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Cancers
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Innovations in Addressing Disparities in Cancer
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Innovations in Addressing Disparities in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Survivorship and Quality of Life".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 5687

Special Issue Editors

Prof. Dr. Rajesh Singh

E-Mail Website
Guest Editor
Department of Microbiology, Immunology and Biochemistry, Morehouse School of Medicine, Atlanta, GA 30310, USA
Interests: cancer immunobiology; health disparity; drug discovery; precision oncology; nanotechnology; chemokines
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shailesh Singh

E-Mail Website
Guest Editor
1. Department of Microbiology, Immunology, and Biochemistry, Morehouse School of Medicine, Atlanta, GA 30310, USA
2. Cancer Health Equity Institute, Morehouse School of Medicine, Atlanta, GA 30310, USA
Interests: cancer progression and metastasis; tumor immunobiology; cancer health disparity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Despite significant progress in cancer prevention, diagnosis, and treatment, disparities in cancer care remain a pressing challenge. Health, recognized as a fundamental human right by the World Health Organization (WHO), should be accessible to all, regardless of background, emphasizing the importance of achieving the highest health standards for all, regardless of background. This Special Issue aims to promote innovation in cancer research to expand possibilities in cancer prevention, early detection, diagnosis, treatment, and patient-centered care. As the financial burden of noncommunicable diseases, particularly cancer, continues to grow, it is essential for health authorities globally to evaluate and adopt the most impactful technologies, policies, and practices. We invite submission on topics such as digital health innovations: enhancing collaboration among multidisciplinary teams; advanced molecular diagnostics: improving pathology services; counseling apps: addressing the psychological effects of HPV testing; and genomic approaches: reducing cancer disparities and promoting health equity.

This Special Issue will feature original research, reviews, and editorials across basic, translational, and clinical studies focused on disparities in all cancer types. Area of interest includes artificial intelligence and machine learning: application in cancer care; social determinants of health (SDOH): their role in cancer disparities; and health informatics and data science: leveraging technology to address disparities, the molecular underpinnings of these disparities, race-related differences in tumor immunity, transcriptomic studies on cancer disparities, and the socioeconomic status (SES) associations with cancer's cellular and molecular signatures that affect disease progression and treatment responses.

We look forward to receiving your contributions.

Prof. Dr. Rajesh Singh
Prof. Dr. Shailesh Singh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence on health equity
  • artificial intelligence and machine learning technologies in cancer care
  • genomic testing and biomarkers
  • AI in social determinants of health
  • health justice
  • precision medicine and health informatics
  • innovative cancer care delivery model for overcoming health disparity
  • equity lens
  • racial differences in the immunological landscape
  • racial differences in treatment and disease outcome

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Published Papers (2 papers)

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Research

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38 pages, 10428 KB  
Article
Conversational AI-Enabled Precision Oncology Reveals Context-Dependent MAPK Pathway Alterations in Hispanic/Latino and Non-Hispanic White Colorectal Cancer Stratified by Age and FOLFOX Exposure
by Fernando C. Diaz, Brigette Waldrup, Francisco G. Carranza, Sophia Manjarrez and Enrique Velazquez-Villarreal
Cancers 2026, 18(2), 293; https://doi.org/10.3390/cancers18020293 - 17 Jan 2026
Cited by 1 | Viewed by 526
Abstract
Background: Colorectal cancer (CRC) demonstrates substantial clinical and biological diversity across age groups, ancestral backgrounds, and treatment settings, alongside a rising incidence of early-onset disease (EOCRC). The mitogen-activated protein kinase (MAPK) pathway is a major driver of CRC development and therapy response; however, [...] Read more.
Background: Colorectal cancer (CRC) demonstrates substantial clinical and biological diversity across age groups, ancestral backgrounds, and treatment settings, alongside a rising incidence of early-onset disease (EOCRC). The mitogen-activated protein kinase (MAPK) pathway is a major driver of CRC development and therapy response; however, the distribution and prognostic value of MAPK alterations across distinct patient subgroups remain unclear. Methods: We analyzed 2515 CRC tumors with harmonized demographic, clinical, genomic, and treatment metadata. Patients were stratified by ancestry (Hispanic/Latino [H/L] vs. non-Hispanic White [NHW]), age at diagnosis (early-onset [EO] vs. late-onset [LO]), and FOLFOX chemotherapy exposure. MAPK pathway alterations were identified using a curated gene set encompassing canonical EGFR-RAS-RAF-MEK-ERK signaling components and regulatory nodes. Conversational artificial intelligence (AI-HOPE and AI-HOPE-MAPK) enabled natural language-driven cohort construction and exploratory analytics; findings were validated using Fisher’s exact testing, chi-square analyses, and Kaplan–Meier survival estimates. Results: MAPK pathway disruption demonstrated marked heterogeneity across ancestry and treatment contexts. Among EO H/L patients, FGFR3, NF1, and RPS6KA6 mutations were significantly enriched in tumors not receiving FOLFOX, whereas PDGFRB alterations were more frequent in FOLFOX-treated EO H/L tumors relative to EO NHW counterparts. In late-onset H/L disease, NTRK2 and PDGFRB mutations were more common in non-FOLFOX tumors. Distinct MAPK-associated alterations were also observed among NHW patients, particularly in non-FOLFOX settings, including AKT3, FGF4, RRAS2, CRKL, DUSP4, JUN, MAPK1, RRAS, and SOS1. Survival analyses provided borderline evidence that MAPK alterations may be linked to improved overall survival in treated EO NHW patients. Conversational AI markedly accelerated analytic throughput and multi-parameter discovery. Conclusions: Although MAPK alterations are pervasive in CRC, their distribution varies meaningfully by ancestry, age, and treatment exposure. These findings highlight NF1, MAPK3, RPS6KA4, and PDGFRB as potential biomarkers in EOCRC and H/L patients, supporting the need for ancestry-aware precision oncology approaches. Full article
(This article belongs to the Special Issue Innovations in Addressing Disparities in Cancer)
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Review

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23 pages, 1540 KB  
Review
Revolutionizing Oncology Through AI: Addressing Cancer Disparities by Improving Screening, Treatment, and Survival Outcomes via Integration of Social Determinants of Health
by Amit Kumar Srivastav, Aryan Singh, Shailesh Singh, Brian Rivers, James W. Lillard, Jr. and Rajesh Singh
Cancers 2025, 17(17), 2866; https://doi.org/10.3390/cancers17172866 - 31 Aug 2025
Cited by 13 | Viewed by 4402
Abstract
Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to [...] Read more.
Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations. Full article
(This article belongs to the Special Issue Innovations in Addressing Disparities in Cancer)
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