PREDIKCIJA METASTAZA U LIMFNIM ČVOROVIMA VRATA KOD PAPILARNOG KARCINOMA ŠTITASTE ŽLEZDE KORIŠĆENJEM PRINCIPA MAŠINSKOG UČENJA
Sažetak
Incidenca papilarnog tiroidnog karcinoma (PTK) se u poslednje tri decenije konstantno uvećava, čineći ga najčešćim malignim oboljenjem štitaste žlezde. Iako pacijenti sa PTK generalno imaju povoljan ishod, prisustvo metastaza u limfnim čvorovima vrata može značajno uticatati na njihovu prognozu, povećavajući verovatnoću ponovnog javljanja bolesti. Trenutna preoperativna dijagnostika limfonodalnih metastaza uglavnom podrazumeva ultrazvučni pregled vrata, ali osnovno ograničenje ove metode je niska senzitivnost. Kao rezultat niske senzitivnosti, limfonodalne metastaze ostaju neotkrivene tokom pre-operativnog stadiranja bolesti i mogu se kasnije manifestovati kao perzistentna ili rekurentna bolest, zahtevajući dalju evaluaciju i potencijalnu reoperaciju.
Kako bi se suočili sa izazovima dijagnostike limfonodalnih metastaza kod pacijenata sa PTK, razvijeni su različiti modeli za njihovu predikciju. Među modelima predikcije posebnu pažnju privlače modeli mašinskog učenja koji mogu sa većom tačnošću predvideti ishod bolesti i omogućiti odabir optimalnog lečenja za svakog pacijenata individualno. Stoga je ovaj mini-pregledni članak pretežno usmeren na objašnjenje osnovnih principa modela mašinskog učenja kroz primer predikcije metastaza u limfnim nodusima vrata kod pacijenata sa PTK. Pored toga, u radu je dat pregled najčešće korišćenih modela mašinskog učenja uz diskusiju njihove efikasnosti u studijama koje su predikciju limfonodalnih metastaza bazirale na ovom pristupu. Na samom kraju, razmatrani su i najčešći izazovi koji limitiraju implementaciju ovih modela u kliničkoj praksi i identifikovana su ključna područja za njihovo poboljšanje.
Trenutno, modeli mašinskog učenja predstavljaju potencijalno korisno sredstvo za predikciju limfonodalnih metastaza kod pacijenata sa PTK ali su neophodna dalja istraživanja kako bi se potpuno iskoristile mogućnosti ovih modela i omogućila njihova implementacija u sisteme za podršku odlučivanju.
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