scholarly journals Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score

2021 ◽  
Vol 11 (1) ◽  
pp. 87
Author(s):  
Alexandros Laios ◽  
Raissa Vanessa De Oliveira Silva ◽  
Daniel Lucas Dantas De Freitas ◽  
Yong Sheung Tan ◽  
Gwendolyn Saalmink ◽  
...  

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

2020 ◽  
Vol 7 (6) ◽  
pp. 1805094
Author(s):  
Maria Bååth ◽  
Sofia Westbom-Fremer ◽  
Laura Martin de la Fuente ◽  
Anna Ebbesson ◽  
Juliette Davis ◽  
...  

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Tomer Feigenberg ◽  
Blaise Clarke ◽  
Carl Virtanen ◽  
Anna Plotkin ◽  
Michelle Letarte ◽  
...  

Epithelial ovarian cancer consists of multiple histotypes differing in etiology and clinical course. The most prevalent histotype is high-grade serous ovarian cancer (HGSOC), which often presents at an advanced stage frequently accompanied with high-volume ascites. While some studies suggest that ascites is associated with poor clinical outcome, most reports have not differentiated between histological subtypes or tumor grade. We compared genome-wide gene expression profiles from a discovery cohort of ten patients diagnosed with stages III-IV HGSOC with high-volume ascites and nine patients with low-volume ascites. An upregulation of immune response genes was detected in tumors from patients presenting with low-volume ascites relative to those with high-volume ascites. Immunohistochemical studies performed on tissue microarrays confirmed higher expression of proteins encoded by immune response genes and increased tumorinfiltrating cells in tumors associated with low-volume ascites. Comparison of 149 advanced-stage HGSOC cases with differential ascites volume at time of primary surgery indicated low-volume ascites correlated with better surgical outcome and longer overall survival. These findings suggest that advanced stage HGSOC presenting with low-volume ascites reflects a unique subgroup of HGSOC, which is associated with upregulation of immune related genes, more abundant tumor infiltrating cells and better clinical outcomes.


2013 ◽  
Vol 19 (4) ◽  
pp. 809-820 ◽  
Author(s):  
Tarrik M. Zaid ◽  
Tsz-Lun Yeung ◽  
Melissa S. Thompson ◽  
Cecilia S. Leung ◽  
Tom Harding ◽  
...  

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17544-e17544
Author(s):  
Wanja Nikolai Kassuhn ◽  
Oliver Klein ◽  
Silvia Darb-Esfahani ◽  
Hedwig Lammert ◽  
Sylwia Handzik ◽  
...  

e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma associated spectra provides tangible improvements to classification quality (AUC = 0.988). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR < 1.0%). Conclusions: Here, we present a concept integrating MALDI-IMS with machine learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for targeted therapies.


2019 ◽  
Author(s):  
I Aluloski ◽  
M Tanturovski ◽  
S Kostadinova-Kunovska ◽  
R Jovanovic ◽  
G Petrusevska

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Se Ik Kim ◽  
Maria Lee ◽  
Hee Seung Kim ◽  
Hyun Hoon Chung ◽  
Jae-Weon Kim ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document