Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data.
Marco PellegriniPublished in: Scientific reports (2023)
Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.
Keyphrases
- prostate cancer
- machine learning
- minimally invasive
- radical prostatectomy
- end stage renal disease
- newly diagnosed
- ejection fraction
- free survival
- public health
- prognostic factors
- high resolution
- coronary artery bypass
- squamous cell carcinoma
- artificial intelligence
- patient reported outcomes
- acute coronary syndrome
- young adults
- climate change
- mass spectrometry
- combination therapy
- data analysis