay J, Gascon S, Gouzevitch M, Grenier G, Ille B, Laktineh IB, Lethuillier M, Mirabito L, Perries S, Torterotot L, Vander Donckt M, Verdier P, Viret S, Lomidze D, Lomidze I, Tsamalaidze Z, Botta V, Feld L, Klein K, Lipinski M, Meuser D, Pauls A, Röwert N, Teroerde M, Diekmann S, Dodonova A, Eich N, Eliseev D, Erdmann M, Fackeldey P, Fasanella D, Fischer B, Hebbeker T, Hoepfner K, Ivone F, Lee MY, Mastrolorenzo L, Merschmeyer M, Meyer A, Mondal S, Mukherjee S, Noll D, Novak A, Nowotny F, Pozdnyakov A, Rath Y, Redjeb W, Rehm F, Reithler H, Schmidt A, Schuler SC, Sharma A, Stein A, Torres Da Silva De Araujo F, Vigilante L, Wiedenbeck S, Zaleski S, Dziwok C, Flügge G, Haj Ahmad W, Hlushchenko O, Kress T, Nowack A, Pooth O, Stahl A, Ziemons T, Zotz A, Petersen HA, Martin MA, Alimena J, Asmuss P, Baxter S, Bayatmakou M, Becerril Gonzalez H, Behnke O, Bhattacharya S, Blekman F, Borras K, Brunner D, Campbell A, Cardini A, Cheng C, Colombina F, Consuegra Rodríguez S, Silva GC, De Silva M, Eckerlin G, Eckstein D, Estevez Banos LI, Filatov O, Gallo E, Geiser A, Giraldi A, Greau G, Grohsjean A, Guglielmi V, Guthoff M, Jafari A, Jomhari NZ, Kaech B, Kasemann M, Kaveh H, Kleinwort C, Kogler R, Komm M, 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A, Ovtin I, Palichik V, Perelygin V, Perfilov M, Petrushanko S, Popov V, Radchenko O, Rusinov V, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. A search for decays of the Higgs boson to invisible particles in events with a top-antitop quark pair or a vector boson in proton-proton collisions at s=13TeV. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2023; 83:933. [PMID: 37855556 PMCID: PMC10579171 DOI: 10.1140/epjc/s10052-023-11952-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/23/2023] [Indexed: 10/20/2023]
Abstract
A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at s = 13 Te V by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138fb - 1 . The 95% confidence level upper limit set on the branching fraction of the 125Ge V Higgs boson to invisible particles, B ( H → inv ) , is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous B ( H → inv ) searches carried out at s = 7 , 8, and 13Te V in complementary production modes. The combined upper limit at 95% confidence level on B ( H → inv ) is 0.15 (0.08 expected).
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Grants
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Ministry of Education and Research
- Estonian Research Council via PRG780, PRG803, and PRG445
- European Regional Development Fund
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Lithuanian Academy of Sciences
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Thailand Center of Excellence in Physics
- Institute for the Promotion of Teaching Science and Technology of Thailand
- Special Task Force for Activating Research
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Individual
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, “Excellence of Science - EOS” - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, “Excellence of Science - EOS” - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University (Thailand)
- CUAASC
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- Kyungpook National University, Daegu
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin-Madison, Madison
- Vanderbilt University, Nashville
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Kravchenko I, Reed I, Siado JE, Snow GR, Tabb W, Wightman A, Yan F, Zecchinelli AG, Agarwal G, Bandyopadhyay H, Hay L, Iashvili I, Kharchilava A, McLean C, Morris M, Nguyen D, Pekkanen J, Rappoccio S, Williams A, Alverson G, Barberis E, Haddad Y, Han Y, Krishna A, Li J, Lidrych J, Madigan G, Marzocchi B, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wamorkar T, Wang B, Wisecarver A, Wood D, Bhattacharya S, Bueghly J, Chen Z, Gilbert A, Hahn KA, Liu Y, Odell N, Schmitt MH, Velasco M, Band R, Bucci R, Castells S, Cremonesi M, Das A, Goldouzian R, Hildreth M, Hurtado Anampa K, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Mohrman K, Moore C, Musienko Y, Nelson H, Ruchti R, Townsend A, Wayne M, Yockey H, Zarucki M, Zygala L, Bylsma B, Carrigan M, Durkin LS, Francis B, Hill C, Lesauvage A, Nunez Ornelas M, Wei K, Winer BL, Yates BR, Addesa FM, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Higginbotham S, Kalogeropoulos A, Kopp G, Kwan S, Lange D, Marlow D, Mei K, Ojalvo I, Olsen J, Stickland D, Tully C, Malik S, Norberg S, Bakshi AS, Barnes VE, Chawla R, Das S, Gutay L, Jones M, Jung AW, Kondratyev D, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Purohit A, Schulte JF, Stojanovic M, Thieman J, Wang F, Xiao R, Xie W, Dolen J, Parashar N, Acosta D, Baty A, Carnahan T, Decaro M, Dildick S, Ecklund KM, Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Kumar A, Li W, Padley BP, Redjimi R, Rotter J, Shi W, Yang S, Yigitbasi E, Zhang L, Zhang Y, Zuo X, Bodek A, de Barbaro P, Demina R, Dulemba JL, Fallon C, Ferbel T, Galanti M, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Ranken E, Taus R, Van Onsem GP, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Hart A, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Osherson M, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Wang H, Acharya H, Delannoy AG, Fiorendi S, Holmes T, Nibigira E, Spanier S, Bouhali O, Dalchenko M, Delgado A, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Lamichhane K, Lee SW, Mengke T, Muthumuni S, Peltola T, Volobouev I, Wang Z, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Cummings G, Hakala J, Hirosky R, Joyce M, Ledovskoy A, Li A, Neu C, Perez Lara CE, Tannenwald B, Karchin PE, Poudyal N, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loeliger A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Bychkova O, Chadeeva M, Chekhovsky V, Dermenev A, Dimova T, Dremin I, Epshteyn V, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanchenko V, Ivanov Y, Kachanov V, Kardapoltsev L, Karjavine V, Karneyeu A, Khein L, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Kodolova O, Konstantinov D, Korenkov V, Korotkikh V, Kozyrev A, Krasnikov N, Kuznetsova E, Lanev A, Levchenko P, Litomin A, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Okhotnikov V, Oskin A, Ovtin I, Palichik V, Parygin P, Perelygin V, Petrushanko S, Pivovarov G, Popov V, Popova E, Radchenko O, Rusinov V, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Stepennov A, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toms M, Toropin A, Uvarov L, Uzunian A, Vardanyan I, Vlasov E, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Observation of τ Lepton Pair Production in Ultraperipheral Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV. PHYSICAL REVIEW LETTERS 2023; 131:151803. [PMID: 37897747 DOI: 10.1103/physrevlett.131.151803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/31/2022] [Accepted: 10/28/2022] [Indexed: 10/30/2023]
Abstract
We present an observation of photon-photon production of τ lepton pairs in ultraperipheral lead-lead collisions. The measurement is based on a data sample with an integrated luminosity of 404 μb^{-1} collected by the CMS experiment at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02 TeV. The γγ→τ^{+}τ^{-} process is observed for τ^{+}τ^{-} events with a muon and three charged hadrons in the final state. The measured fiducial cross section is σ(γγ→τ^{+}τ^{-})=4.8±0.6(stat)±0.5(syst) μb, where the second (third) term corresponds to the statistical (systematic) uncertainty in σ(γγ→τ^{+}τ^{-}) in agreement with leading-order QED predictions. Using σ(γγ→τ^{+}τ^{-}), we estimate a model-dependent value of the anomalous magnetic moment of the τ lepton of a_{τ}=0.001_{-0.089}^{+0.055}.
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Gai YL, Huang HD, Zhang W, Li X, Zhang XQ, Jiao Y, Wang Q, Dong YC, Bai C. [A case of left pulmonary artery sling combined with congenital tracheal stenosis in an adult]. ZHONGHUA JIE HE HE HU XI ZA ZHI = ZHONGHUA JIEHE HE HUXI ZAZHI = CHINESE JOURNAL OF TUBERCULOSIS AND RESPIRATORY DISEASES 2023; 46:1011-1014. [PMID: 37752044 DOI: 10.3760/cma.j.cn112147-20230603-00293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Pulmonary artery sling in adults is a rare congenital vascular malformation usually accompanied by tracheal and bronchial stenosis. Due to its high mortality risk and relatively poor prognosis, it has rarely been reported in adults. We reported a middle-aged patient who presented with shortness of breath, predominantly after activity, since childhood. He was diagnosed with "tracheal stenosis" in another hospital and received symptomatic treatment. The diagnosis of left pulmonary artery sling with congenital tracheal stenosis was confirmed by multi-slice spiral CT (MSCT), airway examination with flexible bronchoscope and 3D image post-processing system. Data from this case and the related literatures have been summarized and analyzed. This will help clinicians to improve their level of diagnosis and treatment.
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Zhang W, Wang M, Liu LT, Cui D, Liu M, Liu DG. [Differential expression of LLGL2 in prostate ductal adenocarcinoma and acinar adenocarcinoma and its significance]. ZHONGHUA BING LI XUE ZA ZHI = CHINESE JOURNAL OF PATHOLOGY 2023; 52:1012-1016. [PMID: 37805392 DOI: 10.3760/cma.j.cn112151-20230216-00138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/09/2023]
Abstract
Objective: To investigate the expression differences of LLGL2 between prostatic ductal adenocarcinoma (PDA) and prostatic acinar adenocarcinoma, and its potential clinical significance. Methods: Eighteen patients diagnosed of PDA or prostatic acinar adenocarcinoma with PDA component by histopathology during January 2015 and December 2019 in the Beijing Hospital, China were retrospectively studied. The transcriptome analysis was conducted using the tissue of PDA and prostatic acinar adenocarcinoma. Differentially expressed genes and the differences in expression profiles were identified. Further, differentially expressed proteins were verified by immunohistochemistry. Results: The tissue from 8 of the 18 patients were used for transcriptome analysis, the results of which were compared with data from public databases. 129 differentially expressed genes were identified. 45 of them were upregulated while 84 were downregulated. The results of gene enrichment analysis and gene oncology (GO) analysis revealed that the differentially expressed genes were mostly enriched in the hypertrophic cardiomyopathy and interleukin-17 related pathways. GPAT2, LLGL2, MAMDC4, PCSK9 and SMIM6 were differentially expressed between PDA and prostatic acinar adenocarcinoma. Moreover, LLGL2 was more likely expressed in the cytoplasm (P=0.04) than the nucleus (P<0.01) in PDA, compared with prostatic acinar adenocarcinoma. Conclusions: The gene expression profiling indicates that PDA are very similar to prostatic acinar adenocarcinoma. Among the differentially expressed proteins screened and verified in this study, the expression of GPAT2, LLGL2, MAMDC4 and PCSK9 is increased in PDA, while that of SMIM6 is reduced in PDA. The expression of LLGL2 shows significantly different patterns between PDA and prostatic acinar carcinoma, and thus may help differentiate PDA from prostatic acinar adenocarcinoma in clinical practice.
