European journal of nuclear medicine and molecular imaging · 2025
[18F]PSMA-1007 PET/CT-based radiomics may help enhance the interpretation of bone focal uptakes in hormone-sensitive prostate cancer patients.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Bauckneht Matteo, Pasini Giovanni, Di Raimondo Tania, Russo Giorgio, Raffa Stefano, Donegani Maria Isabella, Dubois Daniela, Peñuela Leonardo, Sofia Luca, Celesti Greta, Bini Fabiano, Marinozzi Franco, Lanfranchi Francesco, Laudicella Riccardo, Sambuceti Gianmario, Stefano Alessandro
Аннотация
PURPOSE: We hypothesised that applying radiomics to [18F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation.
MATERIALS AND METHODS: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [18F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted features from PET and CT images of each bone uptake and identified the best predictor model for bone metastases using a machine-learning approach to generate a radiomic score. Blinded PET readers with low (n = 2) and high (n = 2) experience rated each bone uptake as either UBU or bone metastasis. The same readers performed a second read three months later, with access to the radiomic score.
RESULTS: Of the 178 [18F]PSMA-1007 bone uptakes, 74 (41.5%) were classified as PCa metastases by the reference standard. A radiomic model combining PET and CT features achieved an accuracy of 84.69%, though it did not surpass expert PET readers in either round. Less-experienced readers had significantly lower diagnostic accuracy at baseline (p < 0.05) but improved with the addition of radiomic scores (p < 0.05 compared to the first round).
CONCLUSION: Radiomics might help to differentiate bone metastases from UBUs. While it did not exceed expert visual assessments, radiomics has the potential to enhance the diagnostic accuracy of less-experienced readers in evaluating [18F]PSMA-1007 PET/CT bone uptakes.
MATERIALS AND METHODS: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [18F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted features from PET and CT images of each bone uptake and identified the best predictor model for bone metastases using a machine-learning approach to generate a radiomic score. Blinded PET readers with low (n = 2) and high (n = 2) experience rated each bone uptake as either UBU or bone metastasis. The same readers performed a second read three months later, with access to the radiomic score.
RESULTS: Of the 178 [18F]PSMA-1007 bone uptakes, 74 (41.5%) were classified as PCa metastases by the reference standard. A radiomic model combining PET and CT features achieved an accuracy of 84.69%, though it did not surpass expert PET readers in either round. Less-experienced readers had significantly lower diagnostic accuracy at baseline (p < 0.05) but improved with the addition of radiomic scores (p < 0.05 compared to the first round).
CONCLUSION: Radiomics might help to differentiate bone metastases from UBUs. While it did not exceed expert visual assessments, radiomics has the potential to enhance the diagnostic accuracy of less-experienced readers in evaluating [18F]PSMA-1007 PET/CT bone uptakes.