Utemeljitev: Zaradi oportunističnega presejanja odkrijemo veliko primerov raka prostate (RP) z ugodnejšo prognozo. Mednje spadajo bolniki z ISUP skupino po Gleasonu 1 in 2 (GG1 in GG2). Kljub dobri prognozi in možnosti aktivnega opazovanja pri teh bolnikih se bo pri nekaterih po radikalni prostatektomiji (RRP) razvila biokemična ponovitev (BKR) ali celo po EAU visokorizična BKR s podvojitvenim časom PSA (PSA–DT) ? 1 leto, ki je povezana s povečano umrljivostjo bolnikov. Vzrok je najverjetneje v heterogenosti RP in neustreznem predoperativnem histopatološkem razvrščanju bolezni. Ocena GG kljub razvoju novih biomarkerjev še vedno velja za najpomembnejši prognostični dejavnik BKR. Slabost ocene GG je subjektivnost in spremenljivost ocene med različnimi patologi. Ploidija DNK je ena od metod, ki je objektivna in ima primerljiv prognostični potencial. Za določanje ploidije DNK potrebujemo veliko rakavega tkiva, zato je pogosto ne moremo določiti na igelnih biopsijah. Z naprednimi tehnološkimi tehnikami lahko na majhnem področju tankih tkivnih rezin izmerimo značilke tkivne arhitekture in jedrne morfologije. Z raziskavo smo preverili, ali lahko s kombinacijo tkivnih značilk pridobimo primerljivo ali celo boljšo napoved BKR in preživetja brez BKR po prostatektomiji v primerjavi z oceno GG ali ploidijo DNK pri populaciji raka prostate z ugodno prognozo. Zasnova raziskave, opis metod: V raziskavo smo vključili 115 bolnikov z nizko– in srednjerizičnim RP, ki so imeli RRP v splošni bolnišnici (SB) Celje v obdobju od 2003––2009. Oceno GG so določili splošni patologi iz SB Celje (GGG) in patolog ekspert z Inštituta za patologijo Medicinske fakultete v Ljubljani (EGG). Ploidijo DNK smo določili na 70 µm debelih rezinah vzorcev, dobljenih po prostatektomijah, saj je bilo rakavega tkiva iz igelnih biopsij premalo ali je bilo izčrpano. Tkivo smo encimsko razgradili, izolirali celice in jih barvali s stehiometričnim barvilom tionin-eozin. S slikovno citometrijo smo določili značilke ploidije DNK in ustvarili oceno PS (ang. Ploidy score). Tkivne značilke smo določili na rezinah igelnih biopsij debeline 5 µm, obarvanih s tioninom. Rezine smo skenirali in določili diagnostično področje na podlagi najslabšega vzorca po Gleasonu. Na podlagi Voronojevega diagrama smo izračunali značilke tkivne arhitekture, iz katerih smo z linearno diskriminantno analizo ustvarili oceno MSTA (ang. Multi-scale tissue architecture). Z dodatnimi segmentacijskimi algoritmi smo določili popolnoma segmentirana jedra z jasnimi mejami (»dobra jedra«), iz katerih smo izračunali jedrne značilke in ustvarili oceno LDO (ang. Large-scale DNA organisation). Iz ocene MSTA in ocene LDO smo ustvarili globalno tkivno oceno QTP (ang. Quantitative tissue phenotype). Rezultati: V univariatnem regresijskem modelu so bile statistično značilne spremenljivke ocena QTP, ocena LDO, ocena MSTA, skupina EGG, PS in cT (ustrezne vrednosti p: 1,2 x 10–7, 0,004, 0,028, 0,0016, 0,0002, 0,016). V multivariatnih modelih ocena MSTA ni bila statistično značilna v kombinaciji z oceno EGG, temveč v kombinaciji z GGG. Ocena LDO je bila neodvisna spremenljivka tudi v modelu z oceno EGG (ustrezne vrednosti p: 0,01 in 0,003). Ocena QTP je dosegla najboljšo prognostično vrednost v vseh modelih – kombinacija z oceno EGG in cT (ustrezne vrednosti p: 3,7 x 10–5, 0,02 in 0,06). Bolniki z višjo oceno QTP so tako imeli največjo verjetnost BKR ali po EAU visokorizične BKR. S kombinacijo ocen GG in MSTA ali ocen GG in LDO smo ustvarili skupine, s katerimi smo dodatno izboljšali razslojenost tveganj za BKR. Z analizo Kaplan-Meierjevih krivulj smo ugotovili, da ocena QTP najbolje napove preživetje brez BKR (test log-rank: p = 4 x 10–12). Ocena LDO ali ocena MSTA sta statistično značilno napovedali preživetje brez BKR, vendar ne bolje od ocene EGG ali PS. Zaključek: Z našo raziskavo smo potrdili, da lahko iz diagnostičnega področja igelne biopsije pridobimo prognostične informacije, ki so celo boljše od standardnih histopatoloških ocen ali ploidije DNK. Nova kvantitativna metoda pokaže potencial zaznavanja najbolj agresivnih fenotipov RP in ima zaradi objektivnih