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Purpose: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups., Materials and Methods: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups., Results: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively., Conclusion: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

Background: Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial., Objective: To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial., Design, Setting, and Participants: Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality)., Outcome Measurements and Statistical Analysis: Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models., Results and Limitations: The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm., Conclusions: We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care., Patient Summary: This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Objective: To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT)., Materials and Methods: An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared., Results: Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP., Conclusion: Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules., (© 2024. The Author(s).)

This study reports the findings from using time-domain nuclear magnetic resonance (TD-NMR) to analyze the pore structures of cotton fibers. Cotton fibers, which swell and soften in water, present challenges for conventional pore measurement techniques. TD-NMR overcomes these by measuring the transverse relaxation time (T 2 ) of water protons within the fibers, indicative of internal pore sizes. We established a T 2 -to-pore size conversion equation using mixed cellulose ester membranes. This enabled differentiation between strongly bound, loosely bound, and free water within the fibers, and detailed the water distribution. A method for measuring the pore size distribution of wet cotton fiber was developed using TD-NMR. We then examined how various pretreatments affect the fibers' internal pores by comparing their pore size distribution and porosity. Specifically, caustic mercerization primarily enlarges the porosity and size of larger pores, while liquid ammonia treatment increases porosity but reduces the size of smaller pores. This research confirms TD-NMR's utility in assessing cotton fabrics' wet processing performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To address the growing challenge of counterfeit prevention, this study developed a novel anti-counterfeiting ink system based on bacterial cellulose nanocrystals (BCNC) and lanthanide (Er, Yb)‑nitrogen (N) co-dropped graphene quantum dots (GQDs), which exhibited both photoluminescence (PL) and upconversion photoluminescence (UCPL) fluorescent properties as well as excellent rheological characteristics. The Er/Yb/N-GQDs with positive charges were synthesized by a one-step hydrothermal method and subsequently assembled with negatively charged BCNC through electrostatic self-assembly to fabricate a novel nanohybrid, Er/Yb/N-GQDs-BCNC. Raman spectroscopy results indicated an enhancement in the graphitization of GQDs due to lanthanide modification. The TEM results demonstrated a homogeneous distribution of Er/Yb/N-GQDs on BCNC, while XRD, FTIR, and XPS analyses confirmed their physical binding, thus validating the successful synthesis of novel nanohybrids. Then, Er/Yb/N-GQDs-BCNC was introduced into PVA waterborne ink and exhibited dual anti-counterfeiting properties by emitting blue fluorescence at Em 440 nm under Ex 370 nm and green fluorescence at Em 550 nm under Ex 980 nm. Furthermore, the incorporation of BCNC significantly enhanced the thixotropic behavior and yield stress of the PVA waterborne ink. This enhancement made the dual anti-counterfeiting fluorescent ink more suitable for diversified applications on different devices and various substrates, thus providing a novel approach for convenient and rapid information encryption and high security anti-counterfeiting., Competing Interests: Declaration of competing interest All authors confirmed no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)

Herein, a carbon felt (CF) cathode modified by the acidic oxidised carbon nanotubes (OCNTs) exhibited a high yield of the H 2 O 2 generation in electro-Fenton. Rotating disk electrode (RDE) measurements showed that the selective generation of H 2 O 2 occurred on the CF cathode coated by OCNTs (OCNTs/CF), which was attributed to the high amount of oxygen-containing functional groups in OCNTs. Moreover, the pollutant degradation efficiency could almost reach 100% within 60 min in electro-Fenton with OCNTs/CF as the cathode. Furthermore, the pollutant removal efficiency was kept constant after five consecutive cycles, indicating the high stability of OCNTs/CF cathode. Besides, the hydrophilicity of OCNTs/CF cathode was significantly enhanced owing to the abundant oxygen-contained functional groups on the surface of the OCNTs/CF cathode, which facilitated the mass transfer between the OCNTs/CF cathode and the reactants in the bulk solution. To reveal the possible mechanism in electro-Fenton equipped with the OCNTs/CF cathode, quenching experiments and electron paramagnetic resonance (EPR) investigations were further conducted. This work provided valuable insights into the fabrication of the non-metallic cathode with a high ability towards H 2 O 2 generation in electro-Fenton for efficient pollutant removal.

