FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2966656188> ?p ?o ?g. }

• W2966656188 abstract "Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK. The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3–25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0–13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006). Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients." @default.
• W2966656188 created "2019-08-13" @default.
• W2966656188 creator A5000005180 @default.
• W2966656188 creator A5008910507 @default.
• W2966656188 creator A5012620880 @default.
• W2966656188 creator A5013480180 @default.
• W2966656188 creator A5019284998 @default.
• W2966656188 creator A5019846100 @default.
• W2966656188 creator A5020854072 @default.
• W2966656188 creator A5028298545 @default.
• W2966656188 creator A5029560641 @default.
• W2966656188 creator A5031181272 @default.
• W2966656188 creator A5075105813 @default.
• W2966656188 date "2019-07-29" @default.
• W2966656188 modified "2023-10-16" @default.
• W2966656188 title "A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk" @default.
• W2966656188 cites W1485617223 @default.
• W2966656188 cites W1597336200 @default.
• W2966656188 cites W1607332788 @default.
• W2966656188 cites W1820655337 @default.
• W2966656188 cites W1835326614 @default.
• W2966656188 cites W1840707476 @default.
• W2966656188 cites W1903446459 @default.
• W2966656188 cites W1956944003 @default.
• W2966656188 cites W1965295175 @default.
• W2966656188 cites W1966934872 @default.
• W2966656188 cites W1969592945 @default.
• W2966656188 cites W1977653087 @default.
• W2966656188 cites W1982767940 @default.
• W2966656188 cites W1984692997 @default.
• W2966656188 cites W1997842320 @default.
• W2966656188 cites W2000999878 @default.
• W2966656188 cites W2002786604 @default.
• W2966656188 cites W2005791328 @default.
• W2966656188 cites W2009719437 @default.
• W2966656188 cites W2011416200 @default.
• W2966656188 cites W2012857662 @default.
• W2966656188 cites W2013536196 @default.
• W2966656188 cites W2021115278 @default.
• W2966656188 cites W2025898002 @default.
• W2966656188 cites W2034714785 @default.
• W2966656188 cites W2036568529 @default.
• W2966656188 cites W2040414046 @default.
• W2966656188 cites W2042147906 @default.
• W2966656188 cites W2045059595 @default.
• W2966656188 cites W2053355503 @default.
• W2966656188 cites W2057418448 @default.
• W2966656188 cites W2071762482 @default.
• W2966656188 cites W2093030207 @default.
• W2966656188 cites W2094048742 @default.
• W2966656188 cites W2097793253 @default.
• W2966656188 cites W2098140880 @default.
• W2966656188 cites W2100349108 @default.
• W2966656188 cites W2104285855 @default.
• W2966656188 cites W2110115018 @default.
• W2966656188 cites W2115971744 @default.
• W2966656188 cites W2124155137 @default.
• W2966656188 cites W2125027853 @default.
• W2966656188 cites W2126096721 @default.
• W2966656188 cites W2127644335 @default.
• W2966656188 cites W2129112648 @default.
• W2966656188 cites W2129925362 @default.
• W2966656188 cites W2130192648 @default.
• W2966656188 cites W2131312255 @default.
• W2966656188 cites W2133594922 @default.
• W2966656188 cites W2134993189 @default.
• W2966656188 cites W2136479159 @default.
• W2966656188 cites W2136737404 @default.
• W2966656188 cites W2141292206 @default.
• W2966656188 cites W2153453882 @default.
• W2966656188 cites W2155146490 @default.
• W2966656188 cites W2157314963 @default.
• W2966656188 cites W2157773071 @default.
• W2966656188 cites W2158335605 @default.
• W2966656188 cites W2158747437 @default.
• W2966656188 cites W2163678566 @default.
• W2966656188 cites W2170051899 @default.
• W2966656188 cites W2207029559 @default.
• W2966656188 cites W2217918011 @default.
• W2966656188 cites W2222141994 @default.
• W2966656188 cites W2235523093 @default.
• W2966656188 cites W2333134109 @default.
• W2966656188 cites W2408733084 @default.
• W2966656188 cites W2409363492 @default.
• W2966656188 cites W2420197930 @default.
• W2966656188 cites W2514628397 @default.
• W2966656188 cites W2519172613 @default.
• W2966656188 cites W2521882103 @default.
• W2966656188 cites W2521963431 @default.
• W2966656188 cites W2580285407 @default.
• W2966656188 cites W2620578070 @default.
• W2966656188 cites W2764168238 @default.
• W2966656188 cites W2765485186 @default.
• W2966656188 cites W2886013728 @default.
• W2966656188 cites W2911964244 @default.
• W2966656188 cites W2922268597 @default.
• W2966656188 cites W3099478002 @default.
• W2966656188 cites W4253449727 @default.
• W2966656188 cites W2069833825 @default.
• W2966656188 doi "https://doi.org/10.1186/s13058-019-1165-5" @default.

• next