Machine learning with PROs in breast cancer surgery; caution: Collecting PROs at baseline is crucial

As high breast cancer survival rates are achieved nowadays, irrespective of type of surgery performed, prediction of long-term physical, sexual, and psychosocial outcomes is very important in treatment decision-making. Patient-reported outcomes (PROs) can help facilitate this shared decision-making. Given the significance of more personalized medicine and the growing trend on the application of machine learning techniques, we are striving to develop an algorithm using machine learning techniques to predict PROs in breast cancer patients treated with breast surgery. This short communication describes the bottlenecks in our attempt to predict PROs

From Multiple Quality Indicators of Breast Cancer Care Toward Hospital Variation of a Summary Measure

Objectives: To improve quality in breast cancer care, large numbers of quality indicators are collected per hospital, but benchmarking remains complex. We aimed to assess the validity of indicators, develop a textbook outcome summary measure, and compare case-mix adjusted hospital performance. Methods: From a nationwide population-based registry, all 79 690 nonmetastatic breast cancer patients surgically treated between 2011 and 2016 in 91 hospitals in The Netherlands were included. Twenty-one indicators were calculated and their construct validity tested by Spearman's rho. Between-hospital variation was expressed by interquartile range (IQR), and all valid indicators were included in the summary measure. Standardized scores (observed/expected based on case mix) were calculated as above (>100) or below (<100) expected. The textbook outcome was presented as a continuous and all-or-none score. Results: The size of between-hospital variation varied between indicators. Sixteen (76%) of 21 quality indicators showed construct validity, and 13 were included in the summary measure after excluding redundant indicators that showed collinearity with others owing to strong construct validity. The median all-or-none textbook outcome score was 49% (IQR 42%-54%) before and 49% (IQR 48%-51%) after case-mix adjustment. From the total of 91 hospitals, 3 hospitals were positive (3%) and 9 (10%) were negative outlier

Screening for distant metastases in patients with ipsilateral breast tumor recurrence: the impact of different imaging modalities on distant recurrence-free interval

Purpose In patients with ipsilateral breast tumor recurrence (IBTR), the detection of distant disease determines whether the intention of the treatment is curative or palliative. Therefore, adequate preoperative staging is imperative for optimal treatment planning. The aim of this study is to evaluate the impact of conventional imaging techniques, including chest X-ray and/or CT thorax-(abdomen), liver ultrasonography(US), and skeletal scintigraphy, on the distant recurrence-free interval (DRFI) in patients with IBTR, and to compare conventional imaging with 18F-FDG PET-CT or no imaging at all. Methods This study was exclusively based on the information available at time of diagnoses of IBTR. To adjust for differences in baseline characteristics between the three imaging groups, a propensity score (PS) weighted method was used. Results Of the 495 patients included in the study, 229 (46.3%) were staged with conventional imaging, 89 patients (19.8%) were staged with 18F-FDG PET-CT, and in 168 of the patients (33.9%) no imaging was used (N=168). After a follow-up of approximately 5 years, 14.5% of all patients developed a distant recurrence as frst event after IBTR. After adjusting for the PS weights, the Cox regression analyses showed that the diferent staging methods had no signifcant impact on the DRFI. Conclusions This study showed a wide variation in the use of imaging modalities for staging IBTR patients in the Netherlands. After using PS weighting, no statistically signifcant impact of the diferent imaging modalities on DRFI was shown. Based on these results, it is not possible to recommend staging for distant metastases using 18F-FDG PET-CT over conventional imaging technique

BRCA2 Polymorphic Stop Codon K3326X and the Risk of Breast, Prostate, and Ovarian Cancers

Background: The K3326X variant in BRCA2 (BRCA2∗c.9976A>T p.Lys3326∗rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormonerelated cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76637 cancer case patients and 83796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9×10-6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8×10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor-negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4×10-5 and ORw = 1.50, 95% CI = 1.28 t