In line with the UN Sustainable Development Goal to Reduce Inequalities (SDG10), this collection aims to bring together articles enhancing our understanding of cancer health disparities across the different dimensions of inequality research.

There are many expanding fields of health inequalities research but this collection will include articles examining disproportionate cancer burdens in populations such as: rural-urban, gender/sexual, racial/ethnic, religious minority and socioeconomic status groups, and of course any group where health inequality needs to be addressed.

This wider collection builds upon our existing collections more specifically focused on 'Racial and ethnic disparities in cancer' and 'Racial and ethnic disparities in cancer in the US'.

We are building an ongoing knowledge base of content from across oncology journals to increase the discoverability of articles in this important research area. Thus, we call for new contributions to this collection and invite you to read recent research below.

We also wish to highlight a related webinar recording 'Roundtable on Black in cancer research and oncology'.

Editors for Supportive Care in Cancer: Dr Devon Check and Prof Janet de Groot

Editors for Prostate Cancer and Prostatic Diseases: Dr Samuel L. Washington III, Dr Jun Luo, Dr Yaw Nyame, Dr Randy Vince Jr and Dr Jennifer Beebe-Dimmer

Please note that other journals participating in the collection do not have specific Guest Editors appointed and manuscripts are handled on a case by case basis by members of our editorial boards.

Artificial intelligence (AI) is becoming integrated into many aspects of our day -to -day life, whether its suggestions on movies we should consider, books we may be interested in reading or apparel that may suit our personal taste. In preclinical research, AI provides tools for rapid and robust evaluation of cancer cell and organoid phenotypes or data from small animal imaging. AI is particularly well suited to radiology where it affords opportunities to enhance the speed, accuracy and quality of image interpretation. Rather than eliminating the need for radiologists anytime soon, AI can serve as a valuable adjunct to them allowing resulting in more dependable interpretations of ever more complex technology used in radiology. However, integration of AI to clinical imaging workflows requires careful evaluation of associated ethical, legal, and regulatory challenges.

In this cross-journal collection, we welcome a wide range of articles on AI in breast imaging, including primary research articles, method-based articles, reviews, and perspectives. To express your interest to contribute, please contact the Editor-in-Chief of the respective journal:

Journal of Mammary Gland Biology and Neoplasia: Zuzana Koledova (koledova@med.muni.cz)

Breast Cancer Research & Treatment: William J. Gradishar (w-gradishar@northwestern.edu)

Breast Cancer Research: Lewis A. Chodosh (chodosh@pennmedicine.upenn.edu)