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Pierik AS, Poell JB, Brink A, Stigter-van Walsum M, de Roest RH, Poli T, Yaromin A, Lambin P, Leemans CR, Brakenhoff RH. Intratumor genetic heterogeneity and head and neck cancer relapse. Radiother Oncol 2024; 191:110087. [PMID: 38185257 DOI: 10.1016/j.radonc.2024.110087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/18/2023] [Accepted: 01/03/2024] [Indexed: 01/09/2024]
Abstract
BACKGROUND Head and neck squamous cell carcinomas are treated by surgery, radiotherapy (RT), chemoradiotherapy (CRT) or combinations thereof, but locoregional recurrences (LRs) occur in 30-40% of treated patients. We have previously shown that in approximately half of the LRs after CRT, cancer driver mutations are not shared with the index tumor. AIM To investigate two possible explanations for these genetically unrelated relapses, treatment-induced genetic changes and intratumor genetic heterogeneity. METHODS To investigate treatment-induced clonal DNA changes, we compared copy number alterations (CNAs) and mutations between primary and recurrent xenografted tumors after treatment with (C)RT. Intratumor genetic heterogeneity was studied by multi-region sequencing on DNA from 31 biopsies of 11 surgically treated tumors. RESULTS Induction of clonal DNA changes by (C)RT was not observed in the xenograft models. Multi-region sequencing demonstrated variations in CNA profiles between paired biopsies of individual tumors, with copy number heterogeneity scores varying from 0.027 to 0.333. In total, 32 cancer driver mutations could be identified and were shared in all biopsies of each tumor. Remarkably, multi-clonal mutations in these same cancer driver genes were observed in 6 of 11 tumors. Genetically distinct heterogeneous cell cultures could also be established from single tumors, with different biomarker profiles and drug sensitivities. CONCLUSION Intratumor genetic heterogeneity at the level of the cancer driver mutations might explain the discordant mutational profiles in LRs after CRT, while there are no indications in xenograft models that these changes are induced by CRT.
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Affiliation(s)
- A S Pierik
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
| | - J B Poell
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
| | - A Brink
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
| | - M Stigter-van Walsum
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
| | - R H de Roest
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands
| | - T Poli
- Maxillofacial Surgery Unit, Department of Medicine and Surgery - University of Parma, University Hospital of Parma, Via Gramsci 14, Parma, Italy
| | - A Yaromin
- Maastricht University, Department of Precision Medicine-UM & Radiology-MUMC, Universiteitssingel 40, Maastricht, the Netherlands
| | - P Lambin
- Maastricht University, Department of Precision Medicine-UM & Radiology-MUMC, Universiteitssingel 40, Maastricht, the Netherlands
| | - C R Leemans
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands
| | - R H Brakenhoff
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Otolaryngology/Head and Neck Surgery, Head and Neck Cancer Biology and Immunology laboratory, De Boelelaan 1117, Amsterdam, the Netherlands; Cancer Center Amsterdam, Cancer Biology and Immunology, De Boelelaan 1117, Amsterdam, the Netherlands.
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2
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Rogers W, Keek SA, Beuque M, Lavrova E, Primakov S, Wu G, Yan C, Sanduleanu S, Gietema HA, Casale R, Occhipinti M, Woodruff HC, Jochems A, Lambin P. Towards texture accurate slice interpolation of medical images using PixelMiner. Comput Biol Med 2023; 161:106701. [PMID: 37244145 DOI: 10.1016/j.compbiomed.2023.106701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 08/06/2022] [Accepted: 11/23/2022] [Indexed: 05/29/2023]
Abstract
Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.
