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5 results on '"Baosen Zhu"'

1. Association between severity of nonalcoholic fatty liver disease and major adverse cardiovascular events in patients assessed by coronary computed tomography angiography

Author

Rongchao Shi, Xuemei Li, Kui Sun, Fangyuan Liu, Bing Kang, Yilin Wang, Ying Wang, Baosen Zhu, Xinya Zhao, Zhiqiang Liu, and Ximing Wang

Subjects
Nonalcoholic fatty liver disease, Severity, Computed tomography, Coronary computed tomography angiography, Major adverse cardiovascular events, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background The effect of nonalcoholic fatty liver disease (NAFLD) on major adverse cardiovascular events (MACEs) can be influenced by the degree of coronary artery stenosis. However, the association between the severity of NAFLD and MACEs in patients who underwent coronary computed tomography angiography (CCTA) is unclear. Methods A total of 341 NAFLD patients who underwent CCTA were enrolled. The severity of NAFLD was divided into mild NAFLD and moderate-severe NAFLD by abdominal CT results. The degree of coronary artery stenosis was evaluated by using Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Cox regression analysis and Kaplan–Meier analysis were used to assess poor prognosis. Results During the follow-up period, 45 of 341 NAFLD patients (13.20%) who underwent CCTA occurred MACEs. The severity of NAFLD (hazard ratio [HR] = 2.95[1.54–5.66]; p = 0.001) and CAD-RADS categories 3–5 (HR = 16.31[6.34–41.92]; p

Published
2024
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2. Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study

Author

Xinxin Yu, Bing Kang, Pei Nie, Yan Deng, Zixin Liu, Ning Mao, Yahui An, Jingxu Xu, Chencui Huang, Yong Huang, Yonggao Zhang, Yang Hou, Longjiang Zhang, Zhanguo Sun, Baosen Zhu, Rongchao Shi, Shuai Zhang, Cong Sun, Ximing Wang, Ting Gao, and Xiuyuan Hao

Subjects
Medicine
Abstract

Abstract. Background:. Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. Methods:. In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. Results:. A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94–0.99), 0.93 (95% CI: 0.85–0.98), and 0.94 (95% CI: 0.87–0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63–0.83), 0.72 (95% CI: 0.62–0.82), and 0.73 (95% CI: 0.62–0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model (P

Published
2023
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3. MRI-based radiomics nomogram for differentiation of solitary metastasis and solitary primary tumor in the spine

Author

Sha Li, Xinxin Yu, Rongchao Shi, Baosen Zhu, Ran Zhang, Bing Kang, Fangyuan Liu, Shuai Zhang, and Ximing Wang

Subjects
Spinal tumor, Solitary spinal metastasis, Nomogram, Radiomics, Magnetic resonance imaging, Medical technology, R855-855.5
Abstract

Abstract Background Differentiating between solitary spinal metastasis (SSM) and solitary primary spinal tumor (SPST) is essential for treatment decisions and prognosis. The aim of this study was to develop and validate an MRI-based radiomics nomogram for discriminating SSM from SPST. Methods One hundred and thirty-five patients with solitary spinal tumors were retrospectively studied and the data set was divided into two groups: a training set (n = 98) and a validation set (n = 37). Demographics and MRI characteristic features were evaluated to build a clinical factors model. Radiomics features were extracted from sagittal T1-weighted and fat-saturated T2-weighted images, and a radiomics signature model was constructed. A radiomics nomogram was established by combining radiomics features and significant clinical factors. The diagnostic performance of the three models was evaluated using receiver operator characteristic (ROC) curves on the training and validation sets. The Hosmer–Lemeshow test was performed to assess the calibration capability of radiomics nomogram, and we used decision curve analysis (DCA) to estimate the clinical usefulness. Results The age, signal, and boundaries were used to construct the clinical factors model. Twenty-six features from MR images were used to build the radiomics signature. The radiomics nomogram achieved good performance for differentiating SSM from SPST with an area under the curve (AUC) of 0.980 in the training set and an AUC of 0.924 in the validation set. The Hosmer–Lemeshow test and decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. Conclusions A radiomics nomogram as a noninvasive diagnostic method, which combines radiomics features and clinical factors, is helpful in distinguishing between SSM and SPST.

