1
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Ocaña-Tienda B, Pérez-Beteta J, Jiménez-Sánchez J, Molina-García D, Ortiz de Mendivil A, Asenjo B, Albillo D, Pérez-Romasanta LA, Valiente M, Zhu L, García-Gómez P, González-Del Portillo E, Llorente M, Carballo N, Arana E, Pérez-García VM. Growth exponents reflect evolutionary processes and treatment response in brain metastases. NPJ Syst Biol Appl 2023; 9:35. [PMID: 37479705 PMCID: PMC10361973 DOI: 10.1038/s41540-023-00298-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023] Open
Abstract
Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.
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Affiliation(s)
| | | | | | | | | | - Beatriz Asenjo
- Hospital Regional Universitario de Málaga, Málaga, Spain
| | | | | | - Manuel Valiente
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Lucía Zhu
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Pedro García-Gómez
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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2
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Ocaña-Tienda B, Pérez-Beteta J, Villanueva-García JD, Romero-Rosales JA, Molina-García D, Suter Y, Asenjo B, Albillo D, Ortiz de Mendivil A, Pérez-Romasanta LA, González-Del Portillo E, Llorente M, Carballo N, Nagib-Raya F, Vidal-Denis M, Luque B, Reyes M, Arana E, Pérez-García VM. A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data. Sci Data 2023; 10:208. [PMID: 37059722 PMCID: PMC10104872 DOI: 10.1038/s41597-023-02123-0] [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: 09/14/2022] [Accepted: 03/30/2023] [Indexed: 04/16/2023] Open
Abstract
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain.
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | | | - José A Romero-Rosales
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Yannick Suter
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Beatriz Asenjo
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - David Albillo
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | | | | | - Manuel Llorente
- Radiology Department, MD Anderson Cancer Center, Madrid, Spain
| | | | - Fátima Nagib-Raya
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Maria Vidal-Denis
- Radiology Department, Hospital Regional Universitario de Málaga, Málaga, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
| | - Mauricio Reyes
- Medical Image Analysis Group, ARTORG Research Center, Bern, Switzerland
| | - Estanislao Arana
- Radiology Department, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MOLAB), University of Castilla-La Mancha, Ciudad Real, Spain
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Ocaña-Tienda B, Pérez-Beteta J, Molina-García D, Asenjo B, Ortiz de Mendivil A, Albillo D, Pérez-Romasanta L, González del Portillo E, Llorente M, Carballo N, Arana E, Pérez-García V. Growth dynamics of brain metastases differentiate radiation necrosis from recurrence. Neurooncol Adv 2022; 5:vdac179. [PMID: 36726366 PMCID: PMC9887079 DOI: 10.1093/noajnl/vdac179] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Radiation necrosis (RN) is a frequent adverse event after fractionated stereotactic radiotherapy (FSRT) or single-session stereotactic radiosurgery (SRS) treatment of brain metastases (BMs). It is difficult to distinguish RN from progressive disease (PD) due to their similarities in the magnetic resonance images. Previous theoretical studies have hypothesized that RN could have faster, although transient, growth dynamics after FSRT/SRS, but no study has proven that hypothesis using patient data. Thus, we hypothesized that lesion size time dynamics obtained from growth laws fitted with data from sequential volumetric measurements on magnetic resonance images may help in discriminating recurrent BMs from RN events. Methods A total of 101 BMs from different institutions, growing after FSRT/SRS (60 PDs and 41 RNs) in 86 patients, displaying growth for at least 3 consecutive MRI follow-ups were selected for the study from a database of 1031 BMs. The 3 parameters of the Von Bertalanffy growth law were determined for each BM and used to discriminate statistically PDs from RNs. Results Growth exponents in patients with RNs were found to be substantially larger than those of PD, due to the faster, although transient, dynamics of inflammatory processes. Statistically significant differences (P < .001) were found between both groups. The receiver operating characteristic curve (AUC = 0.76) supported the ability of the growth law exponent to classify the events. Conclusions Growth law exponents obtained from sequential longitudinal magnetic resonance images after FSRT/SRS can be used as a complementary tool in the differential diagnosis between RN and PD.
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Affiliation(s)
- Beatriz Ocaña-Tienda
- Corresponding Author: Beatriz Ocaña-Tienda, Mathematical Oncology Laboratory, University of Castilla-La Mancha, Avda Camilo José Cela n2 13071, Ciudad Real, Spain ()
| | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Beatriz Asenjo
- Department of Radiology, Hospital Regional Universitario Carlos Haya, Málaga, Spain
| | - Ana Ortiz de Mendivil
- Department of Radiology, Sanchinarro University Hospital, HM Hospitales, Madrid, Spain
| | - David Albillo
- Radiology Unit, MD Anderson Cancer Center, Madrid, Spain
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4
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Pineda-Pardo JA, Gasca-Salas C, Fernández-Rodríguez B, Rodríguez-Rojas R, Del Álamo M, Obeso I, Hernández-Fernández F, Trompeta C, Martínez-Fernández R, Matarazzo M, Mata-Marín D, Guida P, Duque A, Albillo D, Plaza de Las Heras I, Montero JI, Foffani G, Toltsis G, Rachmilevitch I, Blesa J, Obeso JA. Striatal Blood-Brain Barrier Opening in Parkinson's Disease Dementia: A Pilot Exploratory Study. Mov Disord 2022; 37:2057-2065. [PMID: 35765711 DOI: 10.1002/mds.29134] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 05/02/2022] [Accepted: 06/01/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) exhibits a high prevalence of dementia as disease severity and duration progress. Focused ultrasound (FUS) has been applied for transient blood-brain barrier (BBB) opening of cortical regions in neurodegenerative disorders. The striatum is a primary target for delivery of putative therapeutic agents in PD. OBJECTIVE Here, we report a prospective, single-arm, nonrandomized, proof-of-concept, phase I clinical trial (NCT03608553 amended) in PD with dementia to test the safety and feasibility of striatal BBB opening in PD patients. METHODS Seven PD patients with cognitive impairment were treated for BBB opening in the posterior putamen. This was performed in two sessions separated by 2 to 4 weeks, where the second session included bilateral putamina opening in 3 patients. Primary outcome measures included safety and feasibility of focal striatal BBB opening. Changes in motor and cognitive functions, magnetic resonance imaging (MRI), 18 F-fluorodopa (FDOPA), and β-amyloid PET (positron emission tomography) images were determined. RESULTS The procedure was feasible and well tolerated, with no serious adverse events. No neurologically relevant change in motor and cognitive (battery of neuropsychological tests) functions was recognized at follow-up. MRI revealed putamen BBB closing shortly after treatment (24 hours to 14 days) and ruled out hemorrhagic and ischemic lesions. There was a discrete but significant reduction in β-amyloid uptake in the targeted region and no change in FDOPA PET. CONCLUSIONS These initial results indicate that FUS-mediated striatal BBB opening is feasible and safe and therefore could become an effective tool to facilitate the delivery of putative neurorestorative molecules in PD. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- José A Pineda-Pardo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain.,University CEU-San Pablo, Madrid, Spain
| | - Carmen Gasca-Salas
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain.,University CEU-San Pablo, Madrid, Spain
| | - Beatriz Fernández-Rodríguez
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,PhD Program in Neuroscience, Autonoma de Madrid University, Madrid, Spain
| | - Rafael Rodríguez-Rojas
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain
| | - Marta Del Álamo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Ignacio Obeso
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain
| | - Frida Hernández-Fernández
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Clara Trompeta
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Raúl Martínez-Fernández
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain
| | - Michele Matarazzo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain
| | - David Mata-Marín
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Pasqualina Guida
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Alicia Duque
- Neuroradiology Unit, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - David Albillo
- Neuroradiology Unit, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | | | - Juan I Montero
- Intensive Care Unit, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain.,Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
| | | | | | - Javier Blesa
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain
| | - José A Obeso
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Instituto Carlos III, Madrid, Spain.,University CEU-San Pablo, Madrid, Spain
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5
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Pérez-García VM, Calvo GF, Bosque JJ, León-Triana O, Jiménez J, Perez-Beteta J, Belmonte-Beitia J, Valiente M, Zhu L, García-Gómez P, Sánchez-Gómez P, Hernández-San Miguel E, Hortigüela R, Azimzade Y, Molina-García D, Martinez Á, Rojas ÁA, de Mendivil AO, Vallette F, Schucht P, Murek M, Pérez-Cano M, Albillo D, Honguero Martínez AF, Jiménez Londoño GA, Arana E, García Vicente AM. Universal scaling laws rule explosive growth in human cancers. Nat Phys 2020; 16:1232-1237. [PMID: 33329756 PMCID: PMC7116451 DOI: 10.1038/s41567-020-0978-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Most physical and other natural systems are complex entities composed of a large number of interacting individual elements. It is a surprising fact that they often obey the so-called scaling laws relating an observable quantity with a measure of the size of the system. Here we describe the discovery of universal superlinear metabolic scaling laws in human cancers. This dependence underpins increasing tumour aggressiveness, due to evolutionary dynamics, which leads to an explosive growth as the disease progresses. We validated this dynamic using longitudinal volumetric data of different histologies from large cohorts of cancer patients. To explain our observations we put forward increasingly-complex biologically-inspired mathematical models that captured the key processes governing tumor growth. Our models predicted that the emergence of superlinear allometric scaling laws is an inherently three-dimensional phenomenon. Moreover, the scaling laws thereby identified allowed us to define a set of metabolic metrics with prognostic value, thus providing added clinical utility to the base findings.
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Affiliation(s)
- Víctor M. Pérez-García
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
- Correspondence and requests for materials should be addressed to V.M. Pérez-García (>)
| | - Gabriel F. Calvo
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
| | - Jesús J. Bosque
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
| | | | - Juan Jiménez
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
| | | | | | - Manuel Valiente
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Lucía Zhu
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Pedro García-Gómez
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | | | - Rafael Hortigüela
- Neuro-oncology Unit, Health Institute Carlos III-UFIEC, Madrid, Spain
| | | | | | - Álvaro Martinez
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
- Department of Mathematics, Universidad de Cádiz, Spain
| | - Ángel Acosta Rojas
- Department of Radiation Oncology, Sanchinarro University Hospital, HM Hospitales, Spain
| | | | - Francois Vallette
- Inserm U1232, Centre de Recherche en Cancérologie et Immunologie Nantes-Angers, Nantes, F-44007, France
| | | | | | - María Pérez-Cano
- Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Spain
| | - David Albillo
- Radiology Unit, MD Anderson Cancer Center, Madrid, Spain
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6
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Pérez-Beteta J, Molina-García D, Villena M, Rodríguez MJ, Velásquez C, Martino J, Meléndez-Asensio B, Rodríguez de Lope Á, Morcillo R, Sepúlveda JM, Hernández-Laín A, Ramos A, Barcia JA, Lara PC, Albillo D, Revert A, Arana E, Pérez-García VM. Morphologic Features on MR Imaging Classify Multifocal Glioblastomas in Different Prognostic Groups. AJNR Am J Neuroradiol 2019; 40:634-640. [PMID: 30923085 DOI: 10.3174/ajnr.a6019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/25/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND PURPOSE Multifocal glioblastomas (ie, glioblastomas with multiple foci, unconnected in postcontrast pretreatment T1-weighted images) represent a challenge in clinical practice due to their poor prognosis. We wished to obtain imaging biomarkers with prognostic value that have not been found previously. MATERIALS AND METHODS A retrospective review of 1155 patients with glioblastomas from 10 local institutions during 2006-2017 provided 97 patients satisfying the inclusion criteria of the study and classified as having multifocal glioblastomas. Tumors were segmented and morphologic features were computed using different methodologies: 1) measured on the largest focus, 2) aggregating the different foci as a whole, and 3) recording the extreme value obtained for each focus. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell concordance indices (c-indices) were used for the statistical analysis. RESULTS Age (P < .001, hazard ratio = 2.11, c-index = 0.705), surgery (P < .001, hazard ratio = 2.04, c-index = 0.712), contrast-enhancing rim width (P < .001, hazard ratio = 2.15, c-index = 0.704), and surface regularity (P = .021, hazard ratio = 1.66, c-index = 0.639) measured on the largest focus were significant independent predictors of survival. Maximum contrast-enhancing rim width (P = .002, hazard ratio = 2.05, c-index = 0.668) and minimal surface regularity (P = .036, hazard ratio = 1.64, c-index = 0.600) were also significant. A multivariate model using age, surgery, and contrast-enhancing rim width measured on the largest foci classified multifocal glioblastomas into groups with different outcomes (P < .001, hazard ratio = 3.00, c-index = 0.853, median survival difference = 10.55 months). Moreover, quartiles with the highest and lowest individual prognostic scores based on the focus with the largest volume and surgery were identified as extreme groups in terms of survival (P < .001, hazard ratio = 18.67, c-index = 0.967). CONCLUSIONS A prognostic model incorporating imaging findings on pretreatment postcontrast T1-weighted MRI classified patients with glioblastoma into different prognostic groups.
