Search

Numerous studies have shown a correlation between perineural invasion (PNI) in prostate biopsies and outcome. The reporting of PNI varies widely in the literature. While the interobserver variability of prostate cancer grading has been studied extensively, less is known regarding the reproducibility of PNI. A total of 212 biopsy cores from a population-based screening trial were included in this study (106 with and 106 without PNI according to the original pathology reports). The glass slides were scanned and circulated among four pathologists with a special interest in urological pathology for assessment of PNI. Discordant cases were stained by immunohistochemistry for S-100 protein. PNI was diagnosed by all four observers in 34.0% of cases, while 41.5% were considered to be negative for PNI. In 24.5% of cases, there was a disagreement between the observers. The kappa for interobserver variability was 0.67-0.75 (mean 0.73). The observations from one participant were compared with data from the original reports, and a kappa for intraobserver variability of 0.87 was achieved. Based on immunohistochemical findings among discordant cases, 88.6% had PNI while 11.4% did not. The most common diagnostic pitfall was the presence of bundles of stroma or smooth muscle. It was noted in a few cases that collagenous micronodules could be mistaken for a nerve. The distance between cancer and nerve was another cause of disagreement. Although the results suggest that the reproducibility of PNI may be greater than that of prostate cancer grading, there is still a need for improvement and standardization.

Background: A new generation of risk calculators (RCs) for prostate cancer (PCa) incorporating magnetic resonance imaging (MRI) data have been introduced. However, these have not been validated externally, and their clinical benefit compared with alternative approaches remains unclear., Objective: To assess previously published PCa RCs incorporating MRI data, and compare their performance with traditional RCs (European Randomized Study of Screening for Prostate Cancer [ERSPC] 3/4 and Prostate Biopsy Collaborative Group [PBCG]) and the blood-based Stockholm3 test., Design, Setting, and Participants: RCs were tested in a prospective multicenter cohort including 532 men aged 45-74 yr participating in the Stockholm3-MRI study between 2016 and 2017., Outcome Measurements and Statistical Analysis: The probabilities of detection of clinically significant PCa (csPCa) defined as Gleason score ≥3 + 4 were calculated for each patient. For each RC and the Stockholm3 test, discrimination was assessed by area under the curve (AUC), calibration by numerical and graphical summaries, and clinical usefulness by decision curve analysis (DCA)., Results and Limitations: The discriminative ability of MRI RCs 1-4 for the detection of csPCa was superior (AUC 0.81-0.87) to the traditional RCs (AUC 0.76-0.80). The observed prevalence of csPCa in the cohort was 37%, but calibration-in-the-large predictions varied from 14% to 63% across models. DCA identified only one model including MRI data as clinically useful at a threshold probability of 10%. The Stockholm3 test achieved equivalent performance for discrimination (AUC 0.86) and DCA, but was underpredicting the actual risk., Conclusions: Although MRI RCs discriminated csPCa better than traditional RCs, their predicted probabilities were variable in accuracy, and DCA identified only one model as clinically useful., Patient Summary: Novel risk calculators (RCs) incorporating imaging improved the ability to discriminate clinically significant prostate cancer compared with traditional tools. However, all but one predicted divergent compared with actual risks, suggesting that regional modifications be implemented before usage. The Stockholm3 test achieved performance comparable with the best MRI RC without utilization of imaging., (Copyright © 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

