Estimation and residual analysis with R for a linear regression model with an interval-censored covariate

A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment

Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1-L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.

Circulating carotenoids are associated with favorable lipid and fatty acid profiles in an older population at high cardiovascular risk

Carotenoid intake has been reported to be associated with improved cardiovascular health, but there is little information on actual plasma concentrations of these compounds as biomarkers of cardiometabolic risk. The objective was to investigate the association between circulating plasma carotenoids and different cardiometabolic risk factors and the plasma fatty acid profile. This is a cross-sectional evaluation of baseline data conducted in a subcohort (106 women and 124 men) of an ongoing multi-factorial lifestyle trial for primary cardiovascular prevention. Plasma concentrations of carotenoids were quantified by liquid chromatography coupled to mass spectrometry. The associations between carotenoid concentrations and cardiometabolic risk factors were assessed using regression models adapted for interval-censored variables. Carotenoid concentrations were cross-sectionally inversely associated with serum triglyceride concentrations [−2.79 mg/dl (95% CI: −4.25, −1.34) and −5.15 mg/dl (95% CI: −7.38, −2.93), p-values = 0.0002 and <0.00001 in women and men, respectively], lower levels of plasma saturated fatty acids [−0.09% (95% CI: −0.14, −0.03) and −0.15 % (95% CI: −0.23, −0.08), p-values = 0.001 and 0.0001 in women and men, respectively], and higher levels of plasma polyunsaturated fatty acids [(0.12 % (95% CI: −0.01, 0.25) and 0.39 % (95% CI: 0.19, 0.59), p-values = 0.065 and 0.0001 in women and men, respectively] in the whole population. Plasma carotenoid concentrations were also associated with higher plasma HDL-cholesterol in women [0.47 mg/dl (95% CI: 0.23, 0.72), p-value: 0.0002], and lower fasting plasma glucose in men [−1.35 mg/dl (95% CI: −2.12, −0.59), p-value: 0.001].

Regression with interval-censored covariates: Application to cross-sectional incidence estimation

A method for generalized linear regression with interval-censored covariates is described, extending previous approaches. A scenario is considered in which an interval-censored covariate of interest is defined as a function of other variables. Instead of directly modeling the distribution of the interval-censored covariate of interest, the distributions of the variables which determine that covariate are modeled, and the distribution of the covariate of interest is inferred indirectly. This approach leads to an estimation procedure using the Expectation-Maximization (EM) algorithm. The performance of this approach is compared to two alternative approaches, one in which the censoring interval midpoints are used as estimates of the censored covariate values, and another in which the censored values are multiply imputed using uniform distributions over the censoring intervals. A simulation framework is constructed to assess these methods' accuracies across a range of scenarios. The proposed approach is found to have less bias than midpoint analysis and uniform imputation, at the cost of small increases in standard error.

Cox model with interval-censored covariate in cohort studies

In cohort studies the outcome is often time to a particular event, and subjects are followed at regular intervals. Periodic visits may also monitor a secondary irreversible event influencing the event of primary interest, and a significant proportion of subjects develop the secondary event over the period of follow-up. The status of the secondary event serves as a time-varying covariate, but is recorded only at the times of the scheduled visits, generating incomplete time-varying covariates. While information on a typical time-varying covariate is missing for entire follow-up period except the visiting times, the status of the secondary event are unavailable only between visits where the status has changed, thus interval-censored. One may view interval-censored covariate of the secondary event status as missing time-varying covariates, yet missingness is partial since partial information is provided throughout the follow-up period. Current practice of using the latest observed status produces biased estimators, and the existing missing covariate techniques cannot accommodate the special feature of missingness due to interval censoring. To handle interval-censored covariates in the Cox proportional hazards model, we propose an available-data estimator, a doubly robust-type estimator as well as the maximum likelihood estimator via EM algorithm and present their asymptotic properties. We also present practical approaches that are valid. We demonstrate the proposed methods using our motivating example from the Northern Manhattan Study.

Model validation and influence diagnostics for regression models with missing covariates

Missing covariate values are prevalent in regression applications. While an array of methods have been developed for estimating parameters in regression models with missing covariate data for a variety of response types, minimal focus has been given to validation of the response model and influence diagnostics. Previous research has mainly focused on estimating residuals for observations with missing covariates using expected values, after which specialized techniques are needed to conduct proper inference. We suggest a multiple imputation strategy that allows for the use of standard methods for residual analyses on the imputed data sets or a stacked data set. We demonstrate the suggested multiple imputation method by analyzing the Sleep in Mammals data in the context of a linear regression model and the New York Social Indicators Status data with a logistic regression model.

