An ERK5-NRF2 Axis Mediates Senescence-Associated Stemness and Atherosclerosis.

BACKGROUND: ERK5 (extracellular signal-regulated kinase 5) is a dual kinase transcription factor containing an N-terminal kinase domain and a C-terminal transcriptional activation domain. Many ERK5 kinase inhibitors have been developed and tested to treat cancer and inflammatory diseases. However, recent data have raised questions about the role of the catalytic activity of ERK5 in proliferation and inflammation. We aimed to investigate how ERK5 reprograms myeloid cells to the proinflammatory senescent phenotype, subsequently leading to atherosclerosis. METHODS: A ERK5 S496A (dephosphorylation mimic) knock in (KI) mouse model was generated using CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/clustered regularly interspaced short palindromic repeat-associated 9), and atherosclerosis was characterized by hypercholesterolemia induction. The plaque phenotyping in homozygous ERK5 S496A KI and wild type (WT) mice was studied using imaging mass cytometry. Bone marrow-derived macrophages were isolated from hypercholesterolemic mice and characterized using RNA sequencing and functional in vitro approaches, including senescence, mitochondria reactive oxygen species, and inflammation assays, as well as by metabolic extracellular flux analysis. RESULTS: We show that atherosclerosis was inhibited in ERK5 S496A KI mice. Furthermore, ERK5 S496 phosphorylation mediates both senescence-associated secretory phenotype and senescence-associated stemness by upregulating AHR (aryl hydrocarbon receptor) in plaque and bone marrow-derived macrophages isolated from hypercholesterolemic mice. We also discovered that ERK5 S496 phosphorylation could induce NRF2 (NFE2-related factor 2) SUMOylation at a novel K518 site to inhibit NRF2 transcriptional activity without altering ERK5 catalytic activity and mediates oxidized LDL (low-density lipoprotein)-induced senescence-associated secretory phenotype. Specific ERK5 kinase inhibitors (AX15836 and XMD8-92) also inhibited ERK5 S496 phosphorylation, suggesting the involvement of ERK5 S496 phosphorylation in the anti-inflammatory effects of these ERK5 kinase inhibitors. CONCLUSIONS: We discovered a novel mechanism by which the macrophage ERK5-NRF2 axis develops a unique senescence-associated secretory phenotype/stemness phenotype by upregulating AHR to engender atherogenesis. The finding of senescence-associated stemness phenotype provides a molecular explanation to resolve the paradox of senescence in proliferative plaque by permitting myeloid cells to escape the senescence-induced cell cycle arrest during atherosclerosis formation.

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine-learning technique devised to identify patterns of cooccurrence among morphologic features and genomic events. We sequenced 1079 MDS patients and analyzed bone marrow morphologic alterations and other clinical features. A total of 1929 somatic mutations were identified. Five distinct morphologic profiles with unique clinical characteristics were defined. Seventy-seven percent of higher-risk patients clustered in profile 1. All lower-risk (LR) patients clustered into the remaining 4 profiles: profile 2 was characterized by pancytopenia, profile 3 by monocytosis, profile 4 by elevated megakaryocytes, and profile 5 by erythroid dysplasia. These profiles could also separate patients with different prognoses. LR MDS patients were classified into 8 genetic signatures (eg, signature A had TET2 mutations, signature B had both TET2 and SRSF2 mutations, and signature G had SF3B1 mutations), demonstrating association with specific morphologic profiles. Six morphologic profiles/genetic signature associations were confirmed in a separate analysis of an independent cohort. Our study demonstrates that nonrandom or even pathognomonic relationships between morphology and genotype to define clinical features can be identified. This is the first comprehensive implementation of machine-learning algorithms to elucidate potential intrinsic interdependencies among genetic lesions, morphologies, and clinical prognostic in attributes of MDS.

A group-sequential randomized trial design utilizing supplemental trial data.

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

Predicting outcomes of phase III oncology trials with Bayesian mediation modeling of tumor response.

