Gross Dissection of Liver for Hepatocellular Carcinoma Using AJCC Cancer Staging Manual 8th Edition: Anatomical and Practical Considerations

Beach, Marla; Caviedes, Laura Henao; Sergi, Consolato

doi:10.36255/exonpublications.livercancer.2021.ch4

Cited by 1 publication

(1 citation statement)

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“…The current common clinical prognostic evaluation is based mainly on the American Joint Committee on Cancer staging guidelines ( 10 ) that include pathological indicators, such as depth of tumor infiltration and the number of lymph node metastases and bloodstream metastases. However, it fails to adequately consider various factors, such as age, race, Primary malignancy number, and other treatments that are considered important for survival, which limits its value and ability to guide an individualized prognosis.…”

Section: Introductionmentioning

confidence: 99%

Developing a competing risk nomogram that predicts the survival of patients with a primary hepatic neuroendocrine tumor

Lin

Ding

et al. 2022

Front. Med.

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Primary hepatic neuroendocrine tumor (PHNET) is rare liver cancer and related prognostic factors are unclear. The aim of this study was to analyze the prognostic risk factors of patients with PHNETs and establish an assessment model for prognosis. The clinical information of 539 patients with PHNETs who met the criteria for inclusion was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly assigned to the training (269 cases) and validation sets (270 cases). Prognostic factors in patients with PHNETs were screened using the Cox proportional regression model and Fine–Gray competing risk model. Based on the training set analysis using the Fine–Gray competing risk model, a nomogram was constructed to predict cumulative probabilities for PHNET-specific death. The performance of the nomogram was measured by using receiver operating characteristic curves, the concordance index (C-index), calibration curves, and decision curve analysis (DCA). No differences in clinical baseline characteristics between the training and validation sets were observed, and the Fine–Gray analysis showed that surgery and more than one primary malignancy were associated with a low cumulative probability of PHNET-specific death. The training set nomograms were well-calibrated and had good discriminative ability, and good agreement between predicted and observed survival was observed. Patients with PHNETs with a high-risk score had a significantly increased risk of PHNET-specific death and non-PHNET death. Surgical treatment and the number of primary malignancies were found to be independent protective factors for PHNETs. The competing risk nomogram has high accuracy in predicting disease-specific survival (DSS) for patients with PHNETs, which may help clinicians to develop individualized treatment strategies.

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Section: Introductionmentioning

confidence: 99%