Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group.

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

CALGB 80802 (Alliance): Impact of Sorafenib with and without Doxorubicin on Hepatitis C Infection in Patients with Advanced Hepatocellular Carcinoma.

Sorafenib blocks nonstructural protein 5A (NS5A)-recruited c-Raf-mediated hepatitis C virus (HCV) replication and gene expression. Release of Raf-1-Ask-1 dimer and inhibition of Raf-1 via sorafenib putatively differ in the presence or absence of doxorubicin. Cancer and Leukemia Group B (CALGB) 80802 (Alliance) randomized phase III trial of doxorubicin plus sorafenib versus sorafenib in patients with advanced hepatocellular carcinoma (HCC), showed no improvement in median overall survival (OS). Whether HCV viral load impacts therapy and whether any correlation between HCV titers and outcome based on HCV was studied. In patients with HCV, HCV titer levels were evaluated at baseline and at multiple postbaseline timepoints until disease progression or treatment discontinuation. HCV titer levels were evaluated in relation to OS and progression-free survival (PFS). Among 53 patients with baseline HCV data, 12 patients had undetectable HCV (HCV-UN). Postbaseline HCV titer levels did not significantly differ between treatment arms. One patient in each arm went from detectable to HCV-UN with greater than 2 log-fold titer levels reduction. Aside from these 2 HCV-UN patients, HCV titers remained stable on treatment. Patients who had HCV-UN at baseline were 3.5 times more likely to progress and/or die from HCC compared with HCV detectable (HR = 3.51; 95% confidence interval: 1.58-7.78; P = 0.002). HCV titer levels remained unchanged, negating any sorafenib impact onto HCV titer levels. Although an overall negative phase III study, patients treated with doxorubicin plus sorafenib and sorafenib only, on CALGB 80802 had worse PFS if HCV-UN. Higher levels of HCV titers at baseline were associated with significantly improved PFS. SIGNIFICANCE: Sorafenib therapy for HCC may impact HCV replication and viral gene expression. In HCV-positive patients accrued to CLAGB 80802 phase III study evaluating the addition of doxorubicin to sorafenib, HCV titer levels were evaluated at baseline and different timepoints. Sorafenib did not impact HCV titer levels. Despite an improved PFS in patients with detectable higher level HCV titers at baseline, no difference in OS was noted.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. METHODS: We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. RESULTS: Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. CONCLUSION: Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. CLINICAL RELEVANCE STATEMENT: This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. KEY POINTS: • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Three-Dimensional Margin as a Predictor of Local Tumor Progression after Microwave Ablation: Intraprocedural versus 4-8-Week Postablation Assessment.

PURPOSE: To evaluate the prognostic accuracy of intraprocedural and 4-8-week (current standard) post-microwave ablation zone (AZ) and margin assessments for prediction of local tumor progression (LTP) using 3-dimensional (3D) software. MATERIALS AND METHODS: Data regarding 100 colorectal liver metastases (CLMs) in 75 patients were collected from 2 prospective fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT)-guided microwave ablation (MWA) trials. The target CLMs and theoretical 5- and 10-mm margins were segmented and registered intraprocedurally and at 4-8 weeks after MWA contrast-enhanced CT (or magnetic resonance [MR] imaging) using the same methodology and 3D software. Tumor and 5- and 10-mm minimal margin (MM) volumes not covered by the AZ were defined as volumes of insufficient coverage (VICs). The intraprocedural and 4-8-week post-MWA VICs were compared as predictors of LTP using receiver operating characteristic curve analysis. RESULTS: The median follow-up time was 19.6 months (interquartile range, 7.97-36.5 months). VICs for 5- and 10-mm MMs were predictive of LTP at both time assessments. The highest accuracy for the prediction of LTP was documented with the intra-ablation 5-mm VIC (area under the curve [AUC], 0.78; 95% confidence interval, 0.66-0.89). LTP for a VIC of 6-10-mm margin category was 11.4% compared with 4.3% for >10-mm margin category (P < .001). CONCLUSIONS: A 3D 5-mm MM is a critical endpoint of thermal ablation, whereas optimal local tumor control is noted with a 10-mm MM. Higher AUCs for prediction of LTP were achieved for intraprocedural evaluation than for the 4-8-week postablation 3D evaluation of the AZ.

