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2. Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis

Author

Hunter, Benjamin, Argyros, Christos, Inglese, Marianna, Linton-Reid, Kristofer, Pulzato, Ilaria, Nicholson, Andrew G., Kemp, Samuel V., L. Shah, Pallav, Molyneaux, Philip L., McNamara, Cillian, Burn, Toby, Guilhem, Emily, Mestas Nuñez, Marcos, Hine, Julia, Choraria, Anika, Ratnakumar, Prashanthi, Bloch, Susannah, Jordan, Simon, Padley, Simon, Ridge, Carole A., Robinson, Graham, Robbie, Hasti, Barnett, Joseph, Silva, Mario, Desai, Sujal, Lee, Richard W., Aboagye, Eric O., and Devaraj, Anand

Published
2023
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3. Mycophenolate and azathioprine efficacy in interstitial lung disease: a systematic review and meta-analysis

Author

Sally Singh, Mark Jones, Michael Kreuter, Maria Molina-Molina, Imre Noth, Andrew Wilson, Martin Brutsche, Naftali Kaminski, Gary M Hunninghake, Michael Keane, Nazia Chaudhuri, Ian Forrest, Bruno Crestani, Fernando J Martinez, Mohsen Sadatsafavi, Luca Richeldi, Antje Prasse, Fasihul Khan, Iain Stewart, Steve Jones, Gisli Jenkins, Gauri Saini, David Turner, Killian Hurley, Lucilla Piccari, Andrew Palmer, Joseph A Lasky, Simon Hart, Joyce Lee, Anthony Gordon, John Blaikley, Kirsty Hett, Helmut Prosch, Elisabeth Bendstrup, Christopher Huntley, Helen Parfrey, Huzaifa Adamali, Paul Beirne, Stephen Bianchi, George Chalmers, Sophie Fletcher, Peter George, Michael Gibbons, Mark Spears, Laura Fabbri, Felix Chua, Michael Henry, Cormac McCarthy, Sabrina Paganoni, Joseph Jacob, Mark Toshner, Bibek Gooptu, Andrew Briggs, Philip L Molyneaux, Athol Wells, Charlotte Summers, Leticia Kawano-Dourado, Ian Glaspole, Melanie Quintana, Christopher J Ryerson, Paolo Spagnolo, Francesco Bonella, Carisi Anne Polanczyk, Anjali Crawshaw, Laurence Pearmain, Avinash Anil Nair, Raphaël Borie, Alexandre Biasi Cavalcanti, Emanuela Falaschetti, Jonathan Chung, James Eaden, Kate Johnson, Shaney Barratt, Chris Ryerson, Juergen Behr, Andreas Guenther, Nik Hirani, Karin Storrer, Deepak Talwar, Claudia Ravaglia, Katerina Antoniou, Sara Freitas, Carlo Vancheri, Laura Price, Amanda Goodwin, Daniel Chambers, Gunnar Gudmundsson, Roger Lewis, Ingrid Cox, Anne Holland, Erica Farrand, Argyrios Tzouvelekis, Rui Rolo, Duncan Richards, Larissa Schwarzkopf, Sabina Guler, Devesh Dhasmana, Claudia Valenzuela, John S Kim, Louise Crowley, Lisa Watson, Amanda Bravery, Elisabetta Balestro, Wendy Adams, Francesco Lombardi, Ali Mojibian, Ana Etges, Ana Sousa Marcelino Boshoff, Anne Bergeron Anna-MariaHoffmann-Vold, Athina Trachalaki, Barbara Wendelberger, Bhavika Kaul Ben Hope-Gill, Bruno Baldi, Carlos Robalo, Chris Grainge, Christophe von Garnier, Conal Hayton, Dapeng Wang, Daphne Bablis, David Thicket, Deji Adegunsoye, Devaraj Anand, Dhruv Parek, Diane Griffiths, Eliana Santucci, Eliza Tsitoura, Emma Karlsen, Ena Gupta, Harold Collard, Hernan Fainberg, Iazsmin Bauer-Ventura, Irina Strambu, Jacobo Sellares, Janet Johnston, Jeff Swigris, Karina Negrelli, Katarzyna Lewandowska, Katrin Hostettler, Kerri Johannson, Liam Galvin, Lisa G. Spencer, Manuela Funke Chambour, Marlies Wijsenbeek-Lourens, Martina Vasakova, Milena Man Iuliu Hatieganu, Nick Weatherley, Ovidiu Fira Mladinescu Victor Babes, Peter Bryce, Pilar Rivera Ortega, Radu Crisan-Dabija, Rahul Maida, Sara Piciucchi, Shama Malik, Simone Dal Corso, and Stefan Stanel

