Therapy with voretigene neparvovec. How to measure success?

Stingl, Krunoslav; Kempf, Melanie; Jung, Ronja; Kortüm, Friederike; Righetti, Giulia; Reith, Milda; Dimopoulos, Spyridon; Ott, Saskia; Kohl, Susanne; Štingl, Katarína

doi:10.1016/j.preteyeres.2022.101115

Cited by 21 publications

(24 citation statements)

References 86 publications

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“…However, since then, other phase III trials on ocular gene therapy have failed to reach their primary endpoints, e.g., the XIRIUS study of an adeno-associated virus serotype 8 (AAV8) vector-based gene therapy, cotoretigene toliparvovec, targeting the RPGR gene in x-linked retinitis pigmentosa. One of the recurring key questions in all ophthalmological gene therapy studies therefore is: “How to measure success?” [23].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseases

Eckardt,

Mittas,

Horlava

et al. 2023

Preprint

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BackgroundOn optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the outer nuclear layer (ONL) thickness measurement has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time consuming.Methods and MaterialPatients with IRD and the availability of an OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included, to provide training data for the artificial intelligence (AI). We trained a U-net based model on healthy patients and applied a domain adaption technique to IRD patients’ scans.ResultsWe established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice-score of the algorithm was98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient.ConclusionAccurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. The here-presented OCT image segmentation algorithm could provide the basis for further studies on IRDs.

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Section: Discussionmentioning

confidence: 99%

Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseases

Eckardt,

Mittas,

Horlava

et al. 2023

Preprint

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“…One of the recurring key questions in all ophthalmological gene therapy studies therefore is: "How to measure success?" [23]. This question underlines the complexity and importance of a reliable and objective measurement of visual performance for gene therapy studies in IRDs and beyond.…”

Section: Discussionmentioning

confidence: 99%

“…However, since then, other phase III trials on ocular gene therapy have failed to reach their primary endpoints, e.g., the XIRIUS study of an AAV serotype 8 (AAV8) vector-based gene therapy, cotoretigene toliparvovec, targeting the RPGR gene in X-linked retinitis pigmentosa. One of the recurring key questions in all ophthalmological gene therapy studies therefore is: “How to measure success?” 23 . This question underlines the complexity and importance of a reliable and objective measurement of visual performance for gene therapy studies in IRDs and beyond.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases

Eckardt,

Mittas,

Horlava

et al. 2023

Klin Monbl Augenheilkd

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Background: On optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the outer nuclear layer (ONL) thickness measurement has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time consuming. Methods and Material: Patients with IRD and the availability of an OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included, to provide training data for the artificial intelligence (AI). We trained a U-net based model on healthy patients and applied a domain adaption technique to IRD patients’ scans. Results: We established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice-score of the algorithm was 98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient. Conclusion: Accurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. The here-presented OCT image segmentation algorithm could provide the basis for further studies on IRDs. Hintergrund: Bei der optischen Kohärenztomographie (OCT) von Patienten mit erblichen Netzhauterkrankungen hat sich die Messung der äußeren Körnerschichtdicke (ONL) als Marker für den Erhalt der Photorezeptoren bewährt. Derzeitige automatische Segmentierungsprogramme versagen bei der OCT-Segmentierung dieser Patienten, und die manuelle Segmentierung ist zeitaufwendig. Methoden und Material: Für die vorliegende Studie wurden Patienten mit erblichen Netzhauterkrankungen und der Verfügbarkeit eines OCT-Scans eingeschlossen. Zusätzlich wurden OCT-Scans von Patienten ohne Netzhauterkrankung einbezogen, um Trainingsdaten für die künstliche Intelligenz (KI) zu generieren. Wir trainierten ein auf einem U-Netz basierendes Modell an gesunden Patienten und wendeten eine Anpassungsmethode auf die pathologisch veränderten Scans von Patienten an. Ergebnisse: Es wurde ein KI-basierter Bildsegmentierungsalgorithmus entwickelt, der die ONL in OCT-Scans von Patienten mit erblichen Netzhauterkrankungen zuverlässig segmentieren kann. In einem Testdatensatz lag der Dice-Score des Algorithmus bei 98,7 %. Außerdem erstellten wir für jeden Patienten Dickenkarten der gesamten Netzhautdicke und der ONL-Schicht. Schlussfolgerung: Eine präzise Segmentierung anatomischer Schichten auf OCT-Scans ist entscheidend für Prognosemodelle, welche Netzhautstruktur und Sehfunktion korrelieren. Der hier vorgestellte OCT-Bildsegmentierungsalgorithmus könnte die Grundlage für weitere Studien bezüglich erblichen Netzhauterkrankungen darstellen.

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“…Morphological proof of the treatment effect is possible in some cases as well, although the most prominent proof of treatment effects remains the rod functional rescue. An interesting improvement in the area of retinal imaging of patients who underwent RPE65 gene therapy is the repeatedly reported improved signal of the fundus autofluorescence [29,36,37]. This contrasts with the enormous difficulties to obtain FAF images in the natural course of RPE65 retinal dystrophies and before treatment [22,37].…”

Section: Experiences With the Treatment Efficacymentioning

confidence: 99%

RPE65-Associated Retinal Dystrophies: Phenotypes and Treatment Effects with Voretigene Neparvovec

Stingl,

Priglinger,

Herrmann

2024

Klin Monbl Augenheilkd

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Retinal dystrophies linked to the RPE65 gene are mostly fast-progressing retinal diseases, with childhood onset of night blindness and progressive visual loss up to the middle adult age. Rare phenotypes linked to this gene are known with congenital stationary night blindness or slowly progressing retinitis pigmentosa, as well as an autosomal dominant c.1430A>G (p.Asp477Gly) variant. This review gives an overview of the current knowledge of the clinical phenotypes, as well as experience with the efficacy and safety of the approved gene augmentation therapy voretigene neparvovec.

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Therapy with voretigene neparvovec. How to measure success?

Cited by 21 publications

References 86 publications

Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseases

Deep Learning based retinal layer segmentation in optical coherence tomography scans of patients with inherited retinal diseases

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases

RPE65-Associated Retinal Dystrophies: Phenotypes and Treatment Effects with Voretigene Neparvovec

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