2022
DOI: 10.1007/s00259-022-06038-7
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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 pati… Show more

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Cited by 22 publications
(14 citation statements)
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“…In the study, the Random Forest algorithm outperformed others with R2 values ranging from 0.963 to 0.998 in the majority of datasets. While several previous studies reported good performance of all tested models [19,23], different algorithms performed best with specific datasets [22,23]. Machine learning conceptually aims to optimize accuracy and reproducibility as well as avoids human procedural bias in parameter selection.…”
Section: Discussionmentioning
confidence: 93%
“…In the study, the Random Forest algorithm outperformed others with R2 values ranging from 0.963 to 0.998 in the majority of datasets. While several previous studies reported good performance of all tested models [19,23], different algorithms performed best with specific datasets [22,23]. Machine learning conceptually aims to optimize accuracy and reproducibility as well as avoids human procedural bias in parameter selection.…”
Section: Discussionmentioning
confidence: 93%
“…All models performed well, with LR and ENS having better predictive performance than other models. At the same time, the prediction performance of the PET/CT patterns was superior to that of CT. 131 …”
Section: Introductionmentioning
confidence: 99%
“…By analyzing lung ultrasound images of patients with coronavirus disease (COVID-19), some scholars [ 11 ] found that the support vector machine (SVM) model demonstrated better accuracy in assessing the severity of pleural line changes, which is significant for accurately assessing patients’ diseases. A recent study [ 12 ] shows that integrating seven machine learning models selected for the prediction of preoperative 2-deoxy-2-[fluorine-18]fluoro-D-glucose ( [18 F] FDG) positron emission tomography/computed tomography (PET/CT) radiographic features to predict the pathological aggressiveness of lung cancer had the highest diagnostic efficacy and better stability. These studies demonstrated the application of histological imaging in lung diseases.…”
Section: Introductionmentioning
confidence: 99%