Explore the vast range of titles available.

The international Intermediate Clinical Endpoints in Cancer of the Prostate working group has established metastasis-free survival as a surrogate for overall survival in localised prostate cancer based on the findings of 19 predominantly radiotherapy-based trials. We sought to comprehensively assess aggregate trial-level performance of commonly reported intermediate clinical endpoints across all randomised trials in localised prostate cancer. For this meta-analysis, we searched PubMed for all trials in localised or biochemically recurrent prostate cancer published between Jan 1, 1970, and Jan 15, 2020. Eligible trials had to be randomised, therapeutic, reporting overall survival and at least one intermediate clinical endpoint, and with a sample size of at least 70 participants. Trials of metastatic disease were excluded. Intermediate clinical endpoints included biochemical failure, local failure, distant metastases, biochemical failure-free survival, progression-free survival, and metastasis-free survival. Candidacy for surrogacy was assessed using the second condition of the meta-analytical approach (ie, correlation of the treatment effect of the intermediate clinical endpoint and overall survival), using R2 weighted by the inverse variance of the log intermediate clinical endpoint hazard ratio. The intermediate clinical endpoint was deemed to be a surrogate for overall survival if R2 was 0·7 or greater. 75 trials (53 631 patients) were included in our analysis. Median follow-up was 9·1 years (IQR 5·7–10·6). Biochemical failure (R2 0·38 [95% CI 0·11–0·64]), biochemical failure-free survival (R2 0·12 [0·0030–0·33]), biochemical failure and clinical failure (R2 0·28 [0·0045–0·65]), and local failure (R2 0·085 [0·00–0·37]) correlated poorly with overall survival. Progression-free survival (R2 0·46 [95% CI 0·22–0·67]) showed moderate correlation with overall survival, and metastasis-free survival (R2 0·78 [0·59–0·89]) correlated strongly. Intermediate clinical endpoints based on biochemical and local failure did not meet the second condition of the meta-analytical approach and are not surrogate endpoints for overall survival in localised prostate cancer. Our findings validate metastasis-free survival as the only identified surrogate endpoint for overall survival to date. Prostate Cancer Foundation and National Institutes of Health.

Black patients were severely under-represented in the clinical trials that led to the approval of immune checkpoint inhibitors (ICIs) for all cancers. The aim of this study was to characterise the effectiveness and safety of ICIs in Black patients. We did a retrospective cohort study of patients in the US Veterans Health Administration (VHA) system's Corporate Data Warehouse containing electronic medical records for all patients who self-identified as non-Hispanic Black or African American (referred to as Black) or non-Hispanic White (referred to as White) and received PD-1, PD-L1, CTLA-4, or LAG-3 inhibitors between Jan 1, 2010, and Dec 31, 2023. Effectiveness outcomes were overall survival, time to treatment discontinuation, and time to next treatment. The safety outcome was the frequency of immune-related adverse events; assessed among a random sample of 1000 Black patients and 1000 White patients, 892 pairs were matched on the basis of baseline characteristics using 1:1 exact matching without replacement. After manual chart review, patients who did not receive ICI therapy or who had inadequate follow-up were excluded. The adjusted effect of race on each effectiveness outcome was assessed in the whole ICI-treated cohort with propensity-weighted Cox regression with robust standard errors. Immune-related adverse events outcomes were analysed in the random matched sample with multivariable Cox regression, adjusting for baseline characteristics. We identified 26 398 patients, of whom 4943 (18·7%) patients were Black, 21 455 (81·3%) were White, 895 (3·4%) were female, 25 503 (96·6%) were male, 11 859 (45%) had non-small-cell lung cancer, and 26 045 (98·7%) received PD-1 or PD-L1 inhibitors. As of data cutoff (Aug 28, 2024), median follow-up was 40·3 months (95% CI 38·3–42·3) for Black patients and 43·9 months (43·0–45·1) for White patients. Compared with White patients, Black patients had longer time to treatment discontinuation (2-year unadjusted rates 10·7% [95% CI 9·8–11·7] for Black patients vs 8·6% [8·2–9·0] for White patients; adjusted hazard ratio [HR] 0·91, 95% CI 0·87–0·95, p<0·0001), similar time to next treatment (23·5% [22·3–24·8] for Black patients vs 25·6% [25·0–26·2] for White patients; 1·00, 0·95–1·05, p=0·96), and slightly improved overall survival (36·5% [35·2–38·1] for Black patients vs 36·5% [35·8–37·1]; 0·95, 0·90–0·99, p=0·036). 1710 patients (n=862 Black and n=848 White) were analysed for safety outcomes. Compared with White patients, Black patients had a reduced risk of all-grade immune-related adverse events (unadjusted 2-year rate 33·1% [95% CI 28·9–37·1] vs 44·1% [95% CI 39·1–48·7]; adjusted HR 0·75, 95% CI 0·62–0·90, p=0·0026), immune-related adverse events requiring treatment with systemic steroids (0·61, 0·46–0·81, p=0·00051), and immune-related adverse events resulting in permanent ICI discontinuation (0·58, 0·44–0·78, p=0·00024). In exploratory analyses of irAE subtypes, a significant risk reduction in Black patients was found for colitis (0·46, 0·27–0·76, p=0·0026) and hyperthyroidism or hypothyroidism (0·63, 0·44–0·90, p=0·011), and no significant differences were found for any other immune-related adverse event subtypes analysed. Similar results were found in analyses using a steroid-based definition of immune-related adverse events among the entire ICI-treated cohort. Compared with White patients, Black patients had similar ICI effectiveness and lower toxicities among those treated in the national VHA system, potentially reflecting an important difference in the therapeutic ratio (ratio of benefit to harm) of ICIs. Our findings of decreased toxicity among Black patients require further investigation to assess their generalisability. Million Veteran Program, Office of Research and Development, Veterans Health Administration and the LUNGevity foundation.