Subjects
Medicine, Diseases of the respiratory system, RC705-779
Abstract

Objectives Mycophenolate mofetil (MMF) and azathioprine (AZA) are immunomodulatory treatments in interstitial lung disease (ILD). This systematic review aimed to evaluate the efficacy of MMF or AZA on pulmonary function in ILD.Design Population included any ILD diagnosis, intervention included MMF or AZA treatment, outcome was delta change from baseline in per cent predicted forced vital capacity (%FVC) and gas transfer (diffusion lung capacity of carbon monoxide, %DLco). The primary endpoint compared outcomes relative to placebo comparator, the secondary endpoint assessed outcomes in treated groups only.Eligibility criteria Randomised controlled trials (RCTs) and prospective observational studies were included. No language restrictions were applied. Retrospective studies and studies with high-dose concomitant steroids were excluded.Data synthesis The systematic search was performed on 9 May. Meta-analyses according to drug and outcome were specified with random effects, I2 evaluated heterogeneity and Grading of Recommendations, Assessment, Development and Evaluation evaluated certainty of evidence. Primary endpoint analysis was restricted to RCT design, secondary endpoint included subgroup analysis according to prospective observational or RCT design.Results A total of 2831 publications were screened, 12 were suitable for quantitative synthesis. Three MMF RCTs were included with no significant effect on the primary endpoints (%FVC 2.94, 95% CI −4.00 to 9.88, I2=79.3%; %DLco −2.03, 95% CI −4.38 to 0.32, I2=0.0%). An overall 2.03% change from baseline in %FVC (95% CI 0.65 to 3.42, I2=0.0%) was observed in MMF, and RCT subgroup summary estimated a 4.42% change from baseline in %DLCO (95% CI 2.05 to 6.79, I2=0.0%). AZA studies were limited. All estimates were considered very low certainty evidence.Conclusions There were limited RCTs of MMF or AZA and their benefit in ILD was of very low certainty. MMF may support preservation of pulmonary function, yet confidence in the effect was weak. To support high certainty evidence, RCTs should be designed to directly assess MMF efficacy in ILD.PROSPERO registration number CRD42023423223.

Published
2024
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4. Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning

Author

Aslani, Shahab, Alluri, Pavan, Gudmundsson, Eyjolfur, Chandy, Edward, McCabe, John, Devaraj, Anand, Horst, Carolyn, Janes, Sam M, Chakkara, Rahul, Nair, Arjun, Alexander, Daniel C, consortium, SUMMIT, and Jacob, Joseph

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to significantly reduce lung cancer mortality by detecting cancerous lung nodules at an earlier stage. Improving risk stratification of malignancy risk in lung nodules can be enhanced using machine/deep learning algorithms. However most existing algorithms: a) have primarily assessed single time-point CT data alone thereby failing to utilize the inherent advantages contained within longitudinal imaging datasets; b) have not integrated into computer models pertinent clinical data that might inform risk prediction; c) have not assessed algorithm performance on the spectrum of nodules that are most challenging for radiologists to interpret and where assistance from analytic tools would be most beneficial. Here we show the performance of our time-series deep learning model (DeepCAD-NLM-L) which integrates multi-model information across three longitudinal data domains: nodule-specific, lung-specific, and clinical demographic data. We compared our time-series deep learning model to a) radiologist performance on CTs from the National Lung Screening Trial enriched with the most challenging nodules for diagnosis; b) a nodule management algorithm from a North London LCS study (SUMMIT). Our model demonstrated comparable and complementary performance to radiologists when interpreting challenging lung nodules and showed improved performance (AUC=88\%) against models utilizing single time-point data only. The results emphasise the importance of time-series, multi-modal analysis when interpreting malignancy risk in LCS.

Published
2022

6. Is MC Dropout Bayesian?

Author

Folgoc, Loic Le, Baltatzis, Vasileios, Desai, Sujal, Devaraj, Anand, Ellis, Sam, Manzanera, Octavio E. Martinez, Nair, Arjun, Qiu, Huaqi, Schnabel, Julia, and Glocker, Ben

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

MC Dropout is a mainstream "free lunch" method in medical imaging for approximate Bayesian computations (ABC). Its appeal is to solve out-of-the-box the daunting task of ABC and uncertainty quantification in Neural Networks (NNs); to fall within the variational inference (VI) framework; and to propose a highly multimodal, faithful predictive posterior. We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact. To address the need for VI on arbitrary models, we share a generic VI engine within the pytorch framework. The code includes a carefully designed implementation of structured (diagonal plus low-rank) multivariate normal variational families, and mixtures thereof. It is intended as a go-to no-free-lunch approach, addressing shortcomings of mean-field VI with an adjustable trade-off between expressivity and computational complexity.

