Role of brachytherapy in post-operative cervical cancer patients with risk factors other than positive stump.

OBJECTIVE: This study aimed to determine the effectiveness of brachytherapy in post-operative cervical cancer patients with risk factors other than positive stump, and to identify the candidates most likely to benefit. METHODS: Newly diagnosed, non-metastatic cervical cancer patients treated in our hospital between January 2012 and November 2015 were retrospectively reviewed. Early stage patients receiving radical surgery and needing adjuvant external radiotherapy were included, but those with positive stump were excluded. All patients received external radiotherapy. They were divided into two groups: one group received vaginal brachytherapy and the other did not. The 5-year local-regional recurrence free survival (LRRFS) and overall survival (OS) rates in the two groups were compared. RESULTS: Two hundred and twenty-five patients were included in this study; while 99 received brachytherapy, 126 did not. The brachytherapy group had significantly superior 5-year LRRFS (87.7% vs. 72.5%, p = 0.004), but did not show a significant overall survival benefit (78.4% vs. 75.3%, p = 0.055). In multivariate analysis, brachytherapy, pathological type, high-risk factors, duration of radiotherapy, and transfusion were independent prognostic factors for 5-year LRRFS. In stratified analysis, the brachytherapy group showed superior LRRFS in those meeting Sedlis criteria (p = 0.017). CONCLUSION: The combination of external beam radiation therapy and brachytherapy can improve LRRFS in post-operative cervical cancer patients with risk factors other than positive stump. Therefore, brachytherapy should be considered for these patients.

BACH: Grand challenge on breast cancer histology images.

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.