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arXiv 2025年

作者： Pérez-Toro, Paula Andrea Arias-Vergara, Tomás Xing, Fangxu Liu, Xiaofeng Stone, Maureen Zhuo, Jiachen Orozco-Arroyave, Juan Rafael Nöth, Elmar Hutter, Jana Prince, Jerry L. Maier, Andreas Woo, Jonghye Harvard Medical School Massachusetts General Hospital BostonMA02114 United States Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Bayern 91058 Germany GITA Lab Faculty of Engineering Universidad de Antioquia Antioquia Medellín050010 Colombia Department of Radiology & Biomedical Imaging and Biomedical Informatics & Data Science Yale University New HeavenCT06510 United States Department of Neural and Pain Sciences Department of Orthodontics and Pediatrics University of Maryland School of Dentistry BaltimoreMD21210 United States Department of Orthodontics and Pediatrics University of Maryland School of Dentistry BaltimoreMD21201 United States Smart Imaging Lab Radiological Institute University Hospital Erlangen Bayern Erlangen91052 Germany Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States

Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treatment and rehabilitation strategies. Toward this goal, we introduce an audio-to-video generation framework for creating Real Time/cine-Magnetic Resonance Imaging (RT-/cine-MRI) visuals of the vocal tract from speech signals. Our framework first preprocesses RT-/cine-MRI sequences and speech samples to achieve temporal alignment, ensuring synchronization between visual and audio data. We then employ a modified stable diffusion model, integrating structural and temporal blocks, to effectively capture movement characteristics and temporal dynamics in the synchronized data. This process enables the generation of MRI sequences from new speech inputs, improving the conversion of audio into visual data. We evaluated our framework on healthy controls and tongue cancer patients by analyzing and comparing the vocal tract movements in synthesized videos. Our framework demonstrated adaptability to new speech inputs and effective generalization. In addition, positive human evaluations confirmed its effectiveness, with realistic and accurate visualizations, suggesting its potential for outpatient therapy and personalized simulation of vocal tract visualizations. © 2025, CC BY-NC-ND.

关键词： Visualization

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Multiwavelength observations in 2019-2020 of a new very-high-energy γ-ray emitter: the flat spectrum radio quasar QSO B1420+326

Multiwavelength observations in 2019-2020 of a new very-high...

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37th International Cosmic Ray Conference, ICRC 2021

作者： D'Ammando, Filippo Angioni, Roberto Orienti, Monica Sitarek, Julian Nozaki, Seiya Lindfors, Elina Bonnoli, Giacomo Ramazani, Vandad Fallah INAF-IRA Bologna Via P. Gobetti 101 BolognaI-40129 Italy ASI Space Science Data Center Via del Politecnico snc RomeI-00133 Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Pomorska 149 LodzP-90-236 Poland Department of Physics Kyoto University Kitashirakawa-Oiwakecho Sayko-ku Kyoto606-8502 Japan Finnish Centre for Astronomy with ESO University of Turku Quantum Vesilinnantie 5 TurkuFI-20014 Finland Instituto de Astrofisica de Andalucia Glorieta de la Astronomia s/n GranadaS-18008 Spain Ruhr University Bochum Universitätsstraße 150 BochumD-44801 Germany

The flat-spectrum radio quasar QSO B1420+326 underwent an enhanced γ-ray flux state seen by Fermi-LAT at the turn of 2019/2020. Compared to the low state both the position and luminosity of the two spectral energy distribution peaks changed by at least two orders of magnitude. The high state resulted in the discovery of the very-high-energy (>100 GeV) γ-ray emission from the source by the MAGIC telescopes. The organized multiwavelength campaign allow us to trace the broadband emission of the source through different phases of the flaring activity. The source was observed by 20 instruments in radio, near-infrared, optical, ultra-violet, X-ray and γ-ray bands. We use dedicated optical spectroscopy results to estimate the accretion disc and the dust torus luminosity. The optical spectroscopy shows a prominent FeII bump with flux evolving together with the continuum emission and a MgII line with varying equivalent width. The γ-ray flare was accompanied by a rotation of the optical polarization vector and emission of a new superluminal radio knot. We model spectral energy distributions in different flare phases in the framework of combined synchrotron-self-Compton and external Compton scenario in which the shape of the electron energy distribution is determined from cooling processes. Copyright © 2021 owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Resilient restoration scheduling on road network 13

Resilient restoration scheduling on road network

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13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019

作者： Furuta, Hitoshi Ishibashi, Ken Nakatsu, Koichiro Faculty of Informatics Kansai University Osaka Japan Data Science Laboratory Kansai University Osaka Japan Department of Modern Life Osaka Jonan Women's Junior College Osaka Japan

The purpose of this study is to enhance the resilience of restoration scheduling for road networks. Optimal scheduling for resource allocation plays an important role on early restoration. However, circumstances in the post-seismic period are more likely to change due to various factors such as secondary disaster. Furthermore, it is necessary to prepare for unexpected damages. This study examines a method to reduce changes of restoration plan with keeping the adaptability against uncertainties in the post-seismic period. Specifically, an attempt is made to clarify factors strongly related to the delay of restoration in the flexible scheduling which is based on allocation priorities of restoration groups proposed by the existing studies. In this study, numerical simulations are presented to investigate the influence of the change of schedule on early restoration. And then, this paper discussed a method to keep initial restoration plan and to enhance adaptability to circumstances' changes. © 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019. All rights reserved.

