AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment

Repeat multiparametric MRI in prostate cancer patients on active surveillance

Introduction This study was conducted to describe the changes in repeat multiparametric MRI (mpMRI) occurring in prostate cancer (PCa) patients during active surveillance (AS), and to study possible associations between mpMRI-related parameters in predicting prostate biopsy (Bx) Gleason score (GS) upgrading > 3+3 and protocol-based treatment change (TC). Materials and methods The study cohort consisted of 76 AS patients with GS 3+3 PCa and at least two consecutive mpMRIs of the prostate performed between 2006-2015. Patients were followed according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol and an additional mpMRI. The primary end points were GS upgrading (GU) (> 3+3) in protocol-based Bxs and protocol-based TC. Results Out of 76 patients, 53 (69%) had progression (PIRADS upgrade, size increase or new lesion [s]), while 18 (24%) had radiologically stable disease, and 5 (7%) had regression (PIRADS or size decrease, disappearance of lesion[s]) in repeat mpMRIs during AS. PIRADS scores of 4-5 in the initial mpMRI were associated with GU (p = 0.008) and protocol-based TC (p = 0.009). Tumour progression on repeat mpMRIs was associated with TC (p = 0.045) but not with GU (p = 1.00). PIRADS scores of 4-5 predict GU (sensitivity 0.80 [95% confidence interval (CI); 0.51-0.95, specificity 0.62 [95% CI; 0.52-0.77]) with PPV and NPV values of 0.34 (95% CI; 0.21-0.55) and 0.93 (95% CI; 0.80-0.98), respectively. Conclusion mpMRI is a useful tool not only to select but also to monitor PCa patients on AS.Peer reviewe

New prostate cancer grade grouping system predicts survival after radical prostatectomy

Histological Gleason grading of prostate cancer has been through modifications and conjoined into a Grade Grouping system recently. The aim of this study was to determine whether the new Grade Grouping system predicts disease-specific and all-cause mortality after radical prostatectomy. We constructed a clinical database consisting of all consecutively radical prostatectomy treated men between 1983 and 1998 and between 2000 and 2005 at the Helsinki University Hospital and at the Turku University Hospital, respectively. Patients' all-cause and prostate cancer specific mortality information was updated in November 2015 from the Finnish Cancer Registry. Secondary therapy information was also available from the patients' records at Helsinki. Univariate and multivariate statistical analyses were performed to assess predictive significance of the Grade Grouping system. Grade Grouping associated independently with increased risk of prostate cancer specific mortality within 15 years of follow-up in a multivariable model containing age at operation, diagnostic prostate-specific antigen, pathological stage and lymph node status at operation. Additionally, the all-cause mortality-free survival time and time to secondary therapies were different between the Grade Groups, emphasized in the subanalysis of Grade Groups 1-2 versus Grade Groups 3-5. We can conclude that the new Grade Grouping system is feasible in predicting prostate cancer specific survival after radical surgical treatment. Grade Grouping offers a simpler way to interpret the predicted course of the disease to individual patients and thus may help in justifying more conservative follow-up approaches, especially in the lower Grade Group patients. (C) 2018 The Authors. Published by Elsevier Inc.Peer reviewe

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Background To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR). Methods All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used. Results Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR. Conclusions Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.Peer reviewe

Associations of PTEN and ERG with Magnetic Resonance Imaging Visibility and Assessment of Non–organ-confined Pathology and Biochemical Recurrence After Radical Prostatectomy

Background: Diagnosing clinically significant prostate cancer (PCa) is challenging, but may be facilitated by biomarkers and multiparametric magnetic resonance imaging (MRI). Objective: To determine the association between biomarkers phosphatase and tensin homolog (PTEN) and ETS-related gene (ERG) with visible and invisible PCa lesions in MRI, and to predict biochemical recurrence (BCR) and non-organ-confined (non-OC) PCa by integrating clinical, MRI, and biomarker-related data. Design, setting, and participants: A retrospective analysis of a population-based cohort of men with PCa, who underwent preoperative MRI followed by radical prostatectomy (RP) during 2014-2015 in Helsinki University Hospital (n = 346), was conducted. A tissue microarray corresponding to the MRI-visible and MRI-invisible lesions in RP specimens was constructed and stained for PTEN and ERG. Outcome measurements and statistical analysis: Associations of PTEN and ERG with MRI-visible and MRI-invisible lesions were examined (Pearson's chi 2 test), and predictions of non-OC disease together with clinical and MRI parameters were determined (area under the receiver operating characteristic curve and logistic regression analyses). BCR prediction was analyzed by Kaplan-Meier and Cox proportional hazard analyses. Results and limitations: Patients with MRI-invisible lesions (n = 35) had less PTEN loss and ERG-positive expression compared with patients (n = 90) with MRI-visible lesions (17.2% vs 43.3% [p = 0.006]; 8.6% vs 20.0% [p = 0.125]). Patients with invisible lesions had better, but not statistically significantly improved, BCR-free survival probability in Kaplan-Meier analyses (p = 0.055). Rates of BCR (5.7% vs 21.1%; p = 0.039), extraprostatic extension (11.4% vs 44.6%; p < 0.001), seminal vesicle invasion (0% vs 21.1%; p = 0.003), and lymph node metastasis (0% vs 12.2%; p = 0.033) differed between the groups in favor of patients with MRI-invisible lesions. Biomarkers had no independent role in predicting non-OC disease or BCR. The short follow-up period was a limitation. Conclusions: PTEN loss, BCR, and non-OC RP findings were more often encountered with MRI-visible lesions. Patient summary: Magnetic resonance imaging (MRI) of the prostate misses some cancer lesions. MRI-invisible lesions seem to be less aggressive than MRI-visible lesions. (C) 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.Peer reviewe

