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43 results on '"Aerts, Hugo JWL."'

1. Artificial Intelligence-Based Opportunistic Coronary Calcium Screening in the Veterans Affairs National Healthcare System

Author

Hagopian, Raffi, Strebel, Timothy, Bernatz, Simon, Myers, Gregory A, Offerman, Erik, Zuniga, Eric, Kim, Cy Y, Ng, Angie T, Iwaz, James A, Singh, Sunny P, Carey, Evan P, Kim, Michael J, Schaefer, R Spencer, Yu, Jeannie, Gentili, Amilcare, and Aerts, Hugo JWL

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algorithm was developed using 446 expert segmentations to automatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our study differs from prior works as we leverage imaging data across the Veterans Affairs national healthcare system, from 98 medical centers, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on non-gated scans was compared against clinical standard ECG-gated CAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than 100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a year of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox HR 3.49, p < 0.005), and composite first-time stroke, MI, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox HR 3.00, p < 0.005). In a screening dataset of 8,052 patients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%) individuals had AI-CAC >400. Four cardiologists qualitatively reviewed LDCT images from a random sample of >400 AI-CAC patients and verified that 527/531 (99.2%) would benefit from lipid-lowering therapy. To the best of our knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference. We report superior performance relative to previous CAC algorithms evaluated against paired gated scans that included patients with intra-cardiac hardware.

Published
2024

2. LongHealth: A Question Answering Benchmark with Long Clinical Documents

Author

Adams, Lisa, Busch, Felix, Han, Tianyu, Excoffier, Jean-Baptiste, Ortala, Matthieu, Löser, Alexander, Aerts, Hugo JWL., Kather, Jakob Nikolas, Truhn, Daniel, and Bressem, Keno

Subjects
Computer Science - Computation and Language
Abstract

Background: Recent advancements in large language models (LLMs) offer potential benefits in healthcare, particularly in processing extensive patient records. However, existing benchmarks do not fully assess LLMs' capability in handling real-world, lengthy clinical data. Methods: We present the LongHealth benchmark, comprising 20 detailed fictional patient cases across various diseases, with each case containing 5,090 to 6,754 words. The benchmark challenges LLMs with 400 multiple-choice questions in three categories: information extraction, negation, and sorting, challenging LLMs to extract and interpret information from large clinical documents. Results: We evaluated nine open-source LLMs with a minimum of 16,000 tokens and also included OpenAI's proprietary and cost-efficient GPT-3.5 Turbo for comparison. The highest accuracy was observed for Mixtral-8x7B-Instruct-v0.1, particularly in tasks focused on information retrieval from single and multiple patient documents. However, all models struggled significantly in tasks requiring the identification of missing information, highlighting a critical area for improvement in clinical data interpretation. Conclusion: While LLMs show considerable potential for processing long clinical documents, their current accuracy levels are insufficient for reliable clinical use, especially in scenarios requiring the identification of missing information. The LongHealth benchmark provides a more realistic assessment of LLMs in a healthcare setting and highlights the need for further model refinement for safe and effective clinical application. We make the benchmark and evaluation code publicly available., Comment: 11 pages, 3 figures, 5 tables

Published
2024

3. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

4. Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Author

Guevara, Marco, Chen, Shan, Thomas, Spencer, Chaunzwa, Tafadzwa L., Franco, Idalid, Kann, Benjamin, Moningi, Shalini, Qian, Jack, Goldstein, Madeleine, Harper, Susan, Aerts, Hugo JWL, Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EHRs, where they are most commonly documented, and explored the role of synthetic clinical text for improving the extraction of these scarcely documented, yet extremely valuable, clinical data. 800 patient notes were annotated for SDoH categories, and several transformer-based models were evaluated. The study also experimented with synthetic data generation and assessed for algorithmic bias. Our best-performing models were fine-tuned Flan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The benefit of augmenting fine-tuning with synthetic data varied across model architecture and size, with smaller Flan-T5 models (base and large) showing the greatest improvements in performance (delta F1 +0.12 to +0.23). Model performance was similar on the in-hospital system dataset but worse on the MIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models for both tasks. These fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can effectively extracted SDoH information from clinic notes, performing better compare to GPT zero- and few-shot settings. These models could enhance real-world evidence on SDoH and aid in identifying patients needing social support., Comment: Peer-reviewed version published at NPJ Digital Medicine: https://www.nature.com/articles/s41746-023-00970-0