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Affiliation(s)
- J Pérez-Beteta
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | - D Molina-García
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
| | | | - M J Rodríguez
- Radiology (M.J.R.), Hospital General de Ciudad Real, Ciudad Real, Spain
| | - C Velásquez
- Department of Neurosurgery (J.M., C.V.), Hospital Universitario Marqués de Valdecilla and Fundación, Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - J Martino
- Department of Neurosurgery (J.M., C.V.), Hospital Universitario Marqués de Valdecilla and Fundación, Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | | | | | - R Morcillo
- Radiology (R.M.), Hospital Virgen de la Salud, Toledo, Spain
| | | | | | - A Ramos
- Radiology (A. Ramos), Hospital Universitario 12 de Octubre, Madrid, Spain
| | - J A Barcia
- Department of Neurosurgery (J.A.B.), Hospital Clínico San Carlos, Madrid, Spain
| | - P C Lara
- Department of Radiation Oncology (P.C.L.), San Roque University Hospital/Universidad Fernando Pessoa Canarias, Gran Canaria, Spain
| | - D Albillo
- Department of Radiology (D.A.), Hospital Universitario de Salamanca, Salamanca, Spain
| | - A Revert
- Department of Radiology (A. Revert), Hospital de Manises, Valencia, Spain
| | - E Arana
- Department of Radiology (E.A.), Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - V M Pérez-García
- From the Department of Mathematics (J.P.-B., D.M.-G., V.M.P.-G.), Mathematical Oncology Laboratory, Universidad de Castilla-La Mancha, Ciudad Real, Spain
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7
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Pérez-Beteta J, Molina-García D, Martínez-González A, Henares-Molina A, Amo-Salas M, Luque B, Arregui E, Calvo M, Borrás JM, Martino J, Velásquez C, Meléndez-Asensio B, de Lope ÁR, Moreno R, Barcia JA, Asenjo B, Benavides M, Herruzo I, Lara PC, Cabrera R, Albillo D, Navarro M, Pérez-Romasanta LA, Revert A, Arana E, Pérez-García VM. Correction to: Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol 2018; 29:2729. [PMID: 30547198 DOI: 10.1007/s00330-018-5870-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The original version of this article, published on 15 October 2018, unfortunately contained a mistake. The following correction has therefore been made in the original: The name of Mariano Amo-Salas and the affiliation of Ismael Herruzo were presented incorrectly.
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Affiliation(s)
- Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain.
