Using machine learning to predict ovarian cancer

Lu, Mingyang; Fan, Zhenjiang; Xu, Bin; Chen, Lüjun; Zheng, Xiao; Li, Jundong; Znati, Taieb; Mi, Qi; Jiang, Jingting

doi:10.1016/j.ijmedinf.2020.104195

Cited by 82 publications

(51 citation statements)

References 23 publications

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“…Various authors have also used machine learning techniques to predict ovarian cancer. Lu et al [ 28 ] used a decision tree model and feature selection measures to predict the occurrence of ovarian cancer using different blood routine tests, chemistry, and tumor markers. Several other studies also used different classification techniques to predict survival in various types of cancer [ 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

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Section: Related Workmentioning

confidence: 99%

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Kaur

Doja

Ahmad

et al. 2021

Computational Intelligence and Neuroscience

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“…By using machine learning including deep learning, in recent years, many studies on applying machine learning in cancer research have been performed [ 26 , 29 , 30 , 33 , 62 , 63 ]. Using machine learning, predicting the prognoses of ovarian cancer patients and the therapeutic effects of platinum-containing drugs can be widely performed [ 64 – 72 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma

Sato

Shintani

et al. 2022

BMC Cancer

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Background Administration of poly (ADP-ribose) polymerase (PARP) inhibitors after achieving a response to platinum-containing drugs significantly prolonged relapse-free survival compared to placebo administration. PARP inhibitors have been used in clinical practice. However, patients with platinum-resistant relapsed ovarian cancer still have a poor prognosis and there is an unmet need. The purpose of this study was to examine the clinical significance of metabolic genes and focal adhesion kinase (FAK) activity in advanced ovarian high-grade serous carcinoma (HGSC). Methods The RNA sequencing (RNA-seq) data and clinical data of HGSC patients were obtained from the Genomic Data Commons (GDC) Data Portal and analysed (https://portal.gdc.cancer.gov/). In addition, tumour tissue was sampled by laparotomy or screening laparoscopy prior to treatment initiation from patients diagnosed with stage IIIC ovarian cancer (International Federation of Gynecology and Obstetrics (FIGO) classification, 2014) at the Saitama Medical University International Medical Center, and among the patients diagnosed with HGSC, 16 cases of available cryopreserved specimens were included in this study. The present study was reviewed and approved by the Institutional Review Board of Saitama Medical University International Medical Center (Saitama, Japan). Among the 6307 variable genes detected in both The Cancer Genome Atlas-Ovarian (TCGA-OV) data and clinical specimen data, 35 genes related to metabolism and FAK activity were applied. RNA-seq data were analysed using the Subio Platform (Subio Inc, Japan). JMP 15 (SAS, USA) was used for statistical analysis and various types of machine learning. The Kaplan-Meier method was used for survival analysis, and the Wilcoxon test was used to analyse significant differences. P < 0.05 was considered significant. Results In the TCGA-OV data, patients with stage IIIC with a residual tumour diameter of 1-10 mm were selected for K means clustering and classified into groups with significant prognostic correlations (p = 0.0444). These groups were significantly associated with platinum sensitivity/resistance in clinical cases (χ2 test, p = 0.0408) and showed significant relationships with progression-free survival (p = 0.0307). Conclusion In the TCGA-OV data, 2 groups classified by clustering focusing on metabolism-related genes and FAK activity were shown to be associated with platinum resistance and a poor prognosis.

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“…Furthermore, well-known adaptive ML algorithms have been used widely in the literature for cancer classification by integrating different types of data [72] , [73] , [74] , [75] , [76] . Song et al [77] proposed a predictive model for long-term prognosis of bladder cancer based on the learning ability of ML algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

Κούρου

Exarchos

Papaloukas

et al. 2021

Computational and Structural Biotechnology Journal

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Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

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Using machine learning to predict ovarian cancer

Cited by 82 publications

References 23 publications

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques

Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

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