Published
2023
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4. Pneumonic-type invasive mucinous adenocarcinoma and infectious pneumonia: clinical and CT imaging analysis from multiple centers

Author

Shuai Zhang, Xinxin Yu, Yong Huang, Pei Nie, Yan Deng, Ning Mao, Sha Li, Baosen Zhu, Li Wang, Bo Wang, and Ximing Wang

Subjects
Invasive mucinous adenocarcinoma, Infectious pneumonia, Computed tomography, Lung disease, Diseases of the respiratory system, RC705-779
Abstract

Abstract Background Pneumonic-type invasive mucinous adenocarcinoma (IMA) was often misdiagnosed as pneumonia in clinic. However, the treatment of these two diseases is different. Methods A total of 341 patients with pneumonic-type IMA (n = 134) and infectious pneumonia (n = 207) were retrospectively enrolled from January 2017 to January 2022 at six centers. Detailed clinical and CT imaging characteristics of two groups were analyzed and the characteristics between the two groups were compared by χ2 test and Student’s t test. The multivariate logistic regression analysis was performed to identify independent predictors. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of different variables. Results A significant difference was found in age, fever, no symptoms, elevation of white blood cell count and C-reactive protein level, family history of cancer, air bronchogram, interlobular fissure bulging, satellite lesions, and CT attenuation value (all p

Published
2022
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5. Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for 13 Cancer Types

