Publications by authors named "Olivier Morin"

59 Publications

Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.

Radiol Artif Intell 2020 Mar 25;2(2):e190027. Epub 2020 Mar 25.

Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115.

Purpose: To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods.

Materials And Methods: An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR).

Results: A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], = .0571).

Conclusion: A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020.
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http://dx.doi.org/10.1148/ryai.2020190027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017410PMC
March 2020

Quantum Teleportation between Remote Qubit Memories with Only a Single Photon as a Resource.

Phys Rev Lett 2021 Apr;126(13):130502

Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Strasse 1, 85748 Garching, Germany.

Quantum teleportation enables the deterministic exchange of qubits via lossy channels. While it is commonly believed that unconditional teleportation requires a preshared entangled qubit pair, here we demonstrate a protocol that is in principle unconditional and requires only a single photon as an ex-ante prepared resource. The photon successively interacts, first, with the receiver and then with the sender qubit memory. Its detection, followed by classical communication, heralds a successful teleportation. We teleport six mutually unbiased qubit states with average fidelity F[over ¯]=(88.3±1.3)% at a rate of 6 Hz over 60 m.
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http://dx.doi.org/10.1103/PhysRevLett.126.130502DOI Listing
April 2021

A quantum-logic gate between distant quantum-network modules.

Science 2021 02;371(6529):614-617

Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Straße 1, 85748 Garching, Germany.

The big challenge in quantum computing is to realize scalable multi-qubit systems with cross-talk-free addressability and efficient coupling of arbitrarily selected qubits. Quantum networks promise a solution by integrating smaller qubit modules to a larger computing cluster. Such a distributed architecture, however, requires the capability to execute quantum-logic gates between distant qubits. Here we experimentally realize such a gate over a distance of 60 meters. We employ an ancillary photon that we successively reflect from two remote qubit modules, followed by a heralding photon detection, which triggers a final qubit rotation. We use the gate for remote entanglement creation of all four Bell states. Our nonlocal quantum-logic gate could be extended both to multiple qubits and many modules for a tailor-made multi-qubit computing register.
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http://dx.doi.org/10.1126/science.abe3150DOI Listing
February 2021

Closing the Gap Between Machine Learning and Clinical Cancer Care-First Steps Into a Larger World.

JAMA Oncol 2020 Nov;6(11):1731-1732

Department of Radiation Oncology, University of San Francisco, San Francisco, California.

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http://dx.doi.org/10.1001/jamaoncol.2020.4314DOI Listing
November 2020

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V and V of the PTV, V of the rectum, and heterogeneity index.
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http://dx.doi.org/10.1038/s41598-020-68062-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338467PMC
July 2020

Machine and deep learning methods for radiomics.

Med Phys 2020 Jun;47(5):e185-e202

Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.

Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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http://dx.doi.org/10.1002/mp.13678DOI Listing
June 2020

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

Radiology 2020 05 10;295(2):328-338. Epub 2020 Mar 10.

From OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstr 74, PF 41, 01307 Dresden, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z.); Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz Association/Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany (A.Z., S. Leger, E.G.C.T.); German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); Medical Physics Unit, McGill University, Montréal, Canada (M.V., I.E.N.); Image Response Assessment Team Core Facility, Moffitt Cancer Center, Tampa, Fla (M.A.A.); Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Harvard University, Boston, Mass (H.J.W.L.A.); Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland (V.A., A.D., H.M.); Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (A.A.); Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Md (S.A.); Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md (S.A., A.R.); Center for Biomedical image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (R.J.B., R.B., E.A.G.P.); Radiology and Nuclear Medicine, VU University Medical Centre (VUMC), Amsterdam, the Netherlands (R.B.); Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (M.B., M.Guckenberger, S.T.L.); Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (L.B., N.D., R.G., J.L., V.V.); Laboratoire d'Imagerie Translationnelle en Oncologie, Université Paris Saclay, Inserm, Institut Curie, Orsay, France (I.B., C.N., F.O.); Cancer Imaging Dept, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom (G.J.R.C., V.G., M.M.S.); Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland (A.D.); Laboratory of Medical Information Processing (LaTIM)-team ACTION (image-guided therapeutic action in oncology), INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France (M.C.D., M.H., T.U.); Department of Radiation Oncology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (C.V.D.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.E., S.N.); Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, Mich (I.E.N., A.U.K.R.); Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, Mass (A.Y.F.); Department of Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, Fla (R.J.G.); Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (M. Götz, F.I., K.H.M.H., J.S.); The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands (P.L., R.T.H.L.); Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany (F.L., J.S.F., D.T.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (A.L.); Department of Radiation Oncology, University of California, San Francisco, Calif (O.M.); University of Geneva, Geneva, Switzerland (H.M.); Department of Electrical Engineering, Stanford University, Stanford, Calif (S.N.); Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, Calif (S.N.); Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada (A.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich (A.U.K.R.); Department of Radiation Oncology, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (N.M.S., R.J.H.M.S., L.V.v.D.); School of Engineering, Cardiff University, Cardiff, United Kingdom (E.S., P.W.); Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom (E.S.); Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany (E.G.C.T., C.R., S. Löck), Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany (E.G.C.T., C.R.); Department of Nuclear Medicine, CHU Milétrie, Poitiers, France (T.U.); Department of Radiology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (J.v.G.); GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (J.v.G.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (J.v.G.); and Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands (F.H.P.v.V.).

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 See also the editorial by Kuhl and Truhn in this issue.
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http://dx.doi.org/10.1148/radiol.2020191145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193906PMC
May 2020

Reverse engineering cash: Coin designs mark out high value differentials and coin sizes track values logarithmically.

Cognition 2020 05 31;198:104182. Epub 2020 Jan 31.

Max Planck Institute for the Science of Human History, 10, Kahlaische Strasse, 07745 Jena, Germany; Institut Jean Nicod, ENS, EHESS, PSL University, CNRS, Paris, France. Electronic address:

Coins are physical representations of monetary values. Like mental or verbal representations of quantities, coins encode sums of money in formats shaped, in part, by cognitive and communicative needs. Studying the coins circulating today, we consider how their design, colour, and size reflect their value. We show that coin designs solve a trade-off between informativeness-the pressure to highlight distinct denominations-and simplicity-the pressure to limit the number of designs that coin users must memorise. Coinage worldwide is more likely to display distinctive graphic designs and distinct colours on pairs of coins with large differences in value, thus minimising the aggregate cost of mistaking one coin for another. Coin size differentials, in contrast, do not seem to indicate greater value differentials, although absolute coin sizes do reflect monetary values. Log-transformed values predict design and colour distinctiveness in coin pairs, as well as absolute coin sizes, better than raw values, consistent with research suggesting that monetary quantities may recruit the "numerosity system" for magnitude representations, thought to track quantities logarithmically. These results show that coins obey similar informational constraints as linguistic and mental representations.
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http://dx.doi.org/10.1016/j.cognition.2020.104182DOI Listing
May 2020

Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival.

Neurooncol Adv 2019 May-Dec;1(1):vdz011. Epub 2019 Aug 28.

Department of Radiation Oncology, University of California San Francisco, California.

Background: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes.

Methods: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset.

Results: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01-16.7, = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1-3.5, = .02) and worse OS (HR 2.94, 95% CI 1.47-5.56, = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone.

Conclusions: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.
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http://dx.doi.org/10.1093/noajnl/vdz011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777505PMC
August 2019

An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography.

Front Oncol 2019 30;9:810. Epub 2019 Aug 30.

Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.
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http://dx.doi.org/10.3389/fonc.2019.00810DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6730482PMC
August 2019

The Influence of Shared Visual Context on the Successful Emergence of Conventions in a Referential Communication Task.

Cogn Sci 2019 09;43(9):e12783

Minds and Traditions Research Group, Max Planck Institute for the Science of Human History.

Human communication is thoroughly context bound. We present two experiments investigating the importance of the shared context, that is, the amount of knowledge two interlocutors have in common, for the successful emergence and use of novel conventions. Using a referential communication task where black-and-white pictorial symbols are used to convey colors, pairs of participants build shared conventions peculiar to their dyad without experimenter feedback, relying purely on ostensive-inferential communication. Both experiments demonstrate that access to the visual context promotes more successful communication. Importantly, success improves cumulatively, supporting the view that pairs establish conventional ways of using the symbols to communicate. Furthermore, Experiment 2 suggests that dyads with access to the visual context successfully adapt the conventions built for one color space to another color space, unlike dyads lacking it. In linking experimental pragmatics with language evolution, the study illustrates the benefits of exploring the emergence of linguistic conventions using an ostensive-inferential model of communication.
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http://dx.doi.org/10.1111/cogs.12783DOI Listing
September 2019

Radiomics Analysis for Clinical Decision Support in Nuclear Medicine.

Semin Nucl Med 2019 09 20;49(5):438-449. Epub 2019 Jun 20.

Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Maastricht, The Netherlands. Electronic address:

Radiomics - the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.
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http://dx.doi.org/10.1053/j.semnuclmed.2019.06.005DOI Listing
September 2019

When iconicity stands in the way of abbreviation: No Zipfian effect for figurative signals.

PLoS One 2019 7;14(8):e0220793. Epub 2019 Aug 7.

Max Planck for the Science of Human History, Minds and Traditions Research Group, Jena, Germany.

Zipf's law of abbreviation, relating more frequent signals to shorter signal lengths, applies to sounds in a variety of communication systems, both human and non-human. It also applies to writing systems: more frequent words tend to be encoded by less complex graphemes, even when grapheme complexity is decoupled from word length. This study documents an exception to this law of abbreviation. Observing European heraldic motifs, whose frequency of use was documented for the whole continent and over two large corpora (total N = 25115), one medieval, one early modern, we found that they do not obey a robust law of abbreviation. In our early modern corpus, motif complexity and motif frequency are positively, not negatively, correlated, a result driven by iconic motifs. In both our corpora, iconic motifs tend to be more frequent when more complex. They grew in popularity after the invention of printing. Our results suggest that lacking iconicity may be a precondition for a graphic code to exhibit Zipf's Law of Abbreviation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220793PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685622PMC
March 2020

From Context to Code: Information Transfer Constrains the Emergence of Graphic Codes.

Cogn Sci 2019 03;43(3):e12722

Max Planck Institute for the Science of Human History.

Humans commit information to graphic symbols for three basic reasons: as a memory aid, as a tool for thinking, and as a means of communication. Yet, despite the benefits of transmitting information graphically, we still know very little about the biases and constraints acting on the emergence of stable, powerful, and accurate graphic codes (such as writing). Using a reference game, where participants play as Messengers and Recipients, we experimentally manipulate the function of the task (communicative or non-communicative) and investigate whether this shapes the emergence of stable, powerful, and accurate codes for both synchronous and asynchronous modes of information transfer. Only in the Dialogue condition, where Messenger and Recipient are two different persons communicating within the same time frame (i.e., synchronously), do we consistently observe the emergence of stable, powerful, and accurate graphic codes. Such codes are unnecessary for participants in Recall, where Messenger and Recipient are the same person transferring information within the same time frame, and they fail to emerge in Correspondence, where Messenger and Recipient are two different persons communicating across time frames (i.e., asynchronously). Lastly, in the Mnemonic condition, where Messenger and Recipient are the same person at different points in time, participants achieve high accuracy but with codes that are suboptimal in terms of power and stability. Our results suggest that the rarity and late arrival of stable, powerful, and accurate graphic codes in human history largely stems from strong constraints on information transfer. In particular, we suggest that these constraints limit a code's ability to reach an adequate tradeoff between information that needs to be explicitly encoded and information that needs to be inferred from context.
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http://dx.doi.org/10.1111/cogs.12722DOI Listing
March 2019

Optimizing beam models for dosimetric accuracy over a wide range of treatments.

Phys Med 2019 Feb 24;58:47-53. Epub 2019 Jan 24.