关键词： Restoration

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Prediction of individual lifetime cardiovascular risk and potential treatment benefit: development and recalibration of the LIFE-CVD2 model to four European risk regions (May, zwae174, 2024)

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EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY 2024年第15期31卷 1899-1899页

作者： Hageman, Steven H. J. Kaptoge, Stephen de Vries, Tamar, I Lu, Wentian Kist, Janet M. van Os, Hendrikus J. A. Numans, Mattijs E. Lall, Kristi Bobak, Martin Pikhart, Hynek Kubinova, Ruzena Malyutina, Sofia Pajak, Andrzej Tamosiunas, Abdonas Erbel, Raimund Stang, Andreas Schmidt, Boerge Schramm, Sara Bolton, Thomas R. Spackman, Sarah Bakker, Stephan J. L. Blaha, Michael Boer, Jolanda M. A. Bonnefond, Amelie Brenner, Hermann Brunner, Eric J. Cook, Nancy R. Davidson, Karina Dennison, Elaine Donfrancesco, Chiara Doerr, Marcus Floyd, James S. Ford, Ian Fu, Michael Gansevoort, Ron T. Giampaoli, Simona Gillum, Richard F. Gomez-de-la-Camara, Agustin Haheim, Lise Lund Hansson, Per-Olof Harms, Peter Humphries, Steve E. Ikram, M. Kamran Jukema, J. Wouter Kavousi, Maryam Kiechl, Stefan Kucharska-Newton, Anna Pablos, David Lora Matsushita, Kunihiro Meyer, Haakon E. Moons, Karel G. M. Mortensen, Martin Bodtke Muilwijk, Mirthe Nordestgaard, Borge G. Packard, Chris Pamieri, Luigi Panagiotakos, Demosthenes Peters, Annette Potier, Louis Providencia, Rui Psaty, Bruce M. Ridker, Paul M. Rodriguez, Beatriz Rosengren, Annika Sattar, Naveed Schoettker, Ben Schwartz, Joseph E. Shea, Steven Shipley, Martin J. Sofat, Reecha Thorand, Barbara Verschuren, W. M. Monique Voelzke, Henry Wareham, Nicholas J. Westbury, Leo Willeit, Peter Zhou, Bin Danesh, John Visseren, Frank L. J. Di Angelantonio, Emanuele Pennells, Lisa Dorresteijn, Jannick A. N. Department of Vascular Medicine University Medical Center Utrecht Utrecht The Netherlands Department of Public Health and Primary Care University of Cambridge CambridgeUK Department of Epidemiology and Public Health University College London London UK Health Campus The Hague Leiden University Medical Center The Hague the Netherlands National eHealth Living Lab Leiden University Medical Center The Hague the Netherlands Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia RECETOX Masaryk University Brno Czech Republic National Institute of Public Health Prague Czech Republic Independent Researcher Department of Epidemiology and Population Studies Institute of Public Health Faculty of Health Sciences Jagiellonian University Medical College Kraków Poland Institute of Cardiology Lithuanian University of Health Sciences Kaunas Lithuania Institute for Medical Informatics Biometry and Epidemiology University Hospital Essen University Duisburg-Essen Essen Germany British Heart Foundation Data Science Centre Health Data Research UK London UK British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK Victor Phillip Dahdaleh Heart and Lung Research Institute University of Cambridge CambridgeUK Department of Internal Medicine University Medical Centre Groningen University of Groningen Groningen Netherlands Johns Hopkins Ciccarone Center for the Prevention of Heart Disease Johns Hopkins Hospital Baltimore MD USA Centre for Prevention Lifestyle and Health National Institute for Public Health and the Environment Bilthoven The Netherlands Inserm/CNRS UMR 1283/8199 Pasteur Institute of Lille EGID Lille France University of Lille Lille France Department of Metabolism Digestion and Reproduction Imperial College London London UK Division of Clinical Epidemiology and Aging Research German Cancer Research Center Heidelberg Germany Network Aging Research Univers

The study introduces LIFE-CVD2, a new tool that helps predict the risk of heart disease over a person’s lifetime, and highlights how where you live in Europe can affect this risk.

