Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review

Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer

BACKGROUND

An electronic health record-based tool could improve accuracy and eliminate bias in provider estimation of the risk of death from other causes among men with nonmetastatic cancer.

OBJECTIVE

To recalibrate and validate the Veterans Aging Cohort Study Charlson Comorbidity Index (VACS-CCI) to predict non-prostate cancer mortality (non-PCM) and to compare it with a tool predicting prostate cancer mortality (PCM).

DESIGN, SETTING, AND PARTICIPANTS

An observational cohort of men with biopsy-confirmed nonmetastatic prostate cancer, enrolled from 2001 to 2018 in the national US Veterans Health Administration (VA), was divided by the year of diagnosis into the development (2001-2006 and 2008-2018) and validation (2007) sets.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Mortality (all cause, non-PCM, and PCM) was evaluated. Accuracy was assessed using calibration curves and C statistic in the development, validation, and combined sets; overall; and by age (<65 and 65+ yr), race (White and Black), Hispanic ethnicity, and treatment groups.

RESULTS AND LIMITATIONS

Among 107 370 individuals, we observed 24 977 deaths (86% non-PCM). The median age was 65 yr, 4947 were Black, and 5010 were Hispanic. Compared with CCI and age alone (C statistic 0.67, 95% confidence interval [CI] 0.67-0.68), VACS-CCI demonstrated improved validated discrimination (C statistic 0.75, 95% CI 0.74-0.75 for non-PCM). The prostate cancer mortality tool also discriminated well in validation (C statistic 0.81, 95% CI 0.78-0.83). Both were well calibrated overall and within subgroups. Owing to missing data, 18 009/125 379 (14%) were excluded, and VACS-CCI should be validated outside the VA prior to outside application.

CONCLUSIONS

VACS-CCI is ready for implementation within the VA. Electronic health record-assisted calculation is feasible, improves accuracy over age and CCI alone, and could mitigate inaccuracy and bias in provider estimation.

PATIENT SUMMARY

Veterans Aging Cohort Study Charlson Comorbidity Index is ready for application within the Veterans Health Administration. Electronic health record-assisted calculation is feasible, improves accuracy over age and Charlson Comorbidity Index alone, and might help mitigate inaccuracy and bias in provider estimation of the risk of non-prostate cancer mortality.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort

Introduction

Prostate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.

Methods

The TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study's prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.

Results

The cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.

Discussion

This study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.

An Actionable Expert-System Algorithm to Support Nurse-Led Cancer Survivorship Care: Algorithm Development Study

BACKGROUND

Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment.

OBJECTIVE

This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of care.

METHODS

An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists; n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group technique.

RESULTS

Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation.

CONCLUSIONS

The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support timelier decision-making and enhance continuity of care through the automation of more frequent automated checkpoints, while empowering patients to self-manage their symptoms more effectively than standard care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2020-045806.

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

Introduction

Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population.

Patient and methods

Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm.

Results

Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame).

Conclusion

MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09955-w.

Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer

Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients' survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy.

Virtual care for prostate cancer survivorship: protocol for an evaluation of a nurse-led algorithm-enhanced virtual clinic implemented at five cancer centres across Canada

INTRODUCTION

Prostate cancer (PCa) is the most common cancer in Canadian men. Current models of survivorship care are no longer adequate to address the chronic and complex survivorship needs of patients today. Virtual care models for cancer survivorship have recently been associated with comparable clinical outcomes and lower costs to traditional follow-up care, with patients favouring off-site and on-demand visits. Building on their viability, our research group conceived the Ned Clinic-a virtual PCa survivorship model that provides patients with access to lab results, collects patient-reported outcomes, alerts clinicians to emerging issues, and promotes patient self-care. Despite the promise of the Ned Clinic, the model remains limited by its dependence on oncology specialists, lack of an autonomous triage algorithm, and has only been implemented among PCa survivors living in Ontario.

