Dataset Information

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence.

ABSTRACT:

Purpose

Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.

Materials and methods

MSTA was evaluated in needle-biopsy samples from 115 patients with favorable-risk PCa, as defined by GG1 and GG2, a prostate-specific antigen (PSA) level of <10 ng/mL, a clinical stage of cT1c to cT2b, and general Gleason GG (GGG) and expert pathologist-assessed GG (EGG). Algorithms based on Voronoi diagrams were applied to all Feulgen-thionin-stained diagnostic areas. One hundred tissue architecture features were calculated and an MSTA score, a linear combination of the most discriminant features, was generated. Correlation of MSTA score with BCR and other clinical variables was investigated.

Results

In a univariate regression model, EGG, clinical stage, and MSTA were significant predictors of BCR (respective p-values: 0.0016, 0.016, and 0.028). Survival analysis showed that patients with a high MSTA score were more likely to experience BCR than were patients with a low MSTA score (odds ratio, 2.9). Combining MSTA with GG assessment resulted in a significant stratification of risk for BCR.

Conclusions

MSTA score could be used as an objective adjunct risk stratification tool to pathologist assessments and could improve the management of patients with favorable-risk PCa.

SUBMITTER: Pukl M

PROVIDER: S-EPMC7458870 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence.

Pukl Miha M Keyes Sarah S Keyes Mira M Guillaud Martial M Volavšek Metka M

Investigative and clinical urology 20200728 5

<h4>Purpose</h4>Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.<h4>Materials and methods</h4>MSTA ...[more]

PMID: 32734723

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Project description:BackgroundBiochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.MethodsA comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.ResultsA total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratiﬁed analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.ConclusionsThe proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.

Project description:Simple Summary In prostate cancer (PCa), an accurate patient risk stratification, as well as the awareness of a possible biochemical recurrence (BCR) event, are crucial to individualize treatment decisions. Magnetic resonance imaging (MRI) is commonly used in the diagnosis, risk stratification, localization, and staging of PCa. Likewise, radiomics, which allows the extraction of quantitative parameters from medical images, has attracted increased attention in recent years. A combination of both strategies may be useful for predicting important clinical outcomes in these patients. In patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy, we explore the existence of putative prostate region-wise imaging biomarker (radiomic, diffusion, and/or perfusion features) profiles extracted from MRIs in order to discriminate patients according to their risk or the appearance of BCR 10 years after diagnosis, as well as to determine their predictive value alone or in combination with clinical variables. Abstract Background: Identifying prostate cancer (PCa) patients with a worse prognosis and a higher risk of biochemical recurrence (BCR) is essential to guide treatment choices. Here, we aimed to identify possible imaging biomarker (perfusion/diffusion + radiomic features) profiles extracted from MRIs that were able to discriminate patients according to their risk or the occurrence of BCR 10 years after diagnosis, as well as to evaluate their predictive value with or without clinical data. Methods: Patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy were retrospectively evaluated. Imaging features were extracted from MRIs for each prostate region or for the whole gland. Univariate and multivariate analyses were conducted. Results: 128 patients (mean [range] age, 71 [50–83] years) were included. Prostate region-wise imaging biomarker profiles mainly composed of radiomic features allowed discriminating risk groups and patients experiencing BCR. Heterogeneity-related radiomic features were increased in patients with worse prognosis and with BCR. Overall, imaging biomarkers profiles retained good predictive ability (AUC values superior to 0.725 in most cases), which generally improved when clinical data were included (particularly evident for the prediction of the BCR, with AUC values ranging from 0.841 to 0.877 for combined models and sensitivity values above 0.960) and when models were built per prostate region vs. the whole gland. Conclusions: Prostate region-aware imaging profiles enable identification of patients with worse prognosis and with a higher risk of BCR, retaining higher predictive values when combined with clinical variables.

