Can Predictive Models for Prostate Cancer Patients Derived in the United States of America Be Utilized in European Patients? A Validation Study of the Partin Tables

Graefen, Markus; Augustin, Herbert; Karakiewicz, Pierre I.; Hammerer, Peter; Haese, Alexander; Palisaar, Jüri; Blonski, Jakob; Fernandez, S.; Erbersdobler, Andreas; Huland, Hartwig

doi:10.1016/s0302-2838(02)00497-9

Cited by 60 publications

(32 citation statements)

References 20 publications

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“…Area under curve (AUC) values has been assessed previously for evaluating the predictability of Partin tables in external validation studies [11,12,14,15]. The ideal predictions are donated with 100 % accuracy and values of 50 % or less indicates that the model cannot predict the desired outcome but rather describes the only random Despite the apparent differences in pre-operative variables of our cohort and the derivation cohort of updated Partin's table, the predicted accuracy for the final pathological status was good.…”

Section: Discussionmentioning

confidence: 89%

“…Many nomograms have been developed for providing diagnostic, staging and prognostic information in patients with prostate cancer [9]. Oesterling et al [10] [2,11,12] and showed that these tables can be applied to other population cohorts as well. The Partin's table was updated in 2007 by Makarove et al [13] and its accuracy and discriminative properties have been questioned in various external validation studies [7,14,15].…”

Section: Discussionmentioning

confidence: 99%

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Validation of Updated Partin’s Table in Pakistani Patients undergoing Radical Prostatectomy for Prostate Cancer

Nazim¹,

Abbas²,

Islam³

2011

J Cancer Sci Ther

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Objectives: To establish the usefulness and validity of 2007 Partin's table in our population with prostate cancer. Materials and methods:Between January 1998 to June 2009, all patients with clinically localized carcinoma prostate who were treated with intent of radical retro-pubic prostatectomy (RRP) were included. Clinical, operative and pathological data was gathered. All biopsy and final histopathology Gleason scores were re-assigned in a double blind manner. Pre-operative serum PSA, TNM clinical stage and biopsy Gleason scores were plotted on Partin's table and its predictive value and pathological findings of specimen were compared and analyzed by using Receiver operating characteristic (ROC) analysis.Results: A total of 109 of 138 patients were included in final analysis. The median age was 65 ± 5.8 years. The pre-operative serum PSA values and clinical stages were higher in our cohort of patients as compared to Partin's cohort. At pathological assessment of resected specimen, organ confined disease was present in 58 % of patients, seminal vesicles were involved in 22 % and lymph node metastasis was present in 12 % of patients. The accuracy of Partin's table derived probability was high with area under curve (AUC) of 0.82 for organ confinement, 0.805 for seminal vesicle involvement and 0.714 for lymph node involvement respectively. Conclusions:The 2007 Partin's table has a reasonably high predictive value for the final histo-pathological features. This predictive model can be used in Pakistani patients with carcinoma prostate with comparable accuracy.

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Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Validation of Updated Partin’s Table in Pakistani Patients undergoing Radical Prostatectomy for Prostate Cancer

Nazim¹,

Abbas²,

Islam³

2011

J Cancer Sci Ther

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“…After radical prostatectomy (RP), patients with OCPC have an excellent prognosis, as more than 90% of them are free from biochemical recurrence at 5 years [24,25] . Since the introduction of the Partin tables in 1997, investigators have repeatedly validated the nomograms' capacity to help predict the pathologic stage of clinically localized prostate cancer [46][47][48] . In 2001, the nomograms were updated based on a more contemporary cohort of disease features [28] .…”

Section: Detection Of Ocpc (Pt2)mentioning

confidence: 99%

“…In 2001, the nomograms were updated based on a more contemporary cohort of disease features [28] . The accuracy of the Partin tables in predicting OCPC is high, with reports of the area under the receiver operating characteristic (ROC) curve ranging from 0.79 to 0.82 [46][47][48] . MR findings (from endorectal MRI or combined endorectal MRI-MRSI) contribute significant incremental value to clinical staging nomograms in the prediction of OCPC.…”

Section: Detection Of Ocpc (Pt2)mentioning

confidence: 99%

Incremental value of magnetic resonance imaging in the advanced management of prostate cancer

Wang¹

2009

WJR

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Prostate cancer is a major public health burden throughout the world. The high incidence of prostate cancer, combined with earlier detection and downstaging at the time of diagnosis, and the slow natural progression and biological heterogeneity of the disease, has made its management a complex and controversial issue. There is growing demand for patient-specific therapies that can minimize treatment morbidity while maximizing treatment benefits. There are a number of clinical parameters and clinical nomograms to help with the choice of treatment. Magnetic resonance imaging (MRI) is a technique which makes safer, more individualized therapies possible due to high spatial resolution, superior contrast resolution, multiplanar capability, and a large field of view. Other MRI techniques such as MR spectroscopic imaging, dynamic contrast-enhanced MRI or perfusion MRI, and diffusion-weighted imaging complement MRI by reflecting tissue biochemistry, Brownian motion of water molecules, and capillary wall permeability, respectively. This editorial review highlights the incremental value of MRI in the advanced management of prostate cancer to non-invasively improve cancer staging, biologic potential, treatment planning, therapy response, local recurrence, and to guide target biopsy for clinical suspected cancer with previous negative biopsy. Finally, some future prospects for MRI in prostate cancer management are given.

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“…On the other hand their accuracy is still an argument of discussion [21,11]. Moreover, some important limits exists also for the Partins Tables: they consider only a limited part of the population of prostate cancer patients and, furthermore, it has been showed that their algorithm overestimate the presence of lymph node and seminal vesicle involvement in patients with a predicted risk of 40% or more [15].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Machine Learning for Predicting Nodal Status in Prostate Cancer Patients

Vallati

Bari

Gatta

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. Prostate cancer is the second cause of cancer in males. The prophylactic pelvic irradiation is usually needed for treating prostate cancer patients with Subclinical Nodal Metestases. Currently, the physicians decide when to deliver pelvic irradiation in nodal negative patients mainly by using the Roach formula, which gives an approximate estimation of the risk of Subclinical Nodal Metestases. In this paper we study the exploitation of Machine Learning techniques for training models, based on several pre-treatment parameters, that can be used for predicting the nodal status of prostate cancer patients. An experimental retrospective analysis, conducted on the largest Italian database of prostate cancer patients treated with radical External Beam Radiation Therapy, shows that the proposed approaches can effectively predict the nodal status of patients.

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Can Predictive Models for Prostate Cancer Patients Derived in the United States of America Be Utilized in European Patients? A Validation Study of the Partin Tables

Cited by 60 publications

References 20 publications

Validation of Updated Partin’s Table in Pakistani Patients undergoing Radical Prostatectomy for Prostate Cancer

Validation of Updated Partin’s Table in Pakistani Patients undergoing Radical Prostatectomy for Prostate Cancer

Incremental value of magnetic resonance imaging in the advanced management of prostate cancer

Exploiting Machine Learning for Predicting Nodal Status in Prostate Cancer Patients

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