Mayer Rulon, Turkbey Baris, Simone Charles B
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USA.
OncoScore, Garrett Park, MD 20896, USA.
Cancers (Basel). 2024 May 10;16(10):1822. doi: 10.3390/cancers16101822.
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.
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) and -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.
The highest R (R > 0.45), AUC (>0.90), and lowest -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.
The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.
对于确诊为前列腺癌的患者，准确、可靠、无创的评估对于疾病的恰当管理至关重要。多参数磁共振成像的定量评估，例如通过人工智能或光谱/统计方法，可以在无副作用的情况下，对前列腺肿瘤侵袭性进行无创客观判定，同时避免针吸活检可能出现的取样不佳或前列腺血清抗原测量导致的过度诊断。为了简化和加快前列腺肿瘤评估，本研究检验了自动提取肿瘤光谱特征用于空间配准双参数磁共振成像光谱/统计算法的效果。
通过对双参数磁共振成像PI-CAI数据集中42例连续患者的图像序列（表观扩散系数（ADC）、高B值、T2）进行调整大小、平移和裁剪，以数字方式构建空间配准超立方体。前列腺癌团块超过了将配准集归一化为使高B值最大化但使ADC和T2图像最小化的图像时应用于配准集的阈值，在彩色合成图像中呈现为“绿色”。基于大小、平均归一化绿色值、团块内的滑动窗口统计以及超立方体内的位置，选择具有临床意义的团块。团块内的质心和最大化滑动窗口统计确定了与肿瘤特征相关的体素。我们使用相关系数（R）和P值，评估z分数和SCR（处理后的协方差矩阵）与肿瘤侵袭性的线性回归拟合，以及逻辑概率拟合对具有临床意义的前列腺癌的受试者操作特征曲线（ROC）的曲线下面积（AUC）。
使用z分数和应用于协方差矩阵及从所选团块中“最绿”部分选择的肿瘤特征的改进配准，获得了最高的R（R>0.45）、AUC（>0.90）和最低的P值（<0.01）。
应用于空间配准双参数磁共振成像的首个自动肿瘤特征在确定前列腺肿瘤侵袭性方面显示出前景。