Published
2021

7. Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

Author

Folgoc, Loic Le, Baltatzis, Vasileios, Alansary, Amir, Desai, Sujal, Devaraj, Anand, Ellis, Sam, Manzanera, Octavio E. Martinez, Kanavati, Fahdi, Nair, Arjun, Schnabel, Julia, and Glocker, Ben

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Applications, Statistics - Methodology, Statistics - Machine Learning
Abstract

Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the training dataset, introduced upon collecting data or due to the practioner rebalancing the training batches. This paper lays the theoretical and computational framework for training models, and for prediction, in the presence of prevalence bias. Concretely a bias-corrected loss function, as well as bias-corrected predictive rules, are derived under the principles of Bayesian risk minimization. The loss exhibits a direct connection to the information gain. It offers a principled alternative to heuristic training losses and complements test-time procedures based on selecting an operating point from summary curves. It integrates seamlessly in the current paradigm of (deep) learning using stochastic backpropagation and naturally with Bayesian models.

Published
2021

8. High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study

Author

Anderson, Adam, Morgenthau, Adam, Gaser, Adrian, Vizel, Alexander, Speranskaya, Alexandra, Gerke, Alicia, Goksel, Altinisik, Undurraga, Alvaro, Sharma, Amita, Oh, Andrea, Leung, Ann, Larici, Anna Rita, Prasse, Antje, Mazzei, Antonietta, Morais, Antonio, Bhalla, Ashu, Del Rio, Belen, Jankharia, Bhavin, Elicker, Brett, Pereira, Carlos, Biegelman-Aubry, Catherine, White, Charles, Ravaglia, Claudia, Hsia, Connie, Schaefer-Prokop, Cornelia, Launay, David, Talwar, Deepak, Castillo, Diego, Patel, Divya, Israel-Biet, Dominique, Valeyre, Dominique, Britt, Edward James, Bargagli, Elena, Bendstrup, Elisabeth, Crouser, Elliott, Nossent, Esther, Shmelev, Eugeny, Porquera, Eva Carmona, Bonella, Francesco, Cohen-Aubart, Fleur, Jeny, Florence, Ferrara, Giovanni, Jin, Gong Yong, Robbie, Hasti, Prosch, Helmut, Sumikawa, Hiromitsu, Ling-Pei, Ho, Lee, Ho Yun, Strambu, Irina, Buendia-Roldan, Ivette, Grutters, Jan, Ryu, Jay, Swigris, Jeff, Miedema, Jelle, Goo, Jin Mo, Barnett, Joseph, Verschakelen, Johny, Goldin, Jonathan, Seo, Joon Beom, Enghelmayer, Juan, Mana, Juan, Behr, Juergen, Patterson, Karen, Antoniou, Katerina, Brown, Kevin, Fujimoto, Kiminori, Savale, Laurent, Maier, Lisa, Richeldi, Luca, Neto, Manuel Lessa Ribeiro, Humbert, Marc, Judson, Marc, Veltkamp, Marcel, Wilsher, Margaret, Molina, Maria, Otaola, Maria, Revel, Marie-Pierre, Silva, Mario, Drent, Marjolein, Schiebler, Mark, Wijsenbeek-Lourens, Marlies, Bonifazi, Martina, Remy-Jardin, Martine, Koslow, Matthew, Balter, Meyer, Kreuter, Michael, Thillai, Muhunthan, Hamzeh, Nabeel, Chaudhuri, Nazia, Mogulkoc, Nesrin, Todd, Nevins, Screaton, Nick, Goh, Nicole, Shah, Nirav, Obi, Ogugua, Shlobin, Oksana, Baranova, Olga, Bresser, Paul, Rottoli, Paula, Brillet, Pierre-Yves, Said-Hartley, Qonita, Borie, Raphael, Mostard, Remy, van Zyl-smit, Richard, Kairalla, Ronaldo, Egashira, Ryoko, Boussouar, Samia, Lee, Sang Min, Bhalla, Sanjeev, Tomassetti, Sara, Quadrelli, Silvia, Hart, Simon P, Danoff, Sonye, Rajan, Sujeet, Choi, Sun Mi, Copley, Susan, Kim, Tae Jung, Johkoh, Takeshi, Corte, Tamera, McLoud, Theresa, Wessendorf, Thomas, Alfaro, Tiago, Arai, Toru, Costabel, Ulrich, Vicens-Zygmunt, Vanesa, Poletti, Venerino, Cottin, Vincent, Vucinic, Violeta, Wuyts, Wim, van Es, Wouter, Jeong, Yeon Joo, Inoue, Yoshikazu, Desai, Sujal R, Sivarasan, Nishanth, Johannson, Kerri A, George, Peter M, Culver, Daniel A, Devaraj, Anand, Lynch, David A, Milne, David, Renzoni, Elisabetta, Nunes, Hilario, Sverzellati, Nicola, Spagnolo, Paolo, Baughman, Robert P, Yadav, Ruchi, Piciucchi, Sara, Walsh, Simon L F, Kouranos, Vasileios, and Wells, Athol U

