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Objective

To determine the risk of disease progression and conversion to active treatment following a negative biopsy while on active surveillance (AS) for prostate cancer (PCa).

Patients and Methods

Men on an AS programme at a single tertiary hospital (London, UK) between 2003 and 2018 with confirmed low–intermediate-risk PCa, Gleason Grade Group <3, clinical stage <T3 and a diagnostic prostate-specific antigen (PSA) level of <20 ng/mL. This cohort included men diagnosed by transrectal ultrasonography guided (12–14 cores) or transperineal (median 32 cores) biopsy. Multivariate Cox hazards regression analysis was undertaken to determine (i) risk of upgrading, (ii) clinical or radiological suspicion of disease progression, and (iii) transitioning to active treatment. Suspicion of disease progression was defined as any biopsy upgrading, >30% positive cores, magnetic resonance imaging (MRI) Likert score >3/T3 or PSA level of >20 ng/mL. Conversion to treatment included radical or hormonal treatment.

Results

Among the 460 eligible patients, 23% had negative follow-up biopsy findings. The median follow-up was 62 months, with one to two repeat biopsies and two MRIs per patient during that period. Negative biopsy findings at first repeat biopsy were associated with decreased risk of converting to active treatment (hazard ration [HR] 0.18, 95% confidence interval [CI] 0.09–0.37; P < 0.001), suspicion of disease progression (HR 0.56, 95% CI: 0.34–0.94; P = 0.029), and upgrading (HR 0.48, 95% CI 0.23–0.99; P = 0.047). Data are limited by fewer men with multiple follow-up biopsies.

Conclusion

A negative biopsy finding at the first scheduled follow-up biopsy among men on AS for PCa was strongly associated with decreased risk of subsequent upgrading, clinical or radiological suspicion of disease progression, and conversion to active treatment. A less intense surveillance protocol should be considered for this cohort of patients.

Objective

To determine the risk of disease progression and conversion to active treatment following a negative biopsy while on active surveillance (AS) for prostate cancer (PCa).

Patients and Methods

Men on an AS programme at a single tertiary hospital (London, UK) between 2003 and 2018 with confirmed low–intermediate-risk PCa, Gleason Grade Group <3, clinical stage <T3 and a diagnostic prostate-specific antigen (PSA) level of <20 ng/mL. This cohort included men diagnosed by transrectal ultrasonography guided (12–14 cores) or transperineal (median 32 cores) biopsy. Multivariate Cox hazards regression analysis was undertaken to determine (i) risk of upgrading, (ii) clinical or radiological suspicion of disease progression, and (iii) transitioning to active treatment. Suspicion of disease progression was defined as any biopsy upgrading, >30% positive cores, magnetic resonance imaging (MRI) Likert score >3/T3 or PSA level of >20 ng/mL. Conversion to treatment included radical or hormonal treatment.

Results

Among the 460 eligible patients, 23% had negative follow-up biopsy findings. The median follow-up was 62 months, with one to two repeat biopsies and two MRIs per patient during that period. Negative biopsy findings at first repeat biopsy were associated with decreased risk of converting to active treatment (hazard ration [HR] 0.18, 95% confidence interval [CI] 0.09–0.37; P < 0.001), suspicion of disease progression (HR 0.56, 95% CI: 0.34–0.94; P = 0.029), and upgrading (HR 0.48, 95% CI 0.23–0.99; P = 0.047). Data are limited by fewer men with multiple follow-up biopsies.

Conclusion

A negative biopsy finding at the first scheduled follow-up biopsy among men on AS for PCa was strongly associated with decreased risk of subsequent upgrading, clinical or radiological suspicion of disease progression, and conversion to active treatment. A less intense surveillance protocol should be considered for this cohort of patients.

Abstract

Objectives

To develop a classification system for urine cytology with artificial intelligence (AI) using a convolutional neural network algorithm that classifies urine cell images as negative (benign) or positive (atypical or malignant).

Patients and Methods

We collected 195 urine cytology slides from consecutive patients with a histologically-confirmed diagnosis of urothelial cancer (between January 2016 and December 2017). Two certified cytotechnologists independently evaluated and labeled each slide; 4637 cell images with concordant diagnoses were selected, including 3128 benign cells (negative), 398 atypical cells, and 1111 cells that were malignant or suspicious for malignancy (positive). This pathologically-confirmed labeled dataset was used to represent the ground truth for AI training/validation/testing. Customized CutMix (CircleCut) and Refined Data Augmentation were used for image processing. The model architecture included EfficientNet B6 and Arcface. We used 80% of the data for training and validation (4:1 ratio) and 20% for testing. Model performance was evaluated with five-fold cross-validation. A receiver operating characteristic (ROC) analysis was used to evaluate the binary classification model. Bayesian posterior probabilities for the AI performance measure (Y) and cytotechnologist performance measure (X) were compared.