Published
2023
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5. Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Author

Krishnaswamy, Deepa, Bontempi, Dennis, Thiriveedhi, Vamsi, Punzo, Davide, Clunie, David, Bridge, Christopher P, Aerts, Hugo JWL, Kikinis, Ron, and Fedorov, Andrey

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

Published
2023

6. Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

Author

Chen, Shan, Li, Yingya, Lu, Sheng, Van, Hoang, Aerts, Hugo JWL, Savova, Guergana K., and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples as proxies for two fundamental tasks in the clinical domain - classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We compared LLMs with simpler models, such as bag-of-words (BoW) with logistic regression, and fine-tuned BioBERT models. Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment., Comment: 28 pages, 2 tables and 4 figures. Submitting for review

Published
2023
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7. Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy

Author

Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo JWL, Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Subjects
Computer Science - Computation and Language
Abstract

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains., Comment: 17 pages, 6 tables, 1figure, submiting to JCO-CCI for review

Published
2023
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8. MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain

Author

Bressem, Keno K., Papaioannou, Jens-Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo JWL., and Löser, Alexander

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERTde are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community., Comment: Keno K. Bressem and Jens-Michalis Papaioannou and Paul Grundmann contributed equally

Published
2023
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9. What Does DALL-E 2 Know About Radiology?

Author

Adams, Lisa C., Busch, Felix, Truhn, Daniel, Makowski, Marcus R., Aerts, Hugo JWL., and Bressem, Keno K.

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

Generative models such as DALL-E 2 could represent a promising future tool for image generation, augmentation, and manipulation for artificial intelligence research in radiology provided that these models have sufficient medical domain knowledge. Here we show that DALL-E 2 has learned relevant representations of X-ray images with promising capabilities in terms of zero-shot text-to-image generation of new images, continuation of an image beyond its original boundaries, or removal of elements, while pathology generation or CT, MRI, and ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if further fine-tuning and adaptation of these models to the respective domain is required beforehand., Comment: 4 Figures

Published
2022
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10. ModelHub.AI: Dissemination Platform for Deep Learning Models

Author

Hosny, Ahmed, Schwier, Michael, Berger, Christoph, Örnek, Evin P, Turan, Mehmet, Tran, Phi V, Weninger, Leon, Isensee, Fabian, Maier-Hein, Klaus H, McKinley, Richard, Lu, Michael T, Hoffmann, Udo, Menze, Bjoern, Bakas, Spyridon, Fedorov, Andriy, and Aerts, Hugo JWL

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present ModelHub.AI (www.modelhub.ai), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on ModelHub.AI and allows both researchers and developers to utilize models out-of-the-box. ModelHub.AI is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences.

Published
2019

12. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Author

Zwanenburg, Alex, Vallières, Martin, Abdalah, Mahmoud A, Aerts, Hugo JWL, Andrearczyk, Vincent, Apte, Aditya, Ashrafinia, Saeed, Bakas, Spyridon, Beukinga, Roelof J, Boellaard, Ronald, Bogowicz, Marta, Boldrini, Luca, Buvat, Irène, Cook, Gary JR, Davatzikos, Christos, Depeursinge, Adrien, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian, El Naqa, Issam, Fedorov, Andriy Y, Gatta, Roberto, Gillies, Robert J, Goh, Vicky, Götz, Michael, Guckenberger, Matthias, Ha, Sung Min, Hatt, Mathieu, Isensee, Fabian, Lambin, Philippe, Leger, Stefan, Leijenaar, Ralph TH, Lenkowicz, Jacopo, Lippert, Fiona, Losnegård, Are, Maier-Hein, Klaus H, Morin, Olivier, Müller, Henning, Napel, Sandy, Nioche, Christophe, Orlhac, Fanny, Pati, Sarthak, Pfaehler, Elisabeth AG, Rahmim, Arman, Rao, Arvind UK, Scherer, Jonas, Siddique, Muhammad Musib, Sijtsema, Nanna M, Socarras Fernandez, Jairo, Spezi, Emiliano, Steenbakkers, Roel JHM, Tanadini-Lang, Stephanie, Thorwarth, Daniela, Troost, Esther GC, Upadhaya, Taman, Valentini, Vincenzo, van Dijk, Lisanne V, van Griethuysen, Joost, van Velden, Floris HP, Whybra, Philip, Richter, Christian, and Löck, Steffen

