Purpose We have recently reported that ultrasound imaging, together with ultrasound tissue characterization (UTC), can provide quantitative assessment of radiation-induced normal-tissue toxicity. This study’s purpose is to evaluate the reliability of our quantitative ultrasound technology in assessing acute and late normal-tissue toxicity in breast cancer radiotherapy. Method and Materials Our ultrasound technique analyzes radio-frequency echo signals and provides quantitative measures of dermal, hypodermal, and glandular-tissue toxicities. To facilitate easy clinical implementation, we further refined this technique by developing a semi-automatic ultrasound-based toxicity assessment tool (UBTAT). Seventy-two ultrasound studies of 26 patients (720 images) were analyzed. Images of 8 patients were evaluated for acute toxicity (<6 months post radiotherapy) and those of 18 patients were evaluated for late toxicity (≥6 months post radiotherapy). All patients were treated according to a standard radiotherapy protocol. To assess intra-observer reliability, one observer analyzed 720 images in UBTAT and then repeated the analysis 3 months later. To assess inter-observer reliability, three observers (two radiation oncologists and one ultrasound expert) each analyzed 720 images in UBTAT. An intraclass correlation coefficient (ICC) was used to evaluate intra- and inter-observer reliability. Ultrasound assessment and clinical evaluation were also compared. Results Intra-observer ICC was 0.89 for dermal toxicity, 0.74 for hypodermal toxicity, and 0.96 for glandular-tissue toxicity. Inter-observer ICC was 0.78 for dermal toxicity, 0.74 for hypodermal toxicity, and 0.94 for glandular-tissue toxicity. Statistical analysis found significant changes in dermal (p < 0.0001), hypodermal (p=0.0027), and glandular-tissue (p < 0.0001) assessments in the acute toxicity group. Ultrasound measurements correlated with clinical RTOG toxicity scores of patients in the late toxicity group. Patients with RTOG grade 1 or 2 had greater ultrasound-assessed toxicity percentage changes than patients with RTOG grade 0. Conclusion Early and late radiation-induced effects on normal tissue can be reliably assessed using quantitative ultrasound.

In survival analysis, quantile regression has become a useful approach to account for covariate effects on the distribution of an event time of interest. In this paper, we discuss how quantile regression can be extended to model counting processes, and thus lead to a broader regression framework for survival data. We specifically investigate the proposed modeling of counting processes for recurrent events data. We show that the new recurrent events model retains the desirable features of quantile regression such as easy interpretation and good model flexibility, while accommodating various observation schemes encountered in observational studies. We develop a general theoretical and inferential framework for the new counting process model, which unifies with an existing method for censored quantile regression. As another useful contribution of this work, we propose a sample-based covariance estimation procedure, which provides a useful complement to the prevailing bootstrapping approach. We demonstrate the utility of our proposals via simulation studies and an application to a dataset from the US Cystic Fibrosis Foundation Patient Registry (CFFPR).

Summary In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to Polybrominated Biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis.

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