Background: The new STHLM3 test, combining protein markers, genetic markers, and clinical data to assess a man's prostate cancer (PCa) risk, has been investigated in Sweden within the frame of the STHLM3 trial., Objective: To assess whether the STHLM3 test influences men's worry level, PCa knowledge, attitude, and health-related quality of life (HRQoL)., Design Setting and Participants: Invitations with login to the web survey were mailed to 10 000 men, 50-69 yr of age, who were eligible for the STHLM3 trial. The survey was sent 3 mo before invitation to the STHLM3 trial (baseline) and 5 mo after STHLM3 (follow-up). At baseline, the men were unaware of the upcoming invitation to STHLM3. The survey covered the following: PCa-specific worry and perceived vulnerability, knowledge about PCa, attitude toward PCa testing and health behavior, and HRQoL., Outcome Measurements and Statistical Analysis: Survey scores were compared between baseline and follow-up by using the nonparametric Wilcoxon-signed rank tests for paired samples. Analysis of covariance was performed for PCa risk group comparisons., Results and Limitations: A total of 994 men (10%) responded to our survey at baseline and follow-up, and were assessed as follows: low risk: 421 men; intermediate risk: 421 men; and high risk:152, of whom 59 were diagnosed with PCa after further investigation. In men assessed as having low and intermediate risk, level of worrying decreased at follow-up ( p  <  0.001), whereas no changes were observed in men at high risk. Moreover, no HRQoL changes were observed over time. The low response rate is the main limitation., Conclusions: We found that the STHLM3 model, a risk-based PCa test, showed no negative impact on the well-being of men., Patient Summary: Since our results suggest that the risk-based screening as used in STHLM3 did not induce negative psychological effects on the participants, we can recommend this risk-based approach for population-based prostate cancer screening., (© 2020 The Author(s).)

Background: In men aged above 50 yr, lower urinary tract symptoms (LUTS), benign prostate hyperplasia, and prostate cancer are common urological conditions. Current guidelines for general practitioners frequently recommend prostate-specific antigen (PSA) testing in patients with LUTS for the detection of prostate cancer., Objective: To assess the performance of PSA, PSA density, and the Stockholm3 blood test for identification of prostate cancer among men with LUTS., Design Setting and Participants: In this post hoc analysis of a population-based diagnostic trial (STHLM3, n  = 58 588), 4588 men aged 50-69 yr, without previous prostate cancer, with International Prostate Symptom Score (IPSS) data, and having PSA ≥ 3 ng/mL were identified. Men with at least moderate LUTS, defined as an IPSS score of ≥8, were included. PSA density and Stockholm3 scores were calculated., Intervention: Participants underwent 10-12-core systematic prostate biopsies., Outcome Measurements and Statistical Analysis: The primary outcome was significant prostate cancer (sPCa) defined as International Society of Urological Pathology (ISUP) grade ≥2. Logistic regression analysis adjusted for age and previous biopsy status was performed. The area under the receiver operating characteristic curve (AUC) was calculated, and decision curve analysis was performed., Results and Limitations: Out of 4588 men, 1544 (34%) reported at least moderate LUTS. The median age was 64 yr, and 11% had undergone a previous prostate biopsy. The Stockholm3 test showed superior discrimination for sPCa to PSA density, which in turn showed superior discrimination to PSA (AUC 0.77 vs 0.70 vs 0.61, p  <  0.02). Calibration of the Stockholm3 test was adequate. Performing biopsy only in men with PSA ≥5 ng/mL saved 64% of biopsies, but resulted in missing 52% of detectable sPCa. Recommending biopsy for men with PSA density ≥0.07 resulted in sparing 26% of biopsy procedures and delaying the diagnosis of 12% of sPCa cases, with a 6.1% risk of sPCa among unbiopsied men. Recommending men with Stockholm3 ≥ 0.11 for biopsy resulted in sparing 53% of biopsy procedures and delaying the diagnosis of 20% of sPCa cases, with a 5.1% risk of finding sPCa in unbiopsied men., Conclusions: PSA density and the Stockholm3 blood test were superior to PSA for the identification of prostate cancer among men with LUTS., Patient Summary: In this analysis of a large Swedish study, we find that the use of prostate-specific antigen (PSA) density or the Stockholm3 blood test instead of only PSA might improve the detection of prostate cancer among men with lower urinary tract symptoms., (© 2020 The Author(s).)