Subjects
Biomedical Imaging, Cancer, Bioengineering, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, Biomarkers, Calibration, Fluorodeoxyglucose F18, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Phantoms, Imaging, Phenotype, Positron-Emission Tomography, Radiopharmaceuticals, Reproducibility of Results, Sarcoma, Software, Tomography, X-Ray Computed, Medical and Health Sciences, Nuclear Medicine & Medical Imaging
Abstract

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Published
2020

13. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors

Author

Tohidinezhad, Fariba, Bontempi, Dennis, Zhang, Zhen, Dingemans, Anne-Marie, Aerts, Joachim, Bootsma, Gerben, Vansteenkiste, Johan, Hashemi, Sayed, Smit, Egbert, Gietema, Hester, Aerts, Hugo JWL., Dekker, Andre, Hendriks, Lizza E.L., Traverso, Alberto, and De Ruysscher, Dirk

Published
2023
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14. Longitudinal risk prediction for pediatric glioma with temporal deep learning

Author

Tak, Divyanshu, primary, Garomsa, Biniam A., additional, Zapaishchykova, Anna, additional, Ye, Zezhong, additional, Vajapeyam, Sri, additional, Mahootiha, Maryam, additional, Pardo, Juan Carlos Climent, additional, Smith, Ceilidh, additional, Familiar, Ariana M., additional, Liu, Kevin X., additional, Prabhu, Sanjay, additional, Bandopadhayay, Pratiti, additional, Nabavizadeh, Ali, additional, Mueller, Sabine, additional, Aerts, Hugo JWL, additional, Haas-Kogan, Daphne, additional, Poussaint, Tina Y., additional, and Kann, Benjamin H., additional

Published
2024
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15. Repeatability of Multiparametric Prostate MRI Radiomics Features

Author

Schwier, Michael, van Griethuysen, Joost, Vangel, Mark G, Pieper, Steve, Peled, Sharon, Tempany, Clare M, Aerts, Hugo JWL, Kikinis, Ron, Fennessy, Fiona M, and Fedorov, Andrey

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

In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, 2D vs 3D texture computation, and different bin widths for image discretization. Image registration as means to re-identify regions of interest across time points was evaluated against human-expert segmented regions in both time points. Even though we found many radiomics features and preprocessing combinations with a high repeatability (Intraclass Correlation Coefficient (ICC) > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters (under certain configurations, it can be below 0.0). Image normalization, using a variety of approaches considered, did not result in consistent improvements in repeatability. There was also no consistent improvement of repeatability through the use of pre-filtering options, or by using image registration between timepoints to improve consistency of the region of interest localization. Based on these results we urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend making the implementation available.

Published
2018

16. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Author

Thompson, Reid F, Valdes, Gilmer, Fuller, Clifton D, Carpenter, Colin M, Morin, Olivier, Aneja, Sanjay, Lindsay, William D, Aerts, Hugo JWL, Agrimson, Barbara, Deville, Curtiland, Rosenthal, Seth A, Yu, James B, and Thomas, Charles R

Subjects
Humans, Patient Care, Radiotherapy Dosage, Radiation Oncology, Radiology, Decision Support Techniques, Forecasting, Artificial Intelligence, Quality Assurance, Health Care, Machine Learning, Artificial intelligence, Deep learning, Machine learning, Cancer, Other Physical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Published
2018

17. Erratum: Semi-automated pulmonary nodule interval segmentation using the NLST data.

Author

Balagurunathan, Yoganand, Beers, Andrew, Kalpathy-Cramer, Jayashree, McNitt-Gray, Michael, Hadjiiski, Lubomir, Zhao, Bensheng, Zhu, Jianguo, Yang, Hao, Yip, Stephen SF, Aerts, Hugo JWL, Napel, Sandy, Cherezov, Dmitrii, Cha, Kenny, Chan, Heang-Ping, Flores, Carlos, Garcia, Alberto, Gillies, Robert, and Goldgof, Dmitry

Subjects
Nuclear Medicine & Medical Imaging, Other Physical Sciences, Biomedical Engineering
Published
2018

18. Semi‐automated pulmonary nodule interval segmentation using the NLST data

Author

Balagurunathan, Yoganand, Beers, Andrew, Kalpathy‐Cramer, Jayashree, McNitt‐Gray, Michael, Hadjiiski, Lubomir, Zhao, Bensheng, Zhu, Jiangguo, Yang, Hao, Yip, Stephen SF, Aerts, Hugo JWL, Napel, Sandy, Cherezov, Dmitrii, Cha, Kenny, Chan, Heang‐Ping, Flores, Carlos, Garcia, Alberto, Gillies, Robert, and Goldgof, Dmitry

Subjects
Medical and Biological Physics, Engineering, Physical Sciences, Biomedical Engineering, Cancer, Biomedical Imaging, Bioengineering, Aged, Automation, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Mass Screening, Middle Aged, Software, Tomography, X-Ray Computed, change in volume segmentation, CT lung, lung nodule segmentation, volume estimate, Other Physical Sciences, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging, Biomedical engineering, Medical and biological physics
Abstract

PurposeTo study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.MethodsWe obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of

Published
2018

19. Decoding biological age from face photographs using deep learning

Author

Zalay, Osbert, primary, Bontempi, Dennis, additional, Bitterman, Danielle S, additional, Birkbak, Nicolai, additional, Shyr, Derek, additional, Haugg, Fridolin, additional, Qian, Jack M, additional, Roberts, Hannah, additional, Perni, Subha, additional, Prudente, Vasco, additional, Pai, Suraj, additional, Dekker, Andre, additional, Haibe-Kains, Benjamin, additional, Guthier, Christian, additional, Balboni, Tracy, additional, Warren, Laura, additional, Krishan, Monica, additional, Kann, Benjamin H, additional, Swanton, Charles, additional, Ruysscher, Dirk De, additional, Mak, Raymond H, additional, and Aerts, Hugo JWL, additional

Published
2023
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20. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging

Author

Pai, Suraj, primary, Bontempi, Dennis, additional, Hadzic, Ibrahim, additional, Prudente, Vasco, additional, Sokač, Mateo, additional, Chaunzwa, Tafadzwa L., additional, Bernatz, Simon, additional, Hosny, Ahmed, additional, Mak, Raymond H, additional, Birkbak, Nicolai J, additional, and Aerts, Hugo JWL, additional

Published
2023
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21. medBERT.de: A Comprehensive German BERT Model for the Medical Domain

Author

Bressem, Keno K., primary, Papaioannou, Jens-Michalis, additional, Grundmann, Paul, additional, Borchertd, Florian, additional, Adams, Lisa C., additional, Liu, Leonhard, additional, Busch, Felix, additional, Xu, Lina, additional, Loyen, Jan P., additional, Niehues, Stefan M., additional, Augustin, Moritz, additional, Grosser, Lennart, additional, Makowski, Marcus R., additional, Aerts, Hugo JWL., additional, and Löser, Alexander, additional

Published
2023
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22. Data from Multi-institutional prognostic modelling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo JWL, primary, Kann, Benjamin H, primary, Bratman, Scott V., primary, Hope, Andrew J, primary, and Haibe-Kains, Benjamin, primary

