Utility of Volumetric Metabolic Parameters on Preoperative FDG PET/CT for Predicting Tumor Lymphovascular Invasion in Non–Small Cell Lung Cancer

Li, Chongjiao; Tian, Yufei; Ying, Shao‐Yao; Wen, Bing; He, Yong

doi:10.2214/ajr.21.25814

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…A previous report also indicated that enhanced CT is a potential candidate for predicting lymphovascular invasion, although the study subject was not limited to super cial SCC but also included advanced SCC [22]. The bene t of FDG PET/CT for predicting lymphovascular invasion has been described in lung cancer, hepatocellular carcinoma, and colon cancer; [23][24][25] however, it has been poorly studied in super cial ESCC. Furthermore, results showed that the value of SUV max was an independent factor for predicting lymphovascular invasion.…”

Section: Discussionmentioning

confidence: 99%

18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters are associated with histological outcomes in superficial esophageal squamous cell carcinoma

Tanaka,

Hashiguchi,

Tabuchi

et al. 2024

Preprint

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Endoscopic submucosal dissection is one of the standard treatments for early esophageal squamous cell carcinoma. However, submucosal or lymphovascular invasion increases the risk of lymph node metastasis. Although 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) parameters are associated with prognosis in patients with advanced esophageal squamous cell carcinoma, the utility of FDG PET/CT in diagnosing superficial esophageal carcinoma remains unclear. This study aimed to investigate the association between FDG PET/CT parameters and histopathological findings. Fifty-three patients with superficial esophageal cancer who underwent FDG PET/CT scans before undergoing interventions were retrospectively analyzed. The study showed that the maximal standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis were significantly higher in the cases with submucosal invasion (T1b) compared with those confined to the muscularis mucosa (T1a). In contrast, classification of Intrapapillary capillary loops patterns with magnifying endoscopy did not yield statistical differences between T1a and T1b. Multivariable analysis revealed that SUVmax was the only independent predictor of submucosal invasion. Furthermore, SUVmax was the only independent predictor of lymphovascular invasion. This study demonstrated that SUVmax may be useful in predicting submucosal and lymphovascular invasion. The SUVmax value may guide clinical decision-making in superficial esophageal squamous cell carcinoma.

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Section: Discussionmentioning

confidence: 99%

18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters are associated with histological outcomes in superficial esophageal squamous cell carcinoma

Tanaka,

Hashiguchi,

Tabuchi

et al. 2024

Preprint

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“…Several reports have shown that PET findings also effectively predict pathological highly invasive lung cancer. Li et al showed that the MTV obtained from preoperative PET/ CT was an independent predictor of lymphatic invasion, with an AUC of 0.854 when multiple factors were combined in the same cohort (not validation data) [40]. Despite the good statistical performance, however, no machine learning model has been built to predict highly invasive lung cancer based on PET/CT findings.…”

Section: Discussionmentioning

confidence: 99%

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

Onozato

Iwata

Uematsu

et al. 2022

Eur J Nucl Med Mol Imaging

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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 of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. Results In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860–0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR. Conclusion The machine learning model based on preoperative [18F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

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“…Several reports have shown that PET ndings also effectively predict pathological highly invasive lung cancer. Li et al showed that the MTV obtained from preoperative PET/CT was an independent predictor of lymphatic invasion, with an AUC of 0.854 when multiple factors were combined in the same cohort (not validation data) [40]. Despite the good statistical performance, however, no machine learning model has been built to predict highly invasive lung cancer based on PET/CT ndings.…”

Section: Discussionmentioning

confidence: 99%

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

Onozato

Iwata

Uematsu

et al. 2022

Preprint

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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 of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness, and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. Results: In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3% to 98%. Accuracy showed an improvement of more than 8% over the CTR. Conclusion: The machine learning model based on preoperative 18F-FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

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Utility of Volumetric Metabolic Parameters on Preoperative FDG PET/CT for Predicting Tumor Lymphovascular Invasion in Non–Small Cell Lung Cancer

Cited by 13 publications

References 26 publications

18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters are associated with histological outcomes in superficial esophageal squamous cell carcinoma

18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters are associated with histological outcomes in superficial esophageal squamous cell carcinoma

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

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

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