METHODS AND ANALYSIS

Our programme of research comprises two main research objectives: (1) to evaluate the process and cost of implementing and sustaining five nurse-led virtual PCa survivorship clinics in three provinces across Canada and identify barriers and facilitators to implementation success and (2) to assess the impact of these virtual clinics on implementation and effectiveness outcomes of enrolled PCa survivors. The design phase will involve developing an autonomous triage algorithm and redesigning the Ned Clinic towards a nurse-led service model. Site-specific implementation plans will be developed to deploy a localised nurse-led virtual clinic at each centre. Effectiveness will be evaluated using a historical control study comparing the survivorship outcomes of 300 PCa survivors enrolled in the Ned Clinic with 300 PCa survivors receiving traditional follow-up care.

ETHICS AND DISSEMINATION

Appropriate site-specific ethics approval will be secured prior to each research phase. Knowledge translation efforts will include diffusion, dissemination, and application approaches to ensure that knowledge is translated to both academic and lay audiences.

Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database

BACKGROUND

Accurate prognostication is crucial in treatment decisions made for men diagnosed with non-metastatic prostate cancer. Current models rely on prespecified variables, which limits their performance. We aimed to investigate a novel machine learning approach to develop an improved prognostic model for predicting 10-year prostate cancer-specific mortality and compare its performance with existing validated models.

METHODS

We derived and tested a machine learning-based model using Survival Quilts, an algorithm that automatically selects and tunes ensembles of survival models using clinicopathological variables. Our study involved a US population-based cohort of 171 942 men diagnosed with non-metastatic prostate cancer between Jan 1, 2000, and Dec 31, 2016, from the prospectively maintained Surveillance, Epidemiology, and End Results (SEER) Program. The primary outcome was prediction of 10-year prostate cancer-specific mortality. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with nine other prognostic models in clinical use, and decision curve analysis was done.

FINDINGS

647 151 men with prostate cancer were enrolled into the SEER database, of whom 171 942 were included in this study. Discrimination improved with greater granularity, and multivariable models outperformed tier-based models. The Survival Quilts model showed good discrimination (c-index 0·829, 95% CI 0·820-0·838) for 10-year prostate cancer-specific mortality, which was similar to the top-ranked multivariable models: PREDICT Prostate (0·820, 0·811-0·829) and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram (0·787, 0·776-0·798). All three multivariable models showed good calibration with low Brier scores (Survival Quilts 0·036, 95% CI 0·035-0·037; PREDICT Prostate 0·036, 0·035-0·037; MSKCC 0·037, 0·035-0·039). Of the tier-based systems, the Cancer of the Prostate Risk Assessment model (c-index 0·782, 95% CI 0·771-0·793) and Cambridge Prognostic Groups model (0·779, 0·767-0·791) showed higher discrimination for predicting 10-year prostate cancer-specific mortality. c-indices for models from the National Comprehensive Cancer Care Network, Genitourinary Radiation Oncologists of Canada, American Urological Association, European Association of Urology, and National Institute for Health and Care Excellence ranged from 0·711 (0·701-0·721) to 0·761 (0·750-0·772). Discrimination for the Survival Quilts model was maintained when stratified by age and ethnicity. Decision curve analysis showed an incremental net benefit from the Survival Quilts model compared with the MSKCC and PREDICT Prostate models currently used in practice.

INTERPRETATION

A novel machine learning-based approach produced a prognostic model, Survival Quilts, with discrimination for 10-year prostate cancer-specific mortality similar to the top-ranked prognostic models, using only standard clinicopathological variables. Future integration of additional data will likely improve model performance and accuracy for personalised prognostics.

FUNDING

None.

Modified J-CAPRA scoring system in predicting treatment outcomes of metastatic prostate cancer patients undergoing androgen deprivation therapy

The J-CAPRA score is an assessment tool which stratifies risk and predicts outcome of primary androgen deprivation therapy (ADT) using prostate-specific antigen, Gleason score, and clinical TNM staging. Here, we aimed to assess the generalisability of this tool in multi-ethnic Asians. Performance of J-CAPRA was evaluated in 782 Malaysian and 16,946 Japanese patients undergoing ADT from the Malaysian Study Group of Prostate Cancer (M-CaP) and Japan Study Group of Prostate Cancer (J-CaP) databases, respectively. Using the original J-CAPRA, 69.6% metastatic (M1) cases without T and/or N staging were stratified as intermediate-risk disease in the M-CaP database. To address this, we first omitted clinical T and N stage variables, and calculated the score on a 0-8 scale in the modified J-CAPRA scoring system for M1 patients. Notably, treatment decisions of M1 cases were not directly affected by both T and N staging. The J-CAPRA score threshold was adjusted for intermediate (modified J-CAPRA score 3-5) and high-risk (modified J-CAPRA score ≥6) groups in M1 patients. Using J-CaP database, validation analysis showed that overall survival, prostate cancer-specific survival, and progression-free survival of modified intermediate and high-risk groups were comparable to those of original J-CAPRA (p > 0.05) with Cohen's coefficient of 0.65. Around 88% M1 cases from M-CaP database were reclassified into high-risk category. Modified J-CAPRA scoring system is instrumental in risk assessment and treatment outcome prediction for M1 patients without T and/or N staging.

Oncologic Outcomes after Localized Prostate Cancer Treatment: Associations with Pretreatment Prostate Magnetic Resonance Imaging Findings

PURPOSE

We investigated whether T2-weighted magnetic resonance imaging findings could improve upon established prognostic indicators of metastatic disease and prostate cancer specific survival.

MATERIALS AND METHODS

For a cohort of 3,406 consecutive men who underwent prostate magnetic resonance imaging before prostatectomy (2,160) or radiotherapy (1,246) between 2001 and 2006, T2-weighted magnetic resonance imaging exams were retrospectively interpreted and categorized as I) no focal suspicious lesion, II) organ confined focal lesion, III) focal lesion with extraprostatic extension or IV) focal lesion with seminal vesicle invasion. Clinical risk was recorded based on European Association of Urology (EAU) guidelines and the Cancer of the Prostate Risk Assessment (CAPRA) scoring system. Survival probabilities and c-indices were estimated using Cox models and inverse probability censoring weights, respectively.

RESULTS

The median followup was 10.8 years (IQR 8.6-13.0). Higher magnetic resonance imaging categories were associated with a higher likelihood of developing metastases (HR 3.5-18.1, p <0.001 for all magnetic resonance imaging categories) and prostate cancer death (HR 3.1-29.7, p <0.001-0.025); these associations were statistically independent of EAU risk categories, CAPRA scores and treatment type (surgery vs radiation). Combining EAU risk or CAPRA scores with magnetic resonance imaging categories significantly improved prognostication of metastases (c-indices: EAU: 0.798, EAU + magnetic resonance imaging: 0.872; CAPRA: 0.808, CAPRA + magnetic resonance imaging: 0.877) and prostate cancer death (c-indices: EAU 0.813, EAU + magnetic resonance imaging: 0.889; CAPRA: 0.814, CAPRA + magnetic resonance imaging: 0.892; p <0.001 for all).

CONCLUSION

Magnetic resonance imaging findings of localized prostate cancer are associated with clinically relevant long-term oncologic outcomes. Combining magnetic resonance imaging and clinicopathological data results in more accurate prognostication, which could facilitate individualized patient management.

Urokinase-Type Plasminogen Activator Receptor (uPAR) PET/MRI of Prostate Cancer for Noninvasive Evaluation of Aggressiveness: Comparison with Gleason Score in a Prospective Phase 2 Clinical Trial

