{"title":"结合[68Ga]Ga-PSMA-11 PET/CT的生物标志物和放射学特征，基于机器学习的[177Lu] ga - psma -617治疗剂量预测。","authors":"Elmira Yazdani, Mahdi Sadeghi, Najme Karamzade-Ziarati, Parmida Jabari, Payam Amini, Habibeh Vosoughi, Malihe Shahbazi Akbari, Mahboobeh Asadi, Saeed Reza Kheradpisheh, Parham Geramifar","doi":"10.1016/j.ijrobp.2025.05.014","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [<sup>177</sup>Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [<sup>68</sup>Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy.</p><p><strong>Methods: </strong>Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE).</p><p><strong>Results: </strong>The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77.</p><p><strong>Conclusion: </strong>Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort.</p>","PeriodicalId":14215,"journal":{"name":"International Journal of Radiation Oncology Biology Physics","volume":" ","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Dose Prediction in [<sup>177</sup>Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [<sup>68</sup>Ga]Ga-PSMA-11 PET/CT.\",\"authors\":\"Elmira Yazdani, Mahdi Sadeghi, Najme Karamzade-Ziarati, Parmida Jabari, Payam Amini, Habibeh Vosoughi, Malihe Shahbazi Akbari, Mahboobeh Asadi, Saeed Reza Kheradpisheh, Parham Geramifar\",\"doi\":\"10.1016/j.ijrobp.2025.05.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [<sup>177</sup>Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [<sup>68</sup>Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy.</p><p><strong>Methods: </strong>Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE).</p><p><strong>Results: </strong>The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77.</p><p><strong>Conclusion: </strong>Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort.</p>\",\"PeriodicalId\":14215,\"journal\":{\"name\":\"International Journal of Radiation Oncology Biology Physics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2025-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Radiation Oncology Biology Physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ijrobp.2025.05.014\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Radiation Oncology Biology Physics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.ijrobp.2025.05.014","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Machine Learning-Based Dose Prediction in [177Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [68Ga]Ga-PSMA-11 PET/CT.
Purpose: The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [177Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [68Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy.
Methods: Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE).
Results: The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77.
Conclusion: Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort.
期刊介绍：
International Journal of Radiation Oncology • Biology • Physics (IJROBP), known in the field as the Red Journal, publishes original laboratory and clinical investigations related to radiation oncology, radiation biology, medical physics, and both education and health policy as it relates to the field.
This journal has a particular interest in original contributions of the following types: prospective clinical trials, outcomes research, and large database interrogation. In addition, it seeks reports of high-impact innovations in single or combined modality treatment, tumor sensitization, normal tissue protection (including both precision avoidance and pharmacologic means), brachytherapy, particle irradiation, and cancer imaging. Technical advances related to dosimetry and conformal radiation treatment planning are of interest, as are basic science studies investigating tumor physiology and the molecular biology underlying cancer and normal tissue radiation response.
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