Published
2024
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9. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Author

Gleeson, Fergus, Baldwin, David, Batchkala, George, Buchanan, James, Burchardt, Judith, Chakraborty, Rohan, Chana, Ravi, Chen, Yan, Coupland, Carol, Crichton, Charles, Davies, Jim, Devaraj, Anand, Fan, Mengran, Hippisley-Cox, Julia, Koleva-Kolarova, Rositsa, Lee, Richard, Liao, Weiqi, Nair, Arjun, Pickup, Lyndsey, Powell, Anne, Rittscher, Jens, Shadmaan, Amied, Shanmugam, Kandavel, Stokes, Elizabeth, Verrill, Clare, Watkins, Johnathan, Wordsworth, Sarah, Coupland, Carol A C, Baldwin, David R, and Gleeson, Fergus V

Published
2023
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11. Defining the road map to a UK national lung cancer screening programme

Author

O'Dowd, Emma L, Lee, Richard W, Akram, Ahsan R, Bartlett, Emily C, Bradley, Stephen H, Brain, Kate, Callister, Matthew E J, Chen, Yan, Devaraj, Anand, Eccles, Sinan R, Field, John K, Fox, Jesme, Grundy, Seamus, Janes, Sam M, Ledson, Martin, MacKean, Melanie, Mackie, Anne, McManus, Kieran G, Murray, Rachael L, Nair, Arjun, Quaife, Samantha L, Rintoul, Robert, Stevenson, Anne, Summers, Yvonne, Wilkinson, Louise S, Booton, Richard, Baldwin, David R, and Crosbie, Philip

Published
2023
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12. Uptake of invitations to a lung health check offering low-dose CT lung cancer screening among an ethnically and socioeconomically diverse population at risk of lung cancer in the UK (SUMMIT): a prospective, longitudinal cohort study

Author

Dickson, Jennifer L, Hall, Helen, Horst, Carolyn, Tisi, Sophie, Verghese, Priyam, Mullin, Anne-Marie, Teague, Jon, Farrelly, Laura, Bowyer, Vicky, Gyertson, Kylie, Bojang, Fanta, Levermore, Claire, Anastasiadis, Tania, McCabe, John, Navani, Neal, Nair, Arjun, Devaraj, Anand, Hackshaw, Allan, Quaife, Samantha L, and Janes, Sam M

Published
2023
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17. The impact of hiatus hernia in hypersensitivity pneumonitis

Author

Heriot, David A., primary, Stock, Carmel J. W., additional, Mumtaz, Zain‐Ul‐Abideen, additional, Jenkins, R. Gisli, additional, Chua, Felix, additional, Molyneaux, Phillip L., additional, Devaraj, Anand, additional, Kouranos, Vasilis, additional, Wells, Athol U., additional, Renzoni, Elizabetta A., additional, Padley, Simon P. G., additional, Desai, Sujal R., additional, and George, Peter M., additional

Published
2024
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21. Design of the PF-ILD trial: a double-blind, randomised, placebo-controlled phase III trial of nintedanib in patients with progressive fibrosing interstitial lung disease

Author

Flaherty, Kevin R, Brown, Kevin K, Wells, Athol U, Clerisme-Beaty, Emmanuelle, Collard, Harold R, Cottin, Vincent, Devaraj, Anand, Inoue, Yoshikazu, Le Maulf, Florence, Richeldi, Luca, Schmidt, Hendrik, Walsh, Simon, Mezzanotte, William, and Schlenker-Herceg, Rozsa

