Background: Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI. Methods: Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing “green” in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) andp-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer. Results: The highest R (R > 0.45), AUC (>0.90), and lowestp-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the “greenest” parts from the selected blob. Conclusions: The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.
背景：对确诊前列腺癌患者进行准确、可靠且无创的评估是疾病规范管理的关键。通过人工智能或光谱/统计学方法对多参数磁共振成像进行定量评估，能够无创、客观地判定前列腺肿瘤侵袭性，同时避免穿刺活检可能导致的取样误差或前列腺特异性抗原检测可能引发的过度诊断。为简化和加速前列腺肿瘤评估流程，本研究探讨了在空间配准的双参数磁共振成像中，自主提取肿瘤光谱特征用于光谱/统计学算法的效能。方法：基于双参数磁共振成像PI-CAI数据集中42例连续患者的图像序列（表观扩散系数、高B值、T2），通过尺寸调整、平移和裁剪数字化构建空间配准超立方体。在前列腺癌病灶检测中，将配准数据集归一化为最大化高B值、同时最小化表观扩散系数和T2值的合成图像（在彩色合成图像中呈现“绿色”），对超过设定阈值的区域进行识别。依据病灶尺寸、平均归一化绿色值、病灶内滑动窗口统计特征及在超立方体中的空间位置，筛选出具有临床意义的病灶区域。通过计算病灶质心及最大化滑动窗口统计量，确定与肿瘤特征相关的体素。采用相关系数（R）和p值评估z分数与SCR（经协方差矩阵处理后）与肿瘤侵袭性的线性回归拟合度，同时通过逻辑概率拟合临床显著性前列腺癌数据，计算受试者工作特征曲线下面积。结果：当对协方差矩阵应用z分数与改进配准方法，并从选定病灶的“最绿”区域提取肿瘤特征时，获得了最高的相关系数（R > 0.45）、曲线下面积（>0.90）及最低的p值（<0.01）。结论：首次应用于空间配准双参数磁共振成像的自主肿瘤特征提取技术，在判定前列腺肿瘤侵袭性方面展现出良好应用前景。
肿瘤(癌症)患者之家