Author

Sampson, Joshua N. Wheeler, William A. Yeager, Meredith and Panagiotou, Orestis Wang, Zhaoming Berndt, Sonja I. Lan, Qing Abnet, Christian C. Amundadottir, Laufey T. Figueroa, Jonine D. Landi, Maria Teresa Mirabello, Lisa Savage, Sharon A. Taylor, Philip R. De Vivo, Immaculata McGlynn, Katherine A. Purdue, Mark P. Rajaraman, Preetha Adami, Hans-Olov and Ahlbom, Anders Albanes, Demetrius Amary, Maria Fernanda An, She-Juan Andersson, Ulrika Andriole, Jr., Gerald Andrulis, Irene L. Angelucci, Emanuele Ansell, Stephen M. Arici, Cecilia Armstrong, Bruce K. Arslan, Alan A. Austin, Melissa A. Baris, Dalsu Barkauskas, Donald A. Bassig, Bryan A. and Becker, Nikolaus Benavente, Yolanda Benhamou, Simone Berg, Christine Van Den Berg, David Bernstein, Leslie Bertrand, Kimberly A. Birmann, Brenda M. Black, Amanda Boeing, Heiner and Boffetta, Paolo Boutron-Ruault, Marie-Christine Bracci, Paige M. Brinton, Louise Brooks-Wilson, Angela R. and Bueno-de-Mesquita, H. Bas Burdett, Laurie Buring, Julie and Butler, Mary Ann Cai, Qiuyin Cancel-Tassin, Geraldine and Canzian, Federico Carrato, Alfredo Carreon, Tania Carta, Angela Chan, John K. C. Chang, Ellen T. Chang, Gee-Chen and Chang, I-Shou Chang, Jiang Chang-Claude, Jenny Chen, Chien-Jen Chen, Chih-Yi Chen, Chu Chen, Chung-Hsing and Chen, Constance Chen, Hongyan Chen, Kexin Chen, Kuan-Yu and Chen, Kun-Chieh Chen, Ying Chen, Ying-Hsiang Chen, Yi-Song and Chen, Yuh-Min Chien, Li-Hsin Chirlaque, Maria-Dolores and Choi, Jin Eun Choi, Yi Young Chow, Wong-Ho Chung, Charles C. and Clavel, Jacqueline Clavel-Chapelon, Franoise Cocco, Pierluigi Colt, Joanne S. Comperat, Eva Conde, Lucia and Connors, Joseph M. Conti, David Cortessis, Victoria K. and Cotterchio, Michelle Cozen, Wendy Crouch, Simon Crous-Bou, Marta Cussenot, Olivier Davis, Faith G. Ding, Ti Diver, W. Ryan Dorronsoro, Miren Dossus, Laure Duell, Eric J. and Ennas, Maria Grazia Erickson, Ralph L. Feychting, Maria and Flanagan, Adrienne M. Foretova, Lenka Fraumeni, Jr., Joseph F. and Freedman, Neal D. Freeman, Laura E. Beane Fuchs, Charles and Gago-Dominguez, Manuela Gallinger, Steven Gao, Yu-Tang and Gapstur, Susan M. Garcia-Closas, Montserrat Garcia-Closas, Reina and Gascoyne, Randy D. Gastier-Foster, Julie Gaudet, Mia M. and Gaziano, J. Michael Giffen, Carol Giles, Graham G. and Giovannucci, Edward Glimelius, Bengt Goggins, Michael and Gokgoz, Nalan Goldstein, Alisa M. Gorlick, Richard Gross, Myron Grubb, III, Robert Gu, Jian Guan, Peng Gunter, Marc Guo, Huan Habermann, Thomas M. Haiman, Christopher A. and Halai, Dina Hallmans, Goran Hassan, Manal Hattinger, Claudia He, Qincheng He, Xingzhou Helzlsouer, Kathy and Henderson, Brian Henriksson, Roger Hjalgrim, Henrik and Hoffman-Bolton, Judith Hohensee, Chancellor Holford, Theodore R. and Holly, Elizabeth A. Hong, Yun-Chul Hoover, Robert N. and Horn-Ross, Pamela L. Hosain, G. M. Monawar Hosgood, III, H. Dean and Hsiao, Chin-Fu Hu, Nan Hu, Wei Hu, Zhibin Huang, Ming-Shyan Huerta, Jose-Maria Hung, Jen-Yu Hutchinson, Amy and Inskip, Peter D. Jackson, Rebecca D. Jacobs, Eric J. and Jenab, Mazda Jeon, Hyo-Sung Ji, Bu-Tian Jin, Guangfu and Jin, Li Johansen, Christoffer Johnson, Alison Jung, Yoo Jin and Kaaks, Rudolph Kamineni, Aruna Kane, Eleanor Kang, Chang Hyun Karagas, Margaret R. Kelly, Rachel S. Khaw, Kay-Tee and Kim, Christopher Kim, Hee Nam Kim, Jin Hee Kim, Jun Suk and Kim, Yeul Hong Kim, Young Tae Kim, Young-Chul Kitahara, Cari M. Klein, Alison P. Klein, Robert J. Kogevinas, Manolis and Kohno, Takashi Kolonel, Laurence N. Kooperberg, Charles and Kricker, Anne Krogh, Vittorio Kunitoh, Hideo Kurtz, Robert C. Kweon, Sun-Seog LaCroix, Andrea Lawrence, Charles and Lecanda, Fernando Lee, Victor Ho Fun Li, Donghui Li, Haixin and Li, Jihua Li, Yao-Jen