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Background There is great interest in developing artificial intelligence (AI)-based computer-aided detection (CAD) systems for use in screening mammography. Comparative performance benchmarks from true screening cohorts are needed. Purpose To determine the range of human first-reader performance measures within a population-based screening cohort of 1 million screening mammograms to gauge the performance of emerging AI CAD systems. Materials and Methods This retrospective study consisted of all screening mammograms in women aged 40-74 years in Stockholm County, Sweden, who underwent screening with full-field digital mammography between 2008 and 2015. There were 110 interpreting radiologists, of whom 24 were defined as high-volume readers (ie, those who interpreted more than 5000 annual screening mammograms). A true-positive finding was defined as the presence of a pathology-confirmed cancer within 12 months. Performance benchmarks included sensitivity and specificity, examined per quartile of radiologists' performance. First-reader sensitivity was determined for each tumor subgroup, overall and by quartile of high-volume reader sensitivity. Screening outcomes were examined based on the first reader's sensitivity quartile with 10 000 screening mammograms per quartile. Linear regression models were fitted to test for a linear trend across quartiles of performance. Results A total of 418 041 women (mean age, 54 years ± 10 [standard deviation]) were included, and 1 186 045 digital mammograms were evaluated, with 972 899 assessed by high-volume readers. Overall sensitivity was 73% (95% confidence interval [CI]: 69%, 77%), and overall specificity was 96% (95% CI: 95%, 97%). The mean values per quartile of high-volume reader performance ranged from 63% to 84% for sensitivity and from 95% to 98% for specificity. The sensitivity difference was very large for basal cancers, with the least sensitive and most sensitive high-volume readers detecting 53% and 89% of cancers, respectively ( P < .001). Conclusion Benchmarks showed a wide range of performance differences between high-volume readers. Sensitivity varied by tumor characteristics. © RSNA, 2020 Online supplemental material is available for this article.

Aims: Despite being one of the major pathways for the spread of malignant tumours, perineural invasion (PNI) has not conclusively been shown to have an independent prognostic value for prostate cancer. Prostatic biopsy constitutes the major pathology workload in prostate cancer and is the foundation for primary treatment decisions and for this reason we aimed to estimate the prognostic value of PNI in biopsies., Methods: We followed 918 men who underwent radical prostatectomy (RP) from the prospective and population based STHLM3 study until biochemical recurrence with a median follow-up of 4.1 years. To strengthen the evidence, we combined the estimates from the largest studies targeting the prognostic value of PNI in the biopsy. We also estimated the OR of advanced stage as radical prostatectomy for PNI positive and negative men., Results: The estimated prognostic value based on our data suggested an approximately 50% increased risk of biochemical recurrence if PNI was present in the biopsy (p=0.06). Even though not statistically significant on the 5% level, this estimate is consistent with similar studies, and by combining the estimates there is in fact strong evidence in support of an independent prognostic value of PNI in the biopsy (p<0.0001). There was also an independent increased risk of advanced stage at RP for positive men (OR 1.85, p=0.005)., Conclusions: The evidence supporting a clinically relevant and independent prognostic value of PNI is strong enough to be considered for pathology reporting guidelines., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.)

Importance: A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening., Objective: To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists., Design, Setting, and Participants: This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer)., Main Outcomes and Measures: Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%)., Results: The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level., Conclusions and Relevance: To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.

Background: We examined the potential change in cancer detection when using an artificial intelligence (AI) cancer-detection software to triage certain screening examinations into a no radiologist work stream, and then after regular radiologist assessment of the remainder, triage certain screening examinations into an enhanced assessment work stream. The purpose of enhanced assessment was to simulate selection of women for more sensitive screening promoting early detection of cancers that would otherwise be diagnosed as interval cancers or as next-round screen-detected cancers. The aim of the study was to examine how AI could reduce radiologist workload and increase cancer detection., Methods: In this retrospective simulation study, all women diagnosed with breast cancer who attended two consecutive screening rounds were included. Healthy women were randomly sampled from the same cohort; their observations were given elevated weight to mimic a frequency of 0·7% incident cancer per screening interval. Based on the prediction score from a commercially available AI cancer detector, various cutoff points for the decision to channel women to the two new work streams were examined in terms of missed and additionally detected cancer., Findings: 7364 women were included in the study sample: 547 were diagnosed with breast cancer and 6817 were healthy controls. When including 60%, 70%, or 80% of women with the lowest AI scores in the no radiologist stream, the proportion of screen-detected cancers that would have been missed were 0, 0·3% (95% CI 0·0-4·3), or 2·6% (1·1-5·4), respectively. When including 1% or 5% of women with the highest AI scores in the enhanced assessment stream, the potential additional cancer detection was 24 (12%) or 53 (27%) of 200 subsequent interval cancers, respectively, and 48 (14%) or 121 (35%) of 347 next-round screen-detected cancers, respectively., Interpretation: Using a commercial AI cancer detector to triage mammograms into no radiologist assessment and enhanced assessment could potentially reduce radiologist workload by more than half, and pre-emptively detect a substantial proportion of cancers otherwise diagnosed later., Funding: Stockholm City Council., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm 2 vs 34.2 cm 2 ; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively ( P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers. Conclusion Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Bahl in this issue.

