Izhodišča: Retroperitonealni sarkomi so izredno redki, zato naj zdravljenje bolnikov z retroperitonealnimi sarkomi poteka v referenčnem centru. Temeljno zdravljenje je kirurško. Priporočen tip ...operacije je kompartment resekcija.
Metode: Onkološki inštitut Ljubljana je edini referenčni center za sarkome v Sloveniji. V raziskavo so bili vključeni bolniki s primarnim lokaliziranim retroperitonealnim sarkomom, zdravljeni pri nas v obdobju od januarja 1999 do junija 2020. Opredelili smo preživetje, kakovost kirurškega zdravljenja in zaplete.
Rezultati: Vključenih je bilo 100 bolnikov. Srednja starost je bila 62 let. Srednja velikost tumorja je bila 21,5 cm. Najpogostejši histološki podtip je bil dediferenciran liposarkom (39 %). Kompartment resekcija je bila opravljena v 24 %, multivisceralna resekcija pa v 25 %. Zaplete po posegu je imelo po klasifikaciji Clavien-Dindo stopnje 3a ali višje 29 % bolnikov, pri 58,6 % (17/29) je bila potrebna ponovna operacija. Zgodnja in pozna smrtnost po operaciji sta bili 3 % in 5 %. Srednji čas sledenja je bil 55,1 mesecev. 5-letno celokupno preživetje je bilo 67,8 %. Kumulativna verjetnost za lokalno ponovitev bolezni po 5 letih je bila 16,9 %, za oddaljene zasevke pa 21,4 %. Ocena ASA in izguba krvi med operacijo sta bila neodvisna napovedna dejavnika celokupnega preživetja.
Zaključek: Retroperitonealni sarkomi sodijo med redke vrste raka. Naši rezultati zdravljenja bolnikov z retroperitonealnimi sarkomi so zelo dobri in primerljivi z rezultati drugih referenčnih centrov iz tujine. Potrjujejo tudi ključno vlogo referenčnega centra pri obravnavi in zdravljenju teh bolnikov.
The present bottleneck in biosimilar bioprocess development has become evaluation of analytical results, due to recent advances in analytics, such as automated sample preparation and development of ...high‐throughput methods. Currently automated chromatogram integration and annotation is only efficient for simple chromatograms. In an ever more competitive field of biosimilars, this represents a serious drawback because chromatographic analytical methods that provide some of the most valuable physicochemical quality attributes of the product also require careful chromatogram integration and annotation. This work focuses on the glycan mapping analytical method as utilized in development of monoclonal antibody biosimilars, evaluating more than 2000 chromatograms spanning the life cycle of multiple biosimilar development projects. It proposes a modified workflow by implementing automatic machine learning algorithms to determine the proportion of specific relevant glycan species in a sample directly from the chromatogram. Data preparation and analysis is performed using a pipeline approach. Pipeline is a modular design of data processing where signal “travels” through various active modules in a series. Each module performs a specific function or transformation on the signal and propagates the transformed signal to the next module. The pipeline is designed in a way that modules can be independently improved and exchanged. Module functions currently implemented are chromatogram resampling by spline interpolation, baseline removal by asymmetric least squares, peak alignment using parametric time warping, and quantification of the relative proportion of a glycan species using partial least squares regression. Hyper‐parameters of the pipeline are then optimized using the Nelder–Mead method. The approach stands out for its ability to accommodate a broad landscape of samples, covering multiple different proteins in different stages of biosimilar development, analyzed using different adaptations of the glycan map analytical method. The pipeline presents an intuitive, flexible, and creatively simple method design capable of providing reliable results for a wide range of glycan species essential for biosimilar development. It enables transparent, faster, and less subjective evaluation of analytic raw data (from sample to result). Furthermore, our automated approach maintained an accuracy comparable with manual integration thus demonstrating its readiness for implementation in the conservative and highly regulated environment. The presented methodology reduces the cost and time of biosimilar development and should be applicable for any chromatogram‐based analytical method.
The current analytical workflow for biosimilar development is modified by implementing automatic machine learning algorithms that can determine the proportion of specific relevant glycan species in a sample directly from the chromatogram.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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