New prostate cancer grade grouping system predicts survival after radical prostatectomy

Histological Gleason grading of prostate cancer has been through modifications and conjoined into a Grade Grouping system recently. The aim of this study was to determine whether the new Grade Grouping system predicts disease-specific and all-cause mortality after radical prostatectomy. We constructed a clinical database consisting of all consecutively radical prostatectomy–treated men between 1983 and 1998 and between 2000 and 2005 at the Helsinki University Hospital and at the Turku University Hospital, respectively. Patients' all-cause and prostate cancer–specific mortality information was updated in November 2015 from the Finnish Cancer Registry. Secondary therapy information was also available from the patients' records at Helsinki. Univariate and multivariate statistical analyses were performed to assess predictive significance of the Grade Grouping system. Grade Grouping associated independently with increased risk of prostate cancer–specific mortality within 15 years of follow-up in a multivariable model containing age at operation, diagnostic prostate-specific antigen, pathological stage and lymph node status at operation. Additionally, the all-cause mortality-free survival time and time to secondary therapies were different between the Grade Groups, emphasized in the subanalysis of Grade Groups 1-2 versus Grade Groups 3-5. We can conclude that the new Grade Grouping system is feasible in predicting prostate cancer–specific survival after radical surgical treatment. Grade Grouping offers a simpler way to interpret the predicted course of the disease to individual patients and thus may help in justifying more conservative follow-up approaches, especially in the lower Grade Group patients.</p