Background: An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading., Methods: We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50-69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa., Findings: The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994-0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972-0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95-0·97) for the independent test dataset and 0·87 (0·84-0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60-0·73)., Interpretation: An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist., Funding: Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Introduction: The treatment of proximal hamstring avulsions is controversial. While several trials have investigated the outcome for patients treated surgically, there is today no prospective trial comparing operative treatment with non-operative treatment. This protocol describes the design for the proximal hamstring avulsion clinical trial (PHACT)-the first randomised controlled trial of operative versus non-operative treatment for proximal hamstring avulsions., Methods and Analysis: PHACT is a multicentre randomised controlled trial conducted across Sweden, Norway and Finland. Eligible patients (60 participants/treatment arm) with a proximal hamstring avulsion of at least two of three tendons will be randomised to either operative or non-operative treatment. Participants allocated to surgery will undergo reinsertion of the tendons with suture anchors. The rehabilitation programme will be the same for both treatment groups. When patient or surgeon equipoise for treatment alternatives cannot be reached and randomisation therefore is not possible, patients will be invited to participate in a parallel observational non-randomised cohort. The primary outcome will be the patient-reported outcome measure Perth hamstring assessment tool at 24 months. Secondary outcomes include the Lower Extremity Functional Score, physical performance and muscle strength tests, patient satisfaction and MR imaging. Data analysis will be blinded and intention-to-treat analysis will be preformed., Ethics and Dissemination: Ethical approval has been granted by the Ethical Committee of Uppsala University (DNR: 2017-170) and by the Norwegian ethical board (REC: 2017/1911). The study will be conducted in agreement with the Helsinki declaration. The findings will be disseminated in peer-reviewed publications., Trial Registration Number: NCT03311997., Competing Interests: Competing interests: Nothing to declare, (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Risk prediction models and magnetic resonance imaging (MRI) of the prostate can reduce unnecessary biopsies and overdiagnosis of low-risk prostate cancer. However, it is unclear how these tools should be used in concert., Objective: To develop a unified risk prediction model (S3M-MRI) that combines the Stockholm3 score (based on protein and genetic markers and clinical variables) and Prostate Imaging-Reporting and Data System v.2 scores modified for MRI without contrast (modPI-RADS)., Design, Setting, and Participants: We used data for 532 men from the prospective multicentre STHLM3-MRI diagnostic study to construct S3M-MRI. We compared S3M-MRI to Stockholm3 and modPI-RADS alone with respect to model discrimination, calibration, and net benefit. We also compared clinical outcomes for five diagnostic strategies according to the use of combinations of the three models., Results and Limitations: The area under the receiver operating characteristic curve (AUC) was 0.88 (95% confidence interval [CI] 0.85-0.91) for S3M-MRI, which was significantly higher (p=0.04) than for Stockholm3 (0.86, 95% CI 0.83-0.89) and modPI-RADS (0.83, 95% CI 0.79-0.87). S3M-MRI had a higher net benefit on decision curve analysis for clinically relevant probability thresholds for biopsy recommendation in comparison to Stockholm3 and modPI-RADS. However, for different diagnostic strategies, sequential use of Stockholm3 followed by MRI only for Stockholm3-positive men resulted in a similar number of unnecessary biopsies (64 vs 69) and diagnosed International Society of Urological Pathology (ISUP) grade group 1 cancers (56 vs 51) at similar sensitivity for ISUP grade group ≥2 cancers, while avoiding 38% of MRI scans. Limitations include the ethnically homogeneous study population., Conclusions: The unified S3M-MRI model was superior to the Stockholm3 model and modPI-RADS alone. However, the S3M-MRI improvement was marginal compared to sequential use of Stockholm3 followed by MRI, and resulted in 60% more MRI scans., Patient Summary: A new risk prediction model combining clinical variables, genetic and protein biomarkers, and results from prostate magnetic resonance imaging improved the clinical outcome performance of prostate cancer diagnostics., (Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.)