Pivotal cancer trials often fail to yield evidence in support of new therapies thought to offer promising alternatives to standards-of-care. Conducting randomized controlled trials in oncology tends to be considerably more expensive than studies of other diseases with comparable sample size. Moreover, phase III trial design often takes place with a paucity of survival data for experimental therapies. Experts have explained the failures on the basis of design flaws which produce studies with unrealistic expectations. This article presents a framework for predicting outcomes of phase III oncology trials using Bayesian mediation models. Predictions, which arise from interim analyses, derive from multivariate modeling of the relationships among treatment, tumor response, and their conjoint effects on survival. Acting as a safeguard against inaccurate pre-trial design assumptions, the methodology may better facilitate rapid closure of negative studies. Additionally the models can be used to inform re-estimations of sample size for under-powered trials that demonstrate survival benefit via tumor response mediation. The methods are applied to predict the outcomes of two colorectal cancer studies. Simulation is used to evaluate and compare models in the absence versus presence of reliable surrogate markers of survival.

Bayesian basket trial design with false-discovery rate control.

BACKGROUND: Recent advances in developing "tumor agnostic" oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed. METHODS: The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes. RESULTS: Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets. CONCLUSION: Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.

Outcomes with Partial Breast Irradiation vs. Whole Breast Irradiation: a Meta-Analysis.

BACKGROUND: Several randomized trials have been performed comparing partial breast irradiation (PBI) and whole breast irradiation (WBI) though controversy remains, including regarding differences by PBI technique. We performed a meta-analysis to compare results between WBI versus PBI and between PBI techniques. METHODS: A systematic review was performed to identify modern randomized studies listed in MEDLINE from 2005 to 2020. PBI trials were divided into external beam radiation and brachytherapy techniques, with intraoperative radiation excluded. A Bayesian logistic regression model evaluated the risk of ipsilateral breast tumor recurrence (IBTR) and acute and chronic toxicities. The primary outcome was IBTR at 5 years with WBI compared with PBI. RESULTS: A total of 9758 patients from 7 studies were included (4840-WBI, 4918-PBI). At 5 years, no statistically significant difference in the rate of IBTR was noted between PBI (1.8%, 95% HPD 0.68-3.2%) and WBI (1.7%, 95% HPD 0.92-2.4%). By PBI technique, the 5-year rate of IBTR rate for external beam was 1.7% and 2.2% for brachytherapy. Rates of grade 2 + acute toxicity were 7.1% with PBI versus 47.5% with WBI. For late toxicities, grade 2/3 rates were 0%/0% with PBI compared with 1.0%/0% with WBI. CONCLUSIONS: IBTR rates were similar between PBI and WBI with no significant differences noted by PBI technique; PBI had reduced acute toxicities compared to WBI. Because studies did not provide toxicity data in a consistent fashion, definitive conclusions cannot be made with additional data from randomized trials needed to compare toxicity profiles between PBI techniques.

Calibrated dynamic borrowing using capping priors.

Multisource exchangeability models (MEMs), a BayeTsian approach for dynamically integrating information from multiple clinical trials, are a promising approach for gaining efficiency in randomized controlled trials. When the supplementary trials are considerably larger than the primary trial, care must be taken when integrating supplementary data to avoid overwhelming the primary trial. In this paper, we propose "capping priors," which controls the extent of dynamic borrowing by placing an a priori cap on the effective supplemental sample size. We demonstrate the behavior of this technique via simulation, and apply our method to four randomized trials of very low nicotine content cigarettes.

The Diminishing Impact of Margin Definitions and Width on Local Recurrence Rates following Breast-Conserving Therapy for Early-Stage Invasive Cancer: A Meta-Analysis.

BACKGROUND: The optimal tumor-free margin definition and width following breast-conserving therapy (BCT) for early-stage invasive cancers has been evaluated in previous meta-analyses and guidelines. We performed an updated meta-analysis to assess how improvements in treatment over time have affected the impact of margins on local recurrence (LR) rates over time. METHODS: A systematic literature review identified 38 eligible studies comprising 54,502 patients treated between 1968 and 2010. Inclusion criteria included patients treated with BCT and minimum follow-up of 50 months, pathologic definitions of margin status explicitly stated, and LR data in relation to margin status. Data were pooled using a Bayesian logistic regression model to evaluate the risk of LR in relation to both margin status and study enrollment periods. RESULTS: Median follow-up was 7.25 years. Absolute LR rates decreased over time for each margin width cohort, with maximum differences between negative margin groups of less than 1% for the most recent enrollment period. However, relative rates of LR between different margin groups remained stable over time. CONCLUSIONS: With an additional 22,000 patients compared with the previous meta-analysis, this updated meta-analysis supports the consensus guideline of "no tumor on ink" for the majority of patients. Additionally, while concerns exist regarding a benefit with wider margins from previous studies, the analysis demonstrates the impact of margin width on LR rates has declined substantially over time, with very small differences between the narrowest and widest margin groups in the most recent cohort. Hence, older studies appear to have limited value to inform current management guidelines.

