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Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models.
Onozato, Yuki; Iwata, Takekazu; Uematsu, Yasufumi; Shimizu, Daiki; Yamamoto, Takayoshi; Matsui, Yukiko; Ogawa, Kazuyuki; Kuyama, Junpei; Sakairi, Yuichi; Kawakami, Eiryo; Iizasa, Toshihiko; Yoshino, Ichiro.
Affiliation
  • Onozato Y; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan. yukionozato1004@gmail.com.
  • Iwata T; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Uematsu Y; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Shimizu D; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Yamamoto T; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Matsui Y; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Ogawa K; Division of Nuclear Medicine, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Kuyama J; Division of Nuclear Medicine, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Sakairi Y; Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
  • Kawakami E; Department of Artificial Intelligence Medicine, Chiba University Graduate School of Medicine, Chiba, Japan.
  • Iizasa T; Division of Thoracic Surgery, Chiba Cancer Centre, 666-2, Nitona-Cho, Chuo-Ku, Chiba, 260-8717, Japan.
  • Yoshino I; Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
Eur J Nucl Med Mol Imaging ; 50(3): 715-726, 2023 02.
Article in En | MEDLINE | ID: mdl-36385219
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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Positron Emission Tomography Computed Tomography / Lung Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Eur J Nucl Med Mol Imaging Journal subject: MEDICINA NUCLEAR Year: 2023 Document type: Article Affiliation country: Japan Country of publication: Germany

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Positron Emission Tomography Computed Tomography / Lung Neoplasms Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Eur J Nucl Med Mol Imaging Journal subject: MEDICINA NUCLEAR Year: 2023 Document type: Article Affiliation country: Japan Country of publication: Germany