AUTHOR=Arrichiello Gianluca , Pirozzi Mario , Facchini Bianca Arianna , Facchini Sergio , Paragliola Fernando , Nacca Valeria , Nicastro Antonella , Canciello Maria Anna , Orlando Adele , Caterino Marianna , Ciardiello Davide , Della Corte Carminia Maria , Fasano Morena , Napolitano Stefania , Troiani Teresa , Ciardiello Fortunato , Martini Giulia , Martinelli Erika TITLE=Beyond N staging in colorectal cancer: Current approaches and future perspectives JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.937114 DOI=10.3389/fonc.2022.937114 ISSN=2234-943X ABSTRACT=

Traditionally, lymph node metastases (LNM) evaluation is essential to the staging of colon cancer patients according to the TNM (tumor–node–metastasis) system. However, in recent years evidence has accumulated regarding the role of emerging pathological features, which could significantly impact the prognosis of colorectal cancer patients. Lymph Node Ratio (LNR) and Log Odds of Positive Lymph Nodes (LODDS) have been shown to predict patients’ prognosis more accurately than traditional nodal staging and it has been suggested that their implementation in existing classification could help stratify further patients with overlapping TNM stage. Tumor deposits (TD) are currently factored within the N1c category of the TNM classification in the absence of lymph node metastases. However, studies have shown that presence of TDs can affect patients’ survival regardless of LNM. Moreover, evidence suggest that presence of TDs should not be evaluated as dichotomic but rather as a quantitative variable. Extranodal extension (ENE) has been shown to correlate with presence of other adverse prognostic features and to impact survival of colorectal cancer patients. In this review we will describe current staging systems and prognostic/predictive factors in colorectal cancer and elaborate on available evidence supporting the implementation of LNR/LODDS, TDs and ENE evaluation in existing classification to improve prognosis estimation and patient selection for adjuvant treatment.