Individualised axitinib regimen for patients with metastatic renal cell carcinoma after treatment with checkpoint inhibitors: a multicentre, single-arm, phase 2 study.

BACKGROUND: Checkpoint inhibitor therapy is a standard of care for patients with metastatic renal cell carcinoma. Treatment options after checkpoint inhibitor therapy include vascular endothelial growth factor receptor (VEGF-R) tyrosine kinase inhibitors, although no prospective data regarding their use in this setting exist. Axitinib is a VEGF-R inhibitor with clinical data supporting increased activity with dose titration. We aimed to investigate the activity of dose titrated axitinib in patients with metastatic renal cell carcinoma who were previously treated with checkpoint inhibitor. METHODS: We did a multicentre, phase 2 trial of axitinib given on an individualised dosing algorithm. Patients at least 18 years of age with histologically or cytologically confirmed locally recurrent or metastatic renal cell carcinoma with clear cell histology, a Karnofsky Performance Status of 70% or more, and measurable disease who received checkpoint inhibitor therapy as the most recent treatment were eligible. There was no limit on number of previous therapies received. Patients received oral axitinib at a starting dose of 5 mg twice daily with dose titration every 14 days in 1 mg increments (ie, 5 mg twice daily to 6 mg twice daily, up to 10 mg twice daily maximum dose) if there was no axitinib-related grade 2 or higher mucositis, diarrhoea, hand-foot syndrome, or fatigue. If one or more of these grade 2 adverse events occurred, axitinib was withheld for 3 days before the same dose was resumed. Dose reductions were made if recurrent grade 2 adverse events despite treatment breaks or grade 3-4 adverse events occurred. The primary outcome was progression-free survival. Analyses were done per protocol in all patients who received at least one dose of axitinib. Recruitment has been completed and the trial is ongoing. This trial is registered with ClincalTrials.gov, number NCT02579811. FINDINGS: Between Jan 5, 2016 and Feb 21, 2018, 40 patients were enrolled and received at least one dose of study treatment. With a median follow-up of 8·7 months (IQR 3·7-14·2), the median progression-free survival was 8·8 months (95% CI 5·7-16·6). Fatigue (83%) and hypertension (75%) were the most common all-grade adverse events. The most common grade 3 adverse event was hypertension (24 patients [60%]). There was one (3%) grade 4 adverse event (elevated lipase) and no treatment-related deaths occurred. Serious adverse events that were likely related to therapy occurred in eight (20%) patients; the most common were dehydration (n=4) and diarrhoea (n=2). INTERPRETATION: Individualised axitinib dosing in patients with metastatic renal cell inoma previously treated with checkpoint inhibitors did not meet the prespecified threshold for progression free survival, but these data show that this individualised titration scheme is feasible and has robust clinical activity. These prospective results warrant consideration of axitinib in this setting. FUNDING: Pfizer.

Feasibility of fine-needle aspiration for assessing responses to chemotherapy in metastatic nodes marked with clips in breast cancer: A prospective registry study.

BACKGROUND: Clips are often placed to mark axillary nodes with biopsy-confirmed metastases in patients with breast cancer. The evaluation of clipped nodes after chemotherapy can identify patients who have eradication of nodal disease. The goal of this study was to determine whether preoperative fine-needle aspiration (FNA) of clipped nodes after neoadjuvant chemotherapy (NAC) could predict the presence of residual disease. METHODS: This prospective registry study enrolled 50 patients with a clip placed to mark nodes with biopsy-confirmed metastases who had completed NAC. Participants underwent FNA of the clipped node before seed-localized lymph node excision. FNA pathology was compared with surgical pathology. RESULTS: There were 36 patients (72%) with residual disease on surgical pathology: 3 (8%) had a nondiagnostic aspirate, carcinoma was seen in 14 (39%), and 19 (53%) had a false-negative result. The sensitivity of FNA was 42.4%, its specificity was 100%, and its negative predictive value was 40.6%. In a univariate analysis, the odds of a true-positive result increased significantly with the mean initial size of the clipped node (odds ratio [OR], 4.3; P = .004) and the size of the metastatic focus after NAC (OR, 1.3; P = 0.003), whereas normalization of nodes after chemotherapy (OR, 0.1) and a lack of response on ultrasound (OR, 0.11) were associated with a false-negative result (P = .01). CONCLUSIONS: FNA of marked nodes after chemotherapy has a high false-negative rate. This highlights the need for surgical staging of the axilla after NAC to assess the response.

