Individualized Structure–Function Mapping for Glaucoma: Practical Constraints on Map Resolution for Clinical and Research Applications

yesPurpose: We have developed customized maps that relate visual field and optic nerve head (ONH) regions according to individual anatomy. In this study, we aimed to determine feasible map resolution for research use, and to make a principled recommendation of sector size for clinical applications. Methods: Measurement variability in fovea–ONH distance and angle was estimated from 10 repeat OCT scans of 10 healthy people. Errors in estimating axial length from refractive error were determined from published data. Structure–function maps were generated, and customized to varied clinically-plausible anatomical parameters. For each parameter set (n = 210), 200 maps were generated by sampling from measurement/estimation error distributions. Mapped 1° sectors at each visual field location from each parameter set were normalized to difference from their mean. Variation (90% ranges) in normalized mapped sectors represents the precision of individualized maps. Results: Standard deviations of repeated measures of fovea–ONH distance and angle were 61 μm and 0.97° (coefficients of variation 1.3% and 12.0%, respectively). Neither measure varied systematically with mean (Spearmans's ρ = 0.26, P = 0.47 for distance, ρ = −0.31, P = 0.39 for angle). Variation (90% ranges) in normalized mapped sectors varied across the visual field and ranged from 3° to 18° when axial length was measured accurately, and from 6° to 32° when axial length was estimated from refractive error. Conclusions: The 90% ranges represent the minimum feasible ONH sector size at each visual field location. For clinical use an easily interpretable scheme of 30° sectors is suggested

Structure–Function Mapping: Variability and Conviction in Tracing Retinal Nerve Fiber Bundles and Comparison to a Computational Model

yesPurpose: We evaluated variability and conviction in tracing paths of retinal nerve fiber bundles (RNFBs) in retinal images, and compared traced paths to a computational model that produces anatomically-customized structure–function maps. Methods: Ten retinal images were overlaid with 24-2 visual field locations. Eight clinicians and 6 naïve observers traced RNFBs from each location to the optic nerve head (ONH), recording their best estimate and certain range of insertion. Three clinicians and 2 naïve observers traced RNFBs in 3 images, 3 times, 7 to 19 days apart. The model predicted 10° ONH sectors relating to each location. Variability and repeatability in best estimates, certain range width, and differences between best estimates and model-predictions were evaluated. Results: Median between-observer variability in best estimates was 27° (interquartile range [IQR] 20°–38°) for clinicians and 33° (IQR 22°–50°) for naïve observers. Median certain range width was 30° (IQR 14°–45°) for clinicians and 75° (IQR 45°–180°) for naïve observers. Median repeatability was 10° (IQR 5°–20°) for clinicians and 15° (IQR 10°–29°) for naïve observers. All measures were worse further from the ONH. Systematic differences between model predictions and best estimates were negligible; median absolute differences were 17° (IQR 9°–30°) for clinicians and 20° (IQR 10°–36°) for naïve observers. Larger departures from the model coincided with greater variability in tracing. Conclusions: Concordance between the model and RNFB tracing was good, and greatest where tracing variability was lowest. When RNFB tracing is used for structure–function mapping, variability should be considered

Suprathreshold Approaches to Mapping the Visual Field in Advanced Glaucoma

YesMeasuring the spatial extent of defects may be advantageous in advanced glaucoma where conventional perimetric sensitivity measurements are unreliable. We test whether suprathreshold tests on a higher density grid can more efficiently map advanced visual field loss. Data from 97 patients with mean deviationSupported by a College of Optometrists Research Fellowship (to JD)

Effects of criterion bias on perimetric sensitivity and response variability in glaucoma

YesThe purpose of this study was to isolate and quantify the effects of observer response criterion on perimetric sensitivity, response variability, and maximum response probability. Twelve people with glaucoma were tested at three locations in the visual field (age = 47-77 years, mean deviation = -0.61 to -14.54 dB, test location Humphrey field analyzer [HFA] sensitivities = 1 to 30 dB). Frequency of seeing (FoS) curves were measured using a method of constant stimuli with two response paradigms: a "yes-no" paradigm similar to static automated perimetry and a criterion-free two interval forced choice (2IFC) paradigm. Comparison measures of sensitivity, maximum response probability, and response variability were derived from the fitted FoS curves. Sensitivity differences between the tasks varied widely (range = -11.3 dB to 21.6 dB) and did not correlate with visual field sensitivity nor whether the visual field location was in an area of steep sensitivity gradient within the visual field. Due to the wide variation in differences between the methods, there was no significant difference in mean sensitivity between the 2IFC task relative to the yes-no task, but a trend for higher sensitivity (mean = 1.9 dB, SD = 6.0 dB, P = 0.11). Response variability and maximum response probability did not differ between the tasks (P > 0.99 and 0.95, respectively). Perimetric sensitivity estimates are demonstrably altered by observer response criterion but the effect varies widely and unpredictably, even within a single test. Response bias should be considered a factor in perimetric test variability and when comparing sensitivities to nonperimetric data. The effect of response criterion on perimetric response variability varies widely and unpredictably, even within a single test.Supported by ARC LP130100055; ARC LP150100815 (AT and AMM), College of Optometrists Research Fellowship (JD)

