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Current Oncology
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Shaping the Future of Oncology: The Role of Generative AI...
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Shaping the Future of Oncology: The Role of Generative AI in Clinical and Research Environments

A special issue of Current Oncology (ISSN 1718-7729).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6401

Special Issue Editors

Prof. Dr. Matthias May

E-Mail Website
Guest Editor
Department of Urology, St. Elisabeth Hospital Straubing, 94315 Straubing, Germany
Interests: bladder cancer; prostate cancer; renal cell carcinoma; robot-assisted surgery; biomarker-based oncology studies; prognostic outcome models; translational research; multicenter projects in genitourinary oncology research
Special Issues, Collections and Topics in MDPI journals
Dr. Emily Rinderknecht

E-Mail Website
Guest Editor
Department of Urology, St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany
Interests: generative artificial intelligence in medicine; uro-oncology
Dr. Maximilian Haas

E-Mail Website
Guest Editor
Department of Urology, Caritas St. Josef Medical Center, University of Regensburg, 93053 Regensburg, Germany
Interests: urological oncology; bladder cancer; prostate cancer; urologic surgery; cancer research; predictive biomarkers; robot-assisted surgery

Special Issue Information

Dear Colleagues,

The emergence of generative artificial intelligence (AI) marks a pivotal transformation in oncology, with the potential to redefine clinical practice, therapeutic approaches, and research paradigms. This Special Issue of Current Oncology seeks to investigate the diverse and rapidly expanding applications of generative AI in the field of oncology, covering advancements in diagnostics, breakthroughs in treatment modalities, and the optimization of clinical decision making. We invite submissions that showcase how generative AI is being harnessed to enhance cancer diagnostics, individualize treatment regimens, and support decision-making processes for both clinicians and patients through AI-driven decision intelligence.

We particularly welcome manuscripts that evaluate the efficacy of AI-powered tools for cancer patients and explore their clinical applications. Contributions are encouraged from researchers and practitioners highlighting original studies, review articles, case reports, and innovative methodologies demonstrating how AI is addressing the intricate challenges of cancer care, especially how generative AI bridges the gap between research and clinical implementation, fostering a more seamless, integrated, and efficient oncological landscape.

This Special Issue aims to provide a comprehensive exploration of the present and future roles of generative AI in oncology, offering key insights into its transformative potential for cancer care and research. We look forward to your pioneering contributions to this dynamic and swiftly evolving field, which promises to set new standards for oncological treatment and research.

Prof. Dr. Matthias May
Dr. Emily Rinderknecht
Dr. Maximilian Haas
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. Current Oncology is an international peer-reviewed open access monthly 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 2200 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

  • generative AI in oncology
  • AI-driven cancer diagnostics
  • personalized cancer treatment
  • AI in clinical decision making
  • oncology research innovation
  • AI-assisted therapeutic strategies
  • cancer care technology
  • patient-centered AI tools and apps
  • translational oncology and AI
  • future of oncology
  • artificial intelligence

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Published Papers (4 papers)