关键词： Risk prediction Lifetime Prevention Cardiovascular disease Primary prevention

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Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion

arXiv

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arXiv 2023年

作者： Sun, Kaibao Chen, Zhifeng Dan, Guangyu Luo, Qingfei Yan, Lirong Liu, Feng Zhou, Xiaohong Joe Center for Magnetic Resonance Research University of Illinois at Chicago ChicagoIL United States USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Los AngelesCA United States Department of Data Science and AI Faculty of IT Monash University ClaytonVIC Australia Department of Biomedical Engineering University of Illinois at Chicago ChicagoIL United States Department of Radiology Feinberg School of Medicine Northwestern University ChicagoIL United States School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia Department of Radiology and Neurosurgery University of Illinois at Chicago ChicagoIL United States

Purpose: Echo-planar imaging (EPI) with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly functional MRI (fMRI). This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. Methods: A three-dimensional (3D) esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0-field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. Results: The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. Conclusion: The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications. © 2023, CC BY-NC-ND.

关键词： Phantoms

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Multiple lung diseases detection using advanced deep learning model with attention mechanisms and upsampling features

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhu, Jie Al-qaness, Mohammed A.A. AL-Alimi, Dalal Tao, Heng Zhi Alsamhi, Saeed Hamood College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Faculty of Engineering Sana'a University Sana'a12544 Yemen Insight Centre for Data Analytics University of Galway Galway Ireland School of ICT Bahrain Polytechnic Isa Town Bahrain Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Lung diseases represent a major health challenge, necessitating accurate and timely diagnostic methods. X-ray imaging plays a critical role in diagnosing various lung conditions, with physicians traditionally relying on these images for disease identification. However, with rapid advancements in artificial intelligence and imaging technologies, the volume of medical image data has grown significantly, spurring the development of machine learning techniques, particularly deep learning, to improve diagnostic accuracy. Unlike previous studies that focused on binary or three-class classification tasks, this paper presents an efficient deep learning model capable of classifying six types of lung X-ray images. The model employs an efficient feature extraction approach by combining the Visual Geometry Group Network (VGG16) with a Convolutional Neural Network (CNN) feature fusion, further enhanced by a Squeeze-and-Excitation (SE) block and self-attention modules. This comprehensive framework enables effective classification of tuberculosis, pneumonia, COVID-19, normal lungs, lung opacity, and pneumothorax. To validate the model, we constructed a robust dataset consisting of 45,241 X-ray images sourced from multiple databases. The proposed model was evaluated in various conditions to verify its performance. It achieves a remarkable validation accuracy of 93% in detecting the six lung diseases from X-ray images. © 2025 Elsevier Ltd

关键词： Lung cancer

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Patients’ Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study

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Journal of Medical Internet Research 2025年第1期27卷 e70487页

作者： Gundlack, Jana Thiel, Carolin Negash, Sarah Buch, Charlotte Apfelbacher, Timo Denny, Kathleen Christoph, Jan Mikolajczyk, Rafael Unverzagt, Susanne Frese, Thomas Institute of General Practice and Family Medicine Interdisciplinary Center of Health Sciences Medical Faculty of the Martin Luther University Halle-Wittenberg Halle (Saale) Germany Institute for Medical Epidemiology Biometrics and Informatics Interdisciplinary Center for Health Sciences Medical Faculty of the Martin Luther University Halle-Wittenberg Halle (Saale) Germany Institute for History and Ethics of Medicine Interdisciplinary Center of Health Sciences Medical Faculty of the Martin Luther University Halle-Wittenberg Halle (Saale) Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Medical Informatics Erlangen Germany Junior Research Group (Bio-)medical Data Science Medical Faculty of the Martin Luther University Halle-Wittenberg Halle (Saale) Germany

Background: Artificial intelligence (AI) is increasingly used in medical care, particularly in the areas of image recognition and processing. While its practical use in other areas is still limited, an understanding of patients’ needs is essential for the practical and sustainable implementation of AI, which could further acceptance of new innovations. Objective: The objective of this study was to explore patients’ perceptions toward acceptance, challenges of implementation, and potential applications of AI in medical care. Methods: The study used a qualitative research design. To capture a broad range of patient perspectives, we conducted semistructured focus groups (FGs). As a stimulus for the FGs and as an introduction to the topic, we presented a video defining AI and showing 3 potential AI applications in health care. Participants were recruited from different locations in the regions of Halle (Saale) and Erlangen, Germany;all but one group were from outpatient settings. We analyzed the data using a content analysis approach. Results: A total of 35 patients (13 female and 22 male;age: range 23-92, median 50 years) participated in 6 focus groups. They highlighted that AI acceptance in medical care could be improved through user-friendly applications, clear instructions, feedback mechanisms, and a patient-centered approach. Perceived key barriers included data protection concerns, lack of human oversight, and profit-driven motives. Perceived challenges and requirements for AI implementation involved compatibility, training of end users, environmental sustainability, and adherence to quality standards. Potential AI application areas identified were diagnostics, image and data processing, and administrative tasks, though participants stressed that AI should remain a support tool, not an autonomous system. Psychology was an area where its use was opposed due to the need for human interaction. Conclusions: Patients were generally open to the use of AI in medical care

