PICModForPCa · Personalised Image-based Computational Modelling Framework to Forecast Prostate Cancer

Personalised Image-based Computational Modelling Framework to Forecast Prostate Cancer

Prostate cancer (PCa) is a public health burden and a major concern among ageing men worldwide. Although the vast majority of PCa cases are currently detected at an early stage thanks to regular screening, their severity may vary wildly across patients. While a few prostatic tumors (<5%) are metastatic at diagnosis, about 75% cases of PCa show low to intermediate risk and may not produce any symptom nor require treatment for long time periods. However, nearly 85% of PCa patients receive an aggressive treatment (e.g., surgery or radiotherapy) with curative intent after diagnosis. Radical treatment for PCa can eliminate most diagnosed tumors, but the potential side-effects can also adversely impact the patient’s quality of life without prolonging longevity. Ideally, such intervention should be restricted to combat intermediate and high-risk PCa that may constitute a threat to the patient’s life. Additionally, some patients who initially receive no treatment may eventually exhibit rapid growth of aggressive tumors, experience treatment failure, and ultimately succumb to PCa due to an incorrect initial diagnosis. These issues result from the limited individualization of the clinical management of PCa and have led to significant rates of overtreatment and undertreatment. Alternatively, newly-diagnosed PCa patients with favorable pathological features may enroll in active surveillance (AS). In this clinical strategy, patients are closely monitored via multiparametric magnetic resonance imaging (mpMRI), PSA tests, and biopsies until these reveal an increase in PCa risk that warrants treatment. Thus, AS can contribute to reduce the current overtreatment of PCa. Additionally, the longitudinal clinical and imaging data collected for each patient provides key information about tumor growth that can inform a clinician in the selection of appropriate monitoring strategies or in the planning of treatments. Hence, AS also has potential to reduce the undertreatment of PCa patients. However, current AS protocols rely on monitoring a patient’s tumor according to observational population-based studies, which usually prescribe testing types (i.e., PSA, mpMRI, biopsies) at fixed times or following the suspicion of an increase in the risk level of PCa based on the collected data. This approach limits the design of personalized monitoring plans, complicates the early detection of tumor progression, and does not fully exploit the wealth of information about the patient’s tumor development in the longitudinal mpMRI and clinical data collected during AS. This project aims at addressing the current limitations of AS as well as the deficiencies and excesses in PCa treatment by leveraging patient-specific forecasts of PCa dynamics. Hence, the overarching goal of this project is to construct a personalized computational technology that integrates longitudinal clinical and mpMRI data into a predictive biomechanistic model of PCa that enables to run organ-scale simulations to improve diagnosis and obtain forecasts of PCa growth for each individual patient.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз