Prostate Cancer, Version 1.2016

Mohler, James L.; Armstrong, Andrew J.; Bahnson, Robert R.; D’Amico, Anthony V.; Davis, Brian J.; Eastham, James A.; Enke, Charles Arthur; Farrington, Thomas; Higano, Celestia S.; Horwitz, Eric M.; Hurwitz, Michael; Kane, Christopher J.; Kawachi, Mark H.; Kuettel, Michael; Lee, Richard J.; Meeks, Joshua J.; Penson, David F.; Plimack, Elizabeth R.; Pow‐Sang, Julio M.; Raben, David; Richey, Sylvia; Roach, Mack; Rosenfeld, Stan; Schaeffer, Edward M.; Skolarus, Ted A.; Small, Eric J.; Sonpavde, Guru; Srinivas, Sandy; Strope, Seth A.; Tward, Jonathan D.; Shead, Dorothy A.; Freedman-Cass, Deborah A.

doi:10.6004/jnccn.2016.0004

Cited by 548 publications

(306 citation statements)

References 2 publications

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“…Clinical risk grouping has a clear impact on prognosis 12 and affects disease treatment recommendations. 13 With a growing national focus on health care cost and quality, risk-based quality measures have been developed as indicators of the value of care. 14 Pay-for-performance systems, such as the Physician Quality Reporting System, use several quality measures relating to prostate cancer care, including the avoidance of bone scans in patients diagnosed with low-risk disease.…”

Section: Discussionmentioning

confidence: 99%

Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records

Gregg

Lang

Wang

et al. 2017

JCO Clinical Cancer Informatics

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Purpose Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. Methods Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D’Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen’s κ statistic. Results Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. Conclusion NLP can achieve more than 90% accuracy on D’Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known trade off between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.

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Section: Discussionmentioning

confidence: 99%

Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records

Gregg

Lang

Wang

et al. 2017

JCO Clinical Cancer Informatics

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“…Treatment related variables included age at RT, pretreatment PSA, Gleason score, grade group, clinical stage, National Comprehensive Cancer Network (NCCN) risk-group [8], type of RT, target and dose of RT, and the use of hormone suppression during RT. Biochemical variables included PSA nadir, time to nadir from RT, PSA at CholPET, PSA doubling time at CholPET (in months), PSA velocity (in ng/ml/yr) at CholPET, and time to CholPET from RT.…”

Section: Methodsmentioning

confidence: 99%

Identification of Site-specific Recurrence Following Primary Radiation Therapy for Prostate Cancer Using C-11 Choline Positron Emission Tomography/Computed Tomography: A Nomogram for Predicting Extrapelvic Disease

et al. 2017

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Background Management of recurrent prostate cancer (CaP) after radiotherapy (RT) is dependent on accurate localization of the site of recurrent disease. Objective To describe the anatomic patterns and clinical features associated with CaP recurrence following RT identified on advanced imaging. Design, setting, and participants Retrospective review of 184 patients with a rising prostate-specific antigen (PSA) after RT for CaP. Intervention C-11 choline positron emission tomography/computed tomography (CholPET). Outcome measurements and statistical analysis Recurrence patterns were classified as pelvic soft tissue only (as a surrogate for potentially salvageable disease) versus any extrapelvic disease, and clinical features were compared between patterns. Multivariable logistic regression was used to generate a predictive nomogram for extrapelvic recurrence. Discrimination was assessed with a c-index. Results and limitations Recurrence site was identified in 161 (87%) patients, with 95 (59%) sites histologically confirmed. Factors associated with the detection of recurrence included the difference between PSA nadir and PSA at CholPET (odds ratio: 1.30, p < 0.01) and National Comprehensive Cancer Network high-risk classification (odds ratio: 10.83, p = 0.03). One hundred (54.3%) patients recurred in the pelvic soft tissue only, while 61 (33%) had extrapelvic recurrence. Of 21 patients who underwent CholPET prior to meeting the Phoenix criteria of biochemical failure, 15 (71%) had recurrence identified on CholPET with 11 localized to the pelvis. On multivariable analysis, PSA at CholPET, time from RT, and National Comprehensive Cancer Network risk group were predictive of recurrence outside of the pelvis, and a nomogram was generated with a c-index of 0.79. Conclusions CholPET identified the site of recurrence in 87% of patients with a rising PSA after RT; most commonly within the pelvis in potentially salvageable locations. A predictive nomogram was generated, and pending external validation, this may aid in assessing the risk of disease beyond the pelvis. These findings underscore the importance of advanced imaging when considering management strategies for patients with a rising PSA following primary RT. Patient summary We identified anatomic patterns of recurrence in patients with a rising prostate-specific antigen after radiotherapy using C-11 choline positron emission tomography/computed tomography. Most recurrences were localized to the pelvis and we were able to generate a tool to aid in disease localization prior to evaluation with advanced imaging.

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“…The prevalence of prostate cancer in men underscores the need for awareness of pubic symphysis osteomyelitis as a serious consequence of treatment 5. Distinguishing UTIs associated with this entity from those with more common causes is critical.…”

Section: Discussionmentioning

confidence: 99%

Successful treatment of multidrug-resistantPseudomonas aeruginosapubic symphysis osteomyelitis with ceftolozane/tazobactam

et al. 2017

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New antibiotic options are needed for the treatment of multidrug-resistant (MDR) infections. We present a case of a man aged 64 years with a bladder fistula due to radiation, ultimately causing osteomyelitis of the pubic symphysis. Repeated antibiotic courses, without correcting the fistula, resulted in infection with MDR He was successfully treated for his osteomyelitis through cystectomy, aggressive debridement and a prolonged course of antimicrobials directed at the MDR isolate.

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Prostate Cancer, Version 1.2016

Cited by 548 publications

References 2 publications

Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records

Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records

Identification of Site-specific Recurrence Following Primary Radiation Therapy for Prostate Cancer Using C-11 Choline Positron Emission Tomography/Computed Tomography: A Nomogram for Predicting Extrapelvic Disease

Successful treatment of multidrug-resistantPseudomonas aeruginosapubic symphysis osteomyelitis with ceftolozane/tazobactam

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