De Bari B, Vallati M, Gatta R, Simeone C, Girelli G, Ricardi U, Meattini I, Gabriele P, Bellavita R, Krengli M, Cafaro I, Cagna E, Bunkheila F, Borghesi S, Signor M, Di Marco A, Bertoni F, Stefanacci M, Pasinetti N, Buglione M, Magrini S M
1Istituto del Radio "O. Alberti", Radiotherapy Department, Spedali Civili di Brescia and University of Brescia, Brescia, Italy.
Cancer Invest. 2015 Jul;33(6):232-40. doi: 10.3109/07357907.2015.1024317. Epub 2015 May 7.
We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.
我们测试并比较了Roach公式、Partin表以及三种基于决策树的机器学习（ML）算法在识别N+前列腺癌（PC）方面的性能。分析了1555例cN0和50例cN+前列腺癌患者。还在204例已知pN状态（187例pN0，17例pN1患者）的接受手术的cN0患者独立群体中验证了结果。ML表现更佳，在手术人群中进行测试时，其准确率、特异性和敏感性分别在48 - 86%、35 - 91%和17 - 79%之间。ML有可能更好地预测前列腺癌的淋巴结状态，从而有可能更好地定制盆腔放疗方案。
