Purpose Multidisciplinary management improves complex treatment decision making in cancer care, but its impact for bladder cancer (BC) has not been documented. Although radical cystectomy (RC) ...currently is viewed as the standard of care for muscle-invasive bladder cancer (MIBC), radiotherapy-based, bladder-sparing trimodal therapy (TMT) that combines transurethral resection of bladder tumor, chemotherapy for radiation sensitization, and external beam radiotherapy has emerged as a valid treatment option. In the absence of randomized studies, this study compared the oncologic outcomes between patients treated with RC or TMT by using a propensity score matched-cohort analysis. Methods Data from patients treated in a multidisciplinary bladder cancer clinic (MDBCC) from 2008 to 2013 were reviewed retrospectively. Those who received TMT for MIBC were identified and matched (for sex, cT and cN stage, Eastern Cooperative Oncology Group status, Charlson comorbidity score, treatment date, age, carcinoma in situ status, and hydronephrosis) with propensity scores to patients who underwent RC. Overall survival and disease-specific survival (DSS) were assessed with Cox proportional hazards modeling and a competing risk analysis, respectively. Results A total of 112 patients with MIBC were included after matching (56 who had been treated with TMT, and 56 who underwent RC). The median age was 68.0 years, and 29.5% had stage cT3/cT4 disease. At a median follow-up of 4.51 years, there were 20 deaths (35.7%) in the RC group (13 as a result of BC) and 22 deaths (39.3%) in the TMT group (13 as a result of BC). The 5-year DSS rate was 73.2% and 76.6% in the RC and TMT groups, respectively ( P = .49). Salvage cystectomy was performed in 6 (10.7%) of 56 patients who received TMT. Conclusion In the setting of a MDBCC, TMT yielded survival outcomes similar to those of matched patients who underwent RC. Appropriately selected patients with MIBC should be offered the opportunity to discuss various treatment options, including organ-sparing TMT.
Substantial geographical differences in prostate cancer (PCa) incidence and mortality exist, being lower among Asian (ASI) men compared with Caucasian (CAU) men. We prospectively compared PCa ...prevalence in CAU and ASI men from specific populations with low penetrance of prostate-specific antigen screening.
Prostate glands were prospectively obtained during autopsy from men who died from causes other than PCa in Moscow, Russia (CAU), and Tokyo, Japan (ASI). Prostates were removed en-block and analyzed in toto. We compared across the 2 populations PCa prevalence, number and Gleason score (GS) of tumour foci, pathological stage, spatial location, and tumor volume using χ(2), Mann-Whitney-Wilcoxon tests, and multiple logistic regression. All statistical tests were two-sided.
Three hundred twenty prostates were collected, 220 from CAU men and 100 from ASI mean. The mean age was 62.5 in CAU men and 68.5 years in ASI men (P < .001). PCa prevalences of 37.3% in CAU men and 35.0% in ASI men were observed (P = .70). Average tumor volume was 0.303cm(3). In men aged greater than 60 years, PCa was observed in more than 40% of prostates, reaching nearly 60% in men aged greater than 80 years. GS 7 or greater cancers accounted for 23.1% and 51.4% of all PCa in CAU and ASI men, respectively, (P = .003). When adjusted for age and prostate weight, ASI men still had a greater probability of having GS 7 or greater PCa (P = .03).
PCa is found on autopsy in a similar proportion of Russian and Japanese men. More than 50% of cancers in ASI and nearly 25% of cancers in CAU men have a GS of 7 or greater. Our results suggest that the definition of clinically insignificant PCa might be worth re-examining.
Intravesical Bacillus Calmette-Guérin (BCG) immunotherapy is the standard of care for high-risk and intermediate-risk non-muscle-invasive bladder cancer (NMIBC) as well as for Carcinoma in situ ...(CIS). Evidence supports that the different BCG strains, despite genetic variability, are equally effective clinically for preventing the recurrence and progression of papillary NMIBC. The available evidence regarding possible differences in clinical efficacy between various BCG strains in CIS is lacking.
We reviewed the literature on the efficacy of different BCG strains in patients with CIS (whether primary, secondary, concomitant, or unifocal/multifocal), including randomized clinical trials (RCTs), phase II/prospective trials, and retrospective studies with complete response rates (CRR), recurrence-free survival (RFS), or progression-free survival (PFS) as endpoints.