The aim of this study was to evaluate the correlation between uptake of the PET ligand ⁶⁸Ga-NOTA-AE105, targeting the urokinase-type plasminogen activator receptor (uPAR), and Gleason score in patients undergoing prostate biopsy. Methods: Patients with clinical suspicion of prostate cancer (PCa) or previously diagnosed with PCa were prospectively enrolled in this phase 2 trial. A combination of uPAR PET and multiparametric MRI (mpMRI) was performed, and the SUV in the primary tumor, as delineated by mpMRI, was measured by 2 independent readers. The correlation between the SUV and the Gleason score obtained by biopsy was assessed. Results: A total of 27 patients had histologically verified PCa visible on mpMRI and constituted the study population. There was a positive correlation between the SUV_max and the Gleason score (Spearman ρ = 0.55; P = 0.003). Receiver operating characteristic analysis showed an area under the curve of 0.88 (95% CI, 0.67-1.00) for discriminating a Gleason score of greater than or equal to 3 + 4 from a Gleason score of less than or equal to 3 + 3. A cutoff for the tumor SUV_max could be established with a sensitivity of 96% (79%-99%) and a specificity of 75% (30%-95%) for detecting a Gleason score of greater than or equal to 3 + 4. For discriminating a Gleason score of greater than or equal to 4 + 3 from a Gleason score of less than or equal to 3 + 4, a cutoff could be established for detecting a Gleason score of greater than or equal to 4 + 3 with a sensitivity of 93% (69%-99%) and a specificity of 62% (36%-82%). Conclusion: SUV measurements from uPAR PET in primary tumors, as delineated by mpMRI, showed a significant correlation with the Gleason score, and the tumor SUV_max was able to discriminate between low-risk Gleason score profiles and intermediate risk Gleason score profiles with a high diagnostic accuracy. Consequently, uPAR PET/MRI could be a promising method for the noninvasive evaluation of PCa and might reduce the need for repeated biopsies (e.g., in active surveillance).

Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection

Background

Until recently, almost all of these studies have identified multiple risk factors but did not offer practical instruments for routine use in predicting individualized survival in human H7N9 infection cases. The objective of this study is to create a practical instrument for use in predicting an individualized survival probability of H7N9 patients.

Methods

A matched case–control study (1:2 ratios) was performed in Zhejiang Province between 2013 and 2019. We reviewed specific factors and outcomes regarding patients with H7N9 virus infection (VI) to determine relationships and developed a nomogram to calculate individualized survival probability. This tool was used to predict each individual patient’s probability of survival based on results obtained from the multivariable Cox proportional hazard regression analysis.

Results

We examined 227 patients with H7N9 VI enrolled in our study. Stepwise selection was applied to the data, which resulted in a final model with 8 independent predictors [including initial PaO₂/FiO₂ ratio ≤300 mmHg, age ≥60 years, chronic diseases, poor hand hygiene, time from illness onset to the first medical visit, incubation period ≤5 days, peak C-reactive protein ≥120 mg/L], and initial bilateral lung infection. The concordance index of this nomogram was 0.802 [95% confidence interval (CI): 0.694–0.901] and 0.793 (95% CI: 0.611–0.952) for the training and validation sets, respectively, which indicates adequate discriminatory power. The calibration curves for the survival showed optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively.

Conclusion

We established and validated a novel nomogram that can accurately predict the survival probability of patients with H7N9 VI. This nomogram can serve an important role in counseling patients with H7N9 VI and guide treatment decisions.

Impregnation of Curcumin into a Biodegradable (Poly-lactic-co-glycolic acid, PLGA) Support, to Transfer Its Well Known In Vitro Effect to an In Vivo Prostate Cancer Model

Prostate cancer (PCa) is one of the most common cancers in older men and is associated with high mortality. Despite advances in screening for early detection of PCa, a large proportion of patients continue to be diagnosed with metastatic disease, with ~20% of men showing a high tumor grade and stage. Medicinal plant extracts have a great potential to prevent/treat PCa, as well as to reduce its incidence/prevalence and improve survival rates. One of the most promising extracts is curcumin, which is a major, nontoxic, bioactive compound of Curcuma longa. Curcumin has strong antitumor activity in vitro. However, its potential beneficial in vivo affects are limited by its low intestinal absorption and rapid metabolism. In this study, curcumin was impregnated into a biodegradable poly(lactic-co-glycolic) acid (PLGA) support and characterized by FTIR and DSC, and its release by UV spectrophotometry. PLGA-curcumin was tested in different subcutaneous PCa xenograft models (PC3, 22rv1, and DU145 PCa cell-lines), and its effects evaluated by tumor progression an immuno-histochemical analysis (Trichromic, Ki67 and TUNEL stainings), were compared with those of a commercial curcumin preparation. Our results indicate that curcumin-impregnated PLGA is significantly more active (~2-fold increase) with respect to oral curcumin, which supports its use for subcutaneous administration.