External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.

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.

[Effect of nasal swell body on nasal airflow and Artemisia pollen deposition].

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.

Effect of different degrees of adenoid hypertrophy on pediatric upper airway aerodynamics: a computational fluid dynamics study.

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.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

Background: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life and there remain no validated predictive models to guide its use. Methods: Digital pathology image and clinical data from pre-treatment prostate tissue from 5,727 patients enrolled on five phase III randomized trials treated with radiotherapy +/- ADT were used to develop and validate an artificial intelligence (AI)-derived predictive model to assess ADT benefit with the primary endpoint of distant metastasis. After the model was locked, validation was performed on NRG/RTOG 9408 (n = 1,594) that randomized men to radiotherapy +/- 4 months of ADT. Fine-Gray regression and restricted mean survival times were used to assess the interaction between treatment and predictive model and within predictive model positive and negative subgroup treatment effects. Results: In the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis (subdistribution hazard ratio [sHR] = 0.64, 95%CI [0.45-0.90], p = 0.01). The predictive model-treatment interaction was significant (p-interaction = 0.01). In predictive model positive patients (n = 543, 34%), ADT significantly reduced the risk of distant metastasis compared to radiotherapy alone (sHR = 0.34, 95%CI [0.19-0.63], p < 0.001). There were no significant differences between treatment arms in the predictive model negative subgroup (n = 1,051, 66%; sHR = 0.92, 95%CI [0.59-1.43], p = 0.71). Conclusions: Our data, derived and validated from completed randomized phase III trials, show that an AI-based predictive model was able to identify prostate cancer patients, with predominately intermediate-risk disease, who are likely to benefit from short-term ADT.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)­derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine­Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model­positive, i.e., benefited from ADT, and ­negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

Opportunistic screening for osteoporosis using hydroxyapatite measurements of the vertebral by thorax dual-energy spectral CT in postmenopausal females.

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/cm3) 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.

Efficacy of freeze-chilled storage combined with tea polyphenol for controlling melanosis, quality deterioration, and spoilage bacterial growth of Pacific white shrimp (Litopenaeus vannamei).

This study aimed to investigate melanosis, quality attributes, and bacterial growth of freeze-chilled Pacific white shrimp (Litopenaeus vannamei) during 6 days of chilled storage, as well as the preservative effects of tea polyphenol on shrimp. The results showed that freeze-chilled storage retarded the growth of bacteria and the accumulation of putrescine in shrimp. The growth of spoilage bacteria Photobacterium and Shewanella were inhibited. However, freeze-chilled storage aggravated melanosis and lipid oxidation. The total volatile basic nitrogen (TVB-N) slightly accumulated in the thawed shrimp. The incorporation of tea polyphenol preserved freeze-chilled shrimp. Melanosis and lipid oxidation of shrimp were alleviated. The accumulation of biogenic amines, TVB-N, hypoxanthine riboside, and hypoxanthine were retarded. Meanwhile, the growth of spoilage bacteria Pseudoalteromonas, Photobacterium, Psychrobacter, and Carnobacterium were inhibited. Based on sensory analysis, the shelf-life of chilled, freeze-chilled, and freeze-chilled tea polyphenol shrimp were 4 days, 3 days, and 6 days, respectively.

Evaluation of nasal function after endoscopic endonasal surgery for pituitary adenoma: a computational fluid dynamics study.

OBJECTIVE: To analyze the effect of different endoscopic endonasal approaches (EEAs) on nasal airflow and heating and humidification in patients with pituitary adenoma (PA) by computational fluid dynamics (CFD). METHODS: A three-dimensional pre-surgical model (Pre) of the nasal cavity and 6 that were post-EEA surgery were created from computed tomography scans as follows: small posterior septectomy (0.5 cm, sPS), middle posterior septectomy (1.5 cm, mPS), large posterior septectomy (2.5 cm, lPS), and sPS with middle turbinate resection (sPS-MTR), mPS-MTR, and lPS-MTR. Simulations were performed by CFD to compare the changes in different models. RESULTS: The temperature in the nasal vestibule rose more rapidly than in other parts of the nasal cavities in all models. There were no apparent differences in temperature and humidity among the models in sections anterior to the middle turbinate head (C6 section). MTR significantly influenced airflow distribution between the bilateral nasal cavities and the different parts of the nasal cavity, while changes in temperature and humidity in each section were mainly affected by MTR. The temperature and humidity of the choana and nasopharynx of each postoperative model were significantly different from those of the preoperative model and the change in values significantly correlated with the surface-to-volume ratio (SVR) of the airway. CONCLUSIONS: Changes due to the different nasal structures caused different effects on nasal function following the use of EEA surgery for the treatment of PA. CFD provided a new approach to assess nasal function, promising to provide patients with individualized preoperative functional assessment and surgical planning.

ctDNA guiding adjuvant immunotherapy in urothelial carcinoma.

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 relapse1. 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 TGFß 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.

Numerical Simulation of Nasal Airflow Aerodynamics, and Warming and Humidification in Models of Clival Chordoma Pre and Post-Endoscopic Endonasal Surgery.

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 H2O 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.

Predictors of Prostate-specific Membrane Antigen (PSMA/FOLH1) Expression in a Genomic Database.

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.

Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier.

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.

Validation of a genomic classifier for prediction of metastasis and prostate cancer-specific mortality in African-American men following radical prostatectomy in an equal access healthcare setting.

BACKGROUND: The Decipher 22-gene genomic classifier (GC) may help in post-radical prostatectomy (RP) decision making given its superior prognostic performance over clinicopathologic variables alone. However, most studies evaluating the GC have had a modest representation of African-American men (AAM). We evaluated the GC within a large Veteran Affairs cohort and compared its performance to CAPRA-S for predicting outcomes in AAM and non-AAM after RP. METHODS: GC scores were generated for 548 prostate cancer (PC) patients, who underwent RP at the Durham Veteran Affairs Medical Center between 1989 and 2016. This was a clinically high-risk cohort and was selected to have either pT3a, positive margins, seminal vesicle invasion, or received post-RP radiotherapy. Multivariable Cox models and survival C-indices were used to compare the performance of GC and CAPRA-S for predicting the risk of metastasis and PC-specific mortality (PCSM). RESULTS: Median follow-up was 9 years, during which 37 developed metastasis and 20 died from PC. Overall, 55% (n = 301) of patients were AAM. In multivariable analyses, GC (high vs. intermediate and intermediate vs. low) was a significant predictor of metastasis in all men (all p < 0.001). Consistent with prior studies, relative to CAPRA-S, GC had a higher C-index for 5-year metastasis (0.78 vs. 0.72) and 10-year PCSM (0.85 vs. 0.81). There was a suggestion GC was a stronger predictor in AAM than non-AAM. Specifically, the 5-year metastasis risk C-index was 0.86 in AAM vs. 0.69 in non-AAM and the 10-year PCSM risk C-index was 0.91 in AAM vs. 0.78 in non-AAM. However, the test for interaction of race and the performance of the GC in the Cox model was not significant for either metastasis or PCSM (both p ≥ 0.3). CONCLUSIONS: GC was a very strong predictor of poor outcome and performed well in both AAM and non-AAM. Our data support the use of GC for risk stratification in AAM post-RP. While our data suggest that GC may actually work better in AAM, given the limited number of events, further validation is needed.

Prospective study to define the clinical utility and benefit of Decipher testing in men following prostatectomy.