To improve the diagnostic accuracy of adenoid hypertrophy (AH) in children and prevent further complications in time, it is important to study and quantify the effects of different degrees of AH on pediatric upper airway (UA) aerodynamics. In this study, based on computed tomography (CT) scans of a child with AH, UA models with different degrees of obstruction (adenoidal-nasopharyngeal (AN) ratio of 0.9, 0.8, 0.7, and 0.6) and no obstruction (AN ratio of 0.5) were constructed through virtual surgery to quantitatively analyze the aerodynamic characteristics of UA with different degrees of obstruction in terms of the peak velocity, pressure drop (△P), and maximum wall shear stress (WSS). We found that two obvious whirlpools are formed in the anterior upper part of the pediatric nasal cavity and in the oropharynx, which is caused by the sudden increase in the nasal cross-section area, resulting in local flow separation and counterflow. In addition, when the AN ratio was ≥ 0.7, the airflow velocity peaked at the protruding area in the nasopharynx, with an increase 1.1-2.7 times greater than that in the nasal valve area; the △P in the nasopharynx was significantly increased, with an increase 1.1-6.8 times greater than that in the nasal cavity; and the maximum WSS of the posterior wall of the nasopharynx was 1.1-4.4 times larger than that of the nasal cavity. The results showed that the size of the adenoid plays an important role in the patency of the pediatric UA., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: The nasal swell body（NSB） consists of the nasal septal cartilage, nasal bone, and swollen soft tissue, all of which are visible during endoscopic and imaging examinations. Although the function of the NSB remains uncertain, there is evidence to suggest that it plays a vital role in regulating nasal airflow and filtering inhaled air. Based on anatomical and histological evidence, it is hypothesized that the NSB is indispensable in these processes. This study aims to investigate the impact of NSB on nasal aerodynamics and the deposition of allergen particles under physiological conditions. Methods: The three-dimensional （3D） nasal models were reconstructed from computed tomography （CT） scans of the paranasal sinus and nasal cavity in 30 healthy adult volunteers from Northwest China, providing basis for the construction of models without NSB following virtual NSB-removal surgery. To analyze the distribution of airflow in the nasal cavity, nasal resistance, heating and humidification efficiency, and pollen particle deposition rate at various anatomical sites, we employed the computed fluid dynamics（CFD） method for numerical simulation and quantitative analysis. In addition, we created fully transparent segmented nasal cavity models through 3D printing, which were used to conduct bionic experiments to measure nasal resistance and allergen particle deposition. Results: ①The average width and length of the NSB in healthy adults in Northwest China were （12.85±1.74） mm and （28.30±1.92） mm, respectively. ②After NSB removal, there was no significant change in total nasal resistance, and cross-sectional airflow velocity remained essentially unaltered except for a decrease in topical airflow velocity in the NSB plane. ③There was no discernible difference in the nasal heating and humidification function following the removal of the NSB; ④After NSB removal, the deposition fraction（DF） of Artemisia pollen in the nasal septum decreased, and the DFs post-and pre-NSB removal were（22.79±6.61）% vs （30.70±12.27）%, respectively; the DF in the lower airway increased, and the DFs post-and pre-NSB removal were（24.12±6.59）% vs （17.00±5.57）%, respectively. Conclusion: This study is the first to explore the effects of NSB on nasal airflow, heating and humidification, and allergen particle deposition in a healthy population. After NSB removal from the healthy nasal cavities: ①nasal airflow distribution was mildly altered while nasal resistance showed no significantly changed; ②nasal heating and humidification were not significantly changed; ③the nasal septum's ability to filter out Artemisia pollen was diminished, which could lead to increased deposition of Artemisia pollen in the lower airway., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.)

The purpose of this study was to evaluate the feasibility of opportunistic screening for osteoporosis in postmenopausal females using the dual-energy CT(DECT)-derived hydroxyapatite (HAP) concentration and CT value of L1-vertebra. 239 consecutive postmenopausal female patients were enrolled and underwent both chest DECT and Dual energy X-ray absorptiometry (DXA). According to the T-score of the 1st lumbar vertebra on DXA, patients were divided into the osteoporosis group (T [Formula: see text]- 2.5, n = 112) and non-osteoporosis group (T [Formula: see text] - 2.5, n = 127). The HAP values of the 1st lumbar vertebra were measured from the coronal-view HAP(Fat)-based material decomposition(MD) images, and CT values were measured on the 75 keV monochromatic image. The cutoff values of using HAP and CT value for diagnosing osteoporosis were obtained by drawing receiver operating characteristic (ROC) curves. Both HAP and CT value of the 1st lumbar vertebra had moderate-high correlation with bone-mineral-density measurement on DXA (HAP, r = 0.614; CT value, r = 0.625; all p < 0.01). The area-under-the-curve (AUC), sensitivity, specificity, PPV and NPV for diagnosing osteoporosis was 0.754, 0.714, 0.693, 0.68 and 0.752 using HAP (cutoff value: 142.05 mg/cm 3 ) and 0.766, 0.741, 0.7, 0.685 and 0.754 using CT value (cutoff value: 132HU), respectively. HAP measurements on HAP(Fat)-based MD images in DECT could provide reasonably accurate BMD quantification for diagnosing osteoporosis in postmenopausal females. DECT prescribed for lung cancer screening could also provide opportunistic screening for osteoporosis, extending the clinical application of DECT without additional radiation to patients., (© 2022. The Author(s).)

