[The clinical pathway of treatment of hepatocellular carcinoma]. / Vía clínica. Tratamiento del hepatocarcinoma.

INTRODUCTION: The regular clinical practise slows a wide variability present in medical care in patients with the same pathology. The clinical pathways are integrated and systematized care plans for certain processes. The development of the hepatocarcinoma's (HCC) health care treatment is within the care frame of Tumoral Area's departments, Hepatology and General Surgery in the Clinic University of Navarra (CUN). OBJECTIVES: To conduct a clinical pathway able to organize, homogenize and standardize the care of patients diagnosed with hepatocarcinoma and that could be applied from the beginning to the end of the course of this disease. METHODOLOGY: The identification of the set of care procedures wich would be necessary to achieve the best possible result in a patient who comes to the clinic to be treated for an hepatocarcinoma. The study scope has taken place at the CUN, a private clinic that is a part of the University of Navarra. RESULT: The clinical pathway of the hepatocarcinoma's treatment has been developed centered on the surgical treatment and the hepatic transplant. CONCLUSION: The clinical pathways represents a decrease in variability and quality control in clinical practice, and the ability to analyze the information that indicators provide us, the satisfaction surveys and the computerized registration of electronic data.

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse.

BACKGROUND: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. METHODS: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. RESULTS: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. CONCLUSION: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations.