title: Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.
abstract: 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.
authors.affiliation: 1Istituto del Radio "O. Alberti", Radiotherapy Department, Spedali Civili di Brescia and University of Brescia, Brescia, Italy.
authors.forename: B
authors.initials: B
authors.lastname: De Bari
authors.forename: M
authors.initials: M
authors.lastname: Vallati
authors.forename: R
authors.initials: R
authors.lastname: Gatta
authors.forename: C
authors.initials: C
authors.lastname: Simeone
authors.forename: G
authors.initials: G
authors.lastname: Girelli
authors.forename: U
authors.initials: U
authors.lastname: Ricardi
authors.forename: I
authors.initials: I
authors.lastname: Meattini
authors.forename: P
authors.initials: P
authors.lastname: Gabriele
authors.forename: R
authors.initials: R
authors.lastname: Bellavita
authors.forename: M
authors.initials: M
authors.lastname: Krengli
authors.forename: I
authors.initials: I
authors.lastname: Cafaro
authors.forename: E
authors.initials: E
authors.lastname: Cagna
authors.forename: F
authors.initials: F
authors.lastname: Bunkheila
authors.forename: S
authors.initials: S
authors.lastname: Borghesi
authors.forename: M
authors.initials: M
authors.lastname: Signor
authors.forename: A
authors.initials: A
authors.lastname: Di Marco
authors.forename: F
authors.initials: F
authors.lastname: Bertoni
authors.forename: M
authors.initials: M
authors.lastname: Stefanacci
authors.forename: N
authors.initials: N
authors.lastname: Pasinetti
authors.forename: M
authors.initials: M
authors.lastname: Buglione
authors.forename: S M
authors.initials: SM
authors.lastname: Magrini
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D006801:Humans; D008207:Lymphatic Metastasis / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D010388:Pelvis / Q000473:pathology*; D010865:Pilot Projects; D011471:Prostatic Neoplasms / Q000473:pathology*; D012680:Sensitivity and Specificity
keywords: Machine Learning; Nodal metastases; Pelvic irradiation; Prostate cancer; Radiotherapy