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A validation study of new decision algorithms for interpretation of cancer significance on prostate systematic biopsy
Center for Clinical Effectiveness, Office of the Chief Quality Officer, Baylor Scott and White Health, Dallas, Texas, USA
Apr  2017 (Vol.  24, Issue  2, Pages( 8721 - 8727)
PMID: 28436358


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    To test with actual data a new decision algorithm derived by probability modeling of the number of positive cores, for deciding insignificant versus significant prostate cancer, based on prostate volume, Gleason score, tumor length on biopsy cores, and number of positive cores.


    A dataset of 59 cancer-involved autopsied prostate glands from patients aged 42 to 92 years with prostate volumes of 22 cc to 95 cc was used. An 18 core-systematic biopsy was performed on the first 47 patients, and saturation biopsy protocol of 36 cores was performed on the remainder. Clinically insignificant prostate cancer was defined on whole-mount prostates as Gleason score < 7, total tumor volume ≤ 0.5 cc. Separate counts of 'significant' versus 'insignificant' prostate cancer by both the model-based decision algorithm and the actual data were obtained. These yielded specificity (SP), sensitivity (SE), and concordance values for evaluation of the efficacy of the decision algorithm.


    The model-based decision algorithm yielded SP from 83% to 100%, SE from 62% to 100%, and concordance from 78% to 100%. These findings compared favorably with those of currently used study-based algorithms and their individually fitted SP and SE derived from their corresponding studies.


    The model-based decision algorithm performed well with this dataset of autopsied prostates for patients with Gleason score 6 or lower, confirming its practical feasibility and its potential to help reduce over- and under-treatment, especially with marginally positive biopsy cases, by taking prostate volume properly into account. However, additional validation studies with other datasets including higher prostate volumes are needed for further calibration and improvement of the model-based decision algorithm.