The role of husbands in maternal health and safe childbirth in rural Nepal: a qualitative study

Background: The role of husbands in maternal health is often overlooked by health programmes in developing countries and is an under-researched area of study globally. This study examines the role of husbands in maternity care and safe childbirth, their perceptions of the needs of women and children, the factors which influence or discourage their participation, and how women feel about male involvement around childbirth. It also identifies considerations that should be taken into account in the development of health education for husbands. Methods: This qualitative study was conducted in four rural hill villages in the Gorkha district of Nepal. Semi-structured, in-depth interviews were conducted with husbands (n = 17), wives (n = 15), mothers-in-law (n = 3), and health workers (n = 7) in Nepali through a translator. Interviews were transcribed and analysed using axial coding. Results: We found that, in rural Nepal, male involvement in maternal health and safe childbirth is complex and related to gradual and evolving changes in attitudes taking place. Traditional beliefs are upheld which influence male involvement, including the central role of women in the domain of pregnancy and childbirth that cannot be ignored. That said, husbands do have a role to play in maternity care. For example, they may be the only person available when a woman goes into labour. Considerable interest for the involvement of husbands was also expressed by both expectant mothers and fathers. However, it is important to recognise that the husbands’ role is shaped by many factors, including their availability, cultural beliefs, and traditions. Conclusions: This study shows that, although complex, expectant fathers do have an important role in maternal health and safe childbirth. Male involvement needs to be recognised and addressed in health education due to the potential benefits it may bring to both maternal and child health outcomes. This has important implications for health policy and practice, as there is a need for health systems and maternal health interventions to adapt in order to ensure the appropriate and effective inclusion of expectant fathers

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland