Evaluation of the breast cancer care network within the Lazio Region (Central Italy)

Evaluation of Quality Indicators of Breast Cancer Management at a Tertiary Cancer Center in Nepal

PURPOSE

Breast cancer is the second commonest cancer among female in Nepal. This is our first attempt to audit breast cancer management in our institute and compare with standard quality indicators (QIs) available.

METHODS

The retrospective study included 104 female patients with breast cancer who had taken treatment at Bhaktapur Cancer Hospital in 1 year. Participants were selected on the basis of convenience sampling. Of 33 QIs in breast cancer management according to European Society of Breast Cancer Specialists guidelines, 19 QIs were chosen relevant to our setup. These QIs were calculated for all patients and compared with the European Society of Breast Cancer Specialists standard target. Frequencies and percentages were calculated and presented in tables. Binomial 95% of the rates for QI adherence were also calculated for each QI.

RESULTS

One hundred four patients had a median age of 47.5 years (range 24-70 years). Applicable QIs were in the range of 5-15 with a mean of 9.66 per patient. Of 19 evaluable QIs, very high adherence rates were observed in six QIs, high adherence in three Qis, and low adherences in 10 QIs. High adherence rates were for QI 5 and QI 10a, which were 88.46% and 94.73%, respectively. The low compliance was for QI 1, QI 4a, QI 8, QI 9d, QI 10b, QI 11a, QI 11b, QI 13b, QI 13e, and 14b, which were 53.84%, 78.21%, 0%, 83.16%, 76.92%, 36.0%, 33.33%, 4.76%, 30.55%, and 10.81%, respectively.

CONCLUSION

There are several QIs that have low levels of adherence in our setting and suggest that there is significant room for improvement. We will be continuing auditing these QIs regularly to improve our quality of care.

Identification of most important features based on a fuzzy ensemble technique: Evaluation on joint space narrowing progression in knee osteoarthritis patients

OBJECTIVE

Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias.

MATERIALS AND METHODS

To avoid bias and alleviate the defectiveness of single feature selection results, an ensemble FS methodology is proposed in this paper that aggregates the results of several FS algorithms (filter, wrapper and embedded ones). Fuzzy logic is employed to combine multiple feature importance scores thus leading to a more robust selection of informative features. The proposed fuzzy ensemble FS methodology was applied on the problem of knee osteoarthritis (KOA) prediction with special emphasis on the progression of joint space narrowing (JSN). The proposed FS methodology was integrated into an end-to-end machine learning pipeline and a thorough experimental evaluation was conducted using data from the Osteoarthritis Initiative (OAI) database. Several classifiers were investigated for their suitability in the task of JSN prediction and the best performing model was then post-hoc analyzed by using the SHAP method.

RESULTS

The results showed that the proposed method presented a better and more stable performance in contrast to other competitive feature selection methods, leading to an average accuracy of 78.14% using XG Boost at 31 selected features. The post-hoc explainability highlighted the important features that contribute to the classification of patients with JSN progression.

CONCLUSIONS

The proposed fuzzy feature selection approach improves the performance of the predictive models by selecting a small optimal subset of features compared to popular feature selection methods.