Department of Radiation Oncology, University of California San Francisco, 1600 Divisadero Street, Suite H1031, San Francisco, CA 94115, United States.

This work presents a systematic approach for testing a dose calculation algorithm over a variety of conditions designed to span the possible range of clinical treatment plans. Using this method, a TrueBeam STx machine with high definition multi-leaf collimators (MLCs) was commissioned in the RayStation treatment planning system (TPS). The initial model parameters values were determined by comparing TPS calculations with standard measured depth dose and profile curves. The MLC leaf offset calibration was determined by comparing measured and calculated field edges utilizing a wide range of MLC retracted and over-travel positions. The radial fluence was adjusted using profiles through both the center and corners of the largest field size, and through measurements of small fields that were located at highly off-axis positions. The flattening filter source was adjusted to improve the TPS agreement for the output of MLC-defined fields with much larger jaw openings. The MLC leaf transmission and leaf end parameters were adjusted to optimize the TPS agreement for highly modulated intensity-modulated radiotherapy (IMRT) plans. The final model was validated for simple open fields, multiple field configurations, the TG 119 C-shape target test, and a battery of clinical IMRT and volumetric-modulated arc therapy (VMAT) plans. The commissioning process detected potential dosimetric errors of over 10% and resulted in a final model that provided in general 3% dosimetric accuracy. This study demonstrates the importance of using a variety of conditions to adjust a beam model and provides an effective framework for achieving high dosimetric accuracy.
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http://dx.doi.org/10.1016/j.ejmp.2019.01.011DOI Listing
February 2019

Stochastic frontier analysis as knowledge-based model to improve sparing of organs-at-risk for VMAT-treated prostate cancer.

Phys Med Biol 2019 04 8;64(8):085007. Epub 2019 Apr 8.

Département de Radio-Oncologie et Centre de Recherche sur le Cancer de l'université Laval, Pavillon l'Hôtel-Dieu de Québec, Québec, Québec, G1R 2J6, Canada. Département de Physique, de Génie Physique et d'Optique, Université Laval, Québec, Québec, G1K 7P4, Canada.

Stochastic frontier analysis (SFA) is used as a novel knowledge-based technique in order to develop a predictive model of dosimetric features from significant geometric parameters describing a patient morphology. 406 patients treated with VMAT for prostate cancer were analyzed retrospectively. Cases were divided into three prescription-based groups. Seven geometric parameters are extracted to characterize the relationship between the organs-at-risk (bladder and rectum) with the planning volume (PTV). In total, 37 dosimetric parameters are tested for these two OARs. SFA allows the determination of the minimum achievable dose to the OAR based on the geometric parameters. Stochastic frontiers are determined with a maximum likelihood estimation technique. The SFA model was tested using validation cohort (30 patients with prescribed dose between 60 and 70 Gy) where 77% (23 out of 30) of the predicted DVHs present a 5% or less dose deterioration for the bladder and rectum with the planned DVH. SFA can be used in EBRT planning as a predictive model based on anatomical features of previously treated plans.
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http://dx.doi.org/10.1088/1361-6560/ab0b4dDOI Listing
April 2019

Writing, Graphic Codes, and Asynchronous Communication.

Top Cogn Sci 2020 04 10;12(2):727-743. Epub 2018 Oct 10.

Minds and Traditions Research Group, Max Planck Institute for the Science of Human History.

We present a theoretical framework bearing on the evolution of written communication. We analyze writing as a special kind of graphic code. Like languages, graphic codes consist of stable, conventional mappings between symbols and meanings, but (unlike spoken or signed languages) their symbols consist of enduring images. This gives them the unique capacity to transmit information in one go across time and space. Yet this capacity usually remains quite unexploited, because most graphic codes are insufficiently informative. They may only be used for mnemonic purposes or as props for oral communication in real-time encounters. Writing systems, unlike other graphic codes, work by encoding a natural language. This allows them to support asynchronous communication in a more powerful and versatile way than any other graphic code. Yet, writing systems will not automatically unlock the capacity to communicate asynchronously. We argue that this capacity is a rarity in non-literate societies, and not so frequent even in literate ones. Asynchronous communication is intrinsically inefficient because asynchrony constrains the amount of information that the interlocutors share and limits possibilities for repair. This would explain why synchronous, face-to-face communication always fosters the development of sophisticated codes (natural languages), but similar codes for asynchronous communication evolve with more difficulties. It also implies that writing cannot have evolved, at first, for supporting asynchronous communication.
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http://dx.doi.org/10.1111/tops.12386DOI Listing
April 2020

