Detecting panitumumab/FOLFOX responders in K-Ras wild-type metastatic colorectal cancer through an artificial intelligence-based analytical tool

Collective Intelligence, Medicine, Oncology, Publications

Background: Patients with K-RAS wild-type (WT) metastatic colorectal cancer (mCRC) are currently considered the optimal candidates for upfront treatment with combinations of chemotherapy and EGFR inhibitors. These combinations significantly extend overall survival (OS) compared to chemotherapy alone. However, a proportion of patients would not achieve this goal. This study has investigated the ability of an…

Use of deep learning frameworks to detect super-responder and super-survivor stage IV squamous non-small-cell lung cancer (NSCLC) patients treated with a gemcitabine and cisplatin combination

Collective Intelligence, Medicine, Oncology, Publications

Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF). Methods: Through www.projectdatasphere.org, we accessed the control…

Predicting disease progression and mortality in metastatic colorectal cancer patients (mCRC) through an artificial intelligence-based analytical tool

Collective Intelligence, Medicine, Oncology, Publications

Background: Predicting the clinical course of metastatic disease remains a key challenge in CRC. Estimating prognosis of these late-stage patients can avoid undertreatment or overtreatment and also guide the follow-up intensity. This study has investigated the ability of an artificial intelligence-based analytical tool to identify those mCRC patients with high risk of disease progression and…