Absolute Risk Reductions for Local Recurrence after Postoperative Radiotherapy after Sector Resection for Ductal Carcinoma in Situ of the Breast

Missing data in covariates can result in biased estimates and loss of power to detect associations. It can also lead to other challenges in time-to-event analyses including the handling of time-varying effects of covariates, selection of covariates and their flexible modelling. This review aims to describe how researchers approach time-to-event analyses with missing data. Medline and Embase were searched for observational time-to-event studies in oncology published from January 2012 to January 2018. The review focused on proportional hazards models or extended Cox models. We investigated the extent and reporting of missing data and how it was addressed in the analysis. Covariate modelling and selection, and assessment of the proportional hazards assumption were also investigated, alongside the treatment of missing data in these procedures. 148 studies were included. The mean proportion of individuals with missingness in any covariate was 32%. 53% of studies used complete-case analysis, and 22% used multiple imputation. In total, 14% of studies stated an assumption concerning missing data and only 34% stated missingness as a limitation. The proportional hazards assumption was checked in 28% of studies, of which, 17% did not state the assessment method. 58% of 144 multivariable models stated their covariate selection procedure with use of a pre-selected set of covariates being the most popular followed by stepwise methods and univariable analyses. Of 69 studies that included continuous covariates, 81% did not assess the appropriateness of the functional form. While guidelines for handling missing data in epidemiological studies are in place, this review indicates that few report implementing recommendations in practice. Although missing data are present in many studies, we found that few state clearly how they handled it or the assumptions they have made. Easy-to-implement but potentially biased approaches such as complete-case analysis are most commonly used despite these relying on strong assumptions and where often more appropriate methods should be employed. Authors should be encouraged to follow existing guidelines to address missing data, and increased levels of expectation from journals and editors could be used to improve practice.

/pdf/how-are-missing-data-in-covariates-handled-in-observational-1bcm5f7for.pdf

How are missing data in covariates handled in observational time-to-event studies in oncology? A systematic review.

A national multicenter study on 1072 DCIS patients treated with breast-conserving surgery and whole breast radiotherapy (COBCG-01 study).

SUMMARY Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents a fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct stromal expression patterns and immune cell compositions were identified. We found RNA expression patterns that correlate with later events. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS New transcriptomic classification solution reveals 3 major subgroups in DCIS. Four stroma-specific signatures identified. utcome analysis identifies pathways involved in DCIS progression. CNAs characterize high risk of distant relapse IBC subtypes observed in DCIS.

/pdf/dcis-genomic-signatures-define-biology-and-correlate-with-34qifoarif.pdf

DCIS genomic signatures define biology and correlate with clinical outcome: a Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

Ductal carcinoma in situ (DCIS) of the breast is a heterogeneous disease which is increasingly diagnosed through improved screening measures. Multiple prognostic scores have been devised to predict the risk of local recurrence (LR), and the optimal adjuvant management for DCIS is still debated. Hence, the aim of this analysis is to investigate the factors contributing to the prognosis of DCIS, in particular the role of its hormonal status. From 2005 to 2016, a total of 1221 female patients diagnosed with DCIS at the National Cancer Centre Singapore and Singapore General Hospital were studied. The mean age of diagnosis was 54 years of age (sd = 11.0), with estrogen receptor (ER)-positive DCIS tumors presenting earlier (mean age 54 vs 57 years of age; P < .001). DCIS with negative hormonal status (HS) correlates significantly with a larger size (mean 23.5mm vs 13.0 mm, P < .001) and higher grade of tumor (P < .001). Patients with positive HS were more likely to undergo breast conservation surgery over a mastectomy, in contrast to patients with negative HS (P < .001). Patients with negative HS had a poorer prognosis, with a shorter time of overall survival time (HR = 26.3, P = .020). In conclusion, our study shows that the hormonal status, age of diagnosis, and positive margins are important prognostic factors for DCIS, at least in our Asian population.

Impact of hormonal status on ductal carcinoma in situ of the breast: Outcome and prognostic factors

Impact of hormonal status on outcome of ductal carcinoma in situ treated with breast-conserving surgery plus radiotherapy: Long-term experience from two large-institutional series

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Impact of hormonal status on outcome of ductal carcinoma in situ treated with breast-conserving surgery plus radiotherapy: Long-term experience from two large-institutional series