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Zhang X, Feng N, Wu B, Guo Z, Pan T, Tao X, Zheng H, Zhang W. Prognostic value and immune landscapes of cuproptosis-related lncRNAs in esophageal squamous cell carcinoma. Aging (Albany NY) 2023; 15:10473-10500. [PMID: 37812189 PMCID: PMC10599721 DOI: 10.18632/aging.205089] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Precisely forecasting the prognosis of esophageal squamous cell carcinoma (ESCC) patients is a formidable challenge. Cuproptosis has been implicated in ESCC pathogenesis; however, the prognostic value of cuproptosis-associated long noncoding RNAs (CuRLs) in ESCC is unclear. METHODS Transcriptomic and clinical data related to ESCC were sourced from The Cancer Genome Atlas (TCGA). Using coexpression and Cox regression analysis to identify prognostically significant CuRLs, a prognostic signature was created. Nomogram models were established by incorporating the risk score and clinical characteristics. Tumor Immune Dysfunction and Rejection (TIDE) scores were derived by conducting an immune landscape analysis and evaluating the tumor mutational burden (TMB). Drug sensitivity analysis was performed to explore the underlying molecular mechanisms and guide clinical dosing. RESULTS Our risk score based on 5 CuRLs accurately predicted poorer prognosis in high-risk ESCC patients across almost all subgroups. The nomogram that included the risk score provided more precise prognostic predictions. Immune pathways, such as the B-cell receptor signaling pathway, were enriched in the datasets from high-risk patients. High TMB in high-risk patients indicated a relatively poor prognosis. High-risk patients with lower TIDE scores were found to benefit more from immunotherapy. High-risk patients exhibited greater responsiveness to Nilotinib, BI-2536, P22077, Zoledronate, and Fulvestrant, as revealed by drug sensitivity analysis. Real-time PCR validation demonstrated significant differential expression of four CuRLs between ESCC and normal cell lines. CONCLUSIONS The above risk score and nomogram can accurately predict prognosis in ESCC patients and provide guidance for chemotherapy and immunotherapy.
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Wang W, Zhou R, Chen C, Feng X, Zhang W, Li HJ, Jin RH. [Development of auxiliary early predicting model for human brucellosis using machine learning algorithm]. ZHONGHUA YU FANG YI XUE ZA ZHI [CHINESE JOURNAL OF PREVENTIVE MEDICINE] 2023; 57:1601-1607. [PMID: 37859377 DOI: 10.3760/cma.j.cn112150-20221013-00991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
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Zhang X, Feng N, Wu B, Wei Y, Zhang W. Prognostic value of lymph node ratio in stage III non-small-cell lung cancer: A retrospective cohort study. Medicine (Baltimore) 2023; 102:e35341. [PMID: 37800757 PMCID: PMC10553147 DOI: 10.1097/md.0000000000035341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/31/2023] [Indexed: 10/07/2023] Open
Abstract
A growing number of studies have found that the lymph node ratio (LNR) is an important indicator of prognosis in non-small-cell lung cancer (NSCLC). Impact analysis for LNR was performed for survival in patients undergoing surgery for stage III NSCLC compared to the surveillance, epidemiology and end results databank. Clinicopathological variables, such as cancer-specific survival (CSS), were taken from the surveillance epidemiology and end result databank of stage III NSCLC patients who underwent surgery, and the LNR threshold stratification of NSCLC patients was computed by X-tile. CSS was assessed by the Kaplan-Meier method with CSS-independent risk factors calculated by multivariate Cox regression analysis. In total, 7011 lung cancer patients were included. Multifactorial analysis showed that LNR and positive node category had predictive value for stage III NSCLC. In patients with stage IIIA NSCLC, Kaplan-Meier analysis demonstrated that patients with T1-2N2 stage had clearly superior CSS than those with T3-4N1 stage (P < .001), which conflicted with the results from the assessment of primary tumor, lymph nodes, and metastasis/N stage. The cutoff values for LNR were 0.31 and 0.59. Kaplan-Meier analysis demonstrated that the CSS was substantially better in patients with LNR-low than in those with LNR-medium or LNR-high (P < .001), which was also proven by multivariate competing risk regression. Subgroup analysis suggested that the survival advantage of a lower LNR was achieved in all subgroups (sex, race, etc). In stage III NSCLC, the LNR is a valuable factor for assessing prognosis, in which a higher LNR indicates a worse prognosis.
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Tian Y, Shi Z, Wang C, Ke S, Qiu H, Zhao W, Chen J, Gong Y, Wu Y, Zhang W, Xia L, Zhang Y, Chen Y. A Comparison of Clinicopathologic Outcomes and Patterns of Lymphatic Spread across Neoadjuvant Chemotherapy, Neoadjuvant Chemoradiotherapy and Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e345. [PMID: 37785201 DOI: 10.1016/j.ijrobp.2023.06.2412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To evaluate the differences in pathologic complete response (pCR) rates, TRG score, pathologic T stage and the pattern of lymphatic spread among patients receiving neoadjuvant chemotherapy (NCT) or neoadjuvant chemoradiotherapy (NCRT) or neoadjuvant immunochemotherapy (NICT) prior to esophagectomy for locally advanced esophageal squamous cell carcinoma (ESCC). MATERIALS/METHODS A total of 702 patients with ESCC who completed transthoracic esophagectomy followed neoadjuvant therapy at three cancer centers from January 2017 to December 2022 were enrolled. Among the included patients, 382 patients were treated with NCR, 172 with NCRT, and 148 with NICT. Inverse probability of treatment weighting (IPTW) was performed to control potential confounding factors. Pathological response of primary tumor was evaluated using the Chirieac modified tumor regression grade (TRG) system. The complete regression of primary lesion and nodal metastases were considered pCR. Lymph node classification system used the 8th edition of AJCC. Specimens were assessed for pattern of lymphatic spread. RESULTS After adjusting for baseline characteristics, the R0 resection rate did not significantly differ between the patients receiving NCT or NCRT or NICT (99.48% vs.100% vs.98.65%, P = 0.273). Compared with the NCT group, the NCRT group and NICT group had an advantage in pathological response (P<0.05). The pCR rate was 7.07% in the NCT group, 30.23% in the NCRT group, and 22.30% in the NICT group. Compared to the other two groups, the TRG score (P<0.05) and pathologic T stage (P<0.05) in the NCT group were significantly higher. In the NCT group, 9.97% had ypT0 disease, compared with 35.76% in the NCRT group and 25.68% in the NICT group. And in the NCT group, 9.71% had TRG1 disease, compared with 32.76% in the NCRT group and 25% in the NICT group. Compared with NICT, NCRT can significantly reduce the rate of LNM in station 1R (0 vs 3.38%, P<0.05) and 2R (1.15% vs 6.76%, P<0.05). Subgroup analysis according to the tumor location distribution showed that in upper thoracic cases, there was no statistical difference in LNM rates among stations no matter whether patients received NCT or NCRT or NICT. NICT group had higher LNM rates in station 2R (9.1%) in middle thoracic cases (P<0.05) and in station 18 (7.5%) (P<0.05) in lower thoracic cases, compared with the NCRT group and NCT group. CONCLUSION NCRT or NICT followed by surgery may result in a promising pCR rate and show a better performance in therapeutic response of primary lesion. No matter whether patients received NCT or NCRT or NICT, multiple level and skip node metastases are common, and adequate lymphadenectomy should be achieved to ensure the complete removal of metastatic lymph nodes.
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Lin L, Mo Z, Xiao J, Kou J, Guo C, He SM, Zhang W, Sun Y. Identification and Automated Delineation of Radioresistant Biological Tumor Volume in Nasopharyngeal Carcinoma Based on Magnetic Resonance Imaging Radiomics. Int J Radiat Oncol Biol Phys 2023; 117:e598-e599. [PMID: 37785804 DOI: 10.1016/j.ijrobp.2023.06.1958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Widespread use of intensity modulated radiotherapy (IMRT) has improved the tumor control rate of nasopharyngeal carcinoma (NPC). However, nearly 20% of the patients with local-advanced NPC would relapse after precise irradiation and 80% of the recurrent lesions occur within the high dose field, suggesting that there are radiation-resistant cancer cell subsets within the tumor. In this context, identification and contouring of radiation resistance region of NPC for dose escalation at primary IMRT could be advantageous. In this work, we proposed a two-step radiomics workflow to predict local relapse and the recurrent region of NPC before primary IMRT. MATERIALS/METHODS In this single-center, retrospective study, pre-treatment magnetic resonance (MR) sequences of T1-weighted imaging (T1-w) and contrast-enhanced T1-weighted imaging (CET1-w) were collected from 800 patients of newly diagnosed and non-metastatic NPC between April 2009 and December 2015. The primary gross tumor volume (GTVp) of all patients and the actual recurrent lesion (GTVr) of patients who suffered from local recurrence were manually contoured for further analysis. A two-step complete radiomics workflow was designed to predict tumor recurrence and segment the region. First, least absolute shrinkage and selection operator (LASSO) was utilized for radiomics features selection of GTVp and support vector machine (SVM) was adopted to predict the recurrence. If the model predicts a recurrence, then the workflow utilizes an improved 3D U-Net to segment the recurrent region. Area under receiver operating characteristic curve (ROC-AUC) was used to evaluate the performance of tumor recurrence prediction, and Dice similarity coefficient (DSC) was used to assess the consistence between the actual and predicted GTVr. RESULTS Of 800 NPC patients, 95 (11.9%) patients developed in-field local recurrence. For recurrence risk prediction, the SVM ensemble model (T1-w+CET1-w) was selected for further application with higher sensitivity. The average ROC-AUC, specificity, sensitivity of the SVM ensemble model in a 5-fold cross-validation and in the independent test set of 160 patients were 0.922, 0.922, 0.777 and 0.928, 0.915, 0.737, respectively. Moreover, for recurrent region segmentation, the multi-modality (T1-w+CET1-w) model was superior to the single-modality (T1-w or CET1-w) model. In an independent test set of 15 patients, the DSC, sensitivity and 95% Hausdorff Distance between actual and predicted GTVr was 0.549±0.176, 0.696±0.118 and 9.813±4.788 which was superior to 0.444±0.188, 0.497±0.218 and 12.047±5.361 of original 3D U-Net. CONCLUSION The proposed two-step radiomics workflow showed a good performance in predicting tumor recurrence of NPC. The predicted location of the recurrence lesion was all accurate, but there was still a certain difference between the volume of the automated delineated and actual GTVr, which needed to be further optimized to be used as biological tumor volume.
