Starting point for benchmarking outcomes and reporting of pituitary adenoma surgery within the European Reference Network on Rare Endocrine Conditions (Endo-ERN)

Autor: Amir H Zamanipoor Najafabadi, Merel van der Meulen, Ana Luisa Priego Zurita, S Faisal Ahmed, Wouter R van Furth, Evangelia Charmandari, Olaf Hiort, Alberto M Pereira, Mehul Dattani, Diana Vitali, Johan P de Graaf, Nienke R Biermasz, D Steenvoorden, I Bowring, MM van der Klauw, RA Vergeer, M Losa, P Mortini, W Drake, J Grieve, M Didi, C Mallucci, MG Shaikh, S Hassan, JOL Jorgensen, M Albarazi, S Zaharieva, A. Hadzhiyanev, M Tóth, L Sípos, D Unuane, J D'Haens, U Feldt-Rasmussen, L Poulsgaard, T Brue, H Dufour, J Bertherat, S Gaillard, N Karavitaki, S Ahmed, N Unger, I Kreitschmann-Andermahr, S Gaztambide, I Pomposo, H-W Gan, N Dorward, E Fliers, J Hoogmoed, C Scaroni, L Denaro, A Nordenström, M Olsson, B Zilaitiene, A Tamašauskas, L Persani, G Lasio, D Maiter, C Raftopoulos, V Volke, T Rätsep, P Matarazzo, P Peretta, T Deutschbein, J Perez, S Zucchini, D Mazzatenta, S Grottoli, F Zenga, F Devuyst, O De Witte, F Gatto, D Rossi, K Schilbach, W Rachinger, A Beckers, D Martin, M Al-Mrayat, N Mathad, SJCMM Neggers, AHG Dallenga, J Lebl, M Tichy, MM Reincke, AC van de Ven, E van Lindert, P Kotnik, R Bošnjak, B Biagetti, E Cordero, A Colao, P Cappabianca

Publikováno v: Endocrine connections, Vol. 12, no.1, p. e220349 (2023)
Endocrine Connections, 12(1). BIOSCIENTIFICA LTD
Endocrine Connections, 12(1):e220349. Bioscientifica Ltd

Objective The European Reference Network on Rare Endocrine Conditions (Endo-ERN) aims to organize high-quality healthcare throughout Europe, including care for pituitary adenoma patients. As surgery is the mainstay of treatment, we aimed to describe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71c5cda703fb846d23e0c924b9b87a11
http://www.scopus.com/inward/record.url?scp=85158982183&partnerID=8YFLogxK

Automated Pulmonary Fibrosis Segmentation Using a 3D Multi-Scale Convolutional Encoder-Decoder Approach in Thoracic CT for the Rhesus Macaque with Radiation-Induced Lung Damage

Autor: Baoshe Zhang, Dimitris N. Metaxas, Thomas J. MacVittie, Yin Zhang, Jinghao Zhou, B Yi, Dong Yang, Shifeng Chen, G Lasio

Publikováno v: Journal of Signal Processing Systems. 94:473-483

To develop an automated pulmonary fibrosis (PF) segmentation methodology using a 3D multi-scale convolutional encoder-decoder approach following the robust atlas-based active volume model in thoracic CT for Rhesus Macaques with radiation-induced lung

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::101349981cfe6ff08c767b9dc11d3f8c
https://doi.org/10.1007/s11265-020-01605-3

Development and prospective in‐patient proof‐of‐concept validation of a surface photogrammetry + CT‐based volumetric motion model for lung radiotherapy

Autor: Markus D. Foote, Sina Mossahebi, G Lasio, Jian Zhang, Sarang Joshi, D. Leiser, Amit Sawant, Pouya Sabouri, Maida Ranjbar

Publikováno v: Medical Physics. 46:5407-5420

PURPOSE: We develop and validate a motion model that uses real-time surface photogrammetry acquired concurrently with four-dimensional computed tomography (4DCT) to estimate respiration-induced changes within the entire irradiated volume, over arbitr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1332c0ee8874e8c009f75113a48a314
https://doi.org/10.1002/mp.13824

Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation

Autor: Pouya Sabouri, Maida Ranjbar, Sina Mossahebi, Pranshu Mohindra, L D Timme Topoleski, G Lasio, Amit Sawant

Publikováno v: Phys Med Biol

Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdc0ae6c3fe92ebc1b4121f2eeec1ae6
https://pubmed.ncbi.nlm.nih.gov/33207334

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