Results

The area under the ROC curve was 0.99 (95% confidence interval [CI], 0.98–0.99), the highest accuracy was 95% (95%CI, 94–97%), sensitivity was 97% (95%CI, 95–99%), and specificity was 95% (95%CI, 93–97%). The accuracy of AI surpassed the highest level of cytotechnologists for the binary classification [Pr(Y>X) = 0.95]. AI achieved >90% accuracy for all cell subtypes. In the subgroup analysis based on the clinicopathological characteristics of patients who provided the test cells, the accuracy of AI ranged between 89 and 97%.

Conclusion

Our novel AI classification system for urine cytology successfully classified all cell subtypes with an accuracy of higher than 90%, and achieved superior diagnostic accuracy of malignancy to the highest level of cytotechnologists.

Objectives

To identify predictors of early oncological outcomes in patients who opt for robot-assisted laparoscopic radical prostatectomy (RARP) for localized prostate cancer (PCa), including conventional prognostic variables as well as multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography (PET).

Patients and Methods

This observational study included 493 patients who underwent RARP and extended pelvic lymph node dissection (ePLND) for unfavourable intermediate- or high-risk PCa. Outcome measurement was biochemical progression of disease, defined as any postoperative prostate-specific antigen (PSA) value ≥0.2 ng/mL, or the start of additional treatment. Cox regression analysis was performed to assess predictors for biochemical progression, including initial PSA value, biopsy Grade Group (GG), T-stage on mpMRI, and lymph node status on PSMA PET imaging (miN0 vs miN1).

Results

The median (interquartile range) total follow-up of all included patients without biochemical progression was 12.6 (7.5–22.7) months. When assessing biochemical progression after surgery, initial PSA value (per doubling; odds ratio [OR] 1.22, 95% confidence interval [CI] 1.07–1.40; P = 0.004), biopsy GG ≥4 vs GG 1–2 (OR 1.83, 95% CI 1.18–2.85; P = 0.007), T-stage on mpMRI (rT3a vs rT2: OR 2.13, 95% CI 1.39–3.27; P = 0.001; ≥rT3b vs rT2: OR 4.78, 95% CI 3.20–7.16; P < 0.001) and miN1 on PSMA PET imaging (OR 2.94, 95% CI 2.02–4.27; P < 0.001) were independent predictors of early biochemical progression of disease.

Conclusion

Initial PSA value, biopsy GG ≥4, ≥rT3 disease on mpMRI and miN1 disease on PSMA PET were predictors of early biochemical progression after RARP. Identifying these patients with an increased risk of early biochemical progression after surgery may have major implications for patient counselling in radical treatment decisions and on patient selection for modern (neo-)adjuvant and systematic treatments.

ABSTRACT

Objectives

To assess the impact of ECD (expanded criteria donors) on urinary complications in kidney transplantation.

Materials and methods

UNyCORT is a cohort study based on the French prospective DIVAT cohort (Données Informatisées et VAlidées en Transplantation /Computerized and VAlidated Data in Transplantation). Data were extracted between January 1, 2002 and January 1, 2018 with one-year minimum follow-up, in relation to 44 pre-and post-operative variables. ECD status was included according to United Network for Organizational Sharing definition (UNOS). The primary outcome of the UNyCORT study was the association between the donor’s ECD/SCD (standard criteria donors) status and urinary complications at 1 year in univariate and multivariate analysis. Sub-group analysis, stratified analysis on ECD/SCD donor’s status and transplant failure analysis were then conducted.

Results

Between January 1, 2002 and January 1, 2018, 10279 kidney transplants in adult recipients were recorded within the DIVAT network. 8,559 (83.4%) donors were deceased donors and 1,699 (16.6%) were living donors (LD). Among DCD (donation after circulatory death) donors, 224 (2.85%) were uncontrolled DCD and 93 (1.09%) were controlled DCD donors. 3617 (43.9%) deceased donors were ECD. The Overall urological complication rate was 16.26%. The donor’s ECD status was significantly associated with an increased risk of urological complications at 1 year in multivariate analysis (OR: 1.50 (1.31-1.71), p <0.001) and especially with stenosis and ureteric fistulas at 1 year. There is no association with living donors, uncontrolled Donation after Circulary Death (DCD) and controlled DCD. The placement of an endo-ureteric stent was beneficial in preventing urinary complications in all donors and particularly in ECD donors.

Conclusion

The donor’s ECD status is associated with a higher likelihood of stenosis and ureteric fistulas at 1 year. Recipients of grafts from ECD donors should probably be considered for closer urological monitoring and systematic preventive measures.