Objectives: To study the effect of endoscopic endonasal surgery on nasal function for the treatment of clival chordoma., Methods: Pre and post-operative computed tomography (CT) scans of a case of chordoma treated with an endoscopic endonasal approach (EEA) were collected retrospectively, and models of the nasal cavity were reconstructed so that a subsequent numerical simulation of nasal airflow characteristics, warming, and humidification could be conducted., Results: Middle turbinectomy resulted in redistribution of airflow within the nasal cavity, and the most significant changes occurred in the middle section. Consistent with the results of airflow evaluation, it was found that the change in nasal anatomical structure significantly reduced warming and humidification. Nasal humidification decreased substantially when postoperative loss of mucosa was taken into consideration. The H 2 O mass fraction of pharynx in inspiration phase were significantly correlated with airway surface-to-volume ratio (SVR)., Conclusions: The EEA for chordoma significantly affected nasal function. Attention should be paid to the protection of nasal structure and the associated mucosa., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Minimally invasive approaches to detect residual disease after surgery are needed to identify patients with cancer who are at risk for metastatic relapse. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease and relapse 1 . We evaluated outcomes in 581 patients who had undergone surgery and were evaluable for ctDNA from a randomized phase III trial of adjuvant atezolizumab versus observation in operable urothelial cancer. This trial did not reach its efficacy end point in the intention-to-treat population. Here we show that ctDNA testing at the start of therapy (cycle 1 day 1) identified 214 (37%) patients who were positive for ctDNA and who had poor prognosis (observation arm hazard ratio = 6.3 (95% confidence interval: 4.45-8.92); P < 0.0001). Notably, patients who were positive for ctDNA had improved disease-free survival and overall survival in the atezolizumab arm versus the observation arm (disease-free survival hazard ratio = 0.58 (95% confidence interval: 0.43-0.79); P = 0.0024, overall survival hazard ratio = 0.59 (95% confidence interval: 0.41-0.86)). No difference in disease-free survival or overall survival between treatment arms was noted for patients who were negative for ctDNA. The rate of ctDNA clearance at week 6 was higher in the atezolizumab arm (18%) than in the observation arm (4%) (P = 0.0204). Transcriptomic analysis of tumours from patients who were positive for ctDNA revealed higher expression levels of cell-cycle and keratin genes. For patients who were positive for ctDNA and who were treated with atezolizumab, non-relapse was associated with immune response signatures and basal-squamous gene features, whereas relapse was associated with angiogenesis and fibroblast TGFβ signatures. These data suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in patients who are positive for ctDNA and who are at a high risk of relapse. These findings, if validated in other settings, would shift approaches to postoperative cancer care., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date., Methods: A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score., Results: Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79-4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71., Conclusions: Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation.

Objective: To assess predictors of prostate-specific membrane antigen (PSMA) expression in a genomic database; positron emission tomography with PSMA-targeted radiopharmaceuticals is increasingly being utilized., Methods: The de-identified Decipher Biosciences database, which includes expression for more than 46,000 coding and noncoding genes per patient, was queried for expression of FOLH1 (PSMA). Prostate cancer patients who underwent radical prostatectomy and received the Decipher Test were included in the analysis. PSMA expression was compared to the Gleason Grade Group, Decipher risk category (a validated 22 biomarker genomic score), basal versus luminal molecular subtype, and androgen receptor activity. Multivariable regression analyses were performed., Results: The Decipher de-identified Decipher Biosciences database contained 16,807 men who underwent prostatectomy with the average age being 65-year old and most being Gleason Grade Group 2 (35%) or 3 (27%). Higher Grade Group was associated with higher PSMA expression except in Grade Group 5 [Grade group: 1 (0.66), 2 (0.84), 3 (0.99), 4 (1.07), 5 (0.99), P <.001]. Luminal subtype was found to have much higher PSMA expression when compared to basal (1.01 vs 0.68, P <.001). The androgen receptor activity signature demonstrated a dramatic difference between basal (0.19) and luminal (0.62) subtypes (P <.001). In the multivariable model, luminal patients, high androgen receptor activity scores, and high Grade Groups were significantly associated with higher FOLH1 percentile rank (P <.001)., Conclusion: High PSMA expression (FOLH1) was associated with high androgen receptor activity and luminal subtype. Genomic tests could aid in predicting, interpreting, and/or directing PSMA theranostics., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: The optimal management of men with prostate cancer at high risk of recurrence postradical prostatectomy is controversial. The clinical utility of the Decipher test was evaluated prospectively on postoperative treatment decisions and patient-reported outcomes., Methods and Materials: In the study, 246 eligible men across 19 centers were enrolled. Patients were dichotomized into those considering adjuvant or salvage radiation therapy (ART or SRT). Participating providers submitted a management recommendation before and after receiving the Decipher test results. Treatment received within 12 months and a validated survey on prostate cancer-related anxiety were collected longitudinally., Results: Pre-Decipher, treatment was recommended for 12% and 40% for the ART and SRT arms, respectively. Post-Decipher, 17% and 30% of treatment recommendations changed in the ART and SRT arms, respectively. Post-Decipher treatment recommendation was administered 78% and 76% of the time in the ART and SRT arms, respectively. Multivariable analysis confirmed that the Decipher score was an independent predictor for change in management for both adjuvant and salvage patients. The number needed to test to change management for one patient was 4. Cancer-specific anxiety decreased among Decipher risk categories in both arms., Conclusions: Use of Decipher postradical prostatectomy test was associated with postoperative treatment decisions. Overall, high Decipher risk was associated with an increase in treatment intensity whereas low risk scores were associated with a decrease in therapy administered independent of clinical and pathologic risk factors., (Copyright © 2019 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I 2 test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I 2 = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.