in prognostičnih lastnosti potencial personaliziranega pristopa pri obravnavi populacije RP z ugodno prognozo. Background: Prostate cancer (PCa) with ISUP Grade Group (GG) 1 or 2 characteristics have favorable outcomes after radical prostatectomy treatment, yet some of them still progress and present with biochemical recurrence (BCR), and some of those (PSA with doubling time of 䁤 1 year) will be considered a high-risk BCR. This might be due to disease heterogeneity and improper cancer staging and grade classification in pre-operative settings. To date, the GG is the most reliable prognostic tool, and has the largest impact on treatment decisions, however the subjective nature of GG assessment and significant inter-observer variability, in particular amongst general pathologists, have therefore highlighted the need for a more robust and objective assessment to guide treatment recommendations. DNA ploidy is an objective prognostic tool, however it requires a large amount of PCa cells that are often not available from limited biopsy tissue. Technology advances allow us to extract numerous tissue features from a small diagnostic area of PCa tissue sections. We hypothesize that combination of tissue architecture features and nuclear morphometry features could improve the prediction of cancer progression in the form of BCR in comparison to GG assessment or DNA ploidy. Methods: 115 patients from the General Hospital of Celje (GH Celje) with a low– and favorable intermediate risk PCa characteristics that undergone radical retropubic prostatectomy (RRP) between years 2003 and 2009 were included in the study. Pathological evaluation from general pathologists (GGG) was supplemented by an experienced uropathologist from Institute of Pathology in Ljubljana (EGG), who identified diagnostic area with the worst Gleason pattern to be used in the study. DNA ploidy was assessed on prostatectomy samples, which underwent tissue disaggregation, cell isolation and staining with a DNA stoichiometric stain. Using image cytometry, ploidy features were extracted and a Ploidy Score (PS) generated. Tissue features were extracted from thin biopsy tissue sections that were stained with thionin and scanned. Voronoi diagram-based algorithms were applied to diagnostic areas. A linear combination of the most discriminant architecture features generated a MSTA score. A nuclear morphometry features were extracted from perfectly segmented nuclei and LDO score was generated as a linear combination of the most discriminant nuclear features. Using a linear combination of MSTA and LDO score, a global tissue score – QTP score was generated. Results: In a univariate regression model, the variables of QTP score, LDO score, MSTA score, EGG, PS score and cT were significant predictors of BCR (respective p values: 1,2 x 10–7, 0,004, 0,028, 0,0016, 0,0002, 0,016). In a multivariate regression model, MSTA score was significant predictor of BCR in model with GGG, however not with EGG. LDO score reached independent level in model with EGG (respective p values: 0,01 and 0,003). QTP score was best predictive variable of recurrence in any model, including EGG and cT (respective p values: 3,7 x 10–5, 0,02 and 0,06). Thus patients with a high QTP score were more likely to experience BCR or a high-risk BCR than patients with a low QTP score. Combining MSTA or LDO score with GG resulted in a significant stratification of risk of BCR. Survival analysis showed that QTP score is the best variable in predicting biochemical free survival (log-rank test: p = 4 x 10–12). LDO score or MSTA score were significant predictors of biochemical recurrence free survival, however not better in comparison to EGG or PS. Conclusion: We have shown that tissue architecture and nuclear morphometry features extracted from PCa biopsy tissue could improve the prediction of cancer progression in comparison to GG or DNA ploidy. The new quantitative method is objective and has potential to identify aggressive PCa recurrences and could thereby introduce more personalized approach to guide treatment recommendations in population with favorable risk PCa.