Preoperative and postoperative prediction of long-term meningioma outcomes.

PLoS One 2018 20;13(9):e0204161. Epub 2018 Sep 20.

Department of Radiation Oncology, University of California San Francisco, San Francisco, United States of America.

Background: Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.

Methods And Findings: We developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.

Conclusions: Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204161PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6147484PMC
March 2019

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change.

Int J Radiat Oncol Biol Phys 2018 11 28;102(4):1074-1082. Epub 2018 Aug 28.

The D-Lab, Grow Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands.

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.
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http://dx.doi.org/10.1016/j.ijrobp.2018.08.032DOI Listing
November 2018

Clinical Applications of Quantitative 3-Dimensional MRI Analysis for Pediatric Embryonal Brain Tumors.

Int J Radiat Oncol Biol Phys 2018 11 8;102(4):744-756. Epub 2018 Jun 8.

Department of Radiation Oncology, University of California, San Francisco, San Francisco, California; Department of Neurological Surgery, University of California, San Francisco, San Francisco, California. Electronic address:

Purpose: To investigate the prognostic utility of quantitative 3-dimensional magnetic resonance imaging radiomic analysis for primary pediatric embryonal brain tumors.

Methods And Materials: Thirty-four pediatric patients with embryonal brain tumor with concurrent preoperative T1-weighted postcontrast (T1PG) and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance images were identified from an institutional database. The median follow-up period was 5.2 years. Radiomic features were extracted from axial T1PG and FLAIR contours using MATLAB, and 15 features were selected for analysis based on qualitative radiographic features with prognostic significance for pediatric embryonal brain tumors. Logistic regression, linear regression, receiver operating characteristic curves, the Harrell C index, and the Somer D index were used to test the relationships between radiomic features and demographic variables, as well as clinical outcomes.

Results: Pediatric embryonal brain tumors in older patients had an increased normalized mean tumor intensity (P = .05, T1PG), decreased tumor volume (P = .02, T1PG), and increased markers of heterogeneity (P ≤ .01, T1PG and FLAIR) relative to those in younger patients. We identified 10 quantitative radiomic features that delineated medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal tumor, including size and heterogeneity (P ≤ .05, T1PG and FLAIR). Decreased markers of tumor heterogeneity were predictive of neuraxis metastases and trended toward significance (P = .1, FLAIR). Tumors with an increased size (area under the curve = 0.7, FLAIR) and decreased heterogeneity (area under the curve = 0.7, FLAIR) at diagnosis were more likely to recur.

Conclusions: Quantitative radiomic features are associated with pediatric embryonal brain tumor patient age, histology, neuraxis metastases, and recurrence. These data suggest that quantitative 3-dimensional magnetic resonance imaging radiomic analysis has the potential to identify radiomic risk features for pediatric patients with embryonal brain tumors.
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http://dx.doi.org/10.1016/j.ijrobp.2018.05.077DOI Listing
November 2018

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

Radiother Oncol 2018 12 12;129(3):421-426. Epub 2018 Jun 12.

Oregon Health & Science University, Portland, USA.

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
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http://dx.doi.org/10.1016/j.radonc.2018.05.030DOI Listing
December 2018

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.

Med Phys 2018 Jul 13;45(7):3449-3459. Epub 2018 Jun 13.

The D-lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.

Purpose: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.

Methods: We collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen's kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).

Results: Random forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.

