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首页> 外文会议>Medical Imaging Conference >Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary Results
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Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary Results

机译：手术前T2w MRI上前列腺周围脂肪的放射学特征可预测在手术后病理学上确定的前列腺癌的前列腺外扩展：初步结果

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Periprostatic fat composition on T2-weighted (T2w) MRI has been shown to be associated with aggressive prostate cancer and may influence extraprostatic extension (EPE). In this study, we interrogate the periprostatic fat (PPF) region adjacent to cancer lesion on prostate T2w MRI. Patients with pathologic stage ≥ pT3a are considered to experience EPE (EPE+) and those with stage ≤ T2c are without EPE (EPE-) post radical prostatectomy (RP). We use a cohort of N = 45 prostate cancer patients retrospectively acquired from a single institution who underwent 3T multi-parametric MRI prior to RP. Radiomic features including 1st and 2nd order statistics, Haralick, Gabor, CoLlAGe features are extracted from a region of interest (ROI) in the PPF on pre-surgical T2w MRI delineated by an experienced radiologist. Haralick, gradient and CoLlAGe features were observed to be significantly different (p<0.05) in PPF ROIs between EPE+ and EPE- and were significantly over expressed in EPE+ patients compared to EPE- patients, suggesting a higher heterogeneity within the PPF region for EPE+ patients. These features were used to train machine learning classifiers using a 3-fold cross validation approach in conjunction with feature selection methods to predict EPE. The best classification performance was obtained with Support Vector Machine (SVM) classifiers resulting in an AUC = 0.88 (±0.04). On univariable and multivariable analysis, we observed that radiomic classifier predictions resulted in significant separation between EPE+ and EPE- while none of the routinely used clinical parameters including prostate specific antigen (PSA). Gleason Grade Groups (GGG), age. race and prostate imaging reporting and data system (PI-RADS v2) scores showed significant differences. Our results suggest that radiomic features may quantify the underlying heterogeneity in periprostatic fat and predict patients who are likely to experience extraprostatic extension of disease post RP.
机译：T2加权（T2w）MRI上的前列腺周围脂肪成分已显示与侵略性前列腺癌有关，可能会影响前列腺外扩张（EPE）。在这项研究中，我们在前列腺T2w MRI上检查邻近癌灶的前列腺周围脂肪（PPF）区。病理分期≥pT3a的患者被认为经历过EPE（EPE +），而病理分期≥T2c的患者则在根治性前列腺切除术（RP）后没有EPE（EPE-）。我们采用队列研究方法，从一组单一的研究机构中回顾性分析了N = 45名前列腺癌患者，这些患者在RP之前接受了3T多参数MRI检查。放射学特征包括一阶和二阶统计量，Haralick，Gabor和CoLlAGe特征，这些特征是由经验丰富的放射科医生在术前T2w MRI上从PPF中的感兴趣区域（ROI）提取的。在EPE +和EPE-之间，PPF ROIs的Haralick，梯度和CoLlAGe特征被观察到有显着差异（p <0.05），并且在EPE +患者中与EPE-患者相比显着过表达，表明EPE +患者在PPF区域内的异质性更高。这些特征用于结合3种交叉验证方法和特征选择方法来预测EPE，从而训练机器学习分类器。使用支持向量机（SVM）分类器可获得最佳分类性能，其AUC = 0.88（±0.04）。在单变量和多变量分析中，我们观察到放射学分类器预测导致EPE +和EPE-之间的显着分离，而没有常规使用的临床参数包括前列腺特异性抗原（PSA）。格里森成绩组（GGG），年龄。种族和前列腺成像报告和数据系统（PI-RADS v2）得分显示出显着差异。我们的结果表明，放射学特征可能会量化前列腺周围脂肪中的潜在异质性，并预测可能在RP后出现前列腺外疾病扩展的患者。

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