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Lin Y, Zhang W, Prezado Y, Traneus E, Johnson D, Li W, Gan GN, Chen RC, Gao H. Towards Clinical Proton Minibeam Radiation Therapy (pMBRT): Development of Clinical pMBRT System Prototype and pMBRT-Specific Treatment Planning Method. Int J Radiat Oncol Biol Phys 2023; 117:S38. [PMID: 37784487 DOI: 10.1016/j.ijrobp.2023.06.307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Proton minibeam radiation therapy (pMBRT) is an emerging spatially fractionated RT (SFRT) modality that can provide very high therapeutic index compared to conventional radiotherapy methods and clinically-available SFRT methods (GRID and LATTICE). The biological data collected thus far encourage the preparation of clinical trials in pMBRT. This work is to facilitate the clinical translation of pMBRT by developing (1) the first clinical pMBRT system prototype worldwide, readily available for both small-animal biology studies and large-animal pMBRT trials; (2) pMBRT-specific treatment planning method with peak-valley dose ratio (PVDR) optimization capability for large animal and patient pMBRT trials, which is currently unavailable. MATERIALS/METHODS The pMBRT system is based on clinically-used pencil-beam-scanning proton machine equipped with large-field clinical-size pMBRT collimator, pMBRT-dedicated treatment planning system, and KV/CBCT imaging guidance. The multi-slit brass collimator has 10 × 10 cm field size, 0.4mm width per slit and 4 mm center-to-center distance. The divergence of slits is tailed to the divergence of the proton beam. A unique universal collimator design is implemented, so that we can keep the outer fitting to the snout and conveniently inter-change collimators as needed. The pMBRT-specific treatment planning method jointly optimizes PVDR and dose objectives, to meet a minimal PVDR threshold and maximize PVDR, to avoid the situations where meeting dose objectives can compromise PVDR when PVDR were not optimized. In addition, the survival fraction for organs at risk is also optimized. The dose calculation engine is based on the Monte Carlo method using TOPAS. The optimization algorithm utilizes total variation and L1 sparsity regularization to maximize PVDR and iterative convex relaxation method to solve the optimization problem. RESULTS Monte Carlo simulations via TOPAS were performed to design this large-field multi-slit collimator, using the beam structure and the beam data specific to our proton system, with mean dose rate of 8 Gy/min under clinical condition with the collimator in place. The feasibility of using this pMBRT system for small-animal studies has been demonstrated, with customized 3D printed holder for immobilizing small animals, on-board KV imaging system for accurate small-animal positioning, and the GAFchromic film for verifying radiation dose and PVDR. On the other hand, the efficacy of pMBRT-specific treatment planning method (with PVDR optimization capability) to improve PVDR has been demonstrated using retrospective patient planning studies in comparison with standard proton treatment planning method (without PVDR optimization capability). CONCLUSION The initial development of a clinical pMBRT system prototype and pMBRT-specific treatment planning method of PVDR optimization capability has been completed with ongoing efforts to make this system ready for large-animal pMBRT studies.
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Chen L, Wei Y, Sun F, Wang Z, Liu Y, Zhang W, Zhang F, Shi W. An inverse Jiles-Atherton model of nanocrystalline magnetic core for nanoseconds square pulsed magnetization. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:104711. [PMID: 37870442 DOI: 10.1063/5.0165179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023]
Abstract
The magnetic core is a key component of a linear transformer driver (LTD), and the accuracy of the core model affects the calculation of the LTD power flow and the prediction of the output waveform. In this paper, a magnetization model based on the inverse Jiles-Atherton (inverse J-A) model is developed and a particle swarm algorithm is used to identify the parameters and to obtain the variation of the parameters with the excitation characteristic. A nanoseconds square wave LTD magnetic core test platform was built to obtain the magnetization characteristics of nanocrystalline magnetic cores under different excitation characteristic parameters. Under square wave pulses, due to the presence of harmonic components, core loss is more complex. In view of the fitting deviation caused by the traditional J-A model not considering harmonic factors and anisotropy, a dynamic loss correction factor is proposed. Through a comparison of experimental and simulation results, this model can well reflect the magnetization process and has high accuracy in fitting dynamic hysteresis loops and predicting losses, which is important for guiding the design of a square pulse LTD.
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Hu D, Zhang Y, Li W, Zhang W, Reddy K, Chen Y, Gao H. SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data. Int J Radiat Oncol Biol Phys 2023; 117:S178-S179. [PMID: 37784443 DOI: 10.1016/j.ijrobp.2023.06.2523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Limited-angle CBCT (LA-CBCT) is of great clinical interest, because the scanning time and the patient dose are proportional to the scanning range of gantry rotation angles of CBCT. However, the image reconstruction for LA-CBCT remains technically challenging, which suffers from severe wedge artifacts and image distortions. This work aims to improve LA-CBCT by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. MATERIALS/METHODS Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration. RESULTS SEA-Net was validated in comparison with analytic (FDK), iterative (TV), image-domain DL (DDNet and FED-INet, data-domain DL (DCAR), dual-domain DL (Sam'Net), and various unrolling DL (hdNet, CTNet, FSR-Net, CasRedSCAN) methods. Among all methods, the SEA-Net had the best image reconstruction quality as quantified by root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), for various LA-CBCT problems of 90°-180° projection data. In addition, LA-CBCT via SEA-Net provided comparable accuracy for both patient setup (quantified by image registration accuracy from planning CT (pCT) to CBCT) and dose calculation (see the table), with full-view CBCT. CONCLUSION We explored various DL methods and developed an image-domain-based method termed SEA-Net that provided the best image quality for clinical projection data. To the best of our knowledge, this is the first feasibility study of the real clinical-projection-data-based LA-CBCT. Moreover, LA-CBCT via SEA-Net can potentially provide comparable accuracy for patient setup and dose calculation, with full-view CBCT.
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Guan S, Ren K, Yan M, Zhang W, Liu N, Wang J, Zhao L. Induction Immunotherapy vs. Consolidation Immunotherapy for Unresectable Stage III NSCLC. Int J Radiat Oncol Biol Phys 2023; 117:e21. [PMID: 37784874 DOI: 10.1016/j.ijrobp.2023.06.694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Consolidation immunotherapy after chemoradiotherapy (CRT) is the standard of care for unresectable stage III non-small cell lung cancer (NSCLC). However, whether upfront immunotherapy before CRT has similar benefits has not been addressed. This study aimed at exploring the efficacy and safety of induction immunotherapy for unresectable stage III NSCLC through real-world data. MATERIALS/METHODS Patients diagnosed with stage III NSCLC who received immunotherapy in combination with sequential (sCRT) or concurrent CRT (cCRT) between November 2018 and December 2021 were retrospectively identified. Patients were divided into induction (Ind), consolidation (Con) and induction plus consolidation (Ind+Con) immunotherapy groups. Progression-free survival (PFS) and overall survival (OS) were assessed from the initiation of treatment and estimated by Kaplan‒Meier method. The potential factors affecting PFS and OS were analyzed by univariate and multivariate Cox regression models. RESULTS One hundred and two patients were included, with 52 (51.0%) patients in the Ind group, 35 (34.3%) in the Con group and 15 (14.7%) in the Ind+Con group. Median PFS was 24.0 months vs. 36.0 months vs. 19.0 months in the three groups, and 2-year PFS were 43.0% vs 51.1% vs 44.4% (p = 0.940). Median OS was not reached (NR) vs. 44.0 months vs. NR, with a 2-year OS rate of 80.5% vs. 84.4% vs. 86.2% (p = 0.861). In the cCRT setting, 2-year PFS rates were 56.7% vs. 71.6% vs. 100.0% (p = 0.439), 2-year OS rates were 92.3% vs. 89.3% vs. 100.0% in the three groups (p = 0.827). In multivariate analysis, elder (HR = 0.487, p = 0.037) and cCRT (HR = 0.282, p = 0.001) were the independent factors favoring PFS, while only elder (HR = 0.088, p = 0.021) was the independent factors favoring OS. Adverse events were similar in the three arms. Further analysis found the objective response rate (ORR) and disease control rate (DCR) in the Ind and Ind+Con group after induction immunotherapy were 59.7% and 98.5%, respectively. Only 1 (1.5%) patient developed progression. Subgroup analysis showed no significant difference in PFS (p = 0.520) and OS (p = 0.116) between patients who responded to induction immunotherapy (PR+CR) and those who did not (SD+PD). Patients with <4 cycles of induction immunotherapy exhibited numerically better PFS than those with ≥4 cycles of induction immunotherapy (p = 0.113) and improved OS (p = 0.021). CONCLUSION Induction immunotherapy may achieve similar survival benefits to consolidation immunotherapy, and the combination of induction and consolidation immunotherapy with cCRT appears to achieve better outcomes. It seems feasible and safe to upfront immunotherapy before CRT, and further investigations on the combination of induction immunotherapy and CRT are warranted.