Project description:BackgroundGleason scoring represents the standard for diagnosis of prostate cancer (PCa) and assessment of prognosis following radical prostatectomy (RP), but it does not account for patterns in neighboring normal-appearing benign fields that may be predictive of disease recurrence.ObjectiveTo investigate (1) whether computer-extracted image features within tumor-adjacent benign regions on digital pathology images could predict recurrence in PCa patients after surgery and (2) whether a tumor plus adjacent benign signature (TABS) could better predict recurrence compared with Gleason score or features from benign or cancerous regions alone.Design, setting, and participantsWe studied 140 tissue microarray cores (0.6mm each) from 70 PCa patients following surgery between 2000 and 2004 with up to 14 yr of follow-up. Overall, 22 patients experienced recurrence (biochemical [prostate-specific antigen], local, or distant recurrence and cancer death) and 48 did not.InterventionRP was performed in all patients.Outcome measurements and statistical analysisThe top 10 features identified as most predictive of recurrence within both the benign and cancerous regions were combined into a 10-feature signature (TABS). Computer-extracted nuclear shape and architectural features from cancerous regions, adjacent benign fields, and TABS were evaluated via random forest classification accuracy and Kaplan-Meier survival analysis.Results and limitationsTumor-adjacent benign field features were predictive of recurrence (area under the receiver operating characteristic curve [AUC]: 0.72). Tumor-field nuclear shape descriptors and benign-field local nuclear arrangement were the predominant features found for TABS (AUC: 0.77). Combining TABS with Gleason sum further improved identification of recurrence (AUC: 0.81). All experiments were performed using threefold cross-validation without independent test set validation.ConclusionsComputer-extracted nuclear features within cancerous and benign regions predict recurrence following RP. Furthermore, TABS was shown to provide added value to common predictors including Gleason sum and Kattan and Stephenson nomograms.Patient summaryFuture studies may benefit from evaluation of benign regions proximal to the tumor on surgically excised prostate cancer tissue for assessing risk of disease recurrence.

Project description:Background: D'Amico high-risk prostate cancer (Pca) patients experience poor and heterogeneous oncological outcomes. This heterogeneity highlights a need to extensively explore factors associated with poor outcomes to guide decision-making. Objective: To assess predictors of biochemical recurrence (BCR)-free survival in high-risk patients following radical prostatectomy (RP), and subsequently establish a model predicting outcomes. Methods: We retrospectively identified D'Amico high-risk non-metastatic Pca patients who underwent RP between 2013 and 2019 in our hospital. We collected data including PSA level, clinical stage, biopsy Gleason score (GS), number of D'Amico high-risk factors (RF), the inflammatory status (Neutrophil-to-lymphocyte ratio [NLR], derived NLR [dNLR], platelet-to-lymphocyte ratio [PLR] and LDH). Kaplan–Meier methods were used to analyze BCR-free survival. Univariate and multivariate analyses were performed using Cox proportional hazards model to evaluate the association between clinicopathological parameters and BCR-free survival. Results: The median follow-up time for the 101 patients' cohort was 26 months (range: 3–81 months). The number of RF (1RF vs. ?2RF), biopsy GS (<8 vs. ?8), clinical stage (?cT2c vs. >cT2c), pathological stage, and the presence of adverse pathological features were significant predictors of BCR (P < 0.05). Other parameters including inflammatory status (dNLR, NLR, PLR, and LDH) were not of predictive value. On multivariable analysis, biopsy GS (<8 vs. ?8; HR 2.439) and clinical stage (?cT2c vs. >cT2c; HR 3.271) were the independent predictors of BCR. Based on these two independent predictors, patients were stratified into three risk subgroups: favorable (0 risk factor; 47% of patients), intermediate (1 risk factor; 42 %), unfavorable (2 risk factors; 11%). The intermediate and unfavorable subgroups have a significantly shorter median BCR-free survival compared to the favorable subgroup (P < 0.001). Conclusion: Several factors are associated with BCR. Clinical stage (?cT2c vs. >cT2c) and biopsy GS (<8 vs. ?8) are the independent predictors of BCR. The stratification of high-risk patients into risk subgroups based on these two predictors shows that the intermediate and unfavorable subgroups have a significantly shorter median BCR-free survival compared to the favorable subgroup. The preoperative stratification model may help urologists and patients during decision-making. In non-metastatic high-risk patients, preoperative inflammatory markers (NLR, dNLR, PLR, and LDH) are not of prognostic value.

Dataset Information

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence.

Purpose

Materials and methods

Results

Conclusions

Publications

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence.

Similar Datasets

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