Integrating [18F]-Fluorodeoxyglucose Positron Emission Tomography with Computed Tomography with Radiation Therapy and Immunomodulation in Precision Therapy for Solid Tumors.

[18F]-FDG positron emission tomography with computed tomography (PET/CT) imaging is widely used to enhance the quality of care in patients diagnosed with cancer. Furthermore, it holds the potential to offer insight into the synergic effect of combining radiation therapy (RT) with immuno-oncological (IO) agents. This is achieved by evaluating treatment responses both at the RT and distant tumor sites, thereby encompassing the phenomenon known as the abscopal effect. In this context, PET/CT can play an important role in establishing timelines for RT/IO administration and monitoring responses, including novel patterns such as hyperprogression, oligoprogression, and pseudoprogression, as well as immune-related adverse events. In this commentary, we explore the incremental value of PET/CT to enhance the combination of RT with IO in precision therapy for solid tumors, by offering supplementary insights to recently released joint guidelines.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma.

Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.

Vascular Immune Evasion of Mesenchymal Glioblastoma Is Mediated by Interaction and Regulation of VE-Cadherin on PD-L1.

The mesenchymal subtype of glioblastoma (mGBM), which is characterized by rigorous vasculature, resists anti-tumor immune therapy. Here, we investigated the mechanistic link between tumor vascularization and the evasion of immune surveillance. Clinical datasets with GBM transcripts showed that the expression of the mesenchymal markers YKL-40 (CHI3L1) and Vimentin is correlated with elevated expression of PD-L1 and poor disease survival. Interestingly, the expression of PD-L1 was predominantly found in vascular endothelial cells. Orthotopic transplantation of glioma cells GL261 over-expressing YKL-40 in mice showed increased angiogenesis and decreased CD8+ T cell infiltration, resulting in a reduction in mouse survival. The exposure of recombinant YKL-40 protein induced PD-L1 and VE-cadherin (VE-cad) expression in endothelial cells and drove VE-cad-mediated nuclear translocation of ß-catenin/LEF, where LEF upregulated PD-L1 expression. YKL-40 stimulated the dissociation of VE-cad from PD-L1, rendering PD-L1 available to interact with PD-1 from CD8+-positive TALL-104 lymphocytes and inhibit TALL-104 cytotoxicity. YKL-40 promoted TALL-104 cell migration and adhesion to endothelial cells via CCR5-dependent chemotaxis but blocked its anti-vascular immunity. Knockdown of VE-cad or the PD-L1 gene ablated the effects of YKL-40 and reinvigorated TALL-104 cell immunity against vessels. In summary, our study demonstrates a novel vascular immune escape mechanism by which mGBM promotes tumor vascularization and malignant transformation.

Modeling Tumor Growth Using Partly Conditional Survival Models: A Case Study in Colorectal Cancer.

PURPOSE: There are multiple approaches to modeling the relationship between longitudinal tumor measurements obtained from serial imaging and overall survival. Many require strong assumptions that are untestable and debatable. We illustrate how to apply a novel, more flexible approach, the partly conditional (PC) survival model, using images acquired during a phase III, randomized clinical trial in colorectal cancer as an example. METHODS: PC survival approaches were used to model longitudinal volumetric computed tomography data of 1,025 patients in the completed VELOUR trial, which evaluated adding aflibercept to infusional fluorouracil, leucovorin, and irinotecan for treating metastatic colorectal cancer. PC survival modeling is a semiparametric approach to estimating associations of longitudinal measurements with time-to-event outcomes. Overall survival was our outcome. Covariates included baseline tumor burden, change in tumor burden from baseline to each follow-up time, and treatment. Both unstratified and time-stratified models were investigated. RESULTS: Without making assumptions about the distribution of the tumor growth process, we characterized associations between the change in tumor burden and survival. This change was significantly associated with survival (hazard ratio [HR], 1.04; 95% CI, 1.02 to 1.05; P < .001), suggesting that aflibercept works at least in part by altering the tumor growth trajectory. We also found baseline tumor size prognostic for survival even when accounting for the change in tumor burden over time (HR, 1.02; 95% CI, 1.01 to 1.02; P < .001). CONCLUSION: The PC modeling approach offers flexible characterization of associations between longitudinal covariates, such as serially assessed tumor burden, and survival time. It can be applied to a variety of data of this nature and used as clinical trials are ongoing to incorporate new disease assessment information as it is accumulated, as indicated by an example from colorectal cancer.