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Zhu J, Zhang W, Huang W, Zhang Y, Li T, Wang Q. Radical Chemoradiotherapy vs. Radical Surgery plus Adjuvant Chemotherapy or Chemoradiotherapy in Locally Advanced Primary Small Cell Carcinoma of the Esophagus: A Multicenter Retrospective Study in China. Int J Radiat Oncol Biol Phys 2023; 117:e359-e360. [PMID: 37785236 DOI: 10.1016/j.ijrobp.2023.06.2446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The purpose was to compare the treatment outcomes of radical chemoradiotherapy versus radical surgery plus adjuvant chemotherapy or chemoradiotherapy in locally advanced primary small cell carcinoma of the esophagus (LA-PSCCE). The hypothesis was that radical chemoradiotherapy had better overall survival (OS) than radical surgery plus adjuvant chemotherapy or chemoradiotherapy. MATERIALS/METHODS This large-scale multicenter retrospective study in China enrolled patients with newly diagnosed LA-PSCCE (T3-4N0M0 or TanyN+M0, AJCC 8th edition) from 2008 to 2021. According to different curative treatment approaches, patients were divided into two groups: radical chemoradiotherapy (group: CRT), and radical surgery following adjuvant chemotherapy or chemoradiotherapy (group: S + CT/CRT). The propensity score match (PSM) was applied to reduce the effect of confounding biases in clinicopathological characteristics (age, gender, KPS, tumor location, tumor length, and cTNM stage). Univariate Cox-regression analysis and Kaplan-Meier curve were calculated for OS. Statistical results were summarized as hazard ratio (HR), 95% confidence interval (CI) and P value. A two-sided P < 0.05 was regarded to be statistically significant. RESULTS A total of 291 patients with a median follow-up of 4.3 years were retrospectively enrolled. After PSM analysis, 94 and 94 patients were eventually included in group CRT and S + CT/CRT, respectively. Group CRT demonstrated a significantly superior survival than group S + CT/CRT (HR, 0.63; 95% CI, 0.43-0.91; P = 0.01), with a 3-year OS of 49.5% and 27.8% (P = 0.02), respectively. In secondary analysis, patients treated with radical chemoradiotherapy consistently showed significant survival benefit than those with radical surgery plus adjuvant chemoradiotherapy (HR, 0.4; 95% CI, 0.21-0.79; P = 0.008). CONCLUSION For patients with newly diagnosed LA-PSCCE, radical chemoradiotherapy should be a preferred recommendation in real-world clinical practice.
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Zhang R, Liu Y, Yang R, Chen C, Fu C, Pan Z, Cai W, He SM, Zhang W. Deep Learning for Automated Contouring of Primary Gross Tumor Volumes by MRI for Radiation Therapy of Brain Metastasis. Int J Radiat Oncol Biol Phys 2023; 117:e496. [PMID: 37785562 DOI: 10.1016/j.ijrobp.2023.06.1734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Radiotherapy is one of the most effective methods for the treatment of brain metastases (BMs). Traditional manual delineation of primary gross tumor volumes (GTV) of multiple BMs (especially small metastases) in radiotherapy practice is extremely labor intensive and highly dependent on oncologists' experience, achieving the precise and efficient automatic delineation of BMs is of great significance for efficient and homogeneous one-stop adaptive radiotherapy. MATERIALS/METHODS We retrospectively collected 62 MRI (non-enhanced T1-weighted sequences) sequences of 50 patients with BMs from January 2020 to July 2021. An automatic model (BUC-Net) for automatic delineation BMs was proposed in this work, which was based on deep learning by combining 3D bottler layer module and the cascade architecture to improve the accuracy and efficient of BMs' automatic delineation, especially for small metastases with tiny size and relatively low contrast. The prosed method was compared with the existing 3D U-Net (U-Net) and 3D U-Net Cascade (U-Net Cascade). The performance of our proposed method was evaluated by Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and average surface distance (ASD) with human experts. RESULTS The automatic segmentation results of BUC-Net evaluated with 310 BMs in 13 test patients was summarized in Table 1. These BMs in each test patient were automatically delineated by two types of contours: as a whole tumor contour (Whole-delineation) and the multiple tumor contours (Multiple-delineation). BUC-Net performed the best mean DSC and HD95, which is significantly outperformed U-Net (Whole-delineation: 0.911 & 0.894 of DSC, Multiple-delineation: 0.794 & 0.754 of DSC, P < 0.05 for both) and U-Net cascade (Whole-delineation: 0.947 & 7.141 of HD95, Multiple-delineation: 0.902 & 1.171 of HD95, P < 0.05 for both); Additionally, BUC-Net achieved the best mean ASD for Whole-delineation and comparable ASD (0.290 & 0.277, P > 0) for Multiple-delineation with U-Net Cascade. CONCLUSION Our results showed that the proposed approach is promising for the automatic delineation of BMs in MRI, which can be integrated into a radiotherapy workflow to significantly shorten segmentation time.
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Zhang W, Yang X, Sun S, Men Y, Hui Z. Detection of Circulating Tumor DNA Correlates with Recurrence and Survival in Localized Non-Small-Cell Lung Cancer: A Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e80-e81. [PMID: 37786188 DOI: 10.1016/j.ijrobp.2023.06.827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Circulating tumor DNA (ctDNA) is approved recently to use in clinical practice of solid tumor. Several large-scale prospective studies also revealed that minimal residual disease based on ctDNA (ctDNA-MRD) is a potential predictive and prognostic biomarker of localized non-small-cell lung cancer (NSCLC) receiving curative treatment (surgery or radiotherapy). However, there are still barriers to clinical management of ctDNA/ctDNA-MRD in localized NSCLC, and the most significant is effectiveness and detection times of ctDNA/ctDNA-MRD. This meta-analysis aims to clarify the prognostic value of the ctDNA and ctDNA-MRD in predicting the disease recurrence and survival of localized NSCLC. MATERIALS/METHODS Electronic databases (Pubmed/MEDLINE, Web of Science, Cochrane Library, meeting abstracts) were comprehensively searched for eligible studies from January 2001 to January 2023. The Hazard ratio (HR) from relevant reports was extracted to better evaluate the correlation of ctDNA and ctDNA-MRD detected in plasma with clinical outcomes among patients with localized NSCLC. Pooled results including ctDNA detection rate, disease-/relapse-/progression- free survival (DFS/RFS/PFS) and overall survival (OS) were obtained and analyzed by Review Manager 5.4.1. RESULTS A total of 18 eligible studies with 2692 patients were enrolled in the final analysis. The pooled analysis revealed that ctDNA detection in pretreatment plasma indicated poor prognosis for DFS/RFS/PFS (HR 3.82, 95% CI 2.85 - 5.12, p < .00001; Figure 1) with a long-term effect on OS (HR 4.88, 95% CI 3.29 - 7.24, p < .00001; Figure 2). The same result was also observed in patients with positive posttreatment ctDNA-MRD who have shorter DFS/RFS/PFS (HR 7.15, 95% CI 5.50 - 9.31, p < .00001; Figure 3) and OS (HR 4.34, 95% CI: 2.51-7.51, p < .00001; Figure 4) compared to negative groups. CONCLUSION Based on the results from our meta-analysis, the presence of pretreatment ctDNA or posttreatment ctDNA-MRD in radically treated localized NSCLC is associated with higher risk of recurrence and poorer survival. This finding is supportive of ctDNA/ctDNA-MRD becoming a widespread prognostic biomarker in localized NSCLC.
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Peng J, Liu Y, Jiang D, Wang X, Peng P, He SM, Zhang W, Zhou F. Deep Learning and GAN-Synthesis for Auto-Segmentation of Pancreatic Cancer by Non-Enhanced CT for Adaptive Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e499-e500. [PMID: 37785569 DOI: 10.1016/j.ijrobp.2023.06.1742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In conventional adaptive radiotherapy (ART) for pancreatic cancer, contrast-enhanced CT (CECT) helps to more precisely delineate primary gross tumor volume (GTV) than non-enhanced CT (NECT). However, frequent use of contrast medium can damage kidneys and prolong treatment time. Moreover, traditional manual delineation is labor-intensive and highly dependent on the experience of oncologists. Currently, automatic delineation based on deep learning with Generative Adversarial Networks (GAN)-based CT synthesis is one of the most feasible solutions to these problems. MATERIALS/METHODS A dataset of 35 pancreatic cancer patients was retrospectively collected from May 2021 to December 2022. All patients consist of a pair of NECT and CECT. We designed and developed an automatic delineation framework (Proposed) for GTV of pancreatic cancer based on Trans-cycleGAN and a modified 3D U-Net. TranscycleGAN can not only synthesize CECT from NECT, but can also augment the amount of CT images; then all real and synthesized CT images were used to train the modified 3D U-Net for automatic delineation of GTV; finally, our framework was able to automatically delineate GTV by NECT, but not only by CECT. Our framework was evaluated by dice similarity coefficient (DSC), 95% Harsdorff distance (95HD) and average surface distance (ASD) with oncologists' manual delineation ("gold standard"). RESULTS The evaluation results were summarized in Table 1. The proposed framework achieved the best automatic delineation results by NECT, which was superior to that of CECT: 0.917 & 0.903 of DSC, 2.498mm & 3.029mm of HD95, 0.481mm & 0.534mm of ASD, p < 0.05 for DSC and HD95. Specifically, it is significantly superior to the automatic delineation results using U-Net by CECT 0.917 & 0.818 of DSC, 2.498mm & 13.228mm of HD95, 0.481mm & 3.633mm of ASD, p < 0.05 for DSC. CONCLUSION We proposed an automatic delineation framework for contouring GTV in ART of pancreatic cancer based on deep learning and Trans-cycleGAN network. This framework could automatically delineate GTV and achieve better performance with NECT compared to CECT. Our method could not only reduce the use of contrast medium, but also increase the precision and effectiveness of tumor delineation, which could have a positive impact on precision radiotherapy.
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Zhou GQ, Yang YX, Yang X, Jia LC, Jiang X, Zhou J, Chen AQ, Diao WC, Liu L, Li H, Zhang K, He SM, Zhang W, Lin L, Sun Y. All-in-One Online Radiotherapy for Nasopharyngeal Carcinoma: Preliminary Results of Treatment Time, Contouring Accuracy, Treatment Plan Quality and Patient Compliance. Int J Radiat Oncol Biol Phys 2023; 117:e636-e637. [PMID: 37785898 DOI: 10.1016/j.ijrobp.2023.06.2040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To explore the feasibility of Fan-beam CT (FBCT)-based all in one (AIO) online workflow for nasopharyngeal carcinoma (NPC) in radical radiotherapy setting, and to preliminarily describe the timing of different steps in the process, contouring accuracy of regions of interest (ROIs), target coverage, organs at risk (OARs) dose and patient compliance. MATERIALS/METHODS From March 16, 2022 to January 04, 2023, 25 NPC patients (22/25 diagnosed as phase III/IV disease according to 8th edition of the AJCC/UICC staging system) consecutively treated with AIO radiotherapy were prospectively enrolled. All patients received mask fixation and MRI simulation scan in advance. Primary gross tumor volume (GTVp) of nasopharynx was automatically delineated by AI and edited manually on MRI images. AIO online workflow started with an integrated KV-level CT in a CT-integrated linear accelerator. After that GTVp was registrated to CT images and other ROIs was contoured automatically and then modified manually as needed. Subsequently automatic treatment plan was calculated and optimized until the dose of target and OARs was evaluated satisfactory by physicians and physicists. Finally, treatment was delivered using volumetric modulated arc treatment (VMAT), with prescribed dose of 6996 cGy/ 33 fractions to the GTVp. RESULTS Twenty-four patients (24/25, 96%) completed the AIO radiotherapy workflow successfully, with average treatment time of 28.3 min (range: 19.9-42.4 min). the AI-assisted ROIs automatically contouring took 1.55 min in average (range: 1.32-1.77 min), with an average DICE of 97.7% compared with modified contouring, and the average DICE was 95.7% for clinical tumor volume 1 (CTV1), 88.6% for CTV2, 73.6% for GTVn (cervical lymph node), 99.3% for 30 OARs. The automatic treatment plan averagely needed 3.5 min, and the pass rate of radiotherapy planning was 91.7% (22/24). The target coverage for PTVs for GTVp, CTV1, and CTV2 was 99.3%, 99.8%, 98.0% respectively. As for the dose of OARs, the average Dmax of brainstem was 5,583cGy; the Dmax of spinal cord was 3,467cGy; the Dmean of parotid was 3,285 cGy. The average monitor units of all patients was 643 MU and the delivery took 2.93 min. Patient compliance with respect to AIO workflow and total treatment time was excellent. CONCLUSION The AIO online radiotherapy was promising for NPC patients, with clinically acceptable AI assisted ROIs contouring and treatment planning, as well as favorable patient compliance to the AIO online workflow.
