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Bhatt AA, Niell B. Tumor Doubling Time and Screening Interval. Radiol Clin North Am 2024; 62:571-580. [PMID: 38777534 DOI: 10.1016/j.rcl.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The goal of screening is to detect breast cancers when still curable to decrease breast cancer-specific mortality. Breast cancer screening in the United States is routinely performed with digital mammography and digital breast tomosynthesis. This article reviews breast cancer doubling time by tumor subtype and examines the impact of doubling time on breast cancer screening intervals. By the article's end, the reader will be better equipped to have informed discussions with patients and medical professionals regarding the benefits and disadvantages of the currently recommended screening mammography intervals.
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Affiliation(s)
- Asha A Bhatt
- Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA.
| | - Bethany Niell
- Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA; Department of Oncologic Sciences, University of South Florida, 12901 Bruce B. Downs Boulevard MDC 44. Tampa, FL 33612, USA
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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. NATURE PHYSICS 2020; 16:1232-1237. [PMID: 33329756 PMCID: PMC7116451 DOI: 10.1038/s41567-020-0978-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [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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Murphy H, McCarthy G, Dobrovolny HM. Understanding the effect of measurement time on drug characterization. PLoS One 2020; 15:e0233031. [PMID: 32407356 PMCID: PMC7224495 DOI: 10.1371/journal.pone.0233031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/27/2020] [Indexed: 12/24/2022] Open
Abstract
In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times.
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Affiliation(s)
- Hope Murphy
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
| | - Gabriel McCarthy
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
| | - Hana M. Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, United States of America
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Akinduro OO, Lu VM, Izzo A, De Biase G, Vilanilam G, Van Gompel JJ, Bernet V, Donaldson A, Olomu O, Meyer FB, Quinones-Hinojosa A, Chaichana KL. Radiographic and Hormonal Regression in Prolactinomas: An Analysis of Treatment Failure. World Neurosurg 2019; 129:e686-e694. [DOI: 10.1016/j.wneu.2019.05.249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 05/28/2019] [Accepted: 05/29/2019] [Indexed: 12/17/2022]
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