Subjects
Lung, Autoimmune Disease, Clinical Trials and Supportive Activities, Clinical Research, Rare Diseases, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Respiratory, Good Health and Well Being, interstitial fibrosis, rare lung diseases
Abstract

600 patients aged ≥18 years will be randomised in a 1:1 ratio to nintedanib or placebo. Patients with diagnosis of IPF will be excluded. The study population will be enriched with two-thirds having a usual interstitial pneumonia-like pattern on HRCT. The primary endpoint is the annual rate of decline in forced vital capacity over 52 weeks. The main secondary endpoints are the absolute change from baseline in King's Brief Interstitial Lung Disease Questionnaire total score, time to first acute interstitial lung disease exacerbation or death and time to all-cause mortality over 52 weeks.Ethics and disseminationThe trial is conducted in accordance with the Declaration of Helsinki, the International Conference on Harmonisation Tripartite Guideline for Good Clinical Practice (GCP) and Japanese GCP regulations.Trial registration numberNCT02999178.

Published
2017

22. High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study.

Author

Desai, Sujal R, Sivarasan, Nishanth, Johannson, Kerri A, George, Peter M, Culver, Daniel A, Devaraj, Anand, Lynch, David A, Milne, David, Renzoni, Elisabetta, Nunes, Hilario, Sverzellati, Nicola, Spagnolo, Paolo, Baughman, Robert P, Yadav, Ruchi, Piciucchi, Sara, Walsh, Simon L F, Kouranos, Vasileios, and Wells, Athol U

Subjects
DELPHI method, SARCOIDOSIS, PHENOTYPES, LIKERT scale, PHYSICIANS
Abstract

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Stage at Diagnosis Following Delay to Interval Scans for Indeterminate Nodules in Lung Cancer Screening: An Observational Study Examining the Outcomes of CHEST Expert Panel Recommendations.

Author

Creamer, Andrew W., Horst, Carolyn, Dickson, Jennifer L., Tisi, Sophie, Hall, Helen, Verghese, Priyam, Prendecki, Ruth, Bhamani, Amyn, Khaw, Chuen R., McCabe, John, Limani, Tanita, Gyertson, Kylie, Hacker, Anne-Marie, Teague, J., Farrelly, L., Dawadi, Shrinkhala, Hackshaw, Allan, Devaraj, Anand, Nair, Arjun, and Janes, Sam M.

Subjects
DELAYED diagnosis, PULMONARY nodules, LUNG cancer, EARLY detection of cancer, SCIENTIFIC observation
Published
2024
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24. Nintedanib in patients with progressive fibrosing interstitial lung diseases—subgroup analyses by interstitial lung disease diagnosis in the INBUILD trial: a randomised, double-blind, placebo-controlled, parallel-group trial

Author

Abe, S., Aburto, M., Acosta, O., Andrews, C., Antin-Ozerkis, D., Arce, G., Arias, M., Avdeev, S., Barczyk, A., Bascom, R., Bazdyrev, E., Beirne, P., Belloli, E., Bergna, M.A., Bergot, E., Bhatt, N., Blaas, S., Bondue, B., Bonella, F., Britt, E., Buch, K., Burk, J., Cai, H., Cantin, A., Castillo Villegas, D.M., Cazaux, A., Cerri, S., Chaaban, S., Chaudhuri, N., Cottin, V., Crestani, B., Criner, G., Dahlqvist, C., Danoff, S., Dematte D'Amico, J., Dilling, D., Elias, P., Ettinger, N., Falk, J., Fernández Pérez, E.R., Gamez-Dubuis, A., Giessel, G., Gifford, A., Glassberg, M., Glazer, C., Golden, J., Gómez Carrera, L., Guiot, J., Hallowell, R., Hayashi, H., Hetzel, J., Hirani, N., Homik, L., Hope-Gill, B., Hotchkin, D., Ichikado, K., Ilkovich, M., Inoue, Y., Izumi, S., Jassem, E., Jones, L., Jouneau, S., Kaner, R., Kang, J., Kawamura, T., Kessler, R., Kim, Y., Kishi, K., Kitamura, H., Kolb, M., Kondoh, Y., Kono, C., Koschel, D., Kreuter, M., Kulkarni, T., Kus, J., Lebargy, F., León Jiménez, A., Luo, Q., Mageto, Y., Maher, T.M., Makino, S., Marchand-Adam, S., Marquette, C., Martinez, R., Martínez, M., Maturana Rozas, R., Miyazaki, Y., Moiseev, S., Molina-Molina, M., Morrison, L., Morrow, L., Moua, T., Nambiar, A., Nishioka, Y., Nunes, H., Okamoto, M., Oldham, J., Otaola, M., Padilla, M., Park, J.S., Patel, N., Pesci, A., Piotrowski, W., Pitts, L., Poonyagariyagorn, H., Prasse, A., Quadrelli, S., Randerath, W., Refini, R., Reynaud-Gaubert, M., Riviere, F., Rodríguez Portal, J.A., Rosas, I., Rossman, M., Safdar, Z., Saito, T., Sakamoto, N., Salinas Fénero, M., Sauleda, J., Schmidt, S., Scholand, M.B., Schwartz, M., Shapera, S., Shlobin, O., Sigal, B., Silva Orellana, A., Skowasch, D., Song, J.W., Stieglitz, S., Stone, H., Strek, M., Suda, T., Sugiura, H., Takahashi, H., Takaya, H., Takeuchi, T., Thavarajah, K., Tolle, L., Tomassetti, S., Tomii, K., Valenzuela, C., Vancheri, C., Varone, F., Veeraraghavan, S., Villar, A., Weigt, S., Wemeau, L., Wuyts, W., Xu, Z., Yakusevich, V., Yamada, Y., Yamauchi, H., Ziora, D., Wells, Athol U, Flaherty, Kevin R, Brown, Kevin K, Inoue, Yoshikazu, Devaraj, Anand, Richeldi, Luca, Moua, Teng, Crestani, Bruno, Wuyts, Wim A, Stowasser, Susanne, Quaresma, Manuel, Goeldner, Rainer-Georg, Schlenker-Herceg, Rozsa, and Kolb, Martin

Published
2020
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26. Overnight desaturation in interstitial lung diseases: links to pulmonary vasculopathy and mortality

Author

Margaritopoulos, George A., primary, Proklou, Athanasia, additional, Trachalaki, Athina, additional, Badenes Bonet, Diana, additional, Kokosi, Maria, additional, Kouranos, Vasilis, additional, Chua, Felix, additional, George, Peter, additional, Renzoni, Elisabetta A., additional, Devaraj, Anand, additional, Desai, Sujal, additional, Nicholson, Andrew G., additional, Antoniou, Katerina M., additional, and Wells, Athol U., additional

Published
2023
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27. Immediate smoking cessation support during lung cancer screening: long-term outcomes from two randomised controlled trials

Author

Williams, Parris J, primary, Philip, Keir E J, additional, Buttery, Sara C, additional, Perkins, Alexis, additional, Chan, Ley, additional, Bartlett, Emily C, additional, Devaraj, Anand, additional, Kemp, Samuel V, additional, Addis, James, additional, Derbyshire, Jane, additional, Chen, Michelle, additional, Polkey, Michael I, additional, Laverty, Anthony A, additional, and Hopkinson, Nicholas S, additional

Published
2023
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28. ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer

Author

O'Dowd, Emma L., primary, Tietzova, Ilona, additional, Bartlett, Emily, additional, Devaraj, Anand, additional, Biederer, Jürgen, additional, Brambilla, Marco, additional, Brunelli, Alessandro, additional, Chorostowska, Joanna, additional, Decaluwe, Herbert, additional, Deruysscher, Dirk, additional, De Wever, Walter, additional, Donoghue, Matthew, additional, Fabre, Aurelie, additional, Gaga, Mina, additional, van Geffen, Wouter, additional, Hardavella, Georgia, additional, Kauczor, Hans-Ulrich, additional, Kerpel-Fronius, Anna, additional, van Meerbeeck, Jan, additional, Nagavci, Blin, additional, Nestle, Ursula, additional, Novoa, Nuria, additional, Prosch, Helmut, additional, Prokop, Mathias, additional, Putora, Paul Martin, additional, Rawlinson, Janette, additional, Revel, Marie-Pierre, additional, Snoeckx, Annemiek, additional, Veronesi, Giulia, additional, Vliegenthart, Rozemarijn, additional, Weckbach, Sabine, additional, Blum, Torsten G., additional, and Baldwin, David R., additional

Published
2023
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29. Full title: Stage at diagnosis following delay to interval scans for indeterminate nodules in lung cancer screening: An observational study examining the outcomes of CHEST expert panel recommendations

Author

Creamer, Andrew W., primary, Horst, Carolyn, additional, Dickson, Jennifer L., additional, Tisi, Sophie, additional, Hall, Helen, additional, Verghese, Priyam, additional, Prendecki, Ruth, additional, Bhamani, Amyn, additional, Khaw, Cheun R., additional, McCabe, John, additional, Limani, Tanita, additional, Gyertson, Kylie, additional, Hacker, Anne-Marie, additional, Teague, J., additional, Farrelly, L., additional, Dawadi, Shrinkhala, additional, Hackshaw, Allan, additional, Devaraj, Anand, additional, Nair, Arjun, additional, consortium, summit, additional, and Janes, Sam M., additional

Published
2023
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32. Immediate smoking cessation support during lung cancer screening: long-term outcomes from two randomised controlled trials.

