Gibala Sebastian, Obuchowicz Rafal, Lasek Julia, Schneider Zofia, Piorkowski Adam, Pociask Elżbieta, Nurzynska Karolina
Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland.
Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland.
J Clin Med. 2023 Apr 12;12(8):2836. doi: 10.3390/jcm12082836.
Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk.
Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy.
Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy.
There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
前列腺癌与腺体生物学以及环境风险相关，是全球男性人群中的一个严重临床问题。在用于检测前列腺癌的诊断和临床设置方面已取得重要进展，基于前列腺影像报告和数据系统（PIRADS）协议的多参数磁共振诊断过程发挥着关键作用。该方法依赖于影像专家的图像评估。医学界一直希望有能够检测可能表明癌症风险的重要图像特征的图像分析技术。
使用了41例经实验室诊断PSA水平且常规接受前列腺癌扫描患者的匿名扫描数据。在医学监督下手动描绘前列腺的外周区和中央区，并划定可疑肿瘤病灶。使用MaZda软件计算标记区域内7000多个纹理特征。然后，利用这些7000个特征进行区域参数化。进行统计分析以找出与基于PSA水平的诊断的相关性，这些相关性可用于区分可疑（不同）病变。使用多实例支持向量机（MIL-SVM）机器学习进行进一步的多参数分析以获得更高的准确性。
使用MIL-SVM进行多参数分类使我们达到了92%的准确率。
使用PIRADS MR协议生成的前列腺MRI图像的纹理参数与PSA水平>4 mg/mL之间存在重要相关性。所发现的相关性表明具有高癌症标志物的图像特征之间存在依赖性，从而也表明了癌症风险。
