A Statistical-Mining Techniques’ Collaboration for Minimizing Dimensionality in Ovarian Cancer Data

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute to many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well as monitoring the treatment progress. Therefore, identifying the most significant required analysis could be a positive step to reduce the emotional and financial suffering. This research aims to reduce the data dimensionality of the ovarian cancer disease and highlight the most significant analysis using the collaboration of clustering techniques and statistical techniques. The research succeeded to identify twelve significant analysis out of forty-four with a total of fourteen significant attributes for ovarian cancer data.

Application of Modified Generalized–Gamma Mixture Cure Model in the Analysis of Ovarian Cancer Data

The modeling and analysis of lifetime for terminal diseases such as cancer is a significant aspect of statistical work. This study considered data from thirty-seven women diagnosed with Ovarian Cancer and hospitalized for care at theDepartment of Obstetrics and Gynecology, University of Ibadan, Nigeria. Focus was on the application of a parametric mixture cure model that can handle skewness associated with survival data – a modified generalized-gamma mixture cure model (MGGMCM). The effectiveness of MGGMCM was compared with existing parametric mixture cure models using Akaike Information Criterion, median time-to-cure and variance of the cure rate. It was observed that the MGGMCM is an improved parametric model for the mixture cure model.

Combining multiple biomarkers to linearly maximize the diagnostic accuracy under ordered multi-class setting

Either in clinical study or biomedical research, it is a common practice to combine multiple biomarkers to improve the overall diagnostic performance. Despite the fact there exist a large number of statistical methods for biomarker combination under binary classification, research on this topic under multi-class setting is sparse. The overall diagnostic accuracy, i.e. the sum of correct classification rates, directly measures the classification accuracy of the combined biomarkers. Hence the overall accuracy can serve as an important objective function for biomarker combination, especially when the combined biomarkers are used for the purpose of making medical diagnosis. In this paper, we address the problem of combining multiple biomarkers to directly maximize the overall diagnostic accuracy by presenting several grid search methods and derivation-based methods. A comprehensive simulation study was conducted to compare the performances of these methods. An ovarian cancer data set is analyzed in the end.

Impact of chemotherapy alone or in combination with an anti-angiogenic on the immune tumor microenvironment (TME) of ovarian cancer: Data from the randomized CHIVA trial (a GINECO –GINEGEPS study).

6011 Background: The neoadjuvant setting is an excellent opportunity to study ‘ in vivo’ the biological impact of treatment on tumor cells and the immune TME. Both chemotherapy and anti-angiogenics may have immunomodulatory properties which could prime the TME and increase effectiveness of immunotherapeutic agents. We performed comprehensive multiplexed immune biomarker analyses on paired tumor samples at diagnosis and after 3 cycles of neoadjuvant carboplatin+paclitaxel (CP) +/- the anti-angiogenic tyrosine kinase inhibitor nintedanib (N) in the randomized CHIVA trial. Methods: Patients were randomized 2:1 to CP + N or placebo for 3 cycles prior to interval debulking, samples were evaluable for immune profiling for 124 pts at diagnosis and 107 at surgery from the CHIVA trial. For 86 patients matched paired samples were available. Multiplexed IF or IHC panels were performed for CD4, CD3, CD8, CK, Granzyme B, FOXP3, CD68, CD163 and DC-Lamp. Wilcoxon tests were used to compare measurements. Results: At diagnosis the most abundant cells were CD8+ and CD4+ cells (median=118 and 119cells/mm2, respectively) compared to Foxp3+ TRegs (median=30/mm2). Among the myeloid lineage, the proportion of CD68+ (M1) and CD163+ (M2) macrophages was balanced, while mature dentritic cells (DC) represented <5% of myeloid cells. In the whole population, regardless of arm, neoadjuvant platinum-based treatment significantly increased CD4+ (p=0.03) and CD8+ infiltration (p=0.009), decreased FOXP3+ cells (p=0.01), and these differences pre- and post-treatment remained significant when analysis was restricted to pts with paired samples. Mature DC also increased significantly with neoadjuvant treatment (p=0.0003), there was no significant modification in CD68+ or CD163+ macrophages. Changes in immune parameters did not differ significantly between the CP+B vs CP+placebo arms. Conclusions: Neoadjuvant treatment has a profound impact on the immune cell composition of the TME in advanced OC. However this change seems to be mainly mediated by platinum+paclitaxel chemotherapy rather than the anti-angiogenic tyrosine kinase inhibitor nintedanib.