Author

Williams, Parris J., Philip, Keir E. J., Buttery, Sara C., Perkins, Alexis, Ley Chan, Bartlett, Emily C., Devaraj, Anand, Kemp, Samuel V., Addis, James, Derbyshire, Jane, Chen, Michelle, Polkey, Michael I., Laverty, Anthony A., and Hopkinson, Nicholas S.

Subjects
SMOKING cessation, EARLY detection of cancer, LUNG cancer, NICOTINE replacement therapy, COVID-19 pandemic
Abstract

This article summarizes the findings of two randomized controlled trials that examined the impact of immediate smoking cessation support during lung cancer screening. The trials found that providing smoking cessation support alongside targeted lung health checks increased long-term smoking abstinence rates. Participants who received immediate support had higher quit rates compared to those who received usual care, and these results were sustained at 12 months. The study suggests that offering smoking cessation interventions with pharmacotherapy immediately after attending a lung health check can lead to sustained increases in quit rates. The study also highlights the importance of supporting underrepresented groups, such as the most deprived quintile, in engaging with lung health screening. [Extracted from the article]

Published
2024
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35. Coronary artery disease, COPD exacerbation and inflammation

Author

MacLeod, Mairi, primary, Braddy-Green, Aaron, additional, Lopez, Ralph, additional, Webb, Philippa, additional, Sun, Logan, additional, Dalal, Paras, additional, Knott, Kristopher, additional, Davies, Simon, additional, Rubens, M, additional, Nicol, Edward D, additional, Devaraj, Anand, additional, Haskard, Dorian O, additional, Donaldson, Gavin C, additional, Khamis, Ramzi Y, additional, and Wedzicha, Jadwiga A, additional

Published
2023
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37. Growth patterns of screen-detected malignant pulmonary nodules: Accuracy of doubling-time models

Author

Creamer, Andrew, primary, Horst, Carolyn, additional, Dickson, Jennifer, additional, Tisi, Sophie, additional, Hall, Helen, additional, Verghese, Priyam, additional, Prendecki, Ruth, additional, Bhamani, Amyn, additional, Mccabe, John, additional, Teague, Jonathan, additional, Hacker, Anne-Marie, additional, Farrelly, Laura, additional, Gyertson, Kylie, additional, Devaraj, Anand, additional, Nair, Arjun, additional, Hackshaw, Allan, additional, and Janes, Sam, additional

Published
2023
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38. Embedding smoking cessation support into targeted lung health checks in the UK increases long-term smoking abstinence, 12-month follow-up data from two clinical randomised controlled trials.

Author

Williams, Parris, primary, Philip, Keir, additional, Buttery, Sara, additional, Bartlett, Emily, additional, Devaraj, Anand, additional, Addis, Jamie, additional, Derbyshire, Jane, additional, Chen, Michelle, additional, Laverty, Anthony, additional, and Hopkinson, Nicholas, additional

Published
2023
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39. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Author

Liao, Weiqi, primary, Coupland, Carol A C, additional, Burchardt, Judith, additional, Baldwin, David R, additional, Gleeson, Fergus V, additional, Hippisley-Cox, Julia, additional, Gleeson, Fergus, additional, Baldwin, David, additional, Batchkala, George, additional, Buchanan, James, additional, Chakraborty, Rohan, additional, Chana, Ravi, additional, Chen, Yan, additional, Coupland, Carol, additional, Crichton, Charles, additional, Davies, Jim, additional, Devaraj, Anand, additional, Fan, Mengran, additional, Koleva-Kolarova, Rositsa, additional, Lee, Richard, additional, Liao, Weiqi, additional, Nair, Arjun, additional, Pickup, Lyndsey, additional, Powell, Anne, additional, Rittscher, Jens, additional, Shadmaan, Amied, additional, Shanmugam, Kandavel, additional, Stokes, Elizabeth, additional, Verrill, Clare, additional, Watkins, Johnathan, additional, and Wordsworth, Sarah, additional