Bayesian hierarchical modeling based on multisource exchangeability.

Bayesian hierarchical models produce shrinkage estimators that can be used as the basis for integrating supplementary data into the analysis of a primary data source. Established approaches should be considered limited, however, because posterior estimation either requires prespecification of a shrinkage weight for each source or relies on the data to inform a single parameter, which determines the extent of influence or shrinkage from all sources, risking considerable bias or minimal borrowing. We introduce multisource exchangeability models (MEMs), a general Bayesian approach for integrating multiple, potentially non-exchangeable, supplemental data sources into the analysis of a primary data source. Our proposed modeling framework yields source-specific smoothing parameters that can be estimated in the presence of the data to facilitate a dynamic multi-resolution smoothed estimator that is asymptotically consistent while reducing the dimensionality of the prior space. When compared with competing Bayesian hierarchical modeling strategies, we demonstrate that MEMs achieve approximately 2.2 times larger median effective supplemental sample size when the supplemental data sources are exchangeable as well as a 56% reduction in bias when there is heterogeneity among the supplemental sources. We illustrate the application of MEMs using a recently completed randomized trial of very low nicotine content cigarettes, which resulted in a 30% improvement in efficiency compared with the standard analysis.

Phase II study of Dovitinib in recurrent glioblastoma.

INTRODUCTION: Dovitinib is an oral, potent inhibitor of FGFR and VEGFR, and can be a promising strategy in patients with recurrent or progressive glioblastoma (GBM). METHODS: This was an open label phase II study of two arms: Arm 1 included anti-angiogenic naïve patients with recurrent GBM and Arm 2 included patients with recurrent GBM that had progressed on prior anti-angiogenic therapy. Nineteen subjects were enrolled in Arm 1 and 14 subjects in Arm 2. The primary endpoint was 6-month progression-free survival (PFS-6) in Arm 1 and time to progression (TTP) in Arm 2. The secondary endpoints were toxicity, objective response rate (ORR) and overall survival. RESULTS: Patients in Arm 2 (compared to Arm 1) tended to have longer intervals from diagnosis to study entry (median 26.9 vs. 8.9 months, p = 0.002), experienced more recurrences (64%, had 3-4 prior recurrences compared to 0, p < 0.0001) and tended to be heavily pretreated (71% vs. 26-32% p = 0.04 or 0.02). 6-month PFS was 12% ± 6% for the Arm 1 and 0% for Arm 2. TTP was similar in both treatment arms (median 1.8 months Arm 1 and 0.7-1.8 months Arm 2, p = 0.36). Five patients (15%) had grade 4 toxicities and 22 patients (67%) had grade 3 toxicities. There were no significant differences between the two arms with respect to the amount of change in the levels of biomarkers from baseline. CONCLUSION: Dovitinib was not efficacious in prolonging the PFS in patients with recurrent GBM irrespective of prior treatment with anti-angiogenic therapy (including bevacizumab).

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants.

The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.

A Clinical Feasibility Trial for Identification of Exceptional Responders in Whom Breast Cancer Surgery Can Be Eliminated Following Neoadjuvant Systemic Therapy.

OBJECTIVE: To determine the accuracy of fine-needle aspiration (FNA) and vacuum-assisted core biopsy (VACB) in assessing the presence of residual cancer in the breast after neoadjuvant systemic therapy (NST). SUMMARY BACKGROUND DATA: Pathologic complete response (pCR) rates after NST have improved dramatically, suggesting that surgery might be avoided in some patients. Safe avoidance of surgery would require accurate confirmation of no residual invasive/in situ carcinoma. METHODS: Forty patients with T1-3N0-3 triple-negative or HER2-positive cancer receiving NST were enrolled in this single-center prospective trial. Patients underwent ultrasound-guided or mammography-guided FNA and VACB of the initial breast tumor region before surgery. Findings were compared with findings on pathologic evaluation of surgical specimens to determine the performance of biopsy in predicting residual breast disease after NST. RESULTS: Median initial clinical tumor size was 3.3 cm (range, 1.2-7.0 cm); 16 patients (40%) had biopsy-proven nodal metastases. After NST, median clinical tumor size was 1.1 cm (range, 0-4.2 cm). Nineteen patients (47.5%) had a breast pCR and were concordant with pathologic nodal status in 97.5%. Combined FNA/VACB demonstrated an accuracy of 98% (95% CI, 87%-100%), false-negative rate of 5% (95% CI, 0%-24%), and negative predictive value of 95% (95% CI, 75%-100%) in predicting residual breast cancer. VACB alone was more accurate than FNA alone (P = 0.011). CONCLUSIONS: After NST, image-guided FNA/VACB can accurately identify patients with a breast pCR. Based on these results, a prospective clinical trial has commenced in which breast surgery is omitted in patients with a breast pCR after NST according to image-guided biopsy.

