Colorectal cancer represents 10% of all the annual tumors diagnosed worldwide, being often not timely diagnosed, because its symptoms are typically lacking or very mild. Therefore, it is crucial to develop and validate innovative low-invasive techniques to detect it before becoming intractable. To this aim, a device equipped with nanostructured gas sensors has been employed to detect the airborne molecules of blood samples collected from healthy subjects, and from colorectal cancer affected patients at different stages of their pre- and post-surgery therapeutic path. Data was scrutinized by using statistical standard techniques to highlight their statistical differences, and through principal component analysis and support vector machine to classify them. The device was able to readily distinguish between the pre-surgery blood samples (i.e., taken when the patient had cancer), and the ones up to three years post-surgery (i.e., following the tumor removal) or the ones from healthy subjects. Finally, the correlation of the sensor responses with the patient/healthy subject’s gender was investigated, resulting negligible. These results pave the path toward a clinical validation of this device to monitor the patient’s health status by detecting possible relapses, to parallel to clinical follow-up protocols.
MOX Nanosensors to Detect Colorectal Cancer Relapses from Patient’s Blood at Three Years Follow-Up, and Gender Correlation
Astolfi, MichelePrimo
;Zonta, GiuliaSecondo
;Anania, GabrielePenultimo
;Rispoli, Giorgio
Ultimo
2025
Abstract
Colorectal cancer represents 10% of all the annual tumors diagnosed worldwide, being often not timely diagnosed, because its symptoms are typically lacking or very mild. Therefore, it is crucial to develop and validate innovative low-invasive techniques to detect it before becoming intractable. To this aim, a device equipped with nanostructured gas sensors has been employed to detect the airborne molecules of blood samples collected from healthy subjects, and from colorectal cancer affected patients at different stages of their pre- and post-surgery therapeutic path. Data was scrutinized by using statistical standard techniques to highlight their statistical differences, and through principal component analysis and support vector machine to classify them. The device was able to readily distinguish between the pre-surgery blood samples (i.e., taken when the patient had cancer), and the ones up to three years post-surgery (i.e., following the tumor removal) or the ones from healthy subjects. Finally, the correlation of the sensor responses with the patient/healthy subject’s gender was investigated, resulting negligible. These results pave the path toward a clinical validation of this device to monitor the patient’s health status by detecting possible relapses, to parallel to clinical follow-up protocols.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.