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Affiliation(s)
- W Rogers
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - S A Keek
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - M Beuque
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - E Lavrova
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; GIGA Cyclotron Research Centre in Vivo Imaging, University of Liège, Liège, Belgium
| | - S Primakov
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - G Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - C Yan
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - S Sanduleanu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - H A Gietema
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - R Casale
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - M Occhipinti
- Radiomics, Clos Chanmurly 13, 4000, Liege, Belgium
| | - H C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - A Jochems
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - P Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
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3
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Widaatalla Y, Wolswijk T, Adan F, Hillen LM, Woodruff HC, Halilaj I, Ibrahim A, Lambin P, Mosterd K. The application of artificial intelligence in the detection of basal cell carcinoma: A systematic review. J Eur Acad Dermatol Venereol 2023; 37:1160-1167. [PMID: 36785993 DOI: 10.1111/jdv.18963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 01/05/2023] [Indexed: 02/15/2023]
Abstract
Basal cell carcinoma (BCC) is one of the most common types of cancer. The growing incidence worldwide and the need for fast, reliable and less invasive diagnostic techniques make a strong case for the application of different artificial intelligence techniques for detecting and classifying BCC and its subtypes. We report on the current evidence regarding the application of handcrafted and deep radiomics models used for the detection and classification of BCC in dermoscopy, optical coherence tomography and reflectance confocal microscopy. We reviewed all the articles that were published in the last 10 years in PubMed, Web of Science and EMBASE, and we found 15 articles that met the inclusion criteria. We included articles that are original, written in English, focussing on automated BCC detection in our target modalities and published within the last 10 years in the field of dermatology. The outcomes from the selected publications are presented in three categories depending on the imaging modality and to allow for comparison. The majority of articles (n = 12) presented different AI solutions for the detection and/or classification of BCC in dermoscopy images. The rest of the publications presented AI solutions in OCT images (n = 2) and RCM (n = 1). In addition, we provide future directions for the application of these techniques for the detection of BCC. In conclusion, the reviewed publications demonstrate the potential benefit of AI in the detection of BCC in dermoscopy, OCT and RCM.
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Affiliation(s)
- Y Widaatalla
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - T Wolswijk
- Department of Dermatology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - F Adan
- Department of Dermatology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - L M Hillen
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - H C Woodruff
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - I Halilaj
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - A Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - P Lambin
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - K Mosterd
- Department of Dermatology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
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Prades-Sagarra È, Biemans R, Lieuwes N, Lambin P, Yaromina A, Dubois L. PD-0488 Caffeic Acid Phenethyl Ester, a natural radiosensitizer for lung adenocarcinomas. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)02859-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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5
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Hendriks L, Keek S, Chatterjee A, Belderbos J, Bootsma G, van den Borne B, Dingemans AM, Gietema H, Groen H, Herder G, Pitz C, Praag J, De Ruysscher D, Schoenmaekers J, Smit H, Stigt J, Westenend M, Zeng H, Woodruff H, Lambin P. 127P Does radiomics have added value in predicting the development of brain metastases in patients with radically treated stage III non-small cell lung cancer (NSCLC)? Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.02.156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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6
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Bonmatí LM, Miguel A, Suárez A, Aznar M, Beregi JP, Fournier L, Neri E, Laghi A, França M, Sardanelli F, Penzkofer T, Lambin P, Blanquer I, Menzel M, Seymour K, Figueiras S, Krischak K, Martínez R, Mirsky Y, Yang G, Alberich-Bayarri Á. CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools. Front Oncol 2022; 12:742701. [PMID: 35280732 PMCID: PMC8913333 DOI: 10.3389/fonc.2022.742701] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/28/2022] [Indexed: 12/13/2022] Open
Abstract
The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.
