Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

Αρχειοθήκη ιστολογίου

! # Ola via Alexandros G.Sfakianakis on Inoreader

Η λίστα ιστολογίων μου

Πέμπτη 10 Φεβρουαρίου 2022

Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer

xlomafota13 shared this article with you from Inoreader

Am J Cancer Res. 2022 Jan 15;12(1):152-164. eCollection 2022.

ABSTRACT

Triple negative breast cancer (TNBC) is a breast cancer subtype with unfavorable prognosis. We aimed to establish a machine learning-based ultrasound radiomics model to predict disease-free survival (DFS) in TNBC. Invasive TNBC>T1b between January 2009 and June 2018 with preoperative ultrasound were enrolled and assigned to training and independent test cohort. Radiomics and clinicopathological features related with DFS were selected by univariate and multivariate regression analysis. Training cohort of combined features was resampled with SMOTEENN to balance distribution and put into classifiers. Areas Under Curves (AUCs) of models were compared by DeLong's test. 562 women were included with 68 DFS events observed. Twenty prognostic radiomics features were extracted. Machine learning model by Naïve Bayes combining radiomics, clinicopathological features, and SM OTEENN had an AUC of 0.86 (95% CI 0.84-0.88), with sensitivity of 74.7% and specificity of 80.1% in training cohort. In independent test cohort, this three-combination model delivered an AUC of 0.90 (95% CI 0.83-0.95), higher than models based on radiomics (AUC=0.69, P=0.016) or radiomics + SMOTEENN (AUC=0.73, P=0.019). Integrating machine learning radiomics model based on ultrasound and clinicopathological features can predict DFS events for TNBC patients.

PMID:35141010 | PMC:PMC8822271

View on the web

Δεν υπάρχουν σχόλια:

Δημοσίευση σχολίου

Αρχειοθήκη ιστολογίου