Comprehensive analysis of the associations between clinical factors and outcomes by machine learning, using post marketing surveillance data of cabazitaxel in patients with castration-resistant prostate cancer

Abstract

Background We aimed to evaluate relationships between clinical outcomes and explanatory variables by network clustering analysis using data from a post marketing surveillance (PMS) study of castration-resistant prostate cancer (CRPC) patients. Methods The PMS was a prospective, multicenter, observational study of patients with metastatic, docetaxel-refractory CRPC treated with cabazitaxel in Japan after its launch in 2014. Graphical Markov (GM) model-based simulations and network clustering in ‘R’ package were conducted to identify correlations between clinical factors and outcomes. Factors shown to be associated with overall survival (OS) in the machine learning analysis were confirmed according to the clinical outcomes observed in the PMS. Results Among the 660 patients analyzed, median patient age was 70.0 years, and median OS and time-to-treatment failure (TTF) were 319 and 116 days, respectively. In GM-based simulations, factors associated with OS were liver metastases, performance status (PS), TTF, and neutropenia (threshold 0.05), and liver metastases, PS, and TTF (threshold 0.01). Factors associated with TTF were OS and relative dose intensity (threshold 0.05), and OS (threshold 0.01). In network clustering in ‘R’ package, factors associated with OS were number of treatment cycles, discontinuation due to disease progression, and TTF (threshold 0.05), and liver and lung metastases, PS, discontinuation due to adverse events, and febrile neutropenia (threshold 0.01). Kaplan–Meier analysis of patient subgroups demonstrated that visceral metastases and poor PS at baseline were associated with worse OS, while neutropenia or febrile neutropenia and higher number of cabazitaxel cycles were associated with better OS. Conclusions Neutropenia may be a predictive factor for treatment efficacy in terms of survival. Poor PS and distant metastases to the liver and lungs were shown to be associated with worse outcomes, while factors related to treatment duration were shown to positively correlate with better OS.

Baseline characteristics may impact treatment duration of cabazitaxel in patients with mCRPC: a subanalysis of data from a post-marketing surveillance

“…Findings from our study supported a machine learning analysis of cabazitaxel therapy in mCRPC, which suggested association of better OS with neutropenia development and higher number of cabazitaxel cycles ( 12 ). Other patient factors were dependent on the set threshold for correlation—at a higher threshold (0.05), liver metastases, ECOG PS, TTF and neutropenia were associated with OS.…”

“…Other patient factors were dependent on the set threshold for correlation—at a higher threshold (0.05), liver metastases, ECOG PS, TTF and neutropenia were associated with OS. Apart from neutropenia, the same was true at a lower threshold (0.01) ( 12 ).…”

Study on prediction of early adverse events by CapeOX therapy in patients with colorectal cancer

“…Machine learning is intended to use these as sources of information for the prevention, early diagnosis, and treatment of diseases. The number of machine learning papers is increasing in a variety of areas, such as predicting the side effects of drugs in older adults, predicting side effects of anticancer drugs, and correlating survival with clinical data (8)(9)(10). The number of reports on machine learning in oncology has increased in recent years.…”

Evolution from traditional marketing to digital marketing as a formative process

“…The measures taken as alternatives to maintain the functioning of teaching have been multiple, mediated by the use of technological tools and resources that have left consequences and results that need to be carefully analyzed and taken into account. Kazama et al, (2022) in that it alludes to the visions about the actions to be implemented for the recovery, adjustment and achievement of the university processes with the positive and negative consequences of the whole stage of the pandemic. An assessment is made of possible stages or phases of development with the idea of determining these results and the implementation by way of strengthening the mediation of Information and Communication Technologies, and the perspectives of the student body and the teaching staff for the improvement of their performance.…”