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Cancers
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Contemporary Advances and Challenges in Multimodal Management of Gastric Cancer
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Contemporary Advances and Challenges in Multimodal Management of Gastric Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1052

Editors

Prof. Dr. Karol Rawicz-Pruszyński

E-Mail Website
Guest Editor
Department of Surgical Oncology, Medical University of Lublin, Radziwiłłowska 13 St., 20-080 Lublin, Poland
Interests: minimally invasive gastric cancer surgery; conversion surgery for gastric cancer
Special Issues, Collections and Topics in MDPI journals
Dr. Zuzanna Pelc

E-Mail Website
Guest Editor
Department of Surgical Oncology, Medical University of Lublin, Radziwiłłowska 13 St., 20-080 Lublin, Poland
Interests: multimodal treatment of gastric cancer; perioperative therapy in gastric cancer

Special Issue Information

Dear Colleagues,

Multimodal management of gastric cancer is evolving rapidly. However, everyday practice still faces certain gaps: how to tailor the extent of surgery (escalation vs. de-escalation), how to integrate minimally invasive and robotic approaches safely, and how to select patients for intensified perioperative systemic therapy using reliable biomarkers and staging. In stage IV disease, conversion surgery can deliver meaningful survival in carefully selected responders, yet prognostic stratification and timing remain critical. Finally, perioperative nutrition is essential, but evidence is heterogeneous, emphasizing the need for standardized, patient-specific strategies. We invite authors to submit original research and high-quality reviews that address these challenges, provide translational and clinical insights, and help refine evidence-based, patient-centered multimodal strategies in gastric cancer care.

Prof. Dr. Karol Rawicz-Pruszyński
Dr. Zuzanna Pelc
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-anonymized 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

  • gastric cancer
  • multimodal therapy
  • perioperative chemotherapy
  • immunotherapy
  • minimally invasive surgery/robotic gastrectomy
  • surgical escalation and de-escalation
  • conversion surgery
  • patient selection/prognostic factors
  • biomarkers (her2, pd-l1, msi, ctdna)
  • perioperative nutrition/eras

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Published Papers (1 paper)

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Research

13 pages, 1625 KB  
Article
MAGE (Multimodal AI-Enhanced Gastrectomy Evaluation): Comparative Analysis of Machine Learning Models for Postoperative Complications in Central European Gastric Cancer Population
by Wojciech Górski, Marcin Kubiak, Amir Nour Mohammadi, Maksymilian Podleśny, Gian Luca Baiocchi, Manuele Gaioni, S. Vincent Grasso, Andrew Gumbs, Timothy M. Pawlik, Bartłomiej Drop, Albert Chomątowski, Zuzanna Pelc, Katarzyna Sędłak, Michał Woś and Karol Rawicz-Pruszyński
Cancers 2026, 18(3), 443; https://doi.org/10.3390/cancers18030443 - 29 Jan 2026
Viewed by 819
Abstract
Introduction: By leveraging dedicated datasets and predictive modeling, machine-learning (ML) algorithms can estimate the probability of both short- and long-term outcomes after surgery. The aim of this study was to evaluate the ability of ML-based models to predict postoperative complications in patients [...] Read more.
Introduction: By leveraging dedicated datasets and predictive modeling, machine-learning (ML) algorithms can estimate the probability of both short- and long-term outcomes after surgery. The aim of this study was to evaluate the ability of ML-based models to predict postoperative complications in patients with gastric cancer (GC) undergoing multimodal therapy. In particular, we aimed to develop a free, publicly accessible online calculator based on preoperative variables. Materials and Methods: Patients with histologically confirmed locally advanced (cT2-4N0-3M0) GC who underwent multimodal treatment with curative intent between 2013 and 2023 were included in the study. ML models evaluation pipeline was used with Stratified 5-Fold Cross-Validation. Results: A total of 368 patients were included in the final analytic cohort. Among five algorithm classes under 5-fold cross-validation, Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) was 0.9719, 0.9652, 0.9796, 0.8339 and 0.7581 for XGBoost, Catboost, Random Forest, SVM and Logistic Regression, respectively. Macro F1 was 0.8714, 0.5094, 0.8820, 0.8714 and 0.4579 for XGBoost, SVM, Random Forest, CatBoost and Logistic Regression, respectively. Overall Accuracy was 0.8897, 0.5980, 0.8885, 0.8750 and 0.5466 for XGBoost, SVM, Random Forest, CatBoost and Logistic Regression models, respectively. Conclusions: In this Central and Eastern European cohort of patients with locally advanced GC, ML models using non-linear decision rules-particularly Random Forest and XGBoost- substantially outperformed conventional linear approaches in predicting the severity of postoperative complications. Prospective external validation is needed to clarify the model’s clinical utility and its potential role in perioperative decision support. Full article
(This article belongs to the Special Issue Contemporary Advances and Challenges in Multimodal Management of Gastric Cancer)
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