scholarly journals Seven Genes Based Novel Signature Predicts Clinical Outcome and Platinum Sensitivity of High Grade IIIc Serous Ovarian Carcinoma

2018 ◽  
Vol 14 (14) ◽  
pp. 2012-2022 ◽  
Author(s):  
Gang Liu ◽  
Lihua Chen ◽  
He Ren ◽  
Fei Liu ◽  
Chuanpeng Dong ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Ovarian Carcinoma ◽  
High Grade ◽  
Platinum Sensitivity ◽  
Serous Ovarian Carcinoma

scholarly journals Retinoblastoma pathway deregulatory mechanisms determine clinical outcome in high-grade serous ovarian carcinoma

2013 ◽  
Vol 27 (7) ◽  
pp. 991-1001 ◽  
Author(s):  
Anca Milea ◽  
Sophia HL George ◽  
Donco Matevski ◽  
Haiyan Jiang ◽  
Mary Madunic ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Ovarian Carcinoma ◽  
High Grade ◽  
Serous Ovarian Carcinoma

scholarly journals Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1875
Author(s):  
Suhyun Hwangbo ◽  
Se Ik Kim ◽  
Ju-Hyun Kim ◽  
Kyung Jin Eoh ◽  
Chanhee Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Carcinoma ◽  
Model Performance ◽  
Ca 125 ◽  
Lymph Node Status ◽  
High Grade ◽  
Learning Models ◽  
Platinum Sensitivity ◽  
Serous Ovarian Carcinoma ◽  
Machine Learning Models

To support the implementation of individualized disease management, we aimed to develop machine learning models predicting platinum sensitivity in patients with high-grade serous ovarian carcinoma (HGSOC). We reviewed the medical records of 1002 eligible patients. Patients’ clinicopathologic characteristics, surgical findings, details of chemotherapy, treatment response, and survival outcomes were collected. Using the stepwise selection method, based on the area under the receiver operating characteristic curve (AUC) values, six variables associated with platinum sensitivity were selected: age, initial serum CA-125 levels, neoadjuvant chemotherapy, pelvic lymph node status, involvement of pelvic tissue other than the uterus and tubes, and involvement of the small bowel and mesentery. Based on these variables, predictive models were constructed using four machine learning algorithms, logistic regression (LR), random forest, support vector machine, and deep neural network; the model performance was evaluated with the five-fold cross-validation method. The LR-based model performed best at identifying platinum-resistant cases with an AUC of 0.741. Adding the FIGO stage and residual tumor size after debulking surgery did not improve model performance. Based on the six-variable LR model, we also developed a web-based nomogram. The presented models may be useful in clinical practice and research.

Neoplastic cellularity is associated with clinical and molecular features of high-grade serous ovarian carcinoma

2015 ◽  
Vol 139 (1) ◽  
pp. 196
Author(s):  
C. Morse ◽  
B. Norquist ◽  
S. Bernards ◽  
M. Harrell ◽  
K. Agnew ◽  
...  
Keyword(s):  
Ovarian Carcinoma ◽  
High Grade ◽  
Molecular Features ◽  
Serous Ovarian Carcinoma

MiR-182 overexpression in tumourigenesis of high-grade serous ovarian carcinoma

2012 ◽  
Vol 228 (2) ◽  
pp. 204-215 ◽  
Author(s):  
Zhaojian Liu ◽  
Jinsong Liu ◽  
Miguel F Segura ◽  
Changshun Shao ◽  
Peng Lee ◽  
...  
Keyword(s):  
Ovarian Carcinoma ◽  
High Grade ◽  
Serous Ovarian Carcinoma

scholarly journals Molecular analysis of high-grade serous ovarian carcinoma with and without associated serous tubal intra-epithelial carcinoma

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Jennifer Ducie ◽  
Fanny Dao ◽  
Michael Considine ◽  
Narciso Olvera ◽  
Patricia A. Shaw ◽  
...  
Keyword(s):  
Ovarian Carcinoma ◽  
Molecular Analysis ◽  
High Grade ◽  
Serous Ovarian Carcinoma
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