Enhanced structure-function relationship in glaucoma with an anatomically and geometrically accurate neuroretinal rim measurement

yesPurpose: To evaluate the structure–function relationship between disc margin–based rim area (DM-RA) obtained with confocal scanning laser tomography (CSLT), Bruch's membrane opening–based horizontal rim width (BMO-HRW), minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer thickness (RNFLT) obtained with spectral-domain optical coherence tomography (SD-OCT), and visual field sensitivity. Methods: We examined 151 glaucoma patients with CSLT, SD-OCT, and standard automated perimetry on the same day. Optic nerve head (ONH) and RNFL with SD-OCT were acquired relative to a fixed coordinate system (acquired image frame [AIF]) and to the eye-specific fovea-BMO center (FoBMO) axis. Visual field locations were mapped to ONH and RNFL sectors with fixed Garway-Heath (VFGH) and patient-specific (VFPS) maps customized for various biometric parameters. Results: Globally and sectorally, the structure–function relationships between DM-RA and VFGH, BMO-HRWAIF and VFGH, and BMO-HRWFoBMO and VFPS were equally weak. The R2 for the relationship between DM-RA and VFGH ranged from 0.1% (inferonasal) to 11% (superotemporal) whereas that between BMO-HRWAIF and VFGH ranged from 0.1% (nasal) to 10% (superotemporal). Relatively stronger global and sectoral structure–function relationships with BMO-MRWAIF and with BMO-MRWFoBMO were obtained. The R2 between BMO-MRWAIF and VFGH ranged from 5% (nasal) to 30% (superotemporal), whereas that between BMO-MRWFoBMO and VFPS ranged from 5% (nasal) to 25% (inferotemporal). The structure–function relationship with RNFLT was not significantly different from that with BMO-MRW, regardless of image acquisition method. Conclusions: The structure–function relationship was enhanced with BMO-MRW compared with the other neuroretinal rim measurements, due mainly to its geometrically accurate properties

Deterministic mathematical modelling for cancer chronotherapeutics: cell population dynamics and treatment optimisation

Chronotherapeutics has been designed and used for more than twenty years as an effective treatment against cancer by a few teams around the world, among whom one of the first is Francis Lévi's at Paul-Brousse hospital (Villejuif, France), in application of circadian clock physiology to determine best infusion times within the 24-hour span for anticancer drug delivery. Mathematical models have been called in the last ten years to give a rational basis to such optimised treatments, for use in the laboratory and ultimately in the clinic. While actual clinical applications of the theoretical optimisation principles found have remained elusive so far to improve chronotherapeutic treatments in use, mathematical models provide proofs of concepts and tracks to be explored experimentally, to progress from theory to bedside. Starting from a simple ordinary differential equation model that allowed setting and numerically solving a drug delivery optimisation problem with toxicity constraints, this modelling enterprise has been extended to represent the division cycle in proliferating cell populations with different molecular targets, to allow for the representation of anticancer drug combinations that are used in clinical oncology. The main point to be made precise in such a therapeutic optimisation problem is to establish, here in the frame of circadian chronobiology, physiologically based differences between healthy and cancer cell populations in their responses to drugs. To this aim, clear biological evidence at the molecular level is still lacking, so that, starting from indirect observations at the experimental and clinical levels and from theoretical considerations on the model, speculations have been made, that will be exposed in this review of cancer chronotherapeutics models with the corresponding optimisation problems and their numerical solutions, to represent these differences between the two cell populations, with regard to circadian clock control

Towards patient-tailored perimetry: automated perimetry can be improved by seeding procedures with patient-specific structural information

NoTo explore the performance of patient-specific prior information, for example, from structural imaging, in improving perimetric procedures. Computer simulation was used to determine the error distribution and presentation count for Structure–Zippy Estimation by Sequential Testing (ZEST), a Bayesian procedure with prior distribution centered on a threshold prediction from structure. Structure-ZEST (SZEST) was trialled for single locations with combinations of true and predicted thresholds between 1 to 35 dB, and compared with a standard procedure with variability similar to Swedish Interactive Thresholding Algorithm (SITA) (Full-Threshold, FT). Clinical tests of glaucomatous visual fields (n = 163, median mean deviation −1.8 dB, 90% range +2.1 to −22.6 dB) were also compared between techniques. For single locations, SZEST typically outperformed FT when structural predictions were within ± 9 dB of true sensitivity, depending on response errors. In damaged locations, mean absolute error was 0.5 to 1.8 dB lower, SD of threshold estimates was 1.2 to 1.5 dB lower, and 2 to 4 (29%–41%) fewer presentations were made for SZEST. Gains were smaller across whole visual fields (SZEST, mean absolute error: 0.5 to 1.2 dB lower, threshold estimate SD: 0.3 to 0.8 dB lower, 1 [17%] fewer presentation). The 90% retest limits of SZEST were median 1 to 3 dB narrower and more consistent (interquartile range 2–8 dB narrower) across the dynamic range than those for FT. Seeding Bayesian perimetric procedures with structural measurements can reduce test variability of perimetry in glaucoma, despite imprecise structural predictions of threshold. Structural data can reduce the variability of current perimetric techniques. A strong structure–function relationship is not necessary, however, structure must predict function within ±9 dB for gains to be realized