Background The cumulative, health system‐wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real‐world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system‐wide survival impact of ICIs. Methods We identified all patients receiving PD‐1/PD‐L1 or CTLA‐4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non‐ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer‐related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life‐years gained system‐wide were calculated from the difference in adjusted 5‐year restricted mean survival times. Results There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54–0.58, p < 0.001; urothelial: aHR 0.91, 95% CI 0.83–1.01, p = 0.066). The relative benefit of ICI was stable across patient age, comorbidity, and self‐reported race subgroups. Across VHA, 15,859 life‐years gained were attributable to ICI within 5‐years of treatment, with NSCLC contributing the most life‐years gained. Conclusion We demonstrated substantial increase in survival due to ICIs across a national health system, including in patient subgroups poorly represented on clinical trials. In this matched historical cohort study in the Veterans Health Administration system, ICIs had a heterogenous survival benefit by cancer type, and poorer‐prognosis patients derived smaller absolute survival benefits. System‐wide, ICIs accounted for nearly 16,000 life‐years gained within a 5‐year time horizon after treatment.

Purpose Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time‐consuming manual case‐finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real‐time monitoring of novel therapies. Ongoing and potential integrations of AI in oncology.

As the field of medicine strives for equity in care, research showing the association of social determinants of health (SDOH) with poorer health care outcomes is needed to better inform quality improvement strategies. To evaluate the association of SDOH with prostate cancer-specific mortality (PCSM) and overall survival (OS) among Black and White patients with prostate cancer. A MEDLINE search was performed of prostate cancer comparative effectiveness research from January 1, 1960, to June 5, 2020. Two authors independently selected studies conducted among patients within the United States and performed comparative outcome analysis between Black and White patients. Studies were required to report time-to-event outcomes. A total of 251 studies were identified for review. Three authors independently screened and extracted data. End point meta-analyses were performed using both fixed-effects and random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was followed, and 2 authors independently reviewed all steps. All conflicts were resolved by consensus. The primary outcome was PCSM, and the secondary outcome was OS. With the US Department of Health and Human Services Healthy People 2030 initiative, an SDOH scoring system was incorporated to evaluate the association of SDOH with the predefined end points. The covariables included in the scoring system were age, comorbidities, insurance status, income status, extent of disease, geography, standardized treatment, and equitable and harmonized insurance benefits. The scoring system was discretized into 3 categories: high (≥10 points), intermediate (5-9 points), and low (<5 points). The 47 studies identified comprised 1 019 908 patients (176 028 Black men and 843 880 White men; median age, 66.4 years [IQR, 64.8-69.0 years]). The median follow-up was 66.0 months (IQR, 41.5-91.4 months). Pooled estimates found no statistically significant difference in PCSM for Black patients compared with White patients (hazard ratio [HR], 1.08 [95% CI, 0.99-1.19]; P = .08); results were similar for OS (HR, 1.01 [95% CI, 0.95-1.07]; P = .68). There was a significant race-SDOH interaction for both PCSM (regression coefficient, -0.041 [95% CI, -0.059 to 0.023]; P < .001) and OS (meta-regression coefficient, -0.017 [95% CI, -0.033 to -0.002]; P = .03). In studies with minimal accounting for SDOH (<5-point score), Black patients had significantly higher PCSM compared with White patients (HR, 1.29; 95% CI, 1.17-1.41; P < .001). In studies with greater accounting for SDOH variables (≥10-point score), PCSM was significantly lower among Black patients compared with White patients (HR, 0.86; 95% CI, 0.77-0.96; P = .02). The findings of this meta-analysis suggest that there is a significant interaction between race and SDOH with respect to PCSM and OS among men with prostate cancer. Incorporating SDOH variables into data collection and analyses are vital to developing strategies for achieving equity.

