Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis.
Background: Dynamic 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging has been shown to provide additional information for diagnosing lung cancer. The aim of this study was to investigate whether metabolic and flow features directly extracted from time activity curves (TACs) help differentiate between...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 10; pp. 3807 - 3820 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187119036&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187119036 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Aug2025 vid: 52 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187119036 184223195 10.1007/s00259-025-07231-0 187119036 ppf: 3807 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging machine learning with dynamic 18F-FDG PET/CT: integrating metabolic and flow features for lung cancer differential diagnosis. aug: au: Yao, Zhiheng Wang, Yubo Wu, Yaping Zhou, Jinpeng Dang, Na Wang, Meiyun Liang, Ying Sun, Tao affil: https://ror.org/034t30j35 Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China sug: ab: Background: Dynamic 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging has been shown to provide additional information for diagnosing lung cancer. The aim of this study was to investigate whether metabolic and flow features directly extracted from time activity curves (TACs) help differentiate between benign and malignant conditions of lung lesions. Methods: TACs at the primary lesion were extracted from each dynamic 18F-FDG PET/CT scan. The TAC signal was then decomposed into metabolism and blood flow components through kinetic modeling. Dynamic features including area under the curve (AUC), time-to-peak, and slopes were then extracted from each component. The extracted features from 187 patients (mean age, 60.41 ± 11.01 years; 117 males) were used to train a classification model based on bagging, a machine-learning method built with decision trees. The performance of the trained model on differentiating benign and malignant was tested using receiver operating characteristic analysis with cross-validation. External testing was then performed for an independent dataset that consisted of 42 dynamic scans. For the results, SHapley Additive exPlanations (SHAP) were used to assess the relative importance of the contributed features for individuals. Waterfall charts were also plotted, together with assessment of Cohen's effect size to demonstrate the superiority of the proposed model over SUVmax and the net FDG influx rate Ki. Results: The combination of the multiple dynamic features was able to separate benign and malignant lesions. For cross-validation, the trained model had an AUC of 0.89, sensitivity of 0.80, and specificity of 0.88, which was significantly higher than that of either SUVmax (AUC = 0.79, DeLong p < 0.001) or Ki (AUC = 0.76, DeLong test p < 0.001). For the testing dataset, the model had an AUC of 0.86, which again was better than either SUVmax (AUC of 0.72) or Ki (AUC of 0.71). The most important features that contributed to the diagnosis identified by SHAP included the slope and maximum metabolism TAC at the lesion, the AUC, and the peak time of blood TAC at the lesion. The waterfall chart illustrated that the model had significantly different prediction scores between the benign and malignant groups (p < 0.001) with a Cohen's effect size of 1.71, which was higher than that of the values for SUV and Ki (Cohen's effect size 0.96 and 0.81, respectively). Conclusion: An explainable machine learning model that combines dynamic FDG metabolic and flow features can predict benign or malignant lung cancer patients more accurately than conventional parameters such as SUVmax or net influx rate Ki. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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