Begal Julia, Sabo Edmond, Goldberg Natalia, Bitterman Arie, Khoury Wissam
Department of General Surgery, Carmel Medical Center, Haifa 3436212, Israel.
Department of Human Pathology, Carmel Medical Center, Haifa 3436212, Israel.
J Clin Med. 2024 Dec 4;13(23):7383. doi: 10.3390/jcm13237383.
: Patients with locally advanced rectal cancer (LARC) treated by neoadjuvant chemoradiotherapy (nCRT) may experience pathological complete response (pCR). Tools that can identify pCR are required to define candidates suitable for the watch and wait (WW) strategy. Automated image analysis is used for predicting clinical aspects of diseases. Texture analysis of magnetic resonance imaging (MRI) wavelets algorithms provides a novel way to identify pCR. We aimed to evaluate wavelets-based image analysis of MRI for predicting pCR. : MRI images of rectal cancer from 22 patients who underwent nCRT were captured at best representative views of the tumor. The MRI images were digitized and their texture was analyzed using different mother wavelets. Each mother wavelet was used to scan the image repeatedly at different frequencies. Based on these analyses, coefficients of similarity were calculated providing a variety of textural variables that were subsequently correlated with histopathology in each case. This allowed for proper identification of the best mother wavelets able to predict pCR. The predictive formula of complete response was computed using the independent statistical variables that were singled out by the multivariate regression model. : The statistical model used four wavelet variables to predict pCR with an accuracy of 100%, sensitivity of 100%, specificity of 100%, and PPV and NPV of 100%. : Wavelet-transformed texture analysis of radiomic MRI can predict pCR in patients with LARC. It may provide a potential accurate surrogate method for the prediction of clinical outcomes of nCRT, resulting in an effective selection of patients amenable to WW.
接受新辅助放化疗（nCRT）治疗的局部晚期直肠癌（LARC）患者可能会出现病理完全缓解（pCR）。需要能够识别pCR的工具来确定适合观察等待（WW）策略的候选者。自动图像分析用于预测疾病的临床情况。磁共振成像（MRI）小波算法的纹理分析为识别pCR提供了一种新方法。我们旨在评估基于小波的MRI图像分析对pCR的预测能力。：对22例接受nCRT治疗的直肠癌患者的MRI图像，在肿瘤的最佳代表性视图下进行采集。将MRI图像数字化，并使用不同的母小波分析其纹理。每个母小波以不同频率对图像进行重复扫描。基于这些分析，计算相似系数，提供各种纹理变量，随后将其与每个病例的组织病理学相关联。这有助于正确识别能够预测pCR的最佳母小波。使用多变量回归模型挑选出的独立统计变量计算完全缓解的预测公式。：该统计模型使用四个小波变量预测pCR，准确率为100%，敏感性为100%，特异性为100%，阳性预测值和阴性预测值均为100%。：基于小波变换纹理分析的放射组学MRI能够预测LARC患者的pCR。它可能为预测nCRT的临床结果提供一种潜在的准确替代方法，从而有效地选择适合WW的患者。
