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
Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
Nuclear Medicine, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Eur J Nucl Med Mol Imaging. 2025 May;52(6):2076-2086. doi: 10.1007/s00259-025-07085-6. Epub 2025 Jan 28.
We hypothesised that applying radiomics to [F]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.
We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [F]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.
Of the 178 [F]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).
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 [F]PSMA-1007 PET/CT bone uptakes.
我们假设将放射组学应用于[F]PSMA - 1007 PET/CT图像有助于区分前列腺癌(PCa)患者的非特异性骨摄取(UBUs)和骨转移。我们比较了放射组学特征与人类视觉解读的性能。
我们回顾性分析了102例接受[F]PSMA - 1007 PET/CT检查且至少有一处已知临床随访结果的局灶性骨摄取的激素敏感性PCa患者(参考标准)。使用matRadiomics,我们从每个骨摄取的PET和CT图像中提取特征,并使用机器学习方法生成放射组学评分,以确定骨转移的最佳预测模型。经验较少(n = 2)和经验丰富(n = 2)的PET阅片者在不知情的情况下将每个骨摄取评为UBU或骨转移。三个月后,相同的阅片者在知晓放射组学评分的情况下进行第二次阅片。
在178处[F]PSMA - 1007骨摄取中,根据参考标准,74处(41.5%)被分类为PCa转移。结合PET和CT特征的放射组学模型的准确率为84.69%,不过在两轮评估中均未超过专业PET阅片者。经验较少的阅片者在基线时的诊断准确率显著较低(p < 0.05),但在加入放射组学评分后有所提高(与第一轮相比,p < 0.05)。
放射组学可能有助于区分骨转移和UBUs。虽然它没有超过专家的视觉评估,但放射组学有潜力提高经验较少的阅片者在评估[F]PSMA - 1007 PET/CT骨摄取时的诊断准确率。