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PURPOSE: Assuring quality of care, while maintaining sustainability, in complex conditions such as breast cancer (BC) is an important challenge for health systems. Here, we describe a methodology to define a set of quality indicators, assess their computability from administrative data, and apply them to a large cohort of BC cases. MATERIALS AND METHODS: Clinical professionals from the Italian Regional Oncology Networks identified 46 clinically relevant indicators of BC care; 22 were potentially computable using administrative data. Incident cases of BC diagnosed in 2016 in five Italian regions were identified using administrative databases from regional repositories. Each indicator was calculated through record linkage of anonymized individual data. RESULTS: A total of 15,342 incident BC cases were identified. Nine indicators were actually computable from administrative data (two structure and seven process indicators). Although most indicators were consistent with guidelines, for one indicator (blood tumor markers in the year after surgery, 44.2% to 64.5%; benchmark ≤ 20%), deviation was evident throughout the five regions, highlighting systematic overlooking of clinical recommendations. Two indicators (radiotherapy within 4 months after surgery if no adjuvant chemotherapy; 42% to 83.8%; benchmark ≥ 90%; and mammography 6 to 18 months after surgery, 55.1% to 72.6%; benchmark ≥ 90%) showed great regional variability and were lower than expected, possibly as result of an underestimation in indicator calculation by administrative data. CONCLUSION: Despite highlighting some limitations in the use of administrative data to measure health care performance, this study shows that evaluating the quality of BC care at a population level is possible and potentially useful for guiding quality improvement interventions.

The aim of the study is to demonstrate the usefulness of a new, non-linear classifier method, called Hamming clustering (HC), in selecting prognostic variables affecting overall survival in patients with head and neck cancer. In particular, the aim is to identify whether tumour proliferation parameters can be predictive factors of response in a set of 115 patients that receive either alternating chemo-radiotherapy or accelerated or conventional radiotherapy. HC is able to generate a set of understandable rules underlying the study objective; it can also select a subset of input variables that represent good prognostic factors. HC has been compared with other standard classifiers, providing better results in terms of classification accuracy. In particular, HC obtains the best accuracy of 74.8% (sensitivity of 51.1% and specificity of 91.2%) about survival. The rules found show that, besides the classical, well-known variables concerning the tumour dimension and the involved lymphonodes, some biological parameters, such as DNA ploidy, are also useful as predictive factors.