Fluoxetine restrains allergic inflammation by targeting an FcɛRI-ATP positive feedback loop in mast cells.

There is a clinical need for new treatment options addressing allergic disease. Selective serotonin reuptake inhibitors (SSRIs) are a class of antidepressants that have anti-inflammatory properties. We tested the effects of the SSRI fluoxetine on IgE-induced function of mast cells, which are critical effectors of allergic inflammation. We showed that fluoxetine treatment of murine or human mast cells reduced IgE-mediated degranulation, cytokine production, and inflammatory lipid secretion, as well as signaling mediated by the mast cell activator ATP. In a mouse model of systemic anaphylaxis, fluoxetine reduced hypothermia and cytokine production. Fluoxetine was also effective in a model of allergic airway inflammation, where it reduced bronchial responsiveness and inflammation. These data show that fluoxetine suppresses mast cell activation by impeding an FcɛRI-ATP positive feedback loop and support the potential repurposing of this SSRI for use in allergic disease.

The Normal Range of Baseline Tryptase Should Be 1 to 15 ng/mL and Covers Healthy Individuals With HαT.

Physiological levels of basal serum tryptase vary among healthy individuals, depending on the numbers of mast cells, basal secretion rate, copy numbers of the TPSAB1 gene encoding alpha tryptase, and renal function. Recently, there has been a growing debate about the normal range of tryptase because individuals with the hereditary alpha tryptasemia (HαT) trait may or may not be symptomatic, and if symptomatic, uncertainty exists as to whether this trait directly causes clinical phenotypes or aggravates certain conditions. In fact, most HαT-positive cases are regarded as asymptomatic concerning mast cell activation. To address this point, experts of the European Competence Network on Mastocytosis (ECNM) and the American Initiative in Mast Cell Diseases met at the 2022 Annual ECNM meeting and discussed the physiological tryptase range. Based on this discussion, our faculty concluded that the normal serum tryptase range should be defined in asymptomatic controls, inclusive of individuals with HαT, and based on 2 SDs covering the 95% confidence interval. By applying this definition in a literature screen, the normal basal tryptase in asymptomatic controls (HαT-positive persons included) ranges between 1 and 15 ng/mL. This definition should avoid overinterpretation, unnecessary referrals, and unnecessary anxiety or anticipatory fear of illness in healthy individuals.

Immunotherapy in the Treatment of Localized Genitourinary Cancers.

Importance: A true revolution in the management of advanced genitourinary cancers has occurred with the discovery and adoption of immunotherapy (IO). The therapeutic benefits of IO were recently observed not to be solely confined to patients with disseminated disease but also in select patients with localized and locally advanced genitourinary neoplasms. Observations: KEYNOTE-057 demonstrated the benefit of pembrolizumab monotherapy for treating high-risk nonmuscle invasive bladder cancer unresponsive to bacillus Calmette-Guérin (BCG), resulting in recent US Food and Drug Administration approval. Furthermore, a current phase 3 trial (Checkmate274) demonstrated a disease-free survival benefit with the administration of adjuvant nivolumab vs placebo in muscle-invasive urothelial carcinoma after radical cystectomy. In addition, the recent highly publicized phase 3 KEYNOTE 564 trial demonstrated a recurrence-free survival benefit of adjuvant pembrolizumab in patients with high-risk localized/locally advanced kidney cancer. Conclusions and Relevance: The adoption and integration of IO in the management of localized genitourinary cancers exhibiting aggressive phenotypes are becoming an emerging therapeutic paradigm. Clinical oncologists and scientists should become familiar with these trials and indications because they are likely to dramatically change our treatment strategies in the months and years to come.

Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials.

BACKGROUND: Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies. PURPOSE: We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy. METHODS: We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity. RESULTS: In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023). CONCLUSION: This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment. CLINICAL RELEVANCE STATEMENT: Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health. KEY POINTS: •Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).

