Giannini Valentina, Mazzetti Simone, Defeudis Arianna, Stranieri Giuseppe, Calandri Marco, Bollito Enrico, Bosco Martino, Porpiglia Francesco, Manfredi Matteo, De Pascale Agostino, Veltri Andrea, Russo Filippo, Regge Daniele
Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy.
Department of Surgical Sciences, University of Turin, Turin, Italy.
Front Oncol. 2021 Oct 1;11:718155. doi: 10.3389/fonc.2021.718155. eCollection 2021.
In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.
在过去几年中，广泛使用前列腺特异性抗原（PSA）血液检查对将进入前列腺癌（PCa）诊断/治疗路径的患者进行分流，已使PCa特异性死亡率几乎减半。然而，与之相对的是，数百万患有临床意义不显著、不会导致死亡的癌症的男性接受了治疗，这对总体生存率没有有益影响。因此，迫切需要开发能够根据患者风险进行分层的工具，以帮助医生为每个患者选择最合适的治疗方案。本研究的目的是开发并在多供应商数据上验证一种全自动计算机辅助诊断（CAD）系统，以根据PCa的侵袭性对其进行检测和特征描述。我们提出了一种基于人工智能算法的CAD系统，该系统a）记录来自不同MRI序列的所有图像，b）提供可疑为肿瘤的候选区域，c）根据由放射组学特征输入的支持向量机分类器的结果，为每个候选区域提供侵袭性评分。数据集由来自两个不同机构的131名患者（149个肿瘤）组成，这些患者被分为训练集、狭义验证集和外部验证集。该算法在训练集和验证集上区分低侵袭性和高侵袭性肿瘤的受试者操作特征（ROC）曲线下面积分别为0.96和0.81。此外，当将分类器的输出分为三类风险，即惰性、不确定和侵袭性时，我们的方法没有将任何侵袭性肿瘤分类为惰性，这意味着根据我们的评分，所有侵袭性肿瘤都将接受治疗或进一步检查。我们的CAD性能优于先前的研究，并克服了它们的一些局限性，例如需要对肿瘤进行手动分割或分析仅限于单中心数据集。本研究的结果很有前景，可能为PCa患者的个性化决策预测工具铺平道路。
