Predicting pathological highly invasive lung cancer from preoperative [¹⁸F]FDG PET/CT with multiple machine learning models.

ABSTRACT:

Purpose

The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [¹⁸F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features.

Methods

Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability o

SUBMITTER: Onozato Y 

PROVIDER: S-EPMC9852187 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature