Prognostic Factors Associated with Postoperative Outcomes After Resection of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
<p>The Barcelona Clinic Liver Cancer (BCLC) staging system is one of the best-known models used for treatment allocation and prognostic evaluation of patients with hepatocellular carcinoma (HCC). However, there is an increasing body of evidence that other clinical, laboratory, pathologic, and molecu...
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1237183 |
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| Sumario: | <p>The Barcelona Clinic Liver Cancer (BCLC) staging system is one of the best-known models used for treatment allocation and prognostic evaluation of patients with hepatocellular carcinoma (HCC). However, there is an increasing body of evidence that other clinical, laboratory, pathologic, and molecular features, alone or combined with different scoring systems, can reliably identify subgroups of patients with either poor prognosis after liver resection (LR) or better prognosis than those of the patients included in the same American Joint Committee on Cancer (AJCC) or BCLC stage. For example, among patients with HCC at BCLC stage B, several scores were able to identify subgroups with a similar prognosis to BCLC stage A, suggesting that these patients should be candidates for hepatectomy rather than transarterial chemoembolization (TACE). On the contrary, at present, there are available nomograms and on-line calculators that combine clinical and pathologic characteristics to better identify patients with an increased risk of very early recurrence after LR for intrahepatic cholangiocarcinoma (ICC); such patients should undergo alternative therapeutic approaches to improve their survival outcomes and avoid futile operations. Furthermore, some scores that incorporate clinical and laboratory data have outperformed TNM/BCLC staging in predicting prognosis after resection of ICC or HCC. In recent years, machine-learning, classification, and regression tree models have been used to generate homogeneous groups of patients with distinct outcomes. Such models are useful not only for risk stratification but also for guiding treatment allocation. A detailed analysis of this evidence is presented in this chapter.</p> |
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| ISBN: | 978-1-80631-118-7 |
| DOI: | 10.5772/intechopen.1014479 |