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Liu Y, Wang Y, Ma Z, Bao Y, Zhang W, Zhang H, Deng H, Men Y, Zhai Y, Wang X, Liu W, Bi N, Ye F, Men K, Qin J, Xue L, Wang Q, Hui Z. A Machine Learning Method to Predict Pathological Complete Response of Esophageal Cancer after Neoadjuvant Chemoradiotherapy with Clinicohematological Markers and MR Radiomics: A Multi-Center Study. Int J Radiat Oncol Biol Phys 2023; 117:e318. [PMID: 37785139 DOI: 10.1016/j.ijrobp.2023.06.2355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Nearly 30% of patients with local advanced esophageal cancer achieved pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who may benefit from organ-preservation strategy under accurate prediction of pCR. We aimed to develop and validate machine learning models based on clinicohematological markers and MR radiomics to accurately predict pCR of esophageal cancer after nCRT. MATERIALS/METHODS In this multi-center study, eligible patients with esophageal cancer who received baseline MR scan (T2-weighted image) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Pre-nCRT and post-nCRT blood test results were collected to calculate hematological markers. Models were constructed by machine learning based on clinicohematological markers and MR radiomics to predict pCR. Area under the curve (AUC) and cut-off analysis were used to evaluate model performances. RESULTS Totally 154 patients (81 in the training set and 73 in the testing set) were enrolled. The combined model integrating pre-nCRT monocyte-to-lymphocyte ratio and 6 radiomics features achieved AUC of 0.800 (95% CI 0.671-0.918) in the testing set, with sensitivity of 79.2% (95% CI 62.5%-95.8%), specificity of 83.7% (95% CI 73.5%-93.9%), positive predictive value of 76.0% (95% CI 62.5%-90.0%), and negative predictive value of 89.6% (95% CI 82.0%-95.8%). CONCLUSION A machine learning model based on clinicohematological markers and MR radiomics to predict pCR after nCRT for patients with esophageal cancer was developed and validated, providing a novel tool for personalized treatment. It is necessary to further validate in more large datasets.
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Lin L, Wei Z, Jia LC, Guo C, Zhou GQ, Yang YX, He SM, Zhang W, Sun Y. Automated Contouring of Cervical Lymph Nodes and Clinical Target Volumes for Nasopharyngeal Carcinoma Based on Deep Learning and Experience Constraints. Int J Radiat Oncol Biol Phys 2023; 117:e598. [PMID: 37785805 DOI: 10.1016/j.ijrobp.2023.06.1957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Application of artificial intelligence (AI) for automated contouring of tumor volumes and organs at risk (OARs) for radiotherapy of nasopharyngeal carcinoma (NPC) leads to improved contouring accuracy and efficiency. However, few studies have involved the automated contouring of gross tumor volume of cervical lymph nodes (GTVn) and clinical target volumes (CTVs). In this work, we proposed an AI automated contouring tool for GTVn and CTVs for radiotherapy of NPC on the plain scans of planning compute tomography (CT). MATERIALS/METHODS In this retrospective study, plain scan datasets of planning CT covering the nasopharynx and neck from 139 patients with NPC between March 2022 and December 2022 were collected and divided into training, validation, and testing cohorts of 95, 24, and 20 patients, respectively. Ground truth contours of primary gross tumor volume (GTVp), GTVn (divided into GTVn_L in left neck and GTVn_R in right neck), CTVs (including high risk CTV1 contains GTVp and low risk CTV2 contains GTVp and cervical nodal levels) and OARs were delineated and were defined by consensus of two experts. We first proposed a three-dimensional (3D) U-net using GTVp and OARs as experience constrains to guide the automated delineation of GTVn and CTVs. The average Dice similarity coefficient (DSC) and average surface distance (ASD) were used to quantify the performance of the AI tool. Next, five prospective patients were enrolled for clinical evaluation of our AI tool. DSC between automated contours and radiation oncologist-revised contours and time consuming of the revision were record. RESULTS Clinical characteristics of 139 retrospective and 5 prospective patients are list in Table 1. In the independent testing set of 20 patients, our AI tool showed high performance in GTVn and CTVs contouring when compared with the ground truth contours. The mean DSC were 0.73 ± 0.07, 0.74 ± 0.05, 0.93 ± 0.03, and 0.88 ± 0.03, and the mean ASD were 1.01 ± 0.43 mm, 1.14 ± 0.61 mm, 0.51 ± 0.13 mm, 1.17 ± 0.43 mm for GTVn_L, GTVn_R, CTV1 and CTV2, respectively. In the five prospective patients, mean DSC were 0.74 ± 0.07, 0.74 ± 0.10, 0.95 ± 0.01 and 0.89 ± 0.04, respectively. The median time consuming for GTVn and CTVs revision was 2minutes and 10 seconds (range, 1 minutes to 3 minutes). CONCLUSION The proposed AI tool integrating clinical experience as constrains showed high accuracy for contouring GTVn and CTVs of NPC. With the assistance of AI contours, contouring efficiency could be probably increased, which is promising in online adaptive radiotherapy of NPC.
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Lin L, Zhou GQ, Yang X, Yang YX, Jiang X, Li B, Chen AQ, Diao WC, Liu L, He SM, Li H, Jia LC, Zhang W, Zhou J, Sun Y. First Implementation of Full-Workflow Automation for Online Adaptive Radiotherapy of Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e687. [PMID: 37786019 DOI: 10.1016/j.ijrobp.2023.06.2156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The aim of this work is to established the technical characteristics and implementation procedures of an artificial intelligence (AI)-powered radiotherapy workflow that enables full-process automation for online adaptive radiotherapy (ART); and evaluate its feasibility and performance implemented for ART of nasopharyngeal carcinoma (NPC). MATERIALS/METHODS This single center, prospective study has been approved by the ethical committee of the institution. The online ART workflow was developed based on a CT-integrated linear accelerator. During the course of radiotherapy, the patient underwent daily pre-treatment fan-beam CT (FBCT) scan. Then the FBCT was automatically registered to the original planning CT and used to assess the need for the patient to implement ART according to radiation oncologist's discretionary. The online ART workflow incorporates critical radiotherapy procedures from re-simulation, auto-segmentation by integrating image fusion and deep learning method, auto-replanning, beam delivery, and in vivo quality assurance (QA) into one scheme, while the patient is on the treatment couch during the whole process. RESULTS From 2th April 2022 to 5th January 2023, 20 patients with newly-diagnosed, non-metastatic NPC were enrolled in this study. Only one-time online ART was performed for each patient, because that the appropriate timing for triggering online ART was explored in parallel with this study. According to radiation oncologists' discretionary, the median fraction for performing online ART was at 21 fractions (interquartile range, 19-24 fractions). All patients were well tolerated and successfully completed the treatment. For tumor targets contouring, minor revisions were required for automated contours of the primary gross tumor volume (GTVp) and clinical target volumes (CTVs, including CTV1 and CTV2), with the mean DSC between before and after revision of 0.91±0.042, 0.94 ± 0.042 and 0.91 ± 0.061, respectively; and much more revisions for the automated contours of cervical lymph nodes GTV (GTVn), with the mean DSC of 0.74 ± 0.28. The automated contours of normal tissues were clinically acceptable with little modifications. Median time consuming for auto-segmentation and revision was 9.5 minutes (min). For treatment planning, 18 automated plans (90%) were passed at their first auto-optimization and two plans (10%) were passed after further optimization of the dose coverage of CTVs by physicist; and the median time consuming for auto-planning was 6.2 min. Time consuming for other procedures were as follows: re-simulation, 2.3 min; plan evaluation, 3.3 min; beam delivery, 4.6 min; and the duration of the entire process was 25.9 min, range from 19.4 min to 32.5 min. CONCLUSION We successfully established an AI-powered online ART workflow for adaptive radiotherapy of NPC, and confirmed that current auto-segmentation and auto-replanning methods are powered enough to support the clinical application of its online ART.
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Li X, Lin FY, Jia LC, Liu T, He SM, Zhang W, Zhang M, Wang Y. Preserving Structural Consistency in the Generation of Synthetic CT in Pelvic MR-Only Radiation Treatment Planning. Int J Radiat Oncol Biol Phys 2023; 117:e686. [PMID: 37786017 DOI: 10.1016/j.ijrobp.2023.06.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) MR-based synthetic CT (sCT) generation is necessary for MR-only radiotherapy to assist in radiation dose calculation, owing to no electronic density information in MR images. This study investigated the feasibility of synthesizing CT images from magnetic resonance (MR) images using generation antagonism networks (GANs) for MR radiotherapy of rectal cancer. Meanwhile, the transformer module and the contrast learning loss were introduced to improve the sCT. MATERIALS/METHODS The data set used in this study was the T2-weighted MR and CT image data of 108 patients with rectal cancer. Three-fold cross-validation was performed on all data sets. The transformer module was introduced into the plain CycleGAN, and the improved Patch Noise Contrastive Estimation (PatchNCE) loss was used as the loss function. The improved PatchNCE loss maintained the structural consistency of the MR and the synthetic CT by ensuring the consistency of the distribution of image patches on the MR-sCT image pair. The 2.5D images were taken as the input of our model, which refers to taking two consecutive adjacent layers in a specific layer. The CT-to-sCT image similarity was evaluated by metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and Structure Similarity Index Measure (SSIM). The sCT dosimetric accuracy was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed. RESULTS The evaluation indicators of sCT images generated by our model were superior to the plain CycleGAN in the results of the three-fold cross-validation. MAE, PSNR and SSIM of our model were 42.850HU, 26.486 and 0.988, respectively, which were superior to 47.129HU, 25.167 and 0.978 of the plain CycleGAN. In addition, sCT generated by our model exhibited good continuity in the axial direction compared with plain CycleGAN. Furthermore, most of the relative differences in the DVH indicators were less than 1%. CONCLUSION The accuracy of sCT can be effectively improved by introducing a transformer module and comparative learning loss function. Moreover, all dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of the MRI-only workflow for patients with rectal cancer.
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Zhang W, Tang Y, Chen W, Gao Y, Wang W, Liu S, Wei L, Cai Y, Zhu Y, Cheng G, Zhang H, Wang X, Zhu S, Wang J, Li G, Yang J, Zhang K, Li N, Li Y, Jin J. Cost-Effectiveness of Short-Course Radiotherapy Based Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer in China. Int J Radiat Oncol Biol Phys 2023; 117:e356-e357. [PMID: 37785230 DOI: 10.1016/j.ijrobp.2023.06.2439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The phase III STELLAR (NCT02533271) trial demonstrated that four cycles of chemotherapy after short-course radiotherapy (SCRT-TNT) were not inferior to the standard care of long-course concurrent radiotherapy (LCRT) in patients with locally advanced rectal cancer (LARC). This study assessed the cost-effectiveness of SCRT-TNT versus LCRT in locally advanced rectal cancer in China on the basis of the STELLAR trial. MATERIALS/METHODS A Markov model was used to synthesize the healthcare costs and benefits of LARC patients based on results from the STELLAR trial. The model assumes that LARC who meet the inclusion criteria of the STELLAR trial experience four possible states: No Evidence of Disease (NED), locally recurrence, distant metastases, or any death from rectal cancer or other unrelated causes, where local recurrence continues to be classified as resectable and unresectable. The transition status period is 3 month, and 5 years is used to calculate direct medical costs and health benefits. The probabilities of states transition after SCRT-TNT or LCRT were derived from the results of the STELLAR trial and previous published article (Table.1). Costs were evaluated from the Chinese payer's perspective reported in early 2022 US dollars (US$1 = 6.78 Chinese Yuan). Sensitivity analyses were performed for key variables. Cost-effectiveness was evaluated using the incremental cost-effectiveness ratio and net monetary benefits. Effectiveness was defined as quality-adjusted life-years (QALYs). Willingness-to-pay (WTP) threshold was set at $43500/QALY. Data were collected from October 3, 2020, to September 20, 2021, and analyzed from November 15, 2020, to October 25, 2021. RESULTS During the 5-year horizon, for the base case scenario, SCRT-TNT incurred a lower total cost and higher QALYs compared with LCCRT. The total cost was $65767 and QALYs were 1.77 for SCRT-TNT; for LCCRT, the total cost was $72802 and QALYs were 1.64. This resulted in an ICER of -$ 55470.69 per QALY. Therefore, SCRT-TNT was a cost-saving and dominating treatment strategy compared with LCRT. Sensitivity analysis showed that ICERs were most sensitive to the parameters of distant metastases risk after treatment. CONCLUSION SCRT-TNT in locally advanced rectal cancer can be a cost-effective alternative to LCRT in China, and should be considered in appropriately selected patients.
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Tian S, Liu Y, Mao X, Xu X, Wang C, Han G, Yang Y, Wang J, He SM, Zhang W. A Multicenter Study on Deep Learning for Glioblastoma Auto-Segmentation with Prior Knowledge in Multimodal Imaging. Int J Radiat Oncol Biol Phys 2023; 117:e488. [PMID: 37785541 DOI: 10.1016/j.ijrobp.2023.06.2299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) A precise radiotherapy plan is required to ensure accurate delineation of gross tumor volumes (GTV) and clinical target volumes (CTV1 and CTV2) of glioblastomas (GBMs). However, traditional manual delineation is labor intensive and highly dependent on oncologists' experience. To construct and evaluate a deep learning-based automatic delineation method using prior knowledge in multimodal medical imaging to automate precise GTV, CTV1 and CTV2 contouring in GBM patients. MATERIALS/METHODS We retrospectively collected the CT and MRI scans of 55 eligible patients with histologically proven high-grade glioma (HGG) from an institute, these scans were performed with non-enhanced CT (CT), contrast-enhanced T1-weighted (T1C) and T2-FLAIR (T2F) sequences. We proposed a two-stage automatic segmentation framework (PKMI-Net) for GTV, CTV1 and CTV2 based on deep learning using prior knowledge in multimodal medical imaging, and its segmentation performance was evaluated with dice similarity coefficient (DSC), 95% Harsdorff distance (HD95), average surface distance (ASD) and relative volume difference (RVD). To further investigate the generalizability of our method, we designed and conducted two evaluation strategies (Mix and Cross) on four multicenter datasets (including 55 patients, 37 patients, 21 patients and 35 patients). RESULTS The evaluation results with an 11-patient test set from the single institute were summarized in Table 1, the proposed method demonstrated the best accuracy in segmenting, respectively, GTV, CTV1 and CTV, achieving a DSC of 0.94, 0.95 and 0.92; HD95 of 2.07 mm, 1.18 mm and 3.80 mm; ASD of 0.69 mm, 0.39 mm and 1.13 mm and RVE of 5.50%, 3.97% and 9.68%. In the multicenter evaluation, the segmentation performance of our method implemented with the Cross strategy was comparable to that with the Mix strategy, demonstrating that our method had high and stable generalizability across multicenter datasets in automatically segmenting GTV, CTV1 and CTV2. CONCLUSION Our proposed method achieved promising results in automatically segmenting gliomas across various datasets, which could improve the quality and efficiency of glioblastoma radiotherapy.
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Zhang W, Traneus E, Lin Y, Gan GN, Chen RC, Gao H. Virtual-Collimator Based Spatial Dose Modulation for Proton GRID Therapy. Int J Radiat Oncol Biol Phys 2023; 117:e747. [PMID: 37786164 DOI: 10.1016/j.ijrobp.2023.06.2287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Compared to conventional proton therapy, the proton GRID therapy can substantially improve normal tissue protection (with the delivery of spatially-modulated peak-valley dose pattern to normal tissues) while maintaining the tumor control efficacy (with the delivery of uniform dose pattern to tumor targets). The realization of proton GRID often relies on the use of physical collimators to shape the spatial dose distribution. However, the physical collimator may increase neutron dose, decrease delivery efficiency, and limit the freedom for patient positioning. Here we propose a virtual-collimator (VC) method for proton GRID. This new approach can generate peak-to-valley pattern with high peak-to-valley dose ratio (PVDR), without using a physical collimator. MATERIALS/METHODS The principle behind the VC method to modulate the spatial dose distribution consists of two major steps: (1) the primary beam is essentially halved, i.e., the beamlets are interleaved, so that the organ-at-risk (OAR) plane has the peak-valley dose pattern, while the target plane also has the valley dose; (2) the complementary beam is added with half complementary beamlets to fill in the previously valley-dose positions at the target plane, so that the target dose is uniform, while on the other hand, the complementary beam is angled slightly from the primary beam, so that the OAR still has the peak-valley dose pattern. Moreover, on top of VC, we also utilize sparsity regularization method using total variation and L1 sparsity (TVL1) to further jointly optimize PVDR and dose objectives, namely VC-TVL1. RESULTS VC and VC-TVL1 were validated in comparison with conventional proton GRID treatment planning method via IMPT ("CONV") and TVL1-based proton GRID treatment planning method without VC ("TVL1"), for a prostate case with single-beam (270° only) or two-beam (90° and 270°) scenarios. As shown in the table, the results show that VC can indeed modulate spatial dose with higher PVDR than CONV or even TVL1. VC had higher spatial modulation frequency with smaller peak-to-peak distance than TVL1. Moreover, VC+TVL1, as the synergy of VC and TVL1, further improved PVDR from VC or TVL1 alone. CONCLUSION A new way to deliver proton GRID therapy without a physical collimator is developed using the VC method. The VC method can be synergized with TVL1 optimization algorithm to further jointly optimize PVDR and dose objectives.
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Qi W, Li S, Xiao J, Zhang W, Mo Z, He SM, Li H, Chen J, Zhao S. Prediction of Response to Neoadjuvant Chemoradiotherapy Combined with Pembrolizumab in Esophageal Squamous Cell Carcinoma with CT/FDG PET Radiomic Signatures Based on Machine Learning Classification. Int J Radiat Oncol Biol Phys 2023; 117:e358-e359. [PMID: 37785233 DOI: 10.1016/j.ijrobp.2023.06.2443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) PALACE-1 trial has confirm that the addition of pembrolizumab to neoadjuvant chemoradiotherapy (NCRT) improves the pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC), which might be a novel treatment strategy for ESCC. In the present study, we aim to establish a machine learning model to predict the local response to NCRT+ pembrolizumab for ESCC by using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) and contrast-enhanced plan CT images. MATERIALS/METHODS A total of 65 cases treated with NCRT+ pembrolizumab followed by surgery were prospectively enrolled for analysis from 2019-2022. Each patient contains a contrast-enhanced plan CT and FDG PET images. 52 patients were randomly divided into training set and 13 patients were used as test set. The Extraction of radiomics features was performed using an open-source Python library PyRadiomics automatically. Features were computed according to the radiologist-drawn ROIs on both CT and PET images. In the feature selection stage least absolute shrinkage and selection operator (LASSO) was utilized on CT features and PET features separately. Four different machine learning models were implemented: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and XGBoost (XGB). The features selected by LASSO regression were used as model input and the output of the model is "pCR" or "non-pCR". To find the optimal parameter, the 5-fold cross-validation method was used in the training stage. In this study, we use accuracy, sensitivity and specificity as the metrics to evaluate the performance of the model on the testing cohort. The predictive performance of the model was assessed using the area under curve (AUC) of the receiver operating characteristics curve (ROC). RESULTS Of the 65 cases treated with NCRT+pembrolizumab, 35 patients archived pCR (53.8%), and 30 archived non-pCR. 1684 radiomics features were extracted from each case, and half of them (842 features) were from CT and others were from PET. Among the machine learning models mentioned above SVM achieves the most promising performance on the evaluation metrics. Accuracy, sensitivity, specificity and AUC score on test set were 0.692, 0.833, 0.571 and 0.786 for CT features and 0.615, 0.667, 0.571 and 0.762 for PET features, respectively. For CT+FDG PET fused features accuracy, sensitivity, specificity and AUC score on test set were 0.769, 0.667, 0.857 and 0.833. CONCLUSION In this study, we performed several different machine learning models to predict the response to NCRT+ pembrolizumab among ESCC based on the extracted radiomics features from CT and FDG PET images. The best-performing model based on radiomics features of CT and PET images could identify non-pCR to NCRT + pembrolizumab in EC patients.
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Yang YX, Zhou GQ, Lin L, Jiang X, Yang X, Cai W, He SM, Li H, Jia LC, Zhang W, Zhou J, Sun Y. Dosimetric Benefits of Online Adaptive Radiotherapy in Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e635-e636. [PMID: 37785896 DOI: 10.1016/j.ijrobp.2023.06.2038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Online adaptive radiotherapy (ART) has the advantage of compensating for potential underdosing to targets and overdosing to organs-at-risk (OARs) caused by variations in patient anatomy and tumor geometry. Artificial intelligence (AI)-assisted rapid generation of new plans makes online ART possible. We aimed to evaluate the dosimetric benefits of online ART on tumor coverage and OARs sparing in nasopharyngeal carcinoma (NPC). MATERIALS/METHODS Twenty patients diagnosed with NPC (19 with stage III and 1 with stage II according to the 8th edition of the AJCC/UICC staging system) who underwent definitive radiotherapy or concurrent chemoradiotherapy and received online ART on CT-Linac between April 2022 and December 2022 were included in this study, consisting of 14 males and 6 females with a median age of 48 years (range: 29-68 years). The prescription dose was 6996 cGy/33 fractions for primary gross tumor volume (GTVp), 6600-6996 cGy/33 fractions for gross tumor volume of nodes (GTVn), 6006 cGy/33 fractions for high-risk clinical tumor volume (CTV1), 5412 cGy/33 fractions for low-risk clinical tumor volume (CTV2). The majority of the patients (15/20) received online ART during the fourth to fifth week of their radiotherapy treatment The auto-segmented contours and auto-plan generated by AI were manually reviewed and edited by radiotherapists and physicists. The paired samples t-test was used to compare the dose and volumes metrics of targets and OARs between scheduled plan and online ART plan. RESULTS The results of this study showed that compared to the scheduled plan, the online ART plan resulted in significant reductions in the volumes of all targets and 8/12 OARs (temporal lobes, optic nerves, lenses, eyes, parotids, submandibulars, mandibles, and thyroid) (P<0.05). The online ART plan also improved target coverage, with D98% for GTVp in the scheduled plan compared to the online ART plan being 7063.4 ± 76.1 cGy and 7096.1 ± 53.9 cGy (P = 0.1), CTV1 being 6266.7 ± 114.9 cGy and 6208.7 ± 54.7 cGy (P<0.05), and CTV2 being 4142.5 ± 1700.9 cGy and 5416.4 ± 23.8 cGy (P<0.01), respectively. The dose to all 12 OARs was reduced with the use of online ART, with 5/12 OARs showing statistical significance. The D0.03cm3 for the spinal cord in the scheduled plan and online ART plan were 3630.9 ± 197.6 and 3454.1 ± 132.0 cGy; for the temporal lobes were 7075.2 ± 303.0 and 6994.2 ± 345.1 cGy; and 4396.0 ± 2575.0 and for the pituitary were 4214.5 ± 2499.2 cGy. Meanwhile the Dmean for the eyes in the scheduled plan and online ART plan was 769.0 ± 232.0 and 714.8 ± 200.1 cGy; and for the mandibles were 3187.7 ± 211.5 and 3066.0 ± 152.1 cGy. CONCLUSION Online ART was effective in protecting most of the OARs in NPC patients, while simultaneously indicating a trend towards enhancing target coverage. This study demonstrated the promising potential of online ART for patients with NPC. This approach will be tested in an upcoming phase III trial.
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Zhang Y, Hu D, Li W, Zhang W, Chen RC, Chen Y, Gao H. 2V-CBCT: Two-Orthogonal-Projection Based CBCT Reconstruction and Dose Calculation from Real CBCT Projection Data. Int J Radiat Oncol Biol Phys 2023; 117:e748. [PMID: 37786167 DOI: 10.1016/j.ijrobp.2023.06.2289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Not all radiation therapy (RT) treatments/fractions have CBCT acquired, but two orthogonal projections (i.e., KV radiography) are always available. This work demonstrates the feasibility of two-orthogonal-projection-based CBCT (2V-CBCT) reconstruction and dose calculation for RT from real CBCT projection data, which is the first 2V-CBCT feasibility study using real projection data, to the best of our knowledge. MATERIALS/METHODS 2V-CBCT is a severely ill-posed inverse problem for which we propose a coarse-to-fine learning strategy. First, a 3D deep neural network that can extract and exploit the inter-slice and intra-slice information is adopted to predict the initial 3D volumes. Then, a 2D deep neural network is utilized to fine-tune the initial 3D volumes slice-by-slice. During the fine-tuning stage, a perceptual loss based on multi-frequency features is employed to enhance the image reconstruction. Dose calculation results from both photon and proton RT demonstrate that 2V-CBCT provides comparable accuracy with full-view CBCT based on real projection data. RESULTS The proposed method was evaluated on real HN data acquired from on-board CBCT scanners rather than the low-resolution simulated data or down-sampled data. Both visual assessment and quantitative analysis demonstrate that the proposed coarse-to-fine learning strategy has the potential to produce satisfactory volumetric images from two orthogonal projections. Furthermore, we assessed the utility of 2V-CBCT in RT. The results show that the dose distribution maps, dose-volume histograms, and dose parameters calculated using 2V-CBCT have comparable accuracy with the counterparts calculated using the corresponding full-view CBCT for both photon and proton RT. In the table, the methods under comparison are pCT (planning CT), FV-CBCT (CBCT reconstructed with full-view projection data), and 2V-CBCT (CBCT reconstructed with two orthogonal projections). CONCLUSION A new effective 2V-CBCT reconstruction method is proposed and validated using real CBCT projection data, which can potentially provide comparable dose calculation accuracy for both photon and proton RT.
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Zhang W, Zhang L, Dong Q, Wang X, Li Z, Wang Q. Hsa_circ_0003928 regulates the progression of diabetic nephropathy through miR-136-5p/PAQR3 axis. J Endocrinol Invest 2023; 46:2103-2114. [PMID: 37017919 DOI: 10.1007/s40618-023-02061-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/06/2023] [Indexed: 04/06/2023]
Abstract
BACKGROUND Diabetic nephropathy (DN) is one of the complications of diabetes and has a high mortality, but its specific pathogenesis is not clear. In recent years, researches on the mechanism of circRNAs in DN have been proved a lot, whereas the functional mechanism of circ_0003928 in DN remains open and it must be investigated to value its important role in DN prevention. METHODS HK-2 cells were treated with high glucose (HG), normal glucose (NG) or Mannitol. Cell counting kit-8 (CCK8) and 5-ethynyl-2'-deoxyuridine (EdU) assays were performed to detect cell proliferation. Enzyme-linked immunosorbent assay (ELISA) was applied to analyze malondialdehyde (MDA) and superoxide dismutase 1 (SOD) levels. Flow cytometry and western blot were preformed to measure cell apoptosis. Real-time quantitative PCR (RT-qPCR) was used to test the levels of circ_0003928, miR-136-5p and progestin and adipoQ receptor family member 3 (PAQR3) mRNA. Western blot was executed to detect Bcl2 associated X (Bax), B cell leukemia/lymphoma 2 (Bcl2), smooth muscle (αSMA), apolipoprotein (C-IV) and PAQR3 levels. Luciferase reporter assay and RNA pull-down assay were used to analyze the target relationship between miR-136-5p and circ_0003928 or PAQR3. RESULTS Circ_0003928 and PAQR3 expression were up-regulated, whereas miR-136-5p was decreased in DN serum and HG-induced HK-2 cells. Circ_0003928 knockdown promoted cell proliferation, and inhibit cell apoptosis, oxidative stress, and fibrosis in HK-2 cells under HG condition. MiR-136-5p silencing overturned the protective effects of si-circ_0003928 on HG-induced HK-2 cells. MiR-136-5p was targeted by circ_0003928 and directly targeted PAQR3. Overexpression of PAQR3 counteracted the inhibitory functions of circ_0003928 knockdown or miR-136-5p overexpression on HG-induced HK-2 cell injury. CONCLUSION Circ_0003928 acted as a sponge of miR-136-5p to up-regulating PAQR3 expression, and then regulate the proliferation, oxidative stress, fibrosis and apoptosis in HG-induced HK-2 cells.
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Li X, Jia LC, Lin FY, Liu T, He SM, Zhang W, Zhang M, Wang Y. Small Samples and Low-Cost Auto-Segmentation Method for Pelvic Organ-at-Risk Segmentation in Magnetic Resonance Images Using Deep-Learning. Int J Radiat Oncol Biol Phys 2023; 117:e685-e686. [PMID: 37786015 DOI: 10.1016/j.ijrobp.2023.06.2153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In radiotherapy, magnetic resonance (MR) imaging has higher contrast of soft tissue, and no radiation compared with computed tomography (CT) scanning. Due to the high-cost of manual annotation, the deep-learning based automatic organ-at-risk (OAR) and target delineation algorithms are in high-demand, but the collecting of large amounts of high-quality annotated datasets remains difficulty. In this paper, we proposed a low-cost OAR segmentation method with semi-supervised annotation using small annotation samples of pelvic MR images. MATERIALS/METHODS This study consisted of 94 patients diagnosed with rectal cancer from April 2018 to March 2021 at Peking University People's Hospital. We used 17 slices of MR images with annotation and 78 slices without annotation to train a deep-learning based segmentation model. The bladder, femoral heads, rectum and small intestine were selected as OAR. Semi-supervised method and ensemble learning were used for generating training set using small sample with annotation. Post-processing algorithm was used to correct the self-annotation data. Two of 14 annotation samples were set as test set. As for un-labeled images, 40 of them were set as semi-supervised annotation train set, the rest were test set. Besides, both 2D and 3D auto-segmentation networks were evaluated. RESULTS The dice of bladder, femoral head left and right, rectum and small intestine between segmentation results and reference masks is 0.947, 0.983, 0.981, 0.900, 0.845 only using self-annotation and post-processing method of 2D segmentation model. And the dice of corresponding OAR is 0.871, 0.975, 0.975, 0.783, 0.724 using 3D segmentation network, 0.885,0.982, 0.982, 0.882, 0,814 using 2D segmentation network with supervised method (nnUNet). The 2D model outperformed 3D model with better segmentation performance, shorter inference time and fewer parameters. CONCLUSION The results proved that we can train a multi-OAR segmentation model only using small annotation samples and other unlabeled samples. Ensemble learning and post-processing methods are necessary for semi-supervised data annotation. For anisotropy data, 2D model shows better performance than 3D models.