Published
2023
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44. Data from The UK Lung Screen (UKLS): Demographic Profile of First 88,897 Approaches Provides Recommendations for Population Screening

Author

McRonald, Fiona E., primary, Yadegarfar, Ghasem, primary, Baldwin, David R., primary, Devaraj, Anand, primary, Brain, Kate E., primary, Eisen, Tim, primary, Holemans, John A., primary, Ledson, Martin, primary, Screaton, Nicholas, primary, Rintoul, Robert C., primary, Hands, Christopher J., primary, Lifford, Kate, primary, Whynes, David, primary, Kerr, Keith M., primary, Page, Richard, primary, Parmar, Mahesh, primary, Wald, Nicholas, primary, Weller, David, primary, Williamson, Paula R., primary, Myles, Jonathan, primary, Hansell, David M., primary, Duffy, Stephen W., primary, and Field, John K., primary

Published
2023
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45. Supplementary Figures 1 - 2, Tables 1 - 3 from The UK Lung Screen (UKLS): Demographic Profile of First 88,897 Approaches Provides Recommendations for Population Screening

Author

McRonald, Fiona E., primary, Yadegarfar, Ghasem, primary, Baldwin, David R., primary, Devaraj, Anand, primary, Brain, Kate E., primary, Eisen, Tim, primary, Holemans, John A., primary, Ledson, Martin, primary, Screaton, Nicholas, primary, Rintoul, Robert C., primary, Hands, Christopher J., primary, Lifford, Kate, primary, Whynes, David, primary, Kerr, Keith M., primary, Page, Richard, primary, Parmar, Mahesh, primary, Wald, Nicholas, primary, Weller, David, primary, Williamson, Paula R., primary, Myles, Jonathan, primary, Hansell, David M., primary, Duffy, Stephen W., primary, and Field, John K., primary

Published
2023
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46. Prediction of next generation sequencing test failure in lung adenocarcinoma in a genomic laboratory hub setting

Author

Zhang, Yu Zhi, primary, Sherlock, Stuart, additional, MacMahon, Suzanne, additional, Brambilla, Cecilia, additional, Rice, Alexandra, additional, Robertus, Jan Lukas, additional, Wassilew, Katharina, additional, Lim, Eric, additional, Begum, Sofina, additional, Buderi, Silviu, additional, Jordan, Simon, additional, Anikin, Vladimir, additional, Finch, Jonathan, additional, Asadi, Nizar, additional, Beddow, Emma, additional, Garner, Justin L, additional, Morjaria, Jaymin, additional, Lee, Richard, additional, McDonald, Fiona, additional, Antoniou, Georgios, additional, Ridge, Carole, additional, Padley, Simon, additional, Dalal, Paras, additional, Morris-Rosendahl, Deborah, additional, Valganon-Petrizan, Mikel, additional, Shah, Pallav L, additional, Devaraj, Anand, additional, Popat, Sanjay, additional, and Nicholson, Andrew G, additional

Published
2023
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47. Developing a regional hub-and-spoke model for volumetric assessment of indeterminate pulmonary nodules supports efficient and effective management of pulmonary nodule pathways

Author

Ratnakumar, Ratnaprashanthika, primary, Sabarwal, Kivraj, additional, Derbyshire, Jane, additional, Stephenson, Candice, additional, Rhodes, Anita, additional, Morris, Katie, additional, Burchill, John, additional, Devaraj, Anand, additional, Orhan, Orhan, additional, Sheard, Sarah, additional, Mayer, Erik, additional, Quint, Jennifer, additional, and Bloch, Susannah, additional

Published
2023
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50. ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

Author

O'Dowd, Emma L, Tietzova, Ilona, Bartlett, Emily, Devaraj, Anand, Biederer, Jürgen, Brambilla, Marco, Brunelli, Alessandro, Chorostowska, Joanna, Decaluwe, Herbert, Deruysscher, Dirk, Wever, Walter De, Donoghue, Matthew, Fabre, Aurelie, Gaga, Mina, Geffen, Wouter van, Hardavella, Georgia, Kauczor, Hans-Ulrich, Kerpel-Fronius, Anna, Meerbeeck, Jan van, and Nagavci, Blin

Subjects
BRONCHIECTASIS, LUNG cancer, EARLY detection of cancer, AORTIC valve diseases, CORONARY artery calcification, MEDIASTINAL tumors
Abstract

Background Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. Methods A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. Results Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. Conclusions This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible. [ABSTRACT FROM AUTHOR]

Published
2023
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