Study Design: Observational cohort study., Introduction: Investigating prognostic factors using population-based data may be used to improve functional outcome after flexor tendon injury and repair., Purpose of the Study: The aim of this study is to investigate the effect of concomitant nerve transection, combined flexor digitorum profundus (FDP) and flexor digitorum superficialis (FDS) tendon transection and the age of the patient, on digital range of motion (ROM) more than 1 year after FDP tendon transection and repair in zone I and II., Methods: Two hundred seventy-three patients with a total of 311 fingers admitted for FDP injury in zone I and II were treated with active extension-passive flexion with rubber bands and followed for at least 1 year. We compared outcome by evaluating digital mobility using Strickland's evaluation system., Results: At 12 months 72% of patients aged > 50 had fair or poor ROM compared to 17% of patients aged 0-25 years. At 24 months the results for patients aged > 50 had improved to 33% with fair or poor ROM, whereas no improvement had occurred for patients aged 0-25 (17% with fair or poor ROM). Concomitant nerve transection and FDS tendon transection had no negative effects on digital mobility., Discussion: Age above 50 was significantly associated with impaired digital ROM during the first year after flexor tendon injury and repair but not at 2 years follow-up. Concomitant nerve transection and combined transection of FDP and FDS do not affect digital mobility., Conclusions: Older patients are likely to have a slower healing process and impaired digital ROM during the first year after surgery., (Copyright © 2018 Hanley & Belfus. Published by Elsevier Inc. All rights reserved.)

Introduction: Prostate cancer is a leading cause of cancer death among men in the Western world. Early detection of prostate cancer has been shown to decrease mortality, but has limitations with low specificity leading to unnecessary biopsies and overdiagnosis of low-risk cancers. The STHLM3 trial has paved way for improved specificity in early detection of prostate cancer using the blood-based STHLM3 test for identifying men at increased risk of harbouring significant prostate cancer. Targeted prostate biopsies based on MRI images have shown non-inferior sensitivity to detect significant prostate cancer and decrease the number of biopsies and non-significant cancers among men referred for prostate biopsy in clinical practice. The strategy of the STHLM3-MRI project is to study an improved diagnostic pathway including an improved blood-based test for identification of men with increased risk of prostate cancer and use of MRI to select men for diagnostic workup with targeted prostate biopsies., Methods: This study compares prostate cancer detection using prostate-specific antigen (PSA) and systematic biopsies to the improved pathway for prostate cancer detection using the STHLM3 test and targeted biopsies in a screening context. The study will recruit 10 000 participants during 1 June 2018 to 1 June 2020 combining a paired and randomised design. Participants are grouped by PSA and Stockholm3 test level. Men with Stockholm3 ≥11% or PSA ≥3 ng/mL are randomised to systematic or MRI-targeted biopsies. This protocol follows SPIRIT guidelines. Endpoints include the number of detected prostate cancers, number of performed biopsy procedures and number of performed MRIs. Additional aims include to assess the health economic consequences and development of automated image-analysis., Ethics and Dissemination: The study is approved by the regional ethical review board in Stockholm (2017-1280/31). The study findings will be published in peer-review journals. Findings will also be disseminated by conference/departmental presentations and by media., Trial Registration Number: NCT03377881; Pre-results., Competing Interests: Competing interests: HG has five prostate cancer diagnostic related patents pending, has patent applications licensed to Thermo Fisher Scientific and might receive royalties from sales related to these patents. ME is named on four of these five patent applications. Karolinska Institutet collaborates with Thermo Fisher Scientific in developing the technology for the Stockholm3 test., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.)