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Affiliation(s)
- Luis Martí Bonmatí
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group Grupo de Investigación Biomédica en Imagen (GIBI2) at La Fe University and Polytechnic Hospital and Health Research Institute, Valencia, Spain,*Correspondence: Luis Martí Bonmatí,
| | - Ana Miguel
- Medical Imaging Department, La Fe University and Polytechnic Hospital & Biomedical Imaging Research Group Grupo de Investigación Biomédica en Imagen (GIBI2) at La Fe University and Polytechnic Hospital and Health Research Institute, Valencia, Spain
| | | | | | | | - Laure Fournier
- Collège des enseignants en radiologie de France, Paris, France
| | - Emanuele Neri
- Diagnostic Radiology 3, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Andrea Laghi
- Medicina Traslazionale e Oncologia, Sant Andrea Sapienza Rome, Rome, Italy
| | - Manuela França
- Department of Radiology, Centro Hospitalar Universitário do Porto, Porto, Portugal
| | - Francesco Sardanelli
- Servizio di Diagnostica per Immagini, “Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Donato, Milanese, Italy
| | - Tobias Penzkofer
- Department of Radiology, CHARITÉ-Universitätsmedizin Berlin, Berlin, Germany
| | - Phillipe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, Netherlands
| | - Ignacio Blanquer
- Computing Science Department, Universitat Politècnica de València, València, Spain
| | - Marion I. Menzel
- GE Healthcare, München, Germany,Department of Physics, Technical University of Munich, Garching, Germany
| | | | | | - Katharina Krischak
- European Institute for Biomedical Imaging Research, EIBIR gemeinnützige GmbH, Vienna, Austria
| | - Ricard Martínez
- Departamento de Derecho Constitucional, Ciencia Política y Administración, Universitat de València, València, Spain
| | - Yisroel Mirsky
- Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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7
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Granzier RWY, Ibrahim A, Primakov S, Keek SA, Halilaj I, Zwanenburg A, Engelen SME, Lobbes MBI, Lambin P, Woodruff HC, Smidt ML. Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability. J Magn Reson Imaging 2021; 56:592-604. [PMID: 34936160 PMCID: PMC9544420 DOI: 10.1002/jmri.28027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 12/14/2022] Open
Abstract
Background Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test–retest measurements. Study Type Prospective. Population 11 healthy female volunteers. Field Strength/Sequence 1.5 T; MRI exams, comprising T2‐weighted turbo spin‐echo (T2W) sequence, native T1‐weighted turbo gradient‐echo (T1W) sequence, diffusion‐weighted imaging (DWI) sequence using b‐values 0/150/800, and corresponding derived ADC maps. Assessment 18 MRI exams (three test–retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z‐score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z‐score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut‐off value of CCC > 0.90. Results Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z‐score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence 2 Technical Efficacy Stage 1
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Affiliation(s)
- R W Y Granzier
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - A Ibrahim
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium.,Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - S Primakov
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - S A Keek
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - I Halilaj
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Health Innovation Ventures, Maastricht, The Netherlands
| | - A Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden, Rossendorf, Dresden, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany.,Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz Association/Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
| | - S M E Engelen
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - M B I Lobbes
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands
| | - P Lambin
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - H C Woodruff
- GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands.,The D-Lab, Department of Precision Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - M L Smidt
- Department of Surgery, Maastricht University Medical Centre+, Maastricht, The Netherlands.,GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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8
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Yaromina A, Koi L, van der Wiel A, Lieuwes N, Theys J, Dubois L, Krause M, Lambin P. OC-0065 Overcoming radioresistance with the hypoxia-activated prodrug CP-506: a pre-clinical in vivo study. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)06759-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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9