Harnessing imaging tools to guide immunotherapy trials: summary from the National Cancer Institute Cancer Imaging Steering Committee workshop.

As the immuno-oncology field continues the rapid growth witnessed over the past decade, optimising patient outcomes requires an evolution in the current response-assessment guidelines for phase 2 and 3 immunotherapy clinical trials and clinical care. Additionally, investigational tools-including image analysis of standard-of-care scans (such as CT, magnetic resonance, and PET) with analytics, such as radiomics, functional magnetic resonance agents, and novel molecular-imaging PET agents-offer promising advancements for assessment of immunotherapy. To document current challenges and opportunities and identify next steps in immunotherapy diagnostic imaging, the National Cancer Institute Clinical Imaging Steering Committee convened a meeting with diverse representation among imaging experts and oncologists to generate a comprehensive review of the state of the field.

Randomized Phase III Study of Enzalutamide Compared With Enzalutamide Plus Abiraterone for Metastatic Castration-Resistant Prostate Cancer (Alliance A031201 Trial).

PURPOSE: Enzalutamide and abiraterone both target androgen receptor signaling but via different mechanisms. The mechanism of action of one drug may counteract the resistance pathways of the other. We sought to determine whether the addition of abiraterone acetate and prednisone (AAP) to enzalutamide prolongs overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the first-line setting. PATIENTS AND METHODS: Men with untreated mCRPC were randomly assigned (1:1) to receive first-line enzalutamide with or without AAP. The primary end point was OS. Toxicity, prostate-specific antigen declines, pharmacokinetics, and radiographic progression-free survival (rPFS) were also examined. Data were analyzed using an intent-to-treat approach. The Kaplan-Meier estimate and the stratified log-rank statistic were used to compare OS between treatments. RESULTS: In total, 1,311 patients were randomly assigned: 657 to enzalutamide and 654 to enzalutamide plus AAP. OS was not statistically different between the two arms (median, 32.7 [95% CI, 30.5 to 35.4] months for enzalutamide v 34.2 [95% CI, 31.4 to 37.3] months for enzalutamide and AAP; hazard ratio [HR], 0.89; one-sided P = .03; boundary nominal significance level = .02). rPFS was longer in the combination arm (median rPFS, 21.3 [95% CI, 19.4 to 22.9] months for enzalutamide v 24.3 [95% CI, 22.3 to 26.7] months for enzalutamide and AAP; HR, 0.86; two-sided P = .02). However, pharmacokinetic clearance of abiraterone was 2.2- to 2.9-fold higher when administered with enzalutamide, compared with clearance values for abiraterone alone. CONCLUSION: The addition of AAP to enzalutamide for first-line treatment of mCRPC was not associated with a statistically significant benefit in OS. Drug-drug interactions between the two agents resulting in increased abiraterone clearance may partly account for this result, although these interactions did not prevent the combination regimen from having more nonhematologic toxicity.

Is local review of positron emission tomography scans sufficient in diffuse large B-cell lymphoma clinical trials? A CALGB 50303 analysis.

BACKGROUND: Quantitative methods of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) interpretation, including the percent change in FDG uptake from baseline (ΔSUV), are under investigation in lymphoma to overcome challenges associated with visual scoring systems (VSS) such as the Deauville 5-point scale (5-PS). METHODS: In CALGB 50303, patients with DLBCL received frontline R-CHOP or DA-EPOCH-R, and although there were no significant associations between interim PET responses assessed centrally after cycle 2 (iPET) using 5-PS with progression-free survival (PFS) or overall survival (OS), there were significant associations between central determinations of iPET ∆SUV with PFS/OS. In this patient cohort, we retrospectively compared local vs central iPET readings and evaluated associations between local imaging data and survival outcomes. RESULTS: Agreement between local and central review was moderate (kappa = 0.53) for VSS and high (kappa = 0.81) for ∆SUV categories (<66% vs. ≥66%). ∆SUV ≥66% at iPET was significantly associated with PFS (p = 0.03) and OS (p = 0.002), but VSS was not. Associations with PFS/OS when applying local review vs central review were comparable. CONCLUSIONS: These data suggest that local PET interpretation for response determination may be acceptable in clinical trials. Our findings also highlight limitations of VSS and call for incorporation of more objective measures of response assessment in clinical trials.