关键词： artificial intelligence use medical care acceptance patient perceptions

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SMILE-UHURA Challenge - Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms

arXiv

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arXiv 2024年

作者： Chatterjee, Soumick Mattern, Hendrik Dörner, Marc Sciarra, Alessandro Dubost, Florian Schnurre, Hannes Khatun, Rupali Yu, Chun-Chih Hsieh, Tsung-Lin Tsai, Yi-Shan Fang, Yi-Zeng Yang, Yung-Ching Huang, Juinn-Dar Xu, Marshall Liu, Siyu Ribeiro, Fernanda L. Bollmann, Saskia Chintalapati, Karthikesh Varma Radhakrishna, Chethan Mysuru Ram Kumar, Sri Chandana Hudukula Sutrave, Raviteja Qayyum, Abdul Mazher, Moona Razzak, Imran Rodero, Cristobal Niederer, Steven Lin, Fengming Xia, Yan Wang, Jiacheng Qiu, Riyu Wang, Liansheng Panah, Arya Yazdan Jurdi, Rosana El Fu, Guanghui Arslan, Janan Vaillant, Ghislain Valabregue, Romain Dormont, Didier Stankoff, Bruno Colliot, Olivier Vargas, Luisa Chacón, Isai Daniel Pitsiorlas, Ioannis Arbeláez, Pablo Zuluaga, Maria A. Schreiber, Stefanie Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg Germany Department of Neurology Medical Faculty University Hospital of Magdeburg Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany University Hospital Zurich University of Zurich Zurich Switzerland Google Inc. United States Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany National Yang Ming Chiao Tung University Hsinchu Taiwan School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Australian eHealth Research Centre Chile National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Computer Science and Engineering University of New South Wales Sydney Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The Alan Turing Institute London United Kingdom School of Computing University of Leeds Leeds United Kingdom Department of Computer Science School of Informatics Xiamen University Xiamen China Manteia Technologies Co. Ltd Xiamen China Leicester International Institute Dalian University of Technology Dalian China Sorbonne Université Institut du Cerveau - Paris Brain Institute ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Centre of Formation and Research in Artificial Intelligence Universidad de Los Andes Colombia Data Science Department EURECOM Sophia Antipolis Franc

The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address the complexities of mesoscopic vessel segmentation and to highlight the need for advanced techniques to manage the high noise levels and poor vessel-to-background contrast inherent in"ultra-high-resolution" data, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 ± 0.066 and 0.716 ± 0.125 on the respective datasets, with an average performance of up to 0.804 ± 0.15. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Machine Learning Survival Models for Relapse Prediction in a...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mohan Timilsina Samuele Buosi Adrianna Janik Pasquale Minervini Luca Costabello Maria Torrente Mariano Provencio Virginia Calvo Carlos Camps Ana L. Ortega Bartomeu Massutí M.Rosario Garcia Campelo Edel del Barco Joaquim Bosch-Barrera Vit Novacek Data Science Institute University of Galway Galway Ireland Accenture Lab Dublin Ireland University College London London United Kingdom Department of Medical Oncology Puerta de Hierro-Majadahonda University Hospital Madrid Spain Hospital General de Valencia Valencia Spain Hospital Universitario de Jaén Jaen Spain Hospital General Universitario de Alicante Alicante Spain Complejo Hospitalario Universitario A Coruña A Coruna Spain Hospital Universitario de Salamanca Salamanca Spain Institut Català d'Oncologia Hospital Universitari Dr. Josep Trueta Girona Spain Faculty of Informatics Masaryk University Brno Brno Czech Republic

Lung cancer is one of the leading health complications causing high mortality worldwide. The relapsing behavior of medically treated early-stage lung cancer makes this disease even more complicated. Thus predicting such relapse using a data-centric approach provides a complementary perspective for clinicians to understand the disease. In this preliminary work, we explored off-the-shelf survival models to predict the relapse of early-stage lung cancer patients. We analyzed the survival models on a cohort of 1348 early-stage non-small cell lung cancer (NSCLC) patients in different timestamps. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the best-performing survival model's predictions. Our explainable predictive model is a potential tool for oncologists that address an unmet clinical need for post-treatment patient stratification based on the relapse hazard.

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