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Keek S, Wesseling F, Woodruff H, van Timmeren J, Nauta I, Hoffmann T, Cavalieri S, Calareso G, Primakov S, Leijenaar R, Licitra L, Ravanelli M, Scheckenbach K, Poli T, Lanfranco D, Vergeer M, Leemans R, Brakenhoff R, Hoebers F, Lambin P. OC-0642 A radiomics based prognostic model for patients with head and neck squamous cell carcinoma. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)06998-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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van der Wiel A, Yaromina A, Lieuwes N, Biemans R, Theys J, Dubois L, Lambin P. PD-0833 Exploiting tumor DNA repair status with the novel hypoxia-activated DNA alkylating agent CP-506. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)07112-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Oberije C, Lieverse R, Van Wijk Y, Dubois L, Van der Wiel A, Marcus D, Sanduleanu S, Lambin P. PO-1542: Integrating biomarker performance in sample size calculations for therapeutic trials. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)01560-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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12
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Van der Wiel A, Marcus D, Niemans R, Yaromina A, Theys J, Mowday A, Ashoorzadeh A, Anderson R, Bull M, Abbattista M, Heyerick A, Guise C, Smaill J, Patterson A, Dubois L, Lambin P. OC-0562: Exploiting tumor DNA repair status and hypoxia with CP-506, a novel hypoxia-activated prodrug. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00584-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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Vaidyanathan A, Yousif W, Ibrahim A, Miraglio B, Leijenaar R, Woodruff H, Walsh S, Lambin P. PO-1710: A novel AI solution for auto-segmentation of multi-origin liver neoplasms. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)01728-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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14
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Marcus D, Van der Wiel A, Biemans R, Lieuwes N, Heyerick A, Smaill J, Patterson A, Theys J, Yaromina A, Lambin P, Dubois L. OC-0203: Eliminating tumour hypoxia to improve the impact of immunotherapy. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00227-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Chatterjee A, Woodruff H, Lobbes M, Wijk YV, Beuque M, Seuntjens J, Lambin P. Altering the Decision Threshold as a Simple and Effective Method for Machine Learning-Based Classification of Imbalanced Radiation Oncology Data. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.793] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Walsh S, Leijenaar R, Miraglio B, Barakat S, Zerka F, Vaidyanathan A, Lambin P. OC-0587: Prospective Validation of a Radiomics Signature for Chemoradiotherapy Lung Cancer Patients. Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00609-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Lieverse R, Van Limbergen E, Olivo Pimentol V, Oberije C, Neri D, Yaromina A, Dubois L, Lambin P. OC-0084: Combining RT with L19-IL2 and aPDL1: from preclinical results towards a phase II trial (ImmunoSABR). Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00110-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Sanduleanu S, Tamanupadhaya@gmail.com T, Klaassen R, Woodruff H, Hatt M, Kaanders J, Vrieze O, Laarhoven H, Subramiam R, Huang S, Bratman S, Dubois L, Miclea R, Di Perri D, Geets X, Crispin-Ortuzar M, Aptea A, Hun Oh J, Lee N, Humm J, Schoder H, Ruysscher D, Hoebers F, Lambin P. PO-1583: Non-invasive radiomic imaging prediction of tumour hypoxia: biomarker for FLASH irradiation? Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)01601-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Rattay T, Veal C, Azria D, Chang-Claude J, Davidson S, Dunning A, de Ruysscher D, Fachal L, Gutierrez-Enriquez S, Lambin P, Rancati T, Rosenstein B, Seibold P, Sperk E, Symonds R, Vega A, Veldeman L, Webb A, West C, Talbot C. Genome wide association study of acute radiation toxicity and quality of life in breast cancer patients – results from the REQUITE cohort study. Eur J Cancer 2020. [DOI: 10.1016/s0959-8049(20)30554-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Vaidyanathan A, Widaatalla Y, Ibrahim A, Zerka F, Woodruff H, Leijenaar R, Vos W, Walsh S, Lambin P. 4MO A novel AI solution for auto-segmentation of multi-origin liver neoplasms. Ann Oncol 2020. [DOI: 10.1016/j.annonc.2020.08.157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Hermans BCM, Sanduleanu S, Derks JL, Woodruff H, Hillen LM, Casale R, Hoesein FM, de Jong E, Berge DMHJT, Speel EJM, Lambin P, Gietema HA, Dingemans AMC. Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC). Lung Cancer 2020; 148:94-99. [PMID: 32858338 DOI: 10.1016/j.lungcan.2020.08.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/02/2020] [Accepted: 08/04/2020] [Indexed: 01/06/2023]
Abstract
OBJECTIVES Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC. MATERIALS AND METHODS Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients. RESULTS Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like). CONCLUSION LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.