An Anatomically Customizable Computational Model Relating the Visual Field to the Optic Nerve Head in Individual Eyes

NoTo present a computational model mapping visual field (VF) locations to optic nerve head (ONH) sectors accounting for individual ocular anatomy, and to describe the effects of anatomical variability on maps produced. A previous model that related retinal locations to ONH sectors was adapted to model eyes with varying axial length, ONH position and ONH dimensions. Maps (n = 11,550) relating VF locations (24-2 pattern, n = 52 non–blind-spot locations) to 1° ONH sectors were generated for a range of clinically plausible anatomical parameters. Infrequently mapped ONH sectors (5%) were discarded for all locations. The influence of anatomical variables on the maps was explored by multiple linear regression. Across all anatomical variants, for individual VF locations (24-2), total number of mapped 1° ONH sectors ranged from 12 to 90. Forty-one locations varied more than 30°. In five nasal-step locations, mapped ONH sectors were bimodally distributed, mapping to vertically opposite ONH sectors depending on vertical ONH position. Mapped ONH sectors were significantly influenced (P < 0.0002) by axial length, ONH position, and ONH dimensions for 39, 52, and 30 VF locations, respectively. On average across all VF locations, vertical ONH position explained the most variance in mapped ONH sector, followed by horizontal ONH position, axial length, and ONH dimensions. Relations between ONH sectors and many VF locations are strongly anatomy-dependent. Our model may be used to produce customized maps from VF locations to the ONH in individual eyes where some simple biometric parameters are known.ustralian Research Council Linkage Project LP100100250 (with Heidelberg Engineering GmbH, Germany); Australian Research Council Future Fellowship FT0990930 (AMM); Australian Research Council Future Fellowship FT0991326 (AT

Relating optical coherence tomography to visual fields in glaucoma: structure–function mapping, limitations and future applications

YesCombining information from optical coherence tomography (OCT) imaging and visual field testing is useful in the clinical assessment and monitoring of patients with glaucoma. Measurements of retinal nerve fibre layer thickness or neuroretinal rim width taken around the optic nerve head may be related to the visual field using a structure–function map. In this review, the structure–function mapping methods in clinical use are discussed. Typical clinical maps provide a population average, ‘one size fits all’ representation, but in recent years methods for customising structure–function maps to individual eyes have been developed and these are reviewed here. In the macula, visual field stimuli stimulate photoreceptors for which associated retinal ganglion cells are peripherally displaced. Recently developed methods that relate OCT measurements to visual field test locations in the macula are therefore also reviewed. The use of structure–function maps to relate OCT measurements to localised visual field sensitivity in new applications is also explored. These new applications include the selection of visual field test locations and stimulus intensities based on OCT data, and the formal post‐test combination of results across modalities. Such applications promise to exploit the structure–function relationship in glaucoma to improve disease diagnosis and monitoring of progression. Limitations in the validation and use of current structure–function mapping techniques are discussed.>Heidelberg Engineering >Australian Research Council. Grant Number: LP130100055 >College of Optometrists. Grant Number: College of Optometrists Research Fellowshi

Response times across the visual field: Empirical observations and application to threshold determination

noThis study aimed to determine if response times gathered during perimetry can be exploited within a thresholding algorithm to improve the speed and accuracy of the test. Frequency of seeing (FoS) curves were measured at 24 locations across the central 30° of the visual field of 10 subjects using a Method of Constant Stimuli, with response times recorded for each presentation. Spatial locations were interleaved, and built up over multiple 5-min blocks, in order to mimic the attentional conditions of clinical perimetry. FoS curves were fitted to each participant’s data for each location, and response times derived as a function of distance-from-threshold normalised to the slope of each FoS curve. This data was then used to derive a function for the probability of observing response times given the distance-from-threshold, and to seed simulations of a new test procedure (BURTO) that exploited the probability function for stimulus placement. Test time and error were then simulated for patients with various false response rates. When compared with a ZEST algorithm, simulations revealed that BURTO was about one presentation per location faster than ZEST, on average, while sacrificing less precision and bias in threshold estimates than simply terminating the ZEST earlier. Despite response times varying considerably for a given individual and their thresholds, response times can be exploited to reduce the number of presentations required in a visual field test without loss of accuracy