Li, Yuqing Liao, Linda M. and Liebow, Mark Lightfoot, Tracy Lim, Wei-Yen Lin, Chien-Chung and Lin, Dongxin Lindstrom, Sara Linet, Martha S. Link, Brian K. Liu, Chenwei Liu, Jianjun Liu, Li Ljungberg, Boerje Lloreta, Josep Di Lollo, Simonetta Lu, Daru Lund, Eiluv Malats, Nuria Mannisto, Satu Le Marchand, Loic and Marina, Neyssa Masala, Giovanna Mastrangelo, Giuseppe and Matsuo, Keitaro Maynadie, Marc Mckay, James McKean-Cowdin, Roberta Melbye, Mads Melin, Beatrice S. Michaud, Dominique S. Mitsudomi, Tetsuya Monnereau, Alain Montalvan, Rebecca and Moore, Lee E. Mortensen, Lotte Maxild Nieters, Alexandra and North, Kari E. Novak, Anne J. Oberg, Ann L. Offit, Kenneth and Oh, In-Jae Olson, Sara H. Palli, Domenico Pao, William and Park, In Kyu Park, Jae Yong Park, Kyong Hwa and Patino-Garcia, Ana Pavanello, Sofia Peeters, Petra H. M. and Perng, Reury-Perng Peters, Ulrike Petersen, Gloria M. Picci, Piero Pike, Malcolm C. Porru, Stefano Prescott, Jennifer and Prokunina-Olsson, Ludmila Qian, Biyun Qiao, You-Lin Rais, Marco Riboli, Elio Riby, Jacques Risch, Harvey A. and Rizzato, Cosmeri Rodabough, Rebecca Roman, Eve Roupret, Morgan Ruder, Avima M. de Sanjose, Silvia Scelo, Ghislaine and Schned, Alan Schumacher, Fredrick Schwartz, Kendra and Schwenn, Molly Scotlandi, Katia Seow, Adeline Serra, Consol and Serra, Massimo Sesso, Howard D. Setiawan, Veronica Wendy and Severi, Gianluca Severson, Richard K. Shanafelt, Tait D. and Shen, Hongbing Shen, Wei Shin, Min-Ho Shiraishi, Kouya and Shu, Xiao-Ou Siddiq, Afshan Sierrasesumaga, Luis Sihoe, Alan Dart Loon Skibola, Christine F. Smith, Alex Smith, Martyn T. and Southey, Melissa C. Spinelli, John J. Staines, Anthony and Stampfer, Meir Stern, Marianna C. Stevens, Victoria L. and Stolzenberg-Solomon, Rachael S. Su, Jian Su, Wu-Chou Sund, Malin Sung, Jae Sook Sung, Sook Whan Tan, Wen Tang, Wei and Tardon, Adonina Thomas, David Thompson, Carrie A. and Tinker, Lesley F. Tirabosco, Roberto Tjonneland, Anne and Travis, Ruth C. Trichopoulos, Dimitrios Tsai, Fang-Yu Tsai, Ying-Huang Tucker, Margaret Turner, Jenny Vajdic, Claire M. and Vermeulen, Roel C. H. Villano, Danylo J. Vineis, Paolo and Virtamo, Jarmo Visvanathan, Kala Wactawski-Wende, Jean Wang, Chaoyu Wang, Chih-Liang Wang, Jiu-Cun Wang, Junwen Wei, Fusheng Weiderpass, Elisabete Weiner, George J. Weinstein, Stephanie Wentzensen, Nicolas White, Emily Witzig, Thomas E. and Wolpin, Brian M. Wong, Maria Pik Wu, Chen Wu, Guoping and Wu, Junjie Wu, Tangchun Wu, Wei Wu, Xifeng Wu, Yi-Long Wunder, Jay S. Xiang, Yong-Bing Xu, Jun Xu, Ping and Yang, Pan-Chyr Yang, Tsung-Ying Ye, Yuanqing Yin, Zhihua and Yokota, Jun Yoon, Ho-Il Yu, Chong-Jen Yu, Herbert and Yu, Kai Yuan, Jian-Min Zelenetz, Andrew Zeleniuch-Jacquotte, Anne Zhang, Xu-Chao Zhang, Yawei Zhao, Xueying Zhao, Zhenhong Zheng, Hong Zheng, Tongzhang Zheng, Wei Zhou, Baosen Zhu, Meng Zucca, Mariagrazia Boca, Simina M. and Cerhan, James R. Ferri, Giovanni M. Hartge, Patricia Hsiung, Chao Agnes Magnani, Corrado Miligi, Lucia Morton, Lindsay M. and Smedby, Karin E. Teras, Lauren R. Vijai, Joseph Wang, Sophia S. Brennan, Paul Caporaso, Neil E. Hunter, David J. and Kraft, Peter Rothman, Nathaniel Silverman, Debra T. and Slager, Susan L. Chanock, Stephen J. Chatterjee, Nilanjan

Abstract

Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, h(l)(2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (rho = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (rho = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (rho = 0.51, SE = 0.18), and bladder and lung (rho = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. Conclusion: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.

Published
2015

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