Deep Learning Analysis Using F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

Introduction: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma.

Materials And Methods: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center.

Development and Validation of a F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer.

Background: Accurate evaluation of lymph node (LN) status is critical for determining the treatment options in patients with non-small cell lung cancer (NSCLC). This study aimed to develop and validate a F-FDG PET-based radiomic model for the identification of metastatic LNs from the hypermetabolic mediastinal-hilar LNs in NSCLC.

Methods: We retrospectively reviewed 259 patients with hypermetabolic LNs who underwent pretreatment F-FDG PET/CT and were pathologically confirmed as NSCLC from two centers.

Prediction of occult lymph node metastasis using SUV, volumetric parameters and intratumoral heterogeneity of the primary tumor in T1-2N0M0 lung cancer patients staged by PET/CT.

Objective: The aim of this study was to identify whether PET/CT-related metabolic parameters of the primary tumor could predict occult lymph node metastasis (OLM) in patients with T1-2N0M0 NSCLC staged by F-FDG PET/CT.

Methods: 215 patients with clinical T1-2N0M0 (cT1-2N0M0) NSCLC who underwent both preoperative FDG PET/CT and surgical resection with the systematic lymph node dissection were included in the retrospective study. Heterogeneity factor (HF) was obtained by finding the derivative of the volume-threshold function from 40 to 80% of the maximum standardized uptake value (SUVmax).

Prediction of Occult Lymph Node Metastasis Using Tumor-to-Blood Standardized Uptake Ratio and Metabolic Parameters in Clinical N0 Lung Adenocarcinoma.

Purpose: We aimed to investigate whether the tumor-to-blood SUV ratio (SUR) and metabolic parameters of F-FDG uptake could predict occult lymph node metastasis (OLM) in clinically node-negative (cN0) lung adenocarcinoma.

Materials And Methods: We retrospectively reviewed 157 patients with cN0 lung adenocarcinoma who underwent both preoperative F-FDG PET/CT and surgical resection with the systematic lymph node dissection. The SUVmax, SUVmean, MTV, and total lesion glycolysis (TLG) of the primary tumor was measured on the PET/CT workstation.