Quantification of Tumor Vascular Permeability and Blood Volume by Positron Emission Tomography

Purpose: Evans Blue (EB) is an azo dye that binds quantitatively with serum albumin. With an albumin binding, NOTA conjugated truncated Evan's blue (NEB) dye derived PET tracer, we aimed to establish a strategy for evaluating vascular permeability in malignant tumors via non-invasive PET. Experimental design: Sixty-minute dynamic PET using [¹⁸F]FAl-NEB was performed in three xenograft tumor models including INS-1 rat insulinoma, UM-SCC-22B human head and neck carcinoma and U-87 MG human glioblastoma. Tumor vascular permeability was quantified by the difference of the slopes between tumor and blood time-activity curve (TACs, expressed as P_s ). The method was further substantiated by EB extraction and colorimetric assay and correlates with that calculated from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The changes in tumor vasculature at different time points were assessed with NEB PET in U-87 MG and UM-SCC-22B tumor models after treatment with bevacizumab or doxorubicin. Result: The P_s values calculated from tumor and blood TACs from multiple time-point static images are consistent with those from dynamic images. Moreover, the P_s showed a positive and significant correlation with extracted EB concentration and K_PS-MRI generated from DCE-MRI, which further confirmed the soundness of this methodology. The antiangiogenic effect of bevacizumab could be revealed by NEB PET in U-87 MG tumors as early as 8 hrs after therapy, demonstrated by a substantial decrease of P_s. On the contrary, there was no significant change of P_s in bevacizumab treated UM-SCC-22B tumors, compared with control group. However, the significant changes of P_s were overestimated in doxorubicin treated UM-SCC-22B tumors. Conclusions: We successfully developed a relatively convenient and novel strategy to evaluate vascular permeability and blood volume using NEB PET. This method will be advantageous in evaluating vascular permeability, promoting drug delivery, and monitoring tumor response to therapeutics that affect tumor angiogenesis.

Size Dependent Kinetics of Gold Nanorods in EPR Mediated Tumor Delivery

Gold nanorods (AuNR) have been intensively used in nanomedicine for cancer diagnostics and therapy, due to their excellent plasmonic photothermal properties. Tuning the size and aspect ratio of AuNR tailors the localized surface plasmon resonance (LSPR) in the NIR spectrum at which biological tissues are transparent, thus enables specific and effective treatment. The AuNR extravasates into tumor interstitium through enhanced permeation and retention (EPR) effect. Efficient AuNR based cancer therapy requires efficient AuNR tumor delivery. However, the size of AuNR can dramatically affect its blood circulation and tumor accumulation. Here we proposed for the first time a systematic framework to investigate the size-dependent kinetics of AuNRs during EPR mediated tumor delivery. By using ⁶⁴Cu-labeled AuNRs with positron emission tomography (PET) and kinetic modeling, the in vivo uptake and kinetics of ⁶⁴Cu-AuNR during its blood circulation, tumor accumulation and elimination were studied both in vitro and in vivo. The results of different sized AuNRs were compared and the optimum size of AuNR was suggested for EPR mediated tumor delivery. Our study provides a better understanding of the in vivo behavior of AuNR, which can help future design of nanomaterials for cancer imaging and therapy.

Monitoring Tumor Hypoxia Using (18)F-FMISO PET and Pharmacokinetics Modeling after Photodynamic Therapy

Photodynamic therapy (PDT) is an efficacious treatment for some types of cancers. However, PDT-induced tumor hypoxia as a result of oxygen consumption and vascular damage can reduce the efficacy of this therapy. Measuring and monitoring intrinsic and PDT-induced tumor hypoxia in vivo during PDT is of high interest for prognostic and treatment evaluation. In the present study, static and dynamic (18)F-FMISO PET were performed with mice bearing either U87MG or MDA-MB-435 tumor xenografts immediately before and after PDT at different time points. Significant difference in tumor hypoxia in response to PDT over time was found between the U87MG and MDA-MB-435 tumors in both static and dynamic PET. Dynamic PET with pharmacokinetics modeling further monitored the kinetics of (18)F-FMISO retention to hypoxic sites after treatment. The Ki and k3 parametric analysis provided information on tumor hypoxia by distinction of the specific tracer retention in hypoxic sites from its non-specific distribution in tumor. Dynamic (18)F-FMISO PET with pharmacokinetics modeling, complementary to static PET analysis, provides a potential imaging tool for more detailed and more accurate quantification of tumor hypoxia during PDT.