Delgadillo Rodrigo, Spieler Benjamin O, Ford John C, Yang Fei, Studenski Matthew, Padgett Kyle R, Deana Anthony M, Jin William, Abramowitz Matthew C, Dal Pra Alan, Stoyanova Radka, Dogan Nesrin
Radiation Oncology, University of Miami Sylvester Comprehensive Cancer Center, Miller School of Medicine, Miami, USA.
Radiation Oncology, Varian Medical Systems, Phoenix, USA.
Cureus. 2025 Mar 5;17(3):e80090. doi: 10.7759/cureus.80090. eCollection 2025 Mar.
Radiomics extracted from cone beam computed tomography (CBCT) can be assessed at time points during treatment and may provide an advantage over assessments in a pre-treatment setting using diagnostic images, like magnetic resonance imaging (MRI) or computed tomography (CT), for prostate cancer (pCa) patients receiving radiotherapy (RT). The purpose of this study was to analyze correlations between prostate radiomic features (RFs) derived from T2-weighted (T2w) MRI, CT, and first fraction CBCT for patients receiving RT for pCa. Forty-seven patients were analyzed. The prostate volumes were manually segmented, and 42 radiomic features were extracted, of which seven volume-normalized RFs were considered. The absolute Spearman correlation was calculated among the RFs of the aforementioned imaging modalities (R) and prostate volume (R) since the motivation of this paper was to analyze the strength of the correlation. The Benjamini-Hochberg adjustment was applied to p-values to account for multiple comparisons. No high correlations were found between CT/CBCT vs. T2w. The intramodality R demonstrated that CT RFs were much higher than the other modalities. For example, intramodality R≥0.95 the percentage of RFs was 17% for CT, 9% for CBCT, and 4.5% for T2w. The differences in RFs across different modalities can be viewed positively: the lack of correlation between RFs across T2w and CT/CBCT could indicate a fundamental difference in the extractable image information. It could also indicate that some RFs did not have any extractable information. A future study will include evaluating the predictive performance of patient outcomes using radiomic features from CT, CBCT, and T2w, which could help in answering such questions.
从锥形束计算机断层扫描（CBCT）中提取的放射组学特征可在治疗期间的各个时间点进行评估，对于接受放射治疗（RT）的前列腺癌（pCa）患者而言，与使用诊断图像（如磁共振成像（MRI）或计算机断层扫描（CT））在治疗前进行评估相比，它可能具有一定优势。本研究的目的是分析接受pCa放疗患者的T2加权（T2w）MRI、CT和首次分割CBCT所衍生的前列腺放射组学特征（RFs）之间的相关性。对47例患者进行了分析。手动分割前列腺体积，并提取42个放射组学特征，其中7个体积标准化的RFs被纳入考虑。由于本文的目的是分析相关性强度，因此计算了上述成像模态的RFs之间以及与前列腺体积之间的绝对斯皮尔曼相关性（R）。对p值应用Benjamini-Hochberg校正以考虑多重比较。未发现CT/CBCT与T2w之间存在高度相关性。模态内相关性R表明，CT的RFs远高于其他模态。例如，模态内R≥0.95时，CT的RFs百分比为17%，CBCT为9%，T2w为4.5%。不同模态之间RFs的差异可以从积极的方面来看待：T2w与CT/CBCT之间RFs缺乏相关性可能表明可提取图像信息存在根本差异。这也可能表明一些RFs没有任何可提取的信息。未来的研究将包括使用CT、CBCT和T2w的放射组学特征评估患者预后的预测性能，这可能有助于回答此类问题。