Published
2023
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23. Supplementary Figure S3 from Multi-institutional prognostic modelling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo JWL, primary, Kann, Benjamin H, primary, Bratman, Scott V., primary, Hope, Andrew J, primary, and Haibe-Kains, Benjamin, primary

Published
2023
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24. Supplementary Data from Multi-institutional prognostic modelling in head and neck cancer: evaluating impact and generalizability of deep learning and radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo JWL, primary, Kann, Benjamin H, primary, Bratman, Scott V., primary, Hope, Andrew J, primary, and Haibe-Kains, Benjamin, primary

Published
2023
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25. Abstract 5618: Multi-institutional validation of a radiomics-based artificial intelligence method for predicting response to PD-1/PD-L1 immune checkpoint inhibitor (ICI) therapy in stage IV NSCLC

Author

Parikh, Ravi B., primary, Jordan, Petr, additional, Ciaravino, Rita J., additional, Beasley, Ryan A., additional, Patel, Arpan A., additional, Owen, Dwight H., additional, Amini, Arya, additional, Curti, Brendan D., additional, Page, Ray, additional, Swalduz, Aurelie, additional, Beregi, Jean-Paul, additional, Chrusciel, Jan, additional, Snyder, Eric, additional, Mukherjee, Pritam, additional, Selby, Heather M., additional, Lee, Soohee, additional, Weerasinghe, Roshanthi, additional, Pindikuri, Shwetha, additional, Weiss, Jakob B., additional, Wentland, Andrew L., additional, Kirpalani, Anish, additional, Liu, An, additional, Gevaert, Olivier, additional, Simon, George, additional, and Aerts, Hugo JWL, additional

Published
2023
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26. Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC Development and External Validation of a Multi-Institutional Model: development and external validation of a multi-institutional model

Author

Gao, Sarah, Jin, Lan, Meadows, Hugh W, Shafman, Timothy, Gross, Cary P, Yu, James B, Aerts, Hugo Jwl, Miccio, Joseph A, Stahl, John M, Mak, Raymond H, Decker, Roy H, Kann, Benjamin H, Gao, Sarah, Jin, Lan, Meadows, Hugh W, Shafman, Timothy, Gross, Cary P, Yu, James B, Aerts, Hugo Jwl, Miccio, Joseph A, Stahl, John M, Mak, Raymond H, Decker, Roy H, and Kann, Benjamin H

Abstract

OBJECTIVES: Distant metastases (DM) are the primary driver of mortality for patients with early stage non-small cell lung cancer (NSCLC) receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population.METHODS: We utilized a multi-institutional database of 1,280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006-2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared to a random survival forests (RSF) model. The higher performing model was evaluated on an external dataset of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores.RESULTS: The FG model yielded an AUC of 0.71 (95% CI: 0.57-0.86) compared to the RSF's AUC of 0.69 [95% CI: 0.63-0.85] in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC 0.70 (95% CI: 0.57-0.83) with good calibration (Brier score 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% (3/15) v 2.9% (7/241), P=0.001) and external validation 21.4% (3/15) v 7.8% (9/116), P= 0.095). A model nomogram and online application was made available.CONCLUSIONS: We developed and externally validated a practical model that predicts DM risk in NSCLC patients receiving SBRT that may help select patients for systemic therapy.

Published
2023

27. Body composition and lung cancer-associated cachexia in TRACERx

Author

Al-Sawaf, Othman, Weiss, Jakob, Skrzypski, Marcin, Lam, Jie Min, Karasaki, Takahiro, Zambrana, Francisco, Kidd, Andrew C, Frankell, Alexander M, Watkins, Thomas BK, Martinez-Ruiz, Carlos, Puttick, Clare, Black, James RM, Huebner, Ariana, Al Bakir, Maise, Sokac, Mateo, Collins, Susie, Veeriah, Selvaraju, Magno, Neil, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Ward, Sophia, Jayanth, Nick, Salgado, Roberto, Bridge, Christopher P, Christiani, David C, Mak, Raymond H, Bay, Camden, Rosenthal, Michael, Sattar, Naveed, Welsh, Paul, Liu, Ying, Perrimon, Norbert, Popuri, Karteek, Beg, Mirza Faisal, McGranahan, Nicholas, Hackshaw, Allan, Breen, Danna M, O'Rahilly, Stephen, Birkbak, Nicolai J, Aerts, Hugo JWL, Jamal-Hanjani, Mariam, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Van Loo, Peter, Szallasi, Zoltan, Kisistok, Judit, Diossy, Miklos, Demeulemeester, Jonas, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Abbosh, Christopher, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Hiley, Crispin T, Moore, David A, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Cadieux, Elizabeth Larose, Lim, Emilia L, Colliver, Emma, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Hartley, John A, Peggs, Karl S, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Grigoriadis, Kristiana, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Hill, Mark S, Dietzen, Michelle, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Pich, Oriol, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Zaccaria, Simone, Hessey, Sonya, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Male, Proteomics, Cachexia, Lung Neoplasms, Antigens, Neoplasm, Carcinoma, Non-Small-Cell Lung, Body Weight, Body Composition, Humans, Neoplasm Recurrence, Local, Muscle, Skeletal, Neoplasm Proteins
Abstract

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

28. The evolution of non-small cell lung cancer metastases in TRACERx

Author

Al Bakir, Maise, Huebner, Ariana, Martinez-Ruiz, Carlos, Grigoriadis, Kristiana, Watkins, Thomas BK, Pich, Oriol, Moore, David A, Veeriah, Selvaraju, Ward, Sophia, Laycock, Joanne, Johnson, Diana, Rowan, Andrew, Razaq, Maryam, Akther, Mita, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Hessey, Sonya, Dietzen, Michelle, Colliver, Emma, Frankell, Alexander, Bunkum, Abigail, Lim, Emilia L, Karasaki, Takahiro, Abbosh, Christopher, Hiley, Crispin T, Hill, Mark S, Cook, Daniel E, Wilson, Gareth A, Salgado, Roberto, Nye, Emma, Stone, Richard Kevin, Fennell, Dean A, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Summers, Yvonne, Lindsay, Colin R, Blackhall, Fiona H, Cave, Judith, Blyth, Kevin G, Nair, Arjun, Ahmed, Asia, Taylor, Magali N, Procter, Alexander James, Falzon, Mary, Lawrence, David, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Papadatos-Pastos, Dionysis, Forster, Martin D, Lee, Siow Ming, Ahmad, Tanya, Quezada, Sergio, Peggs, Karl S, Van Loo, Peter, Dive, Caroline, Hackshaw, Allan, Birkbak, Nicolai J, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Benafif, Sarah, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Krebs, Matthew G, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Puttick, Clare, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Black, James RM, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Clonal Evolution, Cohort Studies, Evolution, Molecular, Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Disease Progression, Humans, Neoplasm Metastasis, Neoplasm Recurrence, Local, Clone Cells
Abstract

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

29. The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M, Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L, Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S, Grigoriadis, Kristiana, Moore, David A, Black, James RM, Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas BK, Naceur-Lombardelli, Cristina, Cook, Daniel E, Salgado, Roberto, Wilson, Gareth A, Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martinez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G, Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Blyth, Kevin G, Fennell, Dean A, Forster, Martin D, Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J, Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G, Lester, Jason F, Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M, Lawrence, David, Navani, Neal, Janes, Sam M, Dive, Caroline, Blackhall, Fiona H, Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A, Peggs, Karl S, Schwarz, Roland F, Van Loo, Peter, Miedema, Daniel M, Birkbak, Nicolai J, Hiley, Crispin T, Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joa, Primrose, Lindsay, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Kaufmann, Tom L, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran L, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Rane, Jayant K, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Danson, Sarah, Robinson, Lily, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Lung Neoplasms, Treatment Outcome, DNA Copy Number Variations, Mutagenesis, Carcinoma, Non-Small-Cell Lung, Mutation, Smoking, Humans, Adenocarcinoma of Lung, Neoplasm Recurrence, Local, Phylogeny
Abstract