Conclusion: Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one's own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.
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http://dx.doi.org/10.1002/mp.12967DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6095141PMC
July 2018

Commissioning and Evaluation of an Electronic Portal Imaging Device-Based In-Vivo Dosimetry Software.

Cureus 2018 Feb 2;10(2):e2139. Epub 2018 Feb 2.

Radiation Oncology, University of California San Francisco.

This study reports on our experience with the in-vivo dose verification software, EPIgray® (DOSIsoft, Cachan, France). After the initial commissioning process, clinical experiments on phantom treatments were evaluated to assess the level of accuracy of the electronic portal imaging device (EPID) based in-vivo dose verification. EPIgray was commissioned based on the company's instructions. This involved ion chamber measurements and portal imaging of solid water blocks of various thicknesses between 5 and 35 cm. Field sizes varied between 2 x 2 cm and 20 x 20 cm. The determined conversion factors were adjusted through an additional iterative process using treatment planning system calculations. Subsequently, evaluation was performed using treatment plans of single and opposed beams, as well as intensity modulated radiotherapy (IMRT) plans, based on recommendations from the task group report TG-119 to test for dose reconstruction accuracy. All tests were performed using blocks of solid water slabs as a phantom. For single square fields, the dose at isocenter was reconstructed within 3% accuracy in EPIgray compared to the treatment planning system dose. Similarly, the relative deviation of the total dose was accurately reconstructed within 3% for all IMRT plans with points placed inside a high-dose region near the isocenter. Predictions became less accurate than < 5% when the evaluation point was outside the treatment target. Dose at points 5 cm or more away from the isocenter or within an avoidance structure was reconstructed less reliably. EPIgray formalism accuracy is adequate for an efficient error detection system with verifications performed in high-dose volumes. It provides immediate intra-fractional feedback on the delivery of treatment plans without affecting the treatment beam. Besides the EPID, no additional hardware is required. The software evaluates local point dose measurements to verify treatment plan delivery and patient positioning within 5% accuracy, depending on the placement of evaluation points.
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http://dx.doi.org/10.7759/cureus.2139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5880591PMC
February 2018

Histopathological features predictive of local control of atypical meningioma after surgery and adjuvant radiotherapy.

J Neurosurg 2018 04;130(2):443-450

Departments of1Radiation Oncology.

Obejective: The goal of this study was to investigate the impact of adjuvant radiotherapy (RT) on local recurrence and overall survival in patients undergoing primary resection of atypical meningioma, and to identify predictive factors to inform patient selection for adjuvant RT.

Methods: One hundred eighty-two patients who underwent primary resection of atypical meningioma at a single institution between 1993 and 2014 were retrospectively identified. Patient, meningioma, and treatment data were extracted from the medical record and compared using the Kaplan-Meier method, log-rank tests, multivariate analysis (MVA) Cox proportional hazards models with relative risk (RR), and recursive partitioning analysis.

Results: The median patient age and imaging follow-up were 57 years (interquartile range [IQR] 45–67 years) and 4.4 years (IQR 1.8–7.5 years), respectively. Gross-total resection (GTR) was achieved in 114 cases (63%), and 42 patients (23%) received adjuvant RT. On MVA, prognostic factors for death from any cause included GTR (RR 0.4, 95% CI 0.1–0.9, p = 0.02) and MIB1 labeling index (LI) ≤ 7% (RR 0.4, 95% CI 0.1–0.9, p = 0.04). Prognostic factors on MVA for local progression included GTR (RR 0.2, 95% CI 0.1–0.5, p = 0.002), adjuvant RT (RR 0.2, 95% CI 0.1–0.4, p < 0.001), MIB1 LI ≤ 7% (RR 0.2, 95% CI 0.1–0.5, p < 0.001), and a remote history of prior cranial RT (RR 5.7, 95% CI 1.3–18.8, p = 0.03). After GTR, adjuvant RT (0 of 10 meningiomas recurred, p = 0.01) and MIB1 LI ≤ 7% (RR 0.1, 95% CI 0.003–0.3, p < 0.001) were predictive for local progression on MVA. After GTR, 2.2% of meningiomas with MIB1 LI ≤ 7% recurred (1 of 45), compared with 38% with MIB1 LI > 7% (13 of 34; p < 0.001). Recursive partitioning analysis confirmed the existence of a cohort of patients at high risk of local progression after GTR without adjuvant RT, with MIB1 LI > 7%, and evidence of brain or bone invasion. After subtotal resection, adjuvant RT (RR 0.2, 95% CI 0.04–0.7, p = 0.009) and ≤ 5 mitoses per 10 hpf (RR 0.1, 95% CI 0.03–0.4, p = 0.002) were predictive on MVA for local progression.

