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Cancers
Special Issues
Socio-Demographic Factors and Cancer Research
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Socio-Demographic Factors and Cancer Research

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: 10 March 2025 | Viewed by 2836

Special Issue Editors

Dr. Mitchell S. Fourman

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, Montefiore Einstein, Bronx, NY, USA
Interests: socioeconomic disparities; AI/machine learning; metastatic disease to the spine; spine oncology; sarcoma; minimally invasive spine surgery
Dr. Amanda N. Goldin

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, University of California Irvine School of Medicine, Orange CA, USA
Interests: socioeconomic disparities; sarcoma; metastatic cancer care; benign and malignant bone; soft tissue tumors; reconstruction after oncologic surgery

Special Issue Information

Dear Colleagues,

It is well established that socioeconomic factors influence cancer treatment outcomes, even when optimal therapies are employed. These contributors are often modifiable and community/culture-dependent, warranting focused research.

We are pleased to invite you to contribute to a Special Issue focused on the socioeconomic and sociodemographic factors that influence cancer care and research.

This Special Issue aims to address the specific socioeconomic factors that influence the care of individual or a broad spectrum of cancers, with a focus on both global and region-specific factors. It also seeks to further understand the application of unique analysis technologies such as AI for the identification and management of the influence that these socioeconomic factors have on the provision and outcomes of cancer care.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Socioeconomic disparities
  • Culture-specific cancer outcomes
  • Disparities in cancer care delivery across regions
  • AI and machine learning algorithms to predict cancer outcomes using sociodemographic factors
  • Qualitative research on the provision of cancer care in economically deprived regions

We look forward to receiving your contributions.

Dr. Mitchell S. Fourman
Dr. Amanda N. Goldin
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 short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • socioeconomic disparity
  • cancer delivery
  • cancer survival
  • metastatic cancer
  • culture-specific cancer outcomes
  • AI
  • machine learning

Published Papers (2 papers)

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Research

17 pages, 269 KiB  
Article
Healthcare Costs and Resource Utilisation of Italian Metastatic Non-Small Cell Lung Cancer Patients
by Nicola Gentili, William Balzi, Flavia Foca, Valentina Danesi, Mattia Altini, Angelo Delmonte, Giuseppe Bronte, Lucio Crinò, Nicoletta De Luigi, Marita Mariotti, Alberto Verlicchi, Marco Angelo Burgio, Andrea Roncadori, Thomas Burke and Ilaria Massa
Cancers 2024, 16(3), 592; https://doi.org/10.3390/cancers16030592 - 30 Jan 2024
Viewed by 918
Abstract
This study evaluated the economic burden of metastatic non-small cell lung cancer patients before and after the availability of an immuno-oncology (IO) regimen as a first-line (1L) treatment. Patients from 2014 to 2020 were categorized according to mutational status into mutation-positive and negative/unknown [...] Read more.
This study evaluated the economic burden of metastatic non-small cell lung cancer patients before and after the availability of an immuno-oncology (IO) regimen as a first-line (1L) treatment. Patients from 2014 to 2020 were categorized according to mutational status into mutation-positive and negative/unknown groups, which were further divided into pre-1L IO and post-1L IO sub-groups depending on the availability of pembrolizumab monotherapy in 1L. Healthcare costs and HCRU for a 1L treatment and overall follow-up were reported as the mean total and per-month cost per patient by groups. Of 644 patients, 125were mutation-positive and 519 negative/unknown (229 and 290 in pre- and post-1L IO, respectively). The mean total per-patient cost in 1L was lower in pre- (EUR 7804) and post-1L IO (EUR 19,301) than the mutation-positive group (EUR 45,247), persisting throughout overall disease follow-up. However, this difference was less when analyzing monthly costs. Therapy costs were the primary driver in 1L, while hospitalization costs rose during follow-up. In both mutation-positive and post-IO 1L groups, the 1L costs represented a significant portion (70.1% and 66.3%, respectively) of the total costs in the overall follow-up. Pembrolizumab introduction increased expenses but improved survival. Higher hospitalisation and emergency room occupation rates during follow-up reflected worsening clinical conditions of the negative/unknown group than the mutation-positive population. Full article
(This article belongs to the Special Issue Socio-Demographic Factors and Cancer Research)
21 pages, 2751 KiB  
Article
Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums
by Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin and James Blando
Cancers 2024, 16(3), 540; https://doi.org/10.3390/cancers16030540 - 26 Jan 2024
Viewed by 1552
Abstract
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of [...] Read more.
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions. Conclusions: This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study’s methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes. Full article
(This article belongs to the Special Issue Socio-Demographic Factors and Cancer Research)
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