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

30. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Author

Karasaki, Takahiro, Moore, David A, Veeriah, Selvaraju, Naceur-Lombardelli, Cristina, Toncheva, Antonia, Magno, Neil, Ward, Sophia, Al Bakir, Maise, Watkins, Thomas BK, Grigoriadis, Kristiana, Huebner, Ariana, Hill, Mark S, Frankell, Alexander M, Abbosh, Christopher, Puttick, Clare, Zhai, Haoran, Gimeno-Valiente, Francisco, Saghafinia, Sadegh, Kanu, Nnennaya, Dietzen, Michelle, Pich, Oriol, Lim, Emilia L, Martinez-Ruiz, Carlos, Black, James RM, Biswas, Dhruva, Campbell, Brittany B, Lee, Claudia, Colliver, Emma, Enfield, Katey SS, Hessey, Sonya, Hiley, Crispin T, Zaccaria, Simone, Litchfield, Kevin, Birkbak, Nicolai J, Cadieux, Elizabeth Larose, Demeulemeester, Jonas, Van Loo, Peter, Adusumilli, Prasad R, Tan, Kay See, Cheema, Waseem, Sanchez-Vega, Francisco, Jones, David R, Rekhtman, Natasha, Travis, William D, Hackshaw, Allan, Marafioti, Teresa, Salgado, Roberto, Le Quesne, John, Nicholson, Andrew G, McGranahan, Nicholas, Swanton, Charles, Jamal-Hanjani, Mariam, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Castignani, Carla, Bailey, Chris, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Peggs, Karl S, Selvaraju, Kayalvizhi, Thol, Kerstin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Prymas, Paulina, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
TRACERx Consortium
Abstract

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

34. Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

Author

Hope, Andrew, primary, Verduin, Maikel, additional, Dilling, Thomas J, additional, Choudhury, Ananya, additional, Fijten, Rianne, additional, Wee, Leonard, additional, Aerts, Hugo JWL, additional, El Naqa, Issam, additional, Mitchell, Ross, additional, Vooijs, Marc, additional, Dekker, Andre, additional, de Ruysscher, Dirk, additional, and Traverso, Alberto, additional

Published
2021
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35. Defining the biological basis of radiomic phenotypes in lung cancer

Author

Grossmann, Patrick, primary, Stringfield, Olya, additional, El-Hachem, Nehme, additional, Bui, Marilyn M, additional, Rios Velazquez, Emmanuel, additional, Parmar, Chintan, additional, Leijenaar, Ralph TH, additional, Haibe-Kains, Benjamin, additional, Lambin, Philippe, additional, Gillies, Robert J, additional, and Aerts, Hugo JWL, additional

Published
2017
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37. Inter‐scan and inter‐observer tumour volume delineation variability on cone beam computed tomography in patients treated with stereotactic body radiation therapy for early‐stage non‐small cell lung cancer

Author

Hou, Ying, primary, Lee, Stephanie, additional, Agrawal, Vishesh, additional, Romano, John, additional, Baldini, Elizabeth H, additional, Chen, Aileen B, additional, Kozono, David E, additional, Killoran, Joseph H, additional, Wagar, Matthew, additional, Hacker, Fred L, additional, Aerts, Hugo JWL, additional, Lewis, John H, additional, and Mak, Raymond H, additional

Published
2016
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38. Assessment of pharmacogenomic agreement

Author

Safikhani, Zhaleh, primary, El-Hachem, Nehme, additional, Quevedo, Rene, additional, Smirnov, Petr, additional, Goldenberg, Anna, additional, Juul Birkbak, Nicolai, additional, Mason, Christopher, additional, Hatzis, Christos, additional, Shi, Leming, additional, Aerts, Hugo JWL, additional, Quackenbush, John, additional, and Haibe-Kains, Benjamin, additional

Published
2016
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39. Revisiting inconsistency in large pharmacogenomic studies