BACKGROUND: Genomic classifiers (GC) have been shown to improve risk stratification post prostatectomy. However, their clinical benefit has not been prospectively demonstrated. We sought to determine the impact of GC testing on postoperative management in men with prostate cancer post prostatectomy. METHODS: Two prospective registries of prostate cancer patients treated between 2014 and 2019 were included. All men underwent Decipher tumor testing for adverse features post prostatectomy (Decipher Biosciences, San Diego, CA). The clinical utility cohort, which measured the change in treatment decision-making, captured pre- and postgenomic treatment recommendations from urologists across diverse practice settings (n = 3455). The clinical benefit cohort, which examined the difference in outcome, was from a single academic institution whose tumor board predefined "best practices" based on GC results (n = 135). RESULTS: In the clinical utility cohort, providers' recommendations pregenomic testing were primarily observation (69%). GC testing changed recommendations for 39% of patients, translating to a number needed to test of 3 to change one treatment decision. In the clinical benefit cohort, 61% of patients had genomic high-risk tumors; those who received the recommended adjuvant radiation therapy (ART) had 2-year PSA recurrence of 3 vs. 25% for those who did not (HR 0.1 [95% CI 0.0-0.6], p = 0.013). For the genomic low/intermediate-risk patients, 93% followed recommendations for observation, with similar 2-year PSA recurrence rates compared with those who received ART (p = 0.93). CONCLUSIONS: The use of GC substantially altered treatment decision-making, with a number needed to test of only 3. Implementing best practices to routinely recommend ART for genomic-high patients led to larger than expected improvements in early biochemical endpoints, without jeopardizing outcomes for genomic-low/intermediate-risk patients.

Clinical Utility of a Genomic Classifier in Men Undergoing Radical Prostatectomy: The PRO-IMPACT Trial.

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.

Genomic Validation of 3-Tiered Clinical Subclassification of High-Risk Prostate Cancer.

PURPOSE: Recent data and National Comprehensive Cancer Network (NCCN) guidelines suggest that high-risk prostate cancer (cT3-4, Gleason score ≥8, or prostate-specific antigen [PSA] >20 ng/mL) is a heterogenous group in terms of long-term patient outcomes. We sought to determine whether subclassification of high-risk prostate cancer based on clinical factors correlates with genomic markers of risk. METHODS AND MATERIALS: We identified 3220 patients with NCCN unfavorable intermediate-risk (n = 2000) or high-risk (n = 1220) prostate cancer from a prospective multi-institutional registry cohort. We defined the following subclassification of high-risk prostate cancer based on previously published data: favorable high risk (cT1c, Gleason 6, and PSA >20 ng/mL or cT1c, Gleason 4 + 4 = 8, PSA <10 ng/mL); very high risk (cT3b-T4 or primary Gleason pattern 5); and standard high risk (all others with cT3a, Gleason score ≥8, or PSA >20 ng/mL). We used a set of 33 previously developed genomic classifiers, including Decipher, to determine whether high-risk genomic features correlate with clinical subclasses of high-risk prostate cancer. RESULTS: Among those with favorable high-risk, standard high-risk, and very high-risk prostate cancer, 50.4%, 64.2%, and 81.6% had a high-risk Decipher score, respectively (P < .001). Among 32 other genomic signatures, 29 had a similar trend of increasing risk scores across the 3 subclasses of high-risk disease (P < .05 after correction for multiple hypothesis testing). Patients in the 3 subclasses of high-risk disease had a median of 4, 6, and 13 high-risk signatures, respectively. In comparison, among those with unfavorable intermediate-risk prostate cancer, 38.2% had a high-risk Decipher score, and the median number of high-risk signatures was 3. CONCLUSIONS: Although NCCN guidelines currently use a 2-tiered system for high-risk prostate cancer, genomic markers of risk correlate with the clinically validated subclassification of high-risk prostate cancer into favorable high-risk, standard high-risk, and very high-risk disease, further confirming the prognostic utility of this 3-tiered stratification.

Molecular Subtyping of Clinically Localized Urothelial Carcinoma Reveals Lower Rates of Pathological Upstaging at Radical Cystectomy Among Luminal Tumors.

BACKGROUND: Upstaging of clinical T1-T2 urothelial carcinoma (UC) to non-organ-confined (NOC) pathological stage ≥T3 or N+ at radical cystectomy (RC) is common. Tools for stratifying patients who may have NOC disease are limited. OBJECTIVE: To determine an association of a genomic subtyping classifier (GSC) with pathological upstaging in multi-institutional cohort of patients with cT1-T2 UC treated with RC. DESIGN, SETTING, AND PARTICIPANTS: Precystectomy transurethral specimens from 206 patients with high-grade, cT1-T2, N0M0 UC, who underwent RC without neoadjuvant chemotherapy, underwent GSC testing. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Uni- and multivariable logistic regression analyses evaluated GSC for upstaging, defined as pT3/T4 and/or pTanyN1-3 disease at RC. RESULTS AND LIMITATIONS: Pathological upstaging occurred in 23% of cT1 and 57% of cT2 cases. Lower rates of upstaging to NOC was seen for luminal versus nonluminal tumors (34% vs 51%, p=0.02). The differences in upstaging were confined to T stage, with no difference in node positivity for luminal versus nonluminal patients (cT1: 13% for both [p>0.9], cT2: 15% and 23% [p=0.6], respectively). Fewer patients with luminal tumors were upstaged to ≥pT3Nany compared with nonluminal tumors (Mantel-Haenszel p=0.002; cT1: 13% vs 30%, cT2: 34% vs 58%). On multivariable logistic regression analysis, nonluminal patients were more likely to be upstaged to ≥pT3 at RC (p<0.001). Limitations include retrospective design and sample size. CONCLUSIONS: Molecular subtyping of clinically localized UC demonstrated that luminal tumors have lower rates of upstaging to non-organ-confined disease compared with nonluminal tumors. If validated, these data can help inform which patients may need multimodal therapy. PATIENT SUMMARY: Determining whether bladder cancer has spread beyond the bladder is challenging at diagnosis. In this paper, genomics helped identify patients who were more likely to have aggressive disease that has spread outside the bladder. These patients may benefit from chemotherapy prior to surgery.

Genomic Classifier for Guiding Treatment of Intermediate-Risk Prostate Cancers to Dose-Escalated Image Guided Radiation Therapy Without Hormone Therapy.

PURPOSE: The National Comprehensive Cancer Network (NCCN) has recently endorsed the stratification of intermediate-risk prostate cancer (IR-PCa) into favorable and unfavorable subgroups and recommend the addition of androgen deprivation therapy (ADT) to radiation therapy (RT) for unfavorable IR-PCa. Recently, more accurate prognostication was demonstrated by integrating a 22-feature genomic classifier (GC) to the NCCN stratification system. Here, we test the utility of the GC to better identify patients with IR-PCa who are sufficiently treated by RT alone. METHODS AND MATERIALS: We identified a novel cohort comprising 121 patients with IR-PCa treated with dose-escalated image guided RT (78 Gy in 39 fractions) without ADT. GC scores were derived from tumors sampled in diagnostic biopsies. Multivariable analyses, including both NCCN subclassification and GC scores, were performed for biochemical failure (prostate-specific antigen nadir + 2 ng/mL) and metastasis occurrence. RESULTS: By NCCN subclassification, 33 (27.3%) and 87 (71.9%) of men were classified as having favorable and unfavorable IR-PCa, respectively (1 case unclassifiable). GC scores were high in 3 favorable IR-PCa and low in 60 unfavorable IR-PCa. Higher GC scores, but not NCCN risk subgroups, were associated with biochemical relapse (hazard ratio, 1.36; 95% confidence interval [CI], 1.09-1.71] per 10% increase; P = .007) and metastasis (hazard ratio, 2.05; 95% CI, 1.24-4.24; P = .004). GC predicted biochemical failure at 5 years (area under the curve, 0.78; 95% CI, 0.59-0.91), and the combinatorial NCCN + GC model significantly outperformed the NCCN alone model for predicting early-onset metastasis (area under the curve for 5-year metastasis of 0.89 vs 0.86 [GC alone] vs 0.54 [NCCN alone]). CONCLUSIONS: We demonstrated the accuracy of the GC for predicting disease recurrence in IR-PCa treated with dose-escalated image guided RT alone. Our findings highlight the need to evaluate this GC in a prospective clinical trial investigating the role of ADT-RT in clinicogenomic-defined IR-PCa subgroups.

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

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.

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease.

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 I2 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; I2 = 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.