In most studies, being RCTs, phase II prospective trials, or retrospective studies, genetic differences between BCG strains did not translate into meaningful differences in clinical efficacy against CIS, regardless of the CIS subset (primary, secondary, or concurrent) or CIS focality (unifocal or multifocal). CRR, RFS, and PFS were not statistically different between various BCG strains. None of these trials were designed as head-to-head comparisons between BCG strains focusing specifically on CIS. Limitations include the small sample size of many studies and most comparisons between strains being indirect rather than head-to-head.
This review suggests that the clinical efficacy of the various BCG strains appears similar, irrespective of CIS characteristics. However, based on the weak level of evidence available and underpowered studies, randomized studies in this space should be encouraged as no definitive conclusion can be drawn at this stage.
Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There
is a need to discover and validate novel ovarian cancer biomarkers that ...are suitable for early diagnosis, monitoring, and
prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion
chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass â¥30 kDa. After trypsin
digestion, the subproteome (â¤30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass
spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from
different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites
fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as
ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates,
we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel
ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate
biomarkers that warrant further validation.
Ovarian cancer remains a deadly threat to women as the disease is often diagnosed in the late stages when the chance of survival is low. There are no good biomarkers available for early detection and ...only a few markers have shown clinical utility for prognosis, response to therapy and disease recurrence. We mined conditioned media of four ovarian cancer cell lines (HTB75, TOV-112D, TOV-21G and RMUG-S) by two-dimensional liquid chromatography−mass spectrometry. Each cell line represented one of the major histological types of epithelial ovarian cancer. We identified 2039 proteins from which 228 were extracellular and 192 were plasma membrane proteins. Within the latter list, we identified several known markers of ovarian cancer including three that are well established, namely, CA-125, HE4, and KLK6. The list of 420 extracellular and membrane proteins was cross-referenced with the proteome of ascites fluid to generate a shorter list of 51 potential biomarker candidates. According to Ingenuity Pathway Analysis, two of the top 10 diseases associated with the list of 51 proteins were cancer and reproductive diseases. We selected nine proteins for preliminary validation using 20 serum samples from healthy women and 10 from women with ovarian cancer. Of the nine proteins, clusterin (increase) and IGFBP6 (decrease) showed significant differences between women with or without ovarian cancer. We conclude that in-depth proteomic analysis of cell culture supernatants of ovarian cancer cell lines can identify potential ovarian cancer biomarkers that are worth further clinical validation.
: Human tissue kallikreins are a family of 15 trypsin‐ or chymotrypsin‐like secreted serine proteases (KLK1–KLK15). Many KLKs have been identified in normal stratum corneum (SC) and sweat, and are ...candidate desquamation‐related proteases.
We report quantification by enzyme‐linked immunosorbent assay (ELISA) of KLK5, KLK6, KLK7, KLK8, KLK10, KLK11, KLK13 and KLK14 in the SC and serum of atopic dermatitis (AD) patients by ELISA, and examine their variation with clinical phenotype, correlation with blood levels of eosinophils, lactate dehydrogenase (LDH) and immunoglobulin E. The overall SC serine protease activities were also measured.
In the SC of AD, all KLKs, except KLK11, were significantly elevated. The elevation of chymotrypsin‐like KLK7 was predominant, compared with trypsin‐like KLKs. The SC overall plasmin‐ and furin‐like activities were significantly elevated, while trypsin‐ and chymotrypsin‐like activities did not differ significantly. In the serum of AD patients, KLK8 was significantly elevated and KLK5 and KLK11 were significantly decreased. However, their serum levels were not modified by corticosteroid topical agents. The alterations of KLK levels in the SC of AD were more pronounced than those in the serum. KLK7 in the serum was significantly correlated with eosinophil counts in the blood of AD patients, while KLK5, KLK8 and KLK11 were significantly correlated with LDH in the serum.
In conclusion, we report abnormal kallikrein levels in the SC and the serum of AD patients. KLKs might be involved in skin manifestation and/or focal/systemic inflammatory reactions in AD. Our data may contribute to a better understanding of the pathogenesis of AD.