Eturauhassyövän diagnostiikan rakennuspalikat

The most prevalent cancer in men is prostate cancer (PCa). Diagnostic methods currently detect most PCa at an early stage, but the diagnosis of many PCa cases that are clinically insignificant raises concerns about the need for aggressive intervention in all cases. The increased use of multiparametric magnetic resonance imaging (mpMRI) with structured reporting based on Prostate Imaging Reporting and Data System (PI-RADS) and targeted immunohistochemical stains have improved the identification of clinically significant PCa. Studies I-III compared mpMRI visibility of lesions with histopathological findings in radical prostatectomy (RP) specimens. Study I focused on enhancing preoperative risk assessment tools, such as clinical parameters, Cancer of the Prostate Risk Assessment (CAPRA) score, Memorial Sloan Kettering Cancer Center (MSKCC) nomograms, and Partin tables, with the addition of mpMRI visibility. Study II examined the risk of biochemical recurrence (BCR) and evaluated the significance of Phosphatase and Tensin Homolog (PTEN) and ETS-related gene (ERG) biomarker expression. Study III examined the tumor microenvironment (TME) of these lesions. In Study IV, we developed an artificial intelligence (AI) algorithm to diagnose and grade prostate cancer in prostate biopsies and reported its performance as a comparison with pathologists' diagnoses and with BCR. The main results for study I, were that models with mpMRI improved the prediction of postoperative adverse findings such as non-organ confined disease and survival analysis for BCR. Study II confirmed better survival time for BCR and less non-organ-confined disease for mpMRI-invisible lesions. Lesions visible on mpMRI were found to have a more frequent loss of PTEN expression. Study III found lesions with high stromal FAP (Fibroblast Activated Protein) expression in mpMRI had a higher risk of BCR. The presence of a large number of stromal FAP-positive cells was linked to a change in PTEN status in these visible lesions. In study IV, an AI algorithm detected and graded prostate cancer on a par with those of pathologists and predicted adverse staging and the risk for BCR. The main conclusion is that mpMRI visible lesions are associated with signs of higher biological aggressiveness. Aggressiveness is expressed in clinical and biomarker adverse findings such as BCR, non-organ confined disease, altered PTEN, and stromal FAP status. AI algorithms can support clinicians in detecting and grading prostate cancer for biopsies with an added risk stratification value for BCR and higher staging.Den vanligaste cancern hos män är prostatacancer (PCa). Diagnostiska metoder upptäcker för närvarande de flesta PCa i ett tidigt skede, men diagnosen av många kliniskt obetydliga PCa-fall väcker frågor kring behovet av aggressiva ingrepp för alla fall. Den ökade användningen av multiparametrisk magnetisk resonanstomografi (mpMRI) med strukturerad rapportering baserad på Prostate Imaging Reporting and Data System (PI-RADS) och riktade immunhistokemiska färgningar har förbättrat upptäckten av kliniskt signifikant PCa. Studier I-III jämförde mpMRI-synlighet av lesioner med histopatologiska fynd i radikala prostatektomi (RP) fall. Studie I fokuserade på att förbättra preoperativa riskbedömningsverktyg, såsom kliniska parametrar, Cancer of the Prostate Risk Assessment (CAPRA) -score, Memorial Sloan Kettering Cancer Center (MSKCC) -nomogram och Partin-tabeller, genom komplementering med mpMRI-synlighet. Studie II undersökte risken för biokemiskt återfall (BCR) och utvärderade betydelsen av PTEN och ERG biomarköruttryck. Studie III undersökte den mikroskopiska tumörmiljön (TME) för dessa lesioner. I studie IV utvecklade vi en artificiell intelligens (AI) -algoritm för att diagnostisera och gradera prostatacancer i prostatabiopsier och rapporterade dess prestanda i jämförelse med patologernas diagnos och BCR. Som huvudresultat för studie I förbättrade modeller med mpMRI förutsägelsen av postoperativa negativa fynd, såsom lokalt avancerad sjukdom och överlevnadsanalys för BCR. Studie II bekräftade bättre överlevnadstid för BCR och mindre lokalt avancerad sjukdom för mpMRI-osynliga lesioner. Lesioner synliga på mpMRI visade sig ha en mer frekvent förlust av PTEN-uttryck. Enligt studie III hade lesioner med högt stromalt FAP (Fibroblast Activated Protein) -uttryck i mpMRI en högre risk för BCR. Förekomsten av ett stort antal stromala FAP-positiva celler kopplades till en förändring i PTEN-status i dessa synliga lesioner. I studie IV upptäckte och graderade en AI-algoritm prostatacancer i nivå med patologer och förutsåg ogynnsam stadieindelning och risk för BCR. Huvudslutsatsen är att mpMRI-synliga lesioner är förknippade med tecken på högre biologisk aggressivitet. Aggressiviteten uttrycks i kliniska och biomarkörsfynd som BCR, lokalt avancerad sjukdom, förändrad PTEN och stromal FAP-status. AI-algoritmer kan stödja patologerna I upptäckt och gradering av PCa på mellannålsbiopsier med en utökad riskstratifieringsvärde för BCR och högre stadieindelning

Eturauhassyövän diagnostiikka murroksessa

Teema : Akuuttilääketiede. English summaryPeer reviewe

Co-occurrence of CLCN2-related leukoencephalopathy and SPG56

Family Report: Two rare autosomal recessive neurological disorders, leukoencephalopathy with ataxia and spastic paraplegia 56 (SPG56), were found in members of the same family. Two siblings presented with spastic paraplegia, cognitive impairment, bladder and bowel dysfunction and gait ataxia; their consanguineous parents were unaffected. Ophthalmological examination revealed chorioretinopathy. Brain MRI showed T2 hyperintensities and T1 hypointensities in the internal capsules, cerebral peduncles, pyramidal tracts and middle cerebellar peduncles. Both affected siblings were homozygous for CYP2U1 c.947A > T p.(Asp316Val), a known cause for SPG56. However, they were also homozygous for the novel variant CLCN2 c.607G > T, p.(Gly203Cys), classified as a variant of unknown significance. Testing of additional family members revealed homozygosity for both variants in an additional brother, whom we initially considered unaffected. Both male CLCN2 carriers were infertile, and review of the literature revealed one reported case with azoospermia, however the brother had no overt signs of SPG56. His testicular biopsy revealed incomplete maturation arrest in spermatogenesis; clinically we found mild memory impairment and hand tremor and MRI showed similar changes as his siblings. We consider CLCN2 c.607G > T pathogenic because of the neuroradiological and clinical findings, including azoospermia. Conclusion: Considerable workup may be required to determine the pathogenicity of novel variants, and to unambiguously associate phenotype with genotype. In very rare disorders, highly specific clinical or biomarker combinations provide sufficient evidence for a variant’s pathogenicity. Phenotypic variation of monogenic disorders described in the literature may be attributed to a second co-occurring monogenic disorder, especially in consanguineous families. SPG56 may have reduced penetrance