Conclusions: Adjuvant RT improves local control of atypical meningioma irrespective of extent of resection. Although independent validation is required, the authors’ results suggest that MIB1 LI, the number of mitoses per 10 hpf, and brain or bone invasion may be useful guides to the selection of patients who are most likely to benefit from adjuvant RT after resection of atypical meningioma.
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http://dx.doi.org/10.3171/2017.9.JNS171609DOI Listing
April 2018

Deep nets vs expert designed features in medical physics: An IMRT QA case study.

Med Phys 2018 Jun 18;45(6):2672-2680. Epub 2018 Apr 18.

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA.

Purpose: The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA).

Method: A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded. Separately, fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). The CNNs were trained to predict IMRT QA gamma passing rates using TensorFlow and Keras. A set of model architectures, inspired by the convolutional blocks of the VGG-16 ImageNet model, were constructed and implemented. Synthetic data, created by rotating and translating the fluence maps during training, was created to boost the performance of the CNNs. Dropout, batch normalization, and data augmentation were utilized to help train the model. The performance of the CNNs was compared to a generalized Poisson regression model, previously developed for this application, which used 78 expert designed features.

Results: Deep Neural Networks without domain knowledge achieved comparable performance to a baseline system designed by domain experts in the prediction of 3%/3 mm Local gamma passing rates. An ensemble of neural nets resulted in a mean absolute error (MAE) of 0.70 ± 0.05 and the domain expert model resulted in a 0.74 ± 0.06.

Conclusions: Convolutional neural networks (CNNs) with transfer learning can predict IMRT QA passing rates by automatically designing features from the fluence maps without human expert supervision. Predictions from CNNs are comparable to a system carefully designed by physicist experts.
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http://dx.doi.org/10.1002/mp.12890DOI Listing
June 2018

Multiple myeloma and a mischievous pacemaker: A teaching case involving irradiation of a cardiovascular implantable electronic device.

Pract Radiat Oncol 2018 Mar - Apr;8(2):90-94. Epub 2017 Nov 4.

Department of Radiation Oncology, University of California, San Francisco, San Francisco, California. Electronic address:

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http://dx.doi.org/10.1016/j.prro.2017.10.016DOI Listing
November 2018

Spontaneous Emergence of Legibility in Writing Systems: The Case of Orientation Anisotropy.

Authors:
Olivier Morin

Cogn Sci 2018 03 10;42(2):664-677. Epub 2017 Oct 10.

Minds and Tradition Research Group, Max Planck Institute for the Science of Human History.

Cultural forms are constrained by cognitive biases, and writing is thought to have evolved to fit basic visual preferences, but little is known about the history and mechanisms of that evolution. Cognitive constraints have been documented for the topology of script features, but not for their orientation. Orientation anisotropy in human vision, as revealed by the oblique effect, suggests that cardinal (vertical and horizontal) orientations, being easier to process, should be overrepresented in letters. As this study of 116 scripts shows, the orientation of strokes inside written characters massively favors cardinal directions, and it is organized in such a way as to make letter recognition easier: Cardinal and oblique strokes tend not to mix, and mirror symmetry is anisotropic, favoring vertical over horizontal symmetry. Phylogenetic analyses and recently invented scripts show that cultural evolution over the last three millennia cannot be the sole cause of these effects.
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http://dx.doi.org/10.1111/cogs.12550DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887916PMC
March 2018