| | - Alicia Martínez-González
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Araceli Henares-Molina
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Mariano Amo-Salas
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Elena Arregui
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Manuel Calvo
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - José M Borrás
- Department of Neurosurgery, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Juan Martino
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - Carlos Velásquez
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | | | | | - Raquel Moreno
- Department of Radiology, Hospital Virgen de la Salud, Toledo, Spain
| | - Juan A Barcia
- Department of Neurosurgery, Hospital Clínico San Carlos, Madrid, Spain
| | - Beatriz Asenjo
- Department of Radiology, Hospital Carlos Haya, Málaga, Spain
| | - Manuel Benavides
- Department of Medical Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Ismael Herruzo
- Department of Radiation Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Pedro C Lara
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - Raquel Cabrera
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - David Albillo
- Department of Radiology, Hospital Universitario de Salamanca, Salamanca, Spain
| | - Miguel Navarro
- Department of Medical Oncology, Hospital Universitario de Salamanca, Salamanca, Spain
| | | | - Antonio Revert
- Department of Radiology, Hospital de Manises, Valencia, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
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Pérez-Beteta J, Molina-García D, Martínez-González A, Henares-Molina A, Amo-Salas M, Luque B, Arregui E, Calvo M, Borrás JM, Martino J, Velásquez C, Meléndez-Asensio B, de Lope ÁR, Moreno R, Barcia JA, Asenjo B, Benavides M, Herruzo I, Lara PC, Cabrera R, Albillo D, Navarro M, Pérez-Romasanta LA, Revert A, Arana E, Pérez-García VM. Morphological MRI-based features provide pretreatment survival prediction in glioblastoma. Eur Radiol 2018; 29:1968-1977. [PMID: 30324390 DOI: 10.1007/s00330-018-5758-7] [Citation(s) in RCA: 12] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 08/19/2018] [Accepted: 09/12/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVES We wished to determine whether tumor morphology descriptors obtained from pretreatment magnetic resonance images and clinical variables could predict survival for glioblastoma patients. METHODS A cohort of 404 glioblastoma patients (311 discoveries and 93 validations) was used in the study. Pretreatment volumetric postcontrast T1-weighted magnetic resonance images were segmented to obtain the relevant morphological measures. Kaplan-Meier, Cox proportional hazards, correlations, and Harrell's concordance indexes (c-indexes) were used for the statistical analysis. RESULTS A linear prognostic model based on the outstanding variables (age, contrast-enhanced (CE) rim width, and surface regularity) identified a group of patients with significantly better survival (p < 0.001, HR = 2.57) with high accuracy (discovery c-index = 0.74; validation c-index = 0.77). A similar model applied to totally resected patients was also able to predict survival (p < 0.001, HR = 3.43) with high predictive value (discovery c-index = 0.81; validation c-index = 0.92). Biopsied patients with better survival were well identified (p < 0.001, HR = 7.25) by a model including age and CE volume (c-index = 0.87). CONCLUSIONS Simple linear models based on small sets of meaningful MRI-based pretreatment morphological features and age predicted survival of glioblastoma patients to a high degree of accuracy. The partition of the population using the extent of resection improved the prognostic value of those measures. KEY POINTS • A combination of two MRI-based morphological features (CE rim width and surface regularity) and patients' age outperformed previous prognosis scores for glioblastoma. • Prognosis models for homogeneous surgical procedure groups led to even more accurate survival prediction based on Kaplan-Meier analysis and concordance indexes.
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Affiliation(s)
- Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - David Molina-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain.
| | - Alicia Martínez-González
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Araceli Henares-Molina
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Mariano Amo-Salas
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Belén Luque
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
| | - Elena Arregui
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Manuel Calvo
- Department of Radiology, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - José M Borrás
- Department of Neurosurgery, Hospital General de Ciudad Real, Ciudad Real, Spain
| | - Juan Martino
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | - Carlos Velásquez
- Department of Neurosurgery, Hospital Universitario Marqués de Valdecilla and Fundación Instituto de Investigación Marqués de Valdecilla, Santander, Spain
| | | | | | - Raquel Moreno
- Department of Radiology, Hospital Virgen de la Salud, Toledo, Spain
| | - Juan A Barcia
- Department of Neurosurgery, Hospital Clínico San Carlos, Madrid, Spain
| | - Beatriz Asenjo