Author

Safikhani, Zhaleh, primary, Freeman, Mark, additional, Smirnov, Petr, additional, El-Hachem, Nehme, additional, She, Adrian, additional, Quevedo, Rene, additional, Goldenberg, Anna, additional, Juul Birkbak, Nicolai, additional, Hatzis, Christos, additional, Shi, Leming, additional, Beck, Andrew H, additional, Aerts, Hugo JWL, additional, Quackenbush, John, additional, and Haibe-Kains, Benjamin, additional

Published
2015
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40. Defining the biological basis of radiomic phenotypes in lung cancer

Author

Grossmann, Patrick, Stringfield, Olya, El-Hachem, Nehme, Bui, Marilyn M, Rios Velazquez, Emmanuel, Parmar, Chintan, Leijenaar, Ralph TH, Haibe-Kains, Benjamin, Lambin, Philippe, Gillies, Robert J, and Aerts, Hugo JWL

Subjects
radiomics, oncology, computational biology, imaging, genomics, Human
Abstract

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images. DOI: http://dx.doi.org/10.7554/eLife.23421.001

Published
2017
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41. Longitudinal risk prediction for pediatric glioma with temporal deep learning.

Author

Tak D, Garomsa BA, Zapaishchykova A, Ye Z, Vajapeyam S, Mahootiha M, Climent Pardo JC, Smith C, Familiar AM, Chaunzwa T, Liu KX, Prabhu S, Bandopadhayay P, Nabavizadeh A, Mueller S, Aerts HJ, Haas-Kogan D, Poussaint TY, and Kann BH

Abstract

Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient's current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

Published
2024
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42. Decoding biological age from face photographs using deep learning.

Author

Zalay O, Bontempi D, Bitterman DS, Birkbak N, Shyr D, Haugg F, Qian JM, Roberts H, Perni S, Prudente V, Pai S, Dekker A, Haibe-Kains B, Guthier C, Balboni T, Warren L, Krishan M, Kann BH, Swanton C, Ruysscher D, Mak RH, and Aerts HJ

Abstract

Because humans age at different rates, a person's physical appearance may yield insights into their biological age and physiological health more reliably than their chronological age. In medicine, however, appearance is incorporated into medical judgments in a subjective and non-standardized fashion. In this study, we developed and validated FaceAge, a deep learning system to estimate biological age from easily obtainable and low-cost face photographs. FaceAge was trained on data from 58,851 healthy individuals, and clinical utility was evaluated on data from 6,196 patients with cancer diagnoses from two institutions in the United States and The Netherlands. To assess the prognostic relevance of FaceAge estimation, we performed Kaplan Meier survival analysis. To test a relevant clinical application of FaceAge, we assessed the performance of FaceAge in end-of-life patients with metastatic cancer who received palliative treatment by incorporating FaceAge into clinical prediction models. We found that, on average, cancer patients look older than their chronological age, and looking older is correlated with worse overall survival. FaceAge demonstrated significant independent prognostic performance in a range of cancer types and stages. We found that FaceAge can improve physicians' survival predictions in incurable patients receiving palliative treatments, highlighting the clinical utility of the algorithm to support end-of-life decision-making. FaceAge was also significantly associated with molecular mechanisms of senescence through gene analysis, while age was not. These findings may extend to diseases beyond cancer, motivating using deep learning algorithms to translate a patient's visual appearance into objective, quantitative, and clinically useful measures.

Published
2023
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43. Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging.

Author

Pai S, Bontempi D, Prudente V, Hadzic I, Sokač M, Chaunzwa TL, Bernatz S, Hosny A, Mak RH, Birkbak NJ, and Aerts HJ

Abstract

Foundation models represent a recent paradigm shift in deep learning, where a single large-scale model trained on vast amounts of data can serve as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labeled datasets are often scarce. Here, we developed a foundation model for imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of imaging-based biomarkers. We found that they facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed their conventional supervised counterparts on downstream tasks. The performance gain was most prominent when training dataset sizes were very limited. Furthermore, foundation models were more stable to input and inter-reader variations and showed stronger associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering novel imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings., Competing Interests: COMPETING INTERESTS The authors declare no competing interests.

Published
2023
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