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13 pages, 1394 KB  
Article
Readability of Chatbot Responses in Prostate Cancer and Urological Care: Objective Metrics Versus Patient Perceptions
by Lasse Maywald, Lisa Nguyen, Jana Theres Winterstein, Martin Joachim Hetz, Maurin Helen Mangold, Luisa Vivienne Renner, Titus Josef Brinker, Frederik Wessels and Nicolas Carl
Curr. Oncol. 2025, 32(10), 582; https://doi.org/10.3390/curroncol32100582 - 19 Oct 2025
Viewed by 112
Abstract
Large language models (LLMs) are increasingly explored as chatbots for patient education, including applications in urooncology. Since only 12% of adults have proficient health literacy and most patient information materials exceed recommended reading levels, improving readability is crucial. Although LLMs could potentially increase [...] Read more.
Large language models (LLMs) are increasingly explored as chatbots for patient education, including applications in urooncology. Since only 12% of adults have proficient health literacy and most patient information materials exceed recommended reading levels, improving readability is crucial. Although LLMs could potentially increase the readability of medical information, evidence is mixed, underscoring the need to assess chatbot outputs in clinical settings. Therefore, this study evaluates the measured and perceived readability of chatbot responses in speech-based interactions with urological patients. Urological patients engaged in unscripted conversations with a GPT-4-based chatbot. Transcripts were analyzed using three readability indices: Flesch–Reading-Ease (FRE), Lesbarkeitsindex (LIX) and Wiener-Sachtextformel (WSF). Perceived readability was assessed using a survey covering technical language, clarity and explainability. Associations between measured and perceived readability were analyzed. Knowledge retention was not assessed in this study. A total of 231 conversations were evaluated. The most frequently addressed topics were prostate cancer (22.5%), robotic-assisted prostatectomy (19.9%) and follow-up (18.6%). Objectively, responses were classified as difficult to read (FRE 43.1 ± 9.1; LIX 52.8 ± 6.2; WSF 11.2 ± 1.6). In contrast, perceived readability was rated highly for technical language, clarity and explainability (83–90%). Correlation analyses revealed no association between objective and perceived readability. Chatbot responses were objectively written at a difficult reading level, exceeding recommendations for optimized health literacy. Nevertheless, most patients perceived the information as clear and understandable. This discrepancy suggests that perceived comprehensibility is influenced by factors beyond measurable linguistic complexity. Full article
(This article belongs to the Special Issue Shaping the Future of Oncology: The Role of Generative AI in Clinical and Research Environments)
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13 pages, 264 KB  
Article
Modification and Validation of the System Causability Scale Using AI-Based Therapeutic Recommendations for Urological Cancer Patients: A Basis for the Development of a Prospective Comparative Study
by Emily Rinderknecht, Dominik von Winning, Anton Kravchuk, Christof Schäfer, Marco J. Schnabel, Stephan Siepmann, Roman Mayr, Jochen Grassinger, Christopher Goßler, Fabian Pohl, Peter J. Siska, Florian Zeman, Johannes Breyer, Anna Schmelzer, Christian Gilfrich, Sabine D. Brookman-May, Maximilian Burger, Maximilian Haas and Matthias May
Curr. Oncol. 2024, 31(11), 7061-7073; https://doi.org/10.3390/curroncol31110520 - 11 Nov 2024
Cited by 2 | Viewed by 1563
Abstract
The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data [...] Read more.
The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This preparatory study establishes a robust methodological foundation for the forthcoming CONCORDIA trial, including the validation of the System Causability Scale (SCS) and its modified version (mSCS), as well as the selection of LLMs for urological cancer treatment recommendations based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen’s K > 0.74), and internal consistency (all Cronbach’s Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability (p < 0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials. Full article
(This article belongs to the Special Issue Shaping the Future of Oncology: The Role of Generative AI in Clinical and Research Environments)
15 pages, 6277 KB  
Article
Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI
by Laura J. Jensen, Damon Kim, Thomas Elgeti, Ingo G. Steffen, Lars-Arne Schaafs, Matthias Haas, Lukas J. Kurz, Bernd Hamm and Sebastian N. Nagel
Curr. Oncol. 2024, 31(11), 6814-6828; https://doi.org/10.3390/curroncol31110503 - 1 Nov 2024
Cited by 1 | Viewed by 3506
Abstract
Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this [...] Read more.
Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions. Full article
(This article belongs to the Special Issue Shaping the Future of Oncology: The Role of Generative AI in Clinical and Research Environments)
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8 pages, 658 KB  
Brief Report
Mechanistically Explainable AI Model for Predicting Synergistic Cancer Therapy Combinations
by Han Si, Sanyam Kumar, Sneh Lata, Arshad Ahmad, Saurav Ghosh, Karen Stephansen, Deepti Nagarkar, Eda Zhou and Brandon W. Higgs
Curr. Oncol. 2025, 32(10), 548; https://doi.org/10.3390/curroncol32100548 - 30 Sep 2025
Viewed by 378
Abstract
This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic oncology drug combinations with mechanistic insights. Using a retrieval-augmented generation (RAG) approach, over 50,000 in vitro drug pair assay results and 1631 [...] Read more.
This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic oncology drug combinations with mechanistic insights. Using a retrieval-augmented generation (RAG) approach, over 50,000 in vitro drug pair assay results and 1631 human clinical trial and preclinical test entries were integrated to enhance predictive accuracy and explainability. Validation achieved an F1 score of 0.80, demonstrating the framework’s potential to streamline drug discovery and improve translational strategies in cancer treatment. Full article
(This article belongs to the Special Issue Shaping the Future of Oncology: The Role of Generative AI in Clinical and Research Environments)
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