Angiosarcoma (AS) is a rare neoplasm of endothelial origin that has limited treatment options and poor five-year survival. Using tumorgraft models, we previously showed that AS is sensitive to ...small-molecule inhibitors that target mitogen-activated/extracellular-signal-regulated protein kinase kinases 1 and 2 (MEK). The objective of this study was to identify drugs that combine with MEK inhibitors to more effectively inhibit AS growth. We examined the in vitro synergy between the MEK inhibitor PD0325901 and inhibitors of eleven common cancer pathways in melanoma cell lines and canine angiosarcoma cell isolates. Combination indices were calculated using the Chou-Talalay method. Optimized combination therapies were evaluated in vivo for toxicity and efficacy using canine angiosarcoma tumorgrafts. Among the drugs we tested, rapamycin stood out because it showed strong synergy with PD0325901 at nanomolar concentrations. We observed that angiosarcomas are insensitive to mTOR inhibition. However, treatment with nanomolar levels of mTOR inhibitor renders these cells as sensitive to MEK inhibition as a melanoma cell line with mutant BRAF. Similar results were observed in B-Raf wild-type melanoma cells as well as in vivo, where treatment of canine AS tumorgrafts with MEK and mTOR inhibitors was more effective than monotherapy. Our data show that a low dose of an mTOR inhibitor can dramatically enhance angiosarcoma and melanoma response to MEK inhibition, potentially widening the field of applications for MEK-targeted therapy.
Recent advances in semi-supervised learning algorithms (SSL) have made great strides in reducing the training dependency on labeled datasets and requiring that only a subset of the data be labeled. ...The presented work explores a class of semi-supervised learning algorithms that uses consistency regularization and self-ensembling to leverage the unlabeled portion of the dataset. Labeling medical image datasets are time-consuming and prohibitively expensive, requiring hundreds of hours of effort from expert diagnosticians. This research presents an approach for building and training a deep learning model to grade medical images while requiring only a minimal number of labels. Consistency regularization has been used in SSL to great success in datasets of natural images but not for more complex images such as pathology slides where the dataset consists of cell patterns. This research successfully proposes and applies an SSL algorithm based on the VGG-16 neural network, which combines techniques introduced by the Π model and FixMatch algorithms to a cell pattern-based pathology image dataset. The results presented in this research show that using the proposed approach, it is possible to label only 3% of the samples in a dataset, use the remaining 97% of samples as unlabeled data, and achieve a 19% increase over the baseline accuracy. The second contribution of this research shows a ratio of labeled vs. unlabeled images in a dataset beyond which continuing to label the data increases the cost but offers little performance gains.
•Present a deep learning model to grade medical images with a minimal number of labels.•Consistency regularization and self-ensembling usage in the self-supervised algorithm.•A new approach on the usage of SSL Algorithm on diagnosing bladder cancer.•The presented approach saves a lot on cost and time for grading pathology slides.•Using 3% labeled and 97% unlabeled data, achieve 19% accuracy increase over baseline.
Bladder cancer tissue grading, which assigns a numerical grade reflecting how aggressive a tumor looks under a microscope, is essential to determine the proper course of treatment, design a ...therapeutic plan and determine prognosis. The major problem is that there are considerable and clinically relevant variations in grading by pathologists – as they are humans with different opinions and experience – including in bladder cancer. This work presents a solution, i.e., Artificial Intelligence for Bladder Cancer grading (ABC) system, that is developed based on deep neural network architectures to provide a more reliable and accurate diagnosis for patients affected by this deadly disease and ultimately improve management and clinical outcomes. Whole Slide Images (WSI) are split up into equally-sized square tiles and annotated to build a training dataset. ABC introduces a new grading system concept that can provide a percentage distribution of each different grade in a specific tumor, unlike the current numerical grade value between 1 and 3 based on the general impression of the pathologist. This new approach aims to provide a more granular grading of bladder cancer tissues and better capture tumor grade heterogeneity. This new concept may offer a more precise prognosis and optimize management in the future. The ABC learning model is fully configurable, and any deep architecture model can be trained and used by ABC. Some trained models developed by ABC have shown high accuracy and consistency in grading and intra-observer variability. The combination of a loosely coupled architecture and fully integrated tiles’ utilization makes ABC a universal, scalable, and versatile system that could be configured and deployed worldwide.
•Present a deep learning supervised model to grade medical images.•Approach which aims to provide a more granular grading of bladder cancer tissues.•A new system that can provide valuable feedback and recommendations to pathologists.•Fully configurable model that accepts modular deep architecture models to be trained and used.
Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence ...in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA).
Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram non-MRI and MRI), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors.
Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 95% CI 0·75–0·78 and pooled AUPRC of 0·61 0·58–0·63). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 44% in the logistic regression model, none in the Sayyid model, 13 12% in the Soeterik non-MRI model, and five 5% in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives.
We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy.
None.
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