A multi-source adaptive platform design for testing sequential combinatorial therapeutic strategies.

Traditional paradigms for clinical translation are challenged in settings where multiple contemporaneous therapeutic strategies have been identified as potentially beneficial. Platform trials have emerged as an approach for sequentially comparing multiple trials using a single protocol. The Ebola virus disease outbreak in West Africa represents one recent example which utilized a platform design. Specifically, the PREVAIL II master protocol sequentially tested new combinations of therapies against the concurrent, optimal standard of care (oSOC) strategy. Once a treatment demonstrated sufficient evidence of benefit, the treatment was added to the oSOC for all future comparisons (denoted as segments throughout the manuscript). In the interest of avoiding bias stemming from population drift, PREVAIL II considered only within-segment comparisons between the oSOC and novel treatments and failed to leverage data from oSOC patients in prior segments. This article describes adaptive design methodology aimed at boosting statistical power through Bayesian modeling and adaptive randomization. Specifically, the design uses multi-source exchangeability models to combine data from multiple segments and adaptive randomization to achieve information balance within a segment. When compared to the PREVAIL II design, we demonstrate that our proposed adaptive platform design improves power by as much as 51% with limited type-I error inflation. Further, the adaptive platform effectuates more balance with respect to the distribution of acquired information among study arms, with more patients randomized to experimental regimens.

Bayesian basket trial design with exchangeability monitoring.

Precision medicine endeavors to conform therapeutic interventions to the individuals being treated. Implicit to the concept of precision medicine is heterogeneity of treatment benefit among patients and patient subpopulations. Thus, precision medicine challenges conventional paradigms of clinical translational which have relied on estimates of population-averaged effects to guide clinical practice. Basket trials comprise a class of experimental designs used to study solid malignancies that are devised to evaluate the effectiveness of a therapeutic strategy among patients defined by the presence of a particular drug target (often a genetic mutation) rather than a particular tumor histology. Acknowledging the potential for differential effectiveness on the basis of traditional criteria for cancer subtyping, evaluations of treatment effectiveness are conducted with respect to the "baskets" which collectively represent a partition of the targeted patient population consisting of discrete subtypes. Yet, designs of early basket trials have been criticized for their reliance on basketwise analysis strategies that suffered from limited power in the presence of imbalanced enrollment as well as failed to convey to the clinical community evidentiary measures for consistent effectiveness among the studied clinical subtypes. This article presents novel methodology for sequential basket trial design formulated with Bayesian monitoring rules. Interim analyses are based a novel hierarchical modeling strategy for sharing information among a collection of discrete potentially nonexchangeable subtypes. The methodology is demonstrated by analysis as well as permutation and simulation studies based on a recent basket trial designed to estimate the effectiveness of vemurafenib in BRAFV600 mutant non-melanoma among six primary disease sites and histologies.

Differentiation of Malignant and Benign Adrenal Lesions With Delayed CT: Multivariate Analysis and Predictive Models.