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Affiliation(s)
- B C M Hermans
- Department of Pulmonary Diseases, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - S Sanduleanu
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; The D-Lab, Department of Precision Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - J L Derks
- Department of Pulmonary Diseases, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - H Woodruff
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands; The D-Lab, Department of Precision Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - L M Hillen
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Pathology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands
| | - R Casale
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; The D-Lab, Department of Precision Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - F Mohamed Hoesein
- Department of Radiology, University Medical Centre Utrecht, P.O. Box 85500, 3508 GA Utrecht, the Netherlands
| | - E de Jong
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; The D-Lab, Department of Precision Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - D M H J Ten Berge
- Department of Radiology, Erasmus Medical Centre, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands; Department of Pulmonology, Erasmus Medical Centre, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
| | - E J M Speel
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Pathology, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands
| | - P Lambin
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands; The D-Lab, Department of Precision Medicine, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - H A Gietema
- GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, the Netherlands
| | - A-M C Dingemans
- Department of Pulmonary Diseases, Maastricht University Medical Centre+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands; GROW - School for Oncology & Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands; Department of Pulmonology, Erasmus Medical Centre, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands.
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Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G, Refaee T, Granzier R, Widaatalla Y, Hustinx R, Mottaghy FM, Lambin P. Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods 2020; 188:20-29. [PMID: 32504782 DOI: 10.1016/j.ymeth.2020.05.022] [Citation(s) in RCA: 98] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/20/2020] [Accepted: 05/26/2020] [Indexed: 12/13/2022] Open
Abstract
The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.
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Affiliation(s)
- A Ibrahim
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.
| | - S Primakov
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - M Beuque
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - H C Woodruff
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - I Halilaj
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - G Wu
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - T Refaee
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - R Granzier
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Surgery, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Y Widaatalla
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R Hustinx
- Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium
| | - F M Mottaghy
- Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
| | - P Lambin
- The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Lambin P. SP-102: Radiomics: transforming standard imaging into mineable data related for diagnostic and theragnostic applications. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(20)30606-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Ankolekar A, Vanneste B, Bloemen E, Van Roermund J, Van Limbergen E, Van de Beek K, Zambon V, Oelke M, Dekker A, Lambin P, Fijten R, Berlanga A. PO-0855 Development and Validation of a Prostate Cancer Patient Decision Aid: Towards Participative Medicine. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)31275-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Sanduleanu S, Jochems A, Upadhaya T, Even A, Leijenaar R, Dankers F, Klaassen R, Woodruff H, Hatt M, Kaanders H, Hamming-Vrieze O, Van Laarhoven H, Subramiam R, Huang S, O’Sullivan B, Bratman S, Dubois L, Miclea R, Di Perri D, Geets X, De Ruysscher D, Hoebers F, Lambin P. PO-0733 Non-invasive imaging for tumor hypoxia: a novel validated CT and FDG-PET-based Radiomic signature. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)31153-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Osman S, Leijenaar R, Cole A, Hounsell A, Prise K, O'Sullivan J, Lambin P, McGarry C, Jain S. OC-0407 CT-based Radiomics for Risk Stratification in Prostate Cancer. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30827-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Yaromina A, Dubois L, Lambin P. SP-0053 Exploiting low drug uptake volume for dose painting. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30473-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Van Gisbergen M, Masroor S, Smeets E, Verhesen M, Stassen A, Dubois L, Oberije C, Smeets H, Lambin P. OC-0377 Individual radiation toxicity prediction, how does mtDNA influence normal tissue response? Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30797-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ou D, Adam J, Garberis I, Blanchard P, Nguyen F, Levy A, Casiraghi O, Leijenaar R, Gorphe P, Breuskin I, Janot F, Robert C, Lambin P, Temam S, Scoazec J, Deutsch E, Tao Y. OC-0586 Immunological contexture basis of a prognostic radiomics signature in head and neck cancers. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)31006-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Talbot C, Azria D, Burr T, Chang-Claude J, Dunning A, Jacquet MF, Herskind C, De Ruysscher D, Elliott R, Gutiérrez-Enríquez S, Lambin P, Müller A, Rancati T, Rattay T, Rosenstein B, Seibold P, Valdagni R, Vega A, Veldeman L, Veldwijk M, Wenz F, Webb A, West C. OC-0647 Analysis of biomarkers for late radiotherapy toxicity in the REQUITE project. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)31067-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Deist T, Dankers F, Ojha P, Marshall S, Janssen T, Faivre-Finn C, Masciocchi C, Valentini V, Wang J, Chen J, Zhang Z, Spezi E, Button M, Nuyttens J, Vernhout R, Van Soest J, Jochems A, Monshouwer R, Bussink J, Price G, Lambin P, Dekker A. OC-0544 Distributed learning on 20 000+ lung cancer patients. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30964-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Bogowicz M, Jochems A, Huang S, Chan B, Waldron J, O'Sullivan B, Tanadini-Lang S, Riesterer O, Studer G, Unkelbach J, Brakenhoff R, Nauta I, Gazzani S, Calareso G, Scheckenbach K, Hoebers F, Barakat S, Keek S, Sanduleanu S, Vergeer M, Leemans R, Terhaard C, Van den Brekel M, Guckenberger M, Lambin P. PV-0312 Distributed learning in radiomics to predict overall survival in head and neck cancer. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30732-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Lambin P. SP-004 Against the motion. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30170-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Compter I, Verduin M, Woodruff HC, Leijenaar RTH, Postma AA, Hoeben A, Eekers DBP, Lambin P. P01.117 Differentiating high grade gliomas with CT based radiomics. Neuro Oncol 2018. [DOI: 10.1093/neuonc/noy139.159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- I Compter
- Dept. of Radiation-Oncology (MAASTRO), GROW (School for Oncology & Developmental Biology), Maastricht, Netherlands
| | - M Verduin
- Department of Medical Oncology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - H C Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, Netherlands
| | - R T H Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, Netherlands
| | - A A Postma
- Dept. of Radiology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - A Hoeben
- Department of Medical Oncology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - D B P Eekers
- Dept. of Radiation-Oncology (MAASTRO), GROW (School for Oncology & Developmental Biology), Maastricht, Netherlands
- Proton Therapy Department South-East Netherlands (ZON-PTC), Maastricht, Netherlands
| | - P Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology & MCCC, Maastricht University Medical Centre, Maastricht, Netherlands
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Pimentel VO, Marcus D, Van der Wiel A, Biemans R, Lieuwes N, Neri D, Theys J, Yaromina A, Dubois L, Lambin P. PO-428 Radiotherapy, immunocytokines and immune checkpoint inhibitors: finding the optimal combination. ESMO Open 2018. [DOI: 10.1136/esmoopen-2018-eacr25.939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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Olivo Pimentel V, Rekers N, Yaromina A, Lieuwes N, Biemans R, Zegers C, Germeraad W, Van Limbergen E, Neri D, Dubois L, Lambin P. OC-0051: Radiotherapy causes long-lasting antitumor immunological memory when combined with immunotherapy. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30361-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Van Gisbergen M, Offermans K, Voets A, Lieuwes N, Biemans R, Hoffmann R, Dubois L, Lambin P. EP-2329: Mitochondrial dysfunction inhibits HIF-1α stabilization and expression of downstream targets. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)32638-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Van Timmeren J, Leijenaar R, Van Elmpt W, Lambin P. EP-2112: How accurate should a GTV delineation be for radiomics? A study in NSCLC patients. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)32421-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Refaee T, Ibrahim A, Ibrahim H, Leijenaar R, Jochems A, Larue R, De Jong E, Verhaegen F, Dubois L, Lambin P. EP-2015: Radiomics can detect changes in lung after low dose irradiation: a preclinical study. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)32324-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Oberije C, Van Gisbergen M, Smeets E, Smeets B, Lambin P. SP-0115: Predicting radiation toxicity: what is the link between mitochondrial DNA and individual radio sensitivity? Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30425-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Talbot C, Azria D, Burr T, Chang-Claude J, Dunning A, Herskind C, De Ruysscher D, Elliott R, Gutiérrez-Enríquez S, Lambin P, Müller A, Rancati T, Rosenstein B, Rattay T, Seibold P, Veldeman L, Vega A, Wenz F, Valdagni R, Webb A, West C. SP-0483: The REQUITE project: integrating biomarkers and clinical predictors of radiotherapy side effects. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30793-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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42
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Spiegelberg L, Biemans R, Lieuwes N, Niemans R, Theys J, Yaromina A, Verhaegen F, Lambin P, Dubois L. PO-1061: Evofosfamide sensitizes esophageal carcinomas to radiation without increasing normal tissue toxicity. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)31371-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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43
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Masciocchi C, Cordelli E, Sicilia R, Dinapoli N, Damiani A, Barbaro B, Boldrini L, Casà C, Cusumano D, Chiloiro G, Gambacorta M, Gatta R, Lenkowicz J, Van Soest J, Dekker A, Lambin P, Soda P, Iannello G, Valentini V. PO-0799: An externally validated MRI radiomics model for predicting clinical response in rectal cancer. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)31109-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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44
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Lambin P. SP-0016: Immunocytokines the ideal immunotherapy to combine with radiotherapy? Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30327-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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45
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Marcus D, Olivo Pimentel V, Van der Wiel A, Biemans R, Lieuwes N, Neri D, Theys J, Yaromina A, Dubois L, Lambin P. OC-0055: Immunocytokine, immune checkpoint inhibitor and radiotherapy: finding the right combination. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30365-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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46
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De Jong E, Van Elmpt W, Rizzo S, Leijenaar R, Refaee T, Hendriks L, Reymen B, Dingemans A, Lambin P. EP-1380: Can radiomic features describe lung semantic features in NSCLC patients? Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)31689-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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47
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West C, Elliott R, Talbot C, Webb A, Seibold P, Azria D, De Ruysscher D, Symonds R, Veldeman L, Rosenstein B, Lambin P, Burr T, Gutiérrez Enríquez S, Rancati T, Vega A, Chang-Claude J. OC-0154: REQUITE Big Data Resource for Validating Predictive Models and Biomarkers of Radiotherapy Toxicity. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)30464-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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48
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Rattay T, Johnson K, Azria D, Chang-Claude J, Davidson S, Dunning A, De Ruysscher D, Gutierrez-Enriquez S, Lambin P, Rancati T, Rosenstein B, Seibold P, Symonds R, Valdagni R, Vega A, Veldeman L, Webb A, Wenz F, West C, Talbot C. Acute toxicity and quality of life in breast cancer patients treated by radiotherapy – results from the REQUITE multi-centre cohort study. Eur J Cancer 2018. [DOI: 10.1016/s0959-8049(18)30404-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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49
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Yaromina A, Knight J, Dubois L, Bauwens M, Biemans R, Lieuwes N, Cornelissen B, Lambin P. EP-2324: Non-invasive PET imaging of radiosensitive tumour regions using γH2AX-targeted immunoconjugate. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)32633-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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50
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Niemans R, Yaromina A, Theys J, Marcus D, Ashoorzadeh A, Abbattista M, Mowday A, Biemans R, Lieuwes N, Deschoemaeker S, Heyerick A, Guise C, Smaill J, Patterson A, Dubois L, Lambin P. EP-2327: Hypoxic cell killing by CP-506, a novel hypoxia-activated prodrug. Radiother Oncol 2018. [DOI: 10.1016/s0167-8140(18)32636-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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