2024 Asia Conference on Lung Cancer (ACLC) - Poster & ePosters-EP02.13

EP02.13 - Ying Li

Pdf Summary

The study conducted by Ying Li, Junfeng Zhao, and Yintao Li at Shandong Cancer Hospital and Institute focuses on prognostic predictions for patients with Stage IA lung adenocarcinoma (ADC) featuring a micropapillary (MIP) component, a subgroup known for higher recurrence risk post-surgery. Traditionally, the need for adjuvant chemotherapy in this context has been contentious. The research sought to pinpoint factors influencing prognosis and identify high-risk recurrence candidates.<br /><br />Involving 254 patients treated between 2012 and 2018, the study divided participants into training (169 patients) and validation (85 patients) cohorts. It examined clinicopathological and CT radiomic features through univariate and multivariate analyses. Statistically significant indicators were incorporated into a nomogram—a predictive model used to assess each patient’s risk level.<br /><br />Key predictors identified for overall and disease-free survival (OS and DFS) included stage T1c, MIP ≥1%, spread through air spaces, carcinoembryonic antigen levels, and specific radiomic features. The predictive effectiveness of the nomogram was validated with high area under the curve (AUC) values for 3-, 5-, and 7-year OS and DFS predictions in both training and validation groups, indicating strong predictive capability.<br /><br />Risk stratification using the nomogram effectively differentiated patients into low- and high-risk categories, aiding in personalized treatment strategies. The study concluded that this nomogram, integrating clinicopathologic and radiomic factors, is a robust tool for forecasting recurrence and survival in this patient population, potentially guiding clinical decision-making regarding adjuvant therapy post-surgery.

Keywords

lung adenocarcinoma

micropapillary component

prognostic predictions

adjuvant chemotherapy

nomogram

clinicopathological features

radiomic features

recurrence risk

survival prediction

personalized treatment