Background: Several blood-based tests have been suggested to improve prostate cancer testing. The Stockholm3 test has been shown to reduce the number of prostate biopsies, to decrease detection of low-grade cancer and to maintain the detection rate of ISUP Gleason Group (GG) ≥ 2 cancer in a screening-by-invitation setting. We aimed to validate the performance of the Stockholm3 test in an independent, clinical practice cohort., Methods: The study-population consisted of 533 men in ages 45-75 without previous diagnosis of prostate cancer scheduled for prostate biopsy at any of three centers in Norway and Sweden. Blood samples for Stockholm3 analysis were drawn prior to systematic prostate biopsies. Clinically significant prostate cancer was defined as any finding of ISUP Grade Group (GG) 2 or higher. We calculated area under the curve (AUC) for predicting prostate cancer at biopsy and calculated. Models including PSA and PSA-density (PSA/prostate volume) were compared to a model including also clinical information, protein levels and single nucleotide polymorphisms (SNP)., Results: 263 of 533 (49%) participants were diagnosed with prostate cancer. 162 men had prostate cancer with GG ≥ 2. The Stockholm3 test discriminated better for GG ≥ 2 prostate cancer than PSA in combination with PSA-density AUC 8.9 (95% CI 82.7-89.2) and AUC 74.8 (95% CI 70.3-79.3). Using a Stockholm3 cut-off of 10% risk of GG ≥ 2 cancer, 38% of the biopsy procedures were saved, however delaying diagnosis for 6% (n = 10) of men with GG ≥ 2 cancer. Using PSA-density 0.1 as cut-off for biopsy saved 35% of biopsies, delaying diagnosis for 16% (n = 26) of men with GG ≥ 2 cancer., Conclusion: A prediction model including clinical information, protein levels and SNPs was independently validated in a clinical practice cohort and reduces the number of un-necessary biopsies while delaying diagnosis for a limited number of men.

Background: WISDOM (Women Informed to Screen Depending on Measures of Risk) is a randomized trial to assess whether personalized breast cancer screening-where women are screened biannually, annually, biennially, or not at all depending on risk and age-can prevent as many advanced (stage IIB or higher) cancers as annual screening in women ages 40-74 years across 5 years of trial time. The short study time in combination with design choices of not requiring study entry and exit mammograms for all participants may introduce different sources of bias in favor of either the personalized or the annual arm., Methods: We designed a simulation model and performed 5000 virtual WISDOM trials to assess potential biases. Each virtual trial simulated 65 000 randomly assigned participants who were each assigned a risk stratum and a time to stage of at least IIB cancer sampled from an exponential distribution with the hazard rate based on the risk stratum. Results from the virtual trials were used to evaluate two candidate analysis strategies with respect to susceptibility for introducing bias: 1) difference between arms in total number of events over total trial time, and 2) difference in number of events within complete screening cycles., Results: Based on the simulations, about 86 stage IIB or higher cancers will be detected within the trial and the total exposure time will be about 74 000 years in each arm. Potential ascertainment bias is introduced at study entry and exit. Analysis strategy 1 works better for the nonscreened stratum, whereas method 2 is considerably more unbiased for the strata of women screened biennially or every 6 months., Conclusion: Combining the two candidate analysis approaches gives a reasonably unbiased analysis based on the simulations and is the method we will use for the primary analysis in WISDOM. Publishing the WISDOM analysis approach provides transparency and can aid the design and analysis of other individualized screening trials.