BackgroundDecipher Biopsy is a commercially available gene expression classifier used in risk stratification of newly diagnosed prostate cancer (PCa). Currently, there are no prospective data evaluating its clinical utility. We seek to assess the clinical utility of Decipher Biopsy in localized PCa patients.MethodsA multi-institutional study of 855 men who underwent Decipher Biopsy testing between February 2015 and October 2019. All patients were tracked through the prospective Michigan Urological Surgery Improvement Collaborative and linked to the Decipher Genomics Resource Information Database (GRID®; NCT02609269). Patient matching was performed by an independent third-party (ArborMetrix Inc.) using two or more unique identifiers. Cumulative incidence curves for time to treatment (TTT) and time to failure (TTF) were constructed using Kaplan–Meier estimates. Multivariable Cox proportional hazard models were used to evaluate the independent association of high-risk Decipher scores with the conversion from AS to radical therapy and treatment failure (biochemical failure or receipt of salvage therapy).Results and limitationsEight hundred fifty-five patients underwent Decipher Biopsy testing during the study period. Of the 855 men, 264 proceeded to AS (31%), and 454 (53%) received radical therapy. In men electing AS, after adjusting for NCCN risk group, age, PSA, prostate volume, body mass index, and percent positive cores, a high-risk Decipher score was independently associated with shorter TTT (HR 2.51, 95% CI 1.52–4.13 p < 0.001). Similarly, in patients that underwent radical therapy, a high-risk Decipher score was independently associated with TTF (HR 2.98, 95% CI 1.22–7.29, p = 0.01) on multivariable analysis. Follow-up time was a limitation.ConclusionIn a prospective statewide registry, high-risk Decipher Biopsy score was strongly and independently associated with conversion from AS to definitive treatment and treatment failure. These real-world data support the clinical utility of Decipher Biopsy. An ongoing phase 3 randomized trial (NCT04396808) will provide level 1 evidence of the clinical impact of Decipher biopsy testing.

Abstract Background Issues with randomized controlled trial generalizability are well described, but whether these issues result from differences in patient treatment across contexts remains unknown. We studied treatment of patients with high-risk prostate cancer after radical prostatectomy inside and outside a randomized controlled trial evaluating the impact of a genomic classifier on post–radical prostatectomy treatment decision making (Genomics in Michigan Impacting Observation of Radiation [G-MINOR]; ClinicalTrials.gov identifier NCT02783950). Methods G-MINOR enrolled 338 patients; propensity score–matched eligible but unenrolled patient cohorts (pretrial and trial contemporary) from the Michigan Urological Surgery Improvement Collaborative (MUSIC), in which the trial was embedded, were compared for rates and time to secondary treatment (adjuvant or salvage therapy) after prostatectomy. Results Among 338 patients in the G-MINOR cohort, 69 (31 adjuvant, 38 salvage) received secondary treatment compared with 266 (183 adjuvant, 83 salvage) and 104 (60 adjuvant, 44 salvage) in the 1014 contemporary and 338 pretrial–matched MUSIC cohorts. Time to secondary treatment was much shorter in the MUSIC cohort across all comparisons. For example, matching G-MINOR to synchronous MUSIC patients demonstrated 84% vs 74% estimated 2-year treatment-free survival for trial and real-world patients, respectively (P < .001). Conclusions Controlling for key clinicopathologic factors, patients in the G-MINOR randomized controlled trial and MUSIC cohorts were treated differently, even after stratifying by genomic risk. These findings suggest that challenges in randomized controlled trial generalizability extend beyond the representativeness of trial participants. Differences in management may also explain why divergent patient outcomes are observed in randomized controlled trials vs real-world settings.

Quasi-Newton methods in conjunction with the piecewise sequential quadratic programming are investigated for solving mathematical programming with equilibrium constraints, in particular for problems with complementarity constraints. Local convergence as well as superlinear convergence of these quasi-Newton methods can be established under suitable assumptions. In particular, several well-known quasi-Newton methods such as BFGS and DFP are proved to exhibit the local and superlinear convergence. [PUBLICATION ABSTRACT]

Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multimodal tasks, and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention, and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.