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Zhang W, Ma Y, Ibrahim G, Qi X, Zhou Q. Unsupervised Domain Adaptation of Auto-Segmentation on Multi-Source MRIs. Int J Radiat Oncol Biol Phys 2023; 117:e497. [PMID: 37785564 DOI: 10.1016/j.ijrobp.2023.06.1736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Deep learning has achieved great success in medical image segmentation. Most existing deep learning (DL) approaches make no adjustments to the model prior to inference. These models can perform well on the data of the same distribution, but their performance usually degrades when applied to the images from different source, i.e., different scanners. To tackle the problem caused by domain shift, we proposed an unsupervised domain adaptation (UDA) method based on entropy minimization and physical consistency constraints. MATERIALS/METHODS The proposed method combines feature-level and instance-level domain adaptation techniques to transfer knowledge from the source to the target domain. Specifically, the feature-level adaptation technique uses a graph-based entropy minimization to reduce the discrepancy between the source and target domains. The instance-level adaptation technique employs a novel consistency loss to regularize the physical consistency of the same object, such as volume, length, and centroid, thus improving the segmentation accuracy of the target domain. A collection of 93 abdominal MR images, comprising 45 cases from a 0.35T MRI scanner (TRUFI) and 48 cases from a 1.5T MRI scanner (T2), was utilized to evaluate the effectiveness of the proposed method. The contours of 6 organs-at-risk were delineated by a senior radiation oncologist, serving as the ground truth. Three models, the source model (SRC) trained on the source domain, the target model (TGT) trained on the target domain, and the UDA model adapted from the source domain to the target domain, were compared on the target domain using the Dice Similarity Coefficient (DSC). RESULTS In the experiment of 0.35T-to-1.5T, the proposed UDA method outperformed the source model, achieving an average DSC score of 0.82 ± 0.11, compared to 0.58 ± 0.23 (SRC) and 0.85 ± 0.09 (TGT), respectively. In the inverse experiment 1.5T-to-0.35T, the UDA model achieved an average DSC score of 0.79±0.13, compared to DSCs of 0.52 ± 0.25 and 0.81 ± 0.11 for the SRC and TGT respectively. The UDA method yielded a significant improvement of 46%, compared to the SRC. Particularly, OARs (organ at risk) with higher deformability such as the stomach and duodenum achieved a 58% and 63% improvement in performance, respectively. CONCLUSION This work presents a compelling approach of UDA for auto-segmentation on multi-source MRIs. Experimental results demonstrate that the UDA effectively improve the segmentation performance of the source model in the target domain, resulting in a more robust segmentation model.
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Zhang W, Lin Y, Wang F, Badkul RK, Chen RC, Gao H. Vertex Position Optimization for LATTICE Therapy. Int J Radiat Oncol Biol Phys 2023; 117:e747. [PMID: 37786165 DOI: 10.1016/j.ijrobp.2023.06.2288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) LATTICE radiation therapy (RT) aims to deliver 3D heterogenous dose of high peak-to-valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. In current clinical practice the lattice vertex positions are constant during treatment planning. This work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions as plan variables, which is the first lattice vertex position optimization study to the best of our knowledge. MATERIALS/METHODS The new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and organs-at-risk (OAR) sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto the lattice vertex positions. The lattice vertex position optimization problem is solved by iterative convex relaxation method, where lattice vertex positions and photon/proton plan variables are jointly updated via the Quasi-Newton method. RESULTS Both photon and proton LATTICE RT were considered, and the optimal lattice vertex positions in terms of plan objectives were found by solving all possible combinations on given discrete positions via heuristic searching based on standard IMRT/IMPT, which served as the ground truth for validating the new LATTICE method ("NEW"). That is, the plan with the smallest optimization objective ("BEST"), the plan with the median optimization objective ("MID"), and the plan with the largest optimization objective ("WORST") were selected as the reference plans to be compared with NEW. The table was for an abdomen case with the large bowel as the OAR, where the parameters are total optimization objective f, the mean valley dose of target Dvalley, the mean peak dose of target Dpeak, PVDR = Dpeak/Dvalley, and the mean dose of large bowel Dbowel. The unit of doses is Gy. The results in the table show that the new method indeed provided the optimal lattice vertex positions with the smallest optimization objective, the largest target PVDR, and the best OAR sparing. CONCLUSION A new LATTICE treatment planning method is proposed and validated that can optimize lattice vertex positions as well as other photon or proton plan variables for improving target PVDR and OAR sparing.
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Wang J, Liu R, Ma H, Zhang W. The Pathogenesis of COVID-19-Related Taste Disorder and Treatments. J Dent Res 2023; 102:1191-1198. [PMID: 37729625 DOI: 10.1177/00220345231182926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
Abstract
COVID-19, mainly manifested as acute respiratory distress syndrome, has afflicted millions of people worldwide since 2019. Taste dysfunction is a common early-stage symptom of COVID-19 infection that burdens patients for weeks or even permanently in some cases. Owing to its subjectivity and complexity, the mechanism of taste disorder is poorly studied. Previous studies have reported that the COVID-19 entry receptors are highly expressed in taste buds, thereby intensifying the cytocidal effect. Taste receptor cells are vulnerable to inflammation, and the COVID-19-induced cytokine storm causes secondary damage to taste function. Interferon and various proinflammatory cytokines can trigger cell apoptosis and disrupt the renewal of taste bud stem cells. This immune response can be further enhanced by the accumulation of Angiotensin II (Ang II) caused by an unbalanced local renin-angiotensin system (RAS) system. In addition, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is neurotropic and can invade the brain through the olfactory bulb, affecting the nervous system. Other factors, such as host zinc deficiency, genetic susceptibility, sialic acid, and some neurotransmitters, also contribute to the pathogenesis process. Although several medical interventions have displayed effectiveness, only a few strategies exist for the treatment of postinfectious dysgeusia. Stem cell-based taste regeneration offers promise for long-term taste disorders. Clinical studies have demonstrated that stem cells can treat long COVID-19 through immune regulation. In dysgeusia, the differentiation of taste bud stem cells can be stimulated through exogenous epithelial-derived and neural-derived factors to regenerate taste buds. Tongue organoids are also emerging as functional taste buds, offering new insights into the study of taste regeneration. This review presents the current evidence of the pathogenesis of COVID-19-related dysgeusia, summarizes currently available treatments, and suggests future directions of taste regeneration therapy.
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Fei YY, Liu YY, Dong LL, Xiang Y, Zhang W, Zhao Y. [Recommendations for the diagnosis and treatment of IgG 4-related disease in China]. ZHONGHUA NEI KE ZA ZHI 2023; 62:1161-1171. [PMID: 37766434 DOI: 10.3760/cma.j.cn112138-20221105-00830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
IgG4-related disease (IgG4-RD) is an immune-mediated fibroinflammatory condition characterized by tumefactive lesions in multi-organs. It is a novel entity presented by variable manifestations. In recent years, there has been progress toward recognizing IgG4-RD. However, the diagnosis and treatment of IgG4-RD still present challenges due to insufficient experience. To address this, the Chinese Rheumatology Association has developed standardized guidelines for the diagnosis and treatment of IgG4-RD based on domestic and international experience. These guidelines aim to enhance the understanding and management of IgG4-RD, ultimately improving the prognosis for patients with IgG4-RD.
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Lin L, Peng P, Zhou GQ, Huang SM, Hu J, Liu Y, He SM, Sun Y, Zhang W. Deep Learning-Based Synthesis of Contrast-Enhanced MRI for Automated Delineation of Primary Gross Tumor Volume in Radiotherapy of Nasopharyngeal Carcinoma. Int J Radiat Oncol Biol Phys 2023; 117:e475. [PMID: 37785507 DOI: 10.1016/j.ijrobp.2023.06.1687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Contrast-enhanced MRIs are necessary to delineate the primary gross tumor volume (GTVp) in radiotherapy of nasopharyngeal carcinoma (NPC). However, using contrast agents to scan contrast-enhanced MRIs is not applicable to some patients due to metal implants or their allergy, and it increases the treatment cost of patients. To address these problems, this work aims at synthesizing contrast-enhance MRIs from unenhanced MRIs by implementing generative adversarial network (GAN). MATERIALS/METHODS In this work, 324 MRI datasets of patients with NPC were retrospectively collected between September 2016 and September 2017 from a single institute. MRI examinations were performed with un-enhanced T1-weighted (T1) and T2-weighted (T2) sequences, and contrast-enhanced T1-weighted (T1C) and fat-suppressed T1-weighted (T1FSC) sequences. We designed and developed a modified pix2pix network to synthesize T1C (sT1C) and T1FSC (sT1FSC) from real T1. The end of the generator in this network was assembled with multiple heads (the classification head and gradient head) to learn more representation information and features from real images, the discriminator in this network distinguished whether the synthesized image is real and fake and supervised that the generator outputs more realistic synthesized image. We verified the performance of the synthesized images for automated delineation of GTVp. In an independent testing set of 11 patients, the synthesized sT1C and sT1FSC were inputted into the segmentation deep learning network along with their corresponding T1 and T2 sequences to generate GTVp contours. Delineation performance of the synthesized images and real images for automated delineation were evaluated by dice similarity coefficient (DSC), and average surface distance (ASD), using human expert contours as the ground truth. RESULTS In automated contouring of GTVp for NPC, the segmentation deep learning network using one or two synthesized MRIs showed equivalent performance when compared with the automated contours which generated from four real MRI sequences. Mean DSCs between automated contours by sT1C-replaced or sT1C and sT1FSC-replaced network and ground truth contours were 0.726 ± 0.143 and 0.711 ± 0.157, respectively, slightly inferior to that of contours generated from four real MRI sequences (0.740 ± 0.154, both P >0.05). In terms of mean ASD, there was also no significant difference between automated contours generated from synthesized images and real images (3.056 ± 4.216 mm and 3.537 ± 4.793 mm vs. 3.124 ± 4.637 mm; both P > 0.05). CONCLUSION We proposed an MRI-synthesis method based on GAN and the synthesized contrast-enhanced MRIs performed equivalent as the real contrast-enhanced MRIs in the automated delineation of gross tumor volume for radiotherapy of NPC.
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