Background: More specific diagnostic for prostate cancer is needed to decrease overdetection and number of diagnostic procedures., Objective: To assess the performance of combining a blood-based biomarker panel and magnetic resonance imaging (MRI)-targeted biopsies for prostate cancer detection., Design, Setting, and Participants: We used a prospective, multicenter, paired diagnostic study design. A total of 532 men aged 45-74 yr referred for prostate cancer workup were included during 2016-2017., Intervention: Participants underwent blood sampling for analysis of the Stockholm3 test including protein biomarkers, genetic polymorphisms, and clinical variables; 1.5 T MRI; systematic prostate biopsies; and MRI-targeted biopsies to lesions with Prostate Imaging Reporting and Data System version 2 ≥3., Outcome Measurements and Statistical Analysis: The main outcome was numbers of detected prostate cancer characterized by grade group (GG) and the number of performed biopsies using relative sensitivity (RS)., Results and Limitations: Median prostate-specific antigen was 6.3 ng/ml, and mean age was 63.9 yr. Targeted and systematic biopsies detected 170 and 162 GG ≥2 tumors, respectively (RS 1.05; 95% confidence interval [CI] 0.96-1.14). Compared with performing systematic biopsies on all men, performing targeted and systematic biopsies only on men with >10% risk of GG ≥2 cancer, as predicted by the Stockholm3 test, required 62% (95% CI 58-66) of the biopsy procedures and detected 58% (95% CI 48-70) of GG 1 disease, with increased sensitivity for GG ≥2 detection (RS 1.10; 95% CI 1.02-1.17). Performing only targeted biopsies in men with elevated Stockholm3 test altered these results only slightly. Compared with performing systematic and targeted biopsies on all men, performing this only for men with an elevated Stockholm3 test decreased detection of GG ≥2 cancer slightly (RS 0.92; 95% CI 0.88-0.95). Limitations include lacking knowledge of true disease prevalence., Conclusions: These findings provide evidence that strategies combining the blood-based Stockholm3 test and MRI-targeted biopsies can be used to inform biopsy decision making., Patient Summary: In this study, 532 men coming for prostate cancer workup underwent blood sampling, and both traditional and magnetic resonance imaging/fusion-guided prostate biopsies. We report that performing targeted biopsies only in men with an elevated risk as assessed by the Stockholm3 test saved biopsies, decreased overdetection, and maintained the number of detected high-grade cancers., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background: Implementation of risk-based prostate cancer screening has been proposed as a means to reduce the harms of PSA screening. Little is known, however, about the factors influencing men's decision to attend a prostate cancer screening based on a risk assessment., Method: We sent postal invitations with a login to a survey to 10.000 men, three months before invitation to a risk-based prostate cancer screening. Prostate cancer specific worry, prostate cancer-related knowledge, health behaviour, and health related quality of life were used as predictors of subsequent participation. Participation to risk-based prostate cancer screening was defined as providing a blood sample for the STHLM3 trial, a study evaluating a risk-based model that predicts the risk for aggressive prostate cancer., Results: With a response rate of 20%, 1.347 men (70%) participated in ensuing risk-based prostate cancer screening three months later whereas 568 men (30%) declined participation in the STHLM3-study. These decliners reported less worry and feeling less vulnerable to prostate cancer and responded "Do not know" more often than participants when asked questions about prostate cancer knowledge. Participants reported greater benefits of prostate testing (p = 0.0005), less barriers to prostate testing (p<0.0001), and higher intention to attend prostate cancer testing (p<0.0001) than decliners. Finally, participants reported better overall health than decliners (p<0.0001)., Conclusion: Prostate cancer worry, PC knowledge, health behaviour and quality of life were identified as predictors of participation in risk-based prostate cancer screening. Targeting these predictors may improve the participation rates. These results can inform policymaking for future population-based prostate cancer screening programs that should address potential worry in men and lack of knowledge about prostate cancer., Competing Interests: The authors have declared that no competing interests exist.

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Background: Screening for prostate cancer using prostate-specific antigen (PSA) alone leads to un-necessary biopsying and overdiagnosis. PSA density is easily accessible, but early evidence on its use for biopsy decisions was conflicting and use of PSA density is not commonly recommended in guidelines., Methods: We analyzed biopsy outcomes in 5291 men in the population-based STHLM3 study with PSA ≥ 3 ng/ml and ultrasound-guided prostate volume measurements by using percentages and regression models. PSA density was calculated as total PSA (ng/ml) divided by prostate volume (ml). Main endpoint was clinically significant cancer (csPCa) defined as Gleason Score ≥ 7., Results: The median PSA-density was 0.10 ng/ml 2 (IQR 0.075-0.14). PSA-density was associated with the risk of finding csPCa both with and without adjusting for the additional clinical information age, family history, previous biopsies, total PSA and free/total PSA (OR 1.06; 95% CI:1.05-1.07 and OR 1.07, 95% CI 1.06-1.08). Discrimination for csPCa was better when PSA density was added to a model with additional clinical information (AUC 0.75 vs. 0.73, P < 0.05). The proportion of men with Gleason Score 6 (ISUP 1) was similar across stratas of PSA-density. Omitting prostate biopsy for men with PSA-density ≤0.07 ng/ml 2 would save 19.7% of biopsy procedures, while missing 6.9% of csPCa. PSA-density cutoffs of 0.10 ng/ml 2 and 0.15 ng/ml 2 resulted in detection of 77% (729/947) and 49% (461/947) of Gleason Score ≥7 tumors., Conclusions: PSA-density might inform biopsy decisions, and spare some men from the morbidity associated with a prostate biopsy and diagnosis of low-grade prostate cancer.

