Cancer, when identified early, is likely to respond more effectively to treatment, resulting in greater probability of survival, generating less morbidity and reducing cost of treatment. Early diagnosis focusses on identification of symptomatic cancer cases at the earliest possible stage as compared to screening that seeks asymptomatic cancer or pre-cancerous lesions. Due to limitations, it is not yet possible to use extensively screening to prevent cancer (primary prevention). Screening can over-diagnose cancer conditions, implying that premalignant lesions for which medical intervention is not advisable neither desirable receive treatment generating co-morbidities that would be otherwise avoidable. This situation has resulted in the recent past in an epidemic of cancers such as prostate cancer and ductal carcinoma in situ.

Remote patient monitoring (RPM) of cancer diseases can be potentially used to increase current predictive rates, while contributing for a more cost-effective and accessible diagnosis and treatment. Patients at high risk of cancer recurrence would constitute an ideal population for such improved cancer monitoring tools. These novel tools should have the ability to remotely generate personalized patient data, which should be used to detect disease onset or progression. While RPM has been predominantly applied for monitorization of vital signs, the extension of such concept to monitorization of high-risk profile patients would constitute a significant jump forward in the prevention and early diagnosis of cancer patients.

SENTINEL project proposes the innovative development of remote monitoring tools for high-risk profile cancer patients. Such technology aims to increase the positive predictive value (PPV) of cancer screening, while facilitating the remote and ubiquitous monitoring of patients in large scale. The main objective of the SENTINEL project is to develop a minimally invasive and biocompatible implantable biosensor to be used in the early tumor surveillance in post-operative prostate cancer patients, supported by the following accomplishments:

• To develop novel biocompatible and injectable formulations able to assure low foreign body reaction after implantation;
• To establish encapsulation protocols for novel plasmonic particles using hydrogel-based formulations;
• To develop simple, affordable and low risk implantation procedures able to be implemented in a clinical setting;
• To acquire transdermal Raman signals based on implanted biosensor using a handheld Raman device;
• To test the system’s signaling using in vitro, ex vivo and in vivo protocols that model physiologically disease relevant conditions;
• To process and classify acquired data using machine learning algorithms;
• To develop computational predictive diagnostic protocols using acquired biosensor spectra.