Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients?: Preliminary Results of a Pilot Study

B. De Bari; M. Vallati; R. Gatta; C. Simeone; G. Girelli; U. Ricardi; I. Meattini; P. Gabriele; R. Bellavita; M. Krengli; I. Cafaro; E. Cagna; F. Bunkheila; S. Borghesi; M. Signor; A. Di Marco; F. Bertoni; M. Stefanacci; N. Pasinetti; M. Buglione; S. M. Magrini

doi:10.3109/07357907.2015.1024317

Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study

B. De Bari, M. Vallati, R. Gatta, C. Simeone, G. Girelli, U. Ricardi, I. Meattini, P. Gabriele, R. Bellavita, M. Krengli, I. Cafaro, E. Cagna, F. Bunkheila, S. Borghesi, M. Signor, A. Di Marco, F. Bertoni, M. Stefanacci, N. Pasinetti, M. BuglioneS. M. Magrini

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	232-240
Number of pages	9
Journal	Cancer Investigation
Volume	33
Issue number	6
Early online date	7 May 2015
DOIs	https://doi.org/10.3109/07357907.2015.1024317
Publication status	Published - 3 Jul 2015

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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., ... Magrini, S. M. (2015). Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study. Cancer Investigation, 33(6), 232-240. https://doi.org/10.3109/07357907.2015.1024317

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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.",

keywords = "Machine Learning, Nodal metastases, Pelvic irradiation, Prostate cancer, Radiotherapy",

author = "{De Bari}, B. and M. Vallati and R. Gatta and C. Simeone and G. Girelli and U. Ricardi and I. Meattini and P. Gabriele and R. Bellavita and M. Krengli and I. Cafaro and E. Cagna and F. Bunkheila and S. Borghesi and M. Signor and {Di Marco}, A. and F. Bertoni and M. Stefanacci and N. Pasinetti and M. Buglione and Magrini, {S. M.}",

year = "2015",

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doi = "10.3109/07357907.2015.1024317",

language = "English",

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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, SM 2015, 'Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study', Cancer Investigation, vol. 33, no. 6, pp. 232-240. https://doi.org/10.3109/07357907.2015.1024317

TY - JOUR

T1 - Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients?

T2 - Preliminary Results of a Pilot Study

AU - De Bari, B.

AU - Vallati, M.

AU - Gatta, R.

AU - Simeone, C.

AU - Girelli, G.

AU - Ricardi, U.

AU - Meattini, I.

AU - Gabriele, P.

AU - Bellavita, R.

AU - Krengli, M.

AU - Cafaro, I.

AU - Cagna, E.

AU - Bunkheila, F.

AU - Borghesi, S.

AU - Signor, M.

AU - Di Marco, A.

AU - Bertoni, F.

AU - Stefanacci, M.

AU - Pasinetti, N.

AU - Buglione, M.

AU - Magrini, S. M.

PY - 2015/7/3

Y1 - 2015/7/3

N2 - 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.

AB - 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.

KW - Machine Learning

KW - Nodal metastases

KW - Pelvic irradiation

KW - Prostate cancer

KW - Radiotherapy

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U2 - 10.3109/07357907.2015.1024317

DO - 10.3109/07357907.2015.1024317

M3 - Article

C2 - 25950849

AN - SCOPUS:84934287734

VL - 33

SP - 232

EP - 240

JO - Cancer Investigation

JF - Cancer Investigation

SN - 0735-7907

IS - 6

ER -

Could Machine Learning Improve the Prediction of Pelvic Nodal Status of Prostate Cancer Patients? Preliminary Results of a Pilot Study

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