The European Randomised Study of Screening for Prostate Cancer (ERSPC) showed that Prostate-Specific Antigen (PSA) based screening results in a significant prostate cancer mortality reduction. Although there are concerns on overdiagnosis and overtreatment, it has been shown that the benefits can outweigh the harms if screening is stopped in older ages to prevent overdiagnosis. A limited screening program (for example screening at ages 55-59 years), including active surveillance for men with low-risk tumors, can even be cost-saving, compared with testing in an opportunistic setting in the wrong ages, as currently in Europe. Further improvements are expected in the use of active surveillance and in discrimination between indolent and significant disease due to new biomarkers and magnetic resonance imaging. However, these future developments are no reason to postpone feasibility studies of high-quality PSA screening and reduce opportunistic testing at old ages., (© 2017 UICC.)

Background: Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density., Methods: Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI)., Results: Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD., Conclusions: Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

There is a distinct need for clinical improvements to the pipeline for prostate detection. The aim of the STHLM3 magnetic resonance imaging (MRI) study is to provide evidence of an improved and structured multistep pipeline for prostate cancer detection using the bloodbased Stockholm 3 test and MRI to achieve better specificity and reduce the risk of overdiagnosis., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings., Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences., Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform., Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction., Methods: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls., Results: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk., Conclusions: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction., (© 2015 Wiley Periodicals, Inc.)

Background: Controversy exists over how often and at what age mammography screening should be implemented. Given that evidence supports less frequent screening, the cost differences among advocated screening policies should be better understood., Objective: To estimate the aggregate cost of mammography screening in the United States in 2010 and compare the costs of policy recommendations by professional organizations., Design: A model was developed to estimate the cost of mammography screening in 2010 and 3 screening strategies: annual (ages 40 to 84 years), biennial (ages 50 to 69 years), and U.S. Preventive Services Task Force (USPSTF) guidelines (biennial for those aged 50 to 74 years and personalized based on risk for those younger than 50 years and based on comorbid conditions for those 75 years and older)., Setting: United States., Patients: Women aged 40 to 85 years., Intervention: Mammography annually, biennially, or following USPSTF guidelines., Measurements: Cost of screening per year, using Medicare reimbursements., Results: The estimated cost of mammography screening in the United States in 2010 was $7.8 billion, with approximately 70% of women screened. The simulated cost of screening 85% of women was $10.1 billion, $2.6 billion, and $3.5 billion for annual, biennial, and USPSTF guidelines, respectively. The largest drivers of cost (in order) were screening frequency, percentage of women screened, cost of mammography, percentage of women screened with digital mammography, and percentage of mammography recalls., Limitation: Cost estimates and assumptions used in the model were conservative., Conclusion: The cost of mammography varies by at least $8 billion per year on the basis of screening strategy. The USPSTF guidelines are based on the scientific evidence to date to maximize patient benefit and minimize harm but also result in far more effective use of resources., Primary Funding Source: University of California and the Safeway Foundation.

Severe acute malnutrition (SAM) affects approx. 19 million children below five years of age in low and middle income countries. Both shortage and low quality of foods are important determinants of SAM. With the development of special ready-to-use foods and by using simple treatment protocols, it is now possible to treat SAM successfully outside hospitals and treatment centers thereby reducing the case-fatality rate. However, only a small percentage of children with SAM have access to correct treatment.