OBJECTIVE: The purpose of this study is to identify imaging and patient parameters that affect the diagnostic performance of delayed contrast-enhanced CT for distinguishing malignant from benign adrenal lesions larger than 1 cm in adult patients and to derive predictive models. MATERIALS AND METHODS: This retrospective study assessed 97 pathologically proven adrenal lesions that had undergone unenhanced, portal venous, and 15-minute delayed CT. Quantitatively, single-parameter evaluations of lesion attenuation (in Hounsfield units) and absolute percentage enhancement washout (APEW) and relative percentage enhancement washout (RPEW) were performed. In addition, descriptive CT features (lesion size, margin definition, heterogeneity vs homogeneity, fat, and calcification) and patients' demographic characteristics and medical history of malignancy were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Areas under the ROC curve (Az) were determined for univariate and multivariate analyses. Leave-one-lesion-out cross-validation was applied to ascertain the predictive performance of single-parameter and multivariate evaluations. RESULTS: The Az values for unenhanced attenuation, portal venous attenuation, delayed attenuation, APEW, and RPEW were 0.835, 0.534, 0.847, 0.792, and 0.871, respectively. Multivariate analyses revealed that portal venous attenuation, delayed attenuation, and APEW were significant features, with an Az of 0.923 when combined. The addition of the descriptive CT features increased the Az to 0.938; patient age and a history of malignancy were additional significant factors, increasing the Az to 0.956 and 0.972, respectively. The combined predictive classifier yielded 89% accuracy under cross-validation, compared with the best commonly applied single-parameter evaluation (77% for RPEW < 40%). CONCLUSION: Multivariate imaging evaluation applied to delayed contrast-enhanced CT alone, with or without patient characteristics, improves diagnostic performance for characterizing adrenal lesions beyond those of single-parameter evaluations. Predictive formulas assessing the probabilities of lesion benignity or malignancy are provided.

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

OBJECTIVE: The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. MATERIALS AND METHODS: This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (Az) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. RESULTS: Az for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated Az when combined: RPEW and DL (Az = 0.861) when unenhanced images were not available and APEW and UA (Az = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing Az to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. CONCLUSION: When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

Quantification of the Effect of Shuttling on Computed Tomography Perfusion Parameters by Investigation of Aortic Inputs on Different Table Positions From Shuttle-Mode Scans of Lung and Liver Tumors.

OBJECTIVES: The aim of this study was to quantify the effect of shuttling on computed tomography perfusion (CTp) parameters derived from shuttle-mode body CT images using aortic inputs from different table positions. METHODS: Axial shuttle-mode CT scans were acquired from 6 patients (10 phases, 2 nonoverlapping table positions 1.4 seconds apart) after contrast agent administration. Artifacts resulting from the shuttling motion were corrected with nonrigid registration before computing CTp maps from 4 aortic levels chosen from the most superior and inferior slices of each table position scan. The effect of shuttling on CTp parameters was estimated by mean differences in mappings obtained from aortic inputs in different table positions. Shuttling effect was also quantified using 95% limits of agreement of CTp parameter differences within-table and between-table aortic positions from the interaortic mean CTp values. RESULTS: Blood flow, permeability surface, and hepatic arterial fraction differences were insignificant (P > 0.05) for both within-table and between-table comparisons. The 95% limits of agreement for within-table blood volume (BV) value deviations obtained from lung tumor regions were less than 4.7% (P = 0.18) compared with less than 12.2% (P = 0.003) for between-table BV value deviations. The 95% limits of agreement of within-table deviations for liver tumor regions were less than 1.9% (P = 0.55) for BV and less than 3.2% (P = 0.23) for mean transit time, whereas between-table BV and mean transit time deviations were less than 11.7% (P < 0.01) and less than 14.6% (P < 0.01), respectively. Values for normal liver tissue regions were concordant. CONCLUSIONS: Computed tomography perfusion parameters acquired from aortic levels within-table positions generally yielded higher agreement than mappings obtained from aortic levels between-table positions indicating differences due to shuttling effect.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence.

Neuroimaging and genetic studies provide distinct and complementary information about the structural and biological aspects of a disease. Integrating the two sources of data facilitates the investigation of the links between genetic variability and brain mechanisms among different individuals for various medical disorders. This article presents a general statistical framework for integrative Bayesian analysis of neuroimaging-genetic (iBANG) data, which is motivated by a neuroimaging-genetic study in cocaine dependence. Statistical inference necessitated the integration of spatially dependent voxel-level measurements with various patient-level genetic and demographic characteristics under an appropriate probability model to account for the multiple inherent sources of variation. Our framework uses Bayesian model averaging to integrate genetic information into the analysis of voxel-wise neuroimaging data, accounting for spatial correlations in the voxels. Using multiplicity controls based on the false discovery rate, we delineate voxels associated with genetic and demographic features that may impact diffusion as measured by fractional anisotropy (FA) obtained from DTI images. We demonstrate the benefits of accounting for model uncertainties in both model fit and prediction. Our results suggest that cocaine consumption is associated with FA reduction in most white matter regions of interest in the brain. Additionally, gene polymorphisms associated with GABAergic, serotonergic and dopaminergic neurotransmitters and receptors were associated with FA.