- Department of Radiology, Hospital Carlos Haya, Málaga, Spain
| | - Manuel Benavides
- Department of Medical Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Ismael Herruzo
- Department of Radiation Oncology, Hospital Carlos Haya, Málaga, Spain
| | - Pedro C Lara
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - Raquel Cabrera
- Department of Radiation Oncology, Hospital Universitario Doctor Negrín, Gran Canaria, Spain
| | - David Albillo
- Department of Radiology, Hospital Universitario de Salamanca, Salamanca, Spain
| | - Miguel Navarro
- Department of Medical Oncology, Hospital Universitario de Salamanca, Salamanca, Spain
| | | | - Antonio Revert
- Department of Radiology, Hospital de Manises, Valencia, Spain
| | - Estanislao Arana
- Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain
| | - Víctor M Pérez-García
- Mathematical Oncology Laboratory (MôLAB), Department of Mathematics, Universidad de Castilla-La Mancha, 13071, Ciudad Real, Spain
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Vera L, Pérez-Beteta J, Molina D, Borrás JM, Benavides M, Barcia JA, Velásquez C, Albillo D, Lara P, Pérez-García VM. P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods. Neuro Oncol 2017. [DOI: 10.1093/neuonc/nox036.317] [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: 11/12/2022] Open
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Pérez-Beteta J, Martínez-González A, Molina D, Amo-Salas M, Luque B, Arregui E, Calvo M, Borrás JM, López C, Claramonte M, Barcia JA, Iglesias L, Avecillas J, Albillo D, Navarro M, Villanueva JM, Paniagua JC, Martino J, Velásquez C, Asenjo B, Benavides M, Herruzo I, Delgado MDC, Del Valle A, Falkov A, Schucht P, Arana E, Pérez-Romasanta L, Pérez-García VM. Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study. Eur Radiol 2016; 27:1096-1104. [PMID: 27329522 DOI: 10.1007/s00330-016-4453-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [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: 03/29/2016] [Revised: 05/11/2016] [Accepted: 05/25/2016] [Indexed: 10/21/2022]
Abstract
BACKGROUND The potential of a tumour's volumetric measures obtained from pretreatment MRI sequences of glioblastoma (GBM) patients as predictors of clinical outcome has been controversial. Mathematical models of GBM growth have suggested a relation between a tumour's geometry and its aggressiveness. METHODS A multicenter retrospective clinical study was designed to study volumetric and geometrical measures on pretreatment postcontrast T1 MRIs of 117 GBM patients. Clinical variables were collected, tumours segmented, and measures computed including: contrast enhancing (CE), necrotic, and total volumes; maximal tumour diameter; equivalent spherical CE width and several geometric measures of the CE "rim". The significance of the measures was studied using proportional hazards analysis and Kaplan-Meier curves. RESULTS Kaplan-Meier and univariate Cox survival analysis showed that total volume [p = 0.034, Hazard ratio (HR) = 1.574], CE volume (p = 0.017, HR = 1.659), spherical rim width (p = 0.007, HR = 1.749), and geometric heterogeneity (p = 0.015, HR = 1.646) were significant parameters in terms of overall survival (OS). Multivariable Cox analysis for OS provided the later two parameters as age-adjusted predictors of OS (p = 0.043, HR = 1.536 and p = 0.032, HR = 1.570, respectively). CONCLUSION Patients with tumours having small geometric heterogeneity and/or spherical rim widths had significantly better prognosis. These novel imaging biomarkers have a strong individual and combined prognostic value for GBM patients. KEY POINTS • Three-dimensional segmentation on magnetic resonance images allows the study of geometric measures. • Patients with small width of contrast enhancing areas have better prognosis. • The irregularity of contrast enhancing areas predicts survival in glioblastoma patients.
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Affiliation(s)
- Julián Pérez-Beteta
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain.
| | - Alicia Martínez-González
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain
| | - David Molina
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain
| | - Mariano Amo-Salas
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain
| | - Belén Luque
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain
| | - Elena Arregui
- Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain
| | - Manuel Calvo
- Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain
| | - José M Borrás
- Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain
| | - Carlos López
- Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain
| | - Marta Claramonte
- Hospital General de Ciudad Real, c/ Obispo Rafael Torija, Ciudad Real, Spain
| | | | | | | | - David Albillo
- Hospital Universitario de Salamanca, Salamanca, Spain
| | | | | | | | - Juan Martino
- Hospital Marqués de Valdecilla, Santander, Spain
| | | | | | | | | | | | - Ana Del Valle
- Facultad de Matemáticas, Universidad de Sevilla, Sevilla, Spain
| | | | | | | | | | - Víctor M Pérez-García
- Laboratory of Mathematical Oncology, Edificio Politécnico, Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Universidad de Castilla-La Mancha, Avenida de Camilo José Cela, 3, 13071, Ciudad Real, Spain
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