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Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 12785

Special Issue Editor

Dr. Wei Wu

E-Mail Website
Guest Editor
Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143, USA
Interests: cancer genomics; computational biology; deep learning in cancer research; long non-coding RNA; drug resistance; single cell; spatial transcriptomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence (AI) have revolutionized various sectors and are rapidly reshaping cancer research and personalized clinical care. Big data and our powerful computing capacity have led to the transformative potential of AI-based approaches, particularly deep learning and generative AI, in the field of oncology, specifically in cancer diagnosis, treatment, and prognosis. These applications have demonstrated remarkable capabilities in early detection and accurate diagnosis, optimizing cancer treatment protocols, predicting disease progression, recurrence, and patient survival, and in drug discovery, repurposing, and combination therapy strategies. We expect that the integration of AI technologies in cancer care will enhance the precision, efficiency, and personalization of patient management, ultimately improving clinical outcomes and quality of life for cancer patients.

This Special Issue invites research that explores the development and application of AI-based diagnostic tools, including imaging analysis, histopathological evaluations, and biomarker identification. Contributions that delve into AI-assisted treatment planning, including radiotherapy, chemotherapy, and immunotherapy, are sought. Research on AI models that predict treatment responses, suggest personalized therapy regimens, and manage treatment-related side effects are of particular interest. Generative AI approaches that can accelerate the identification of de novo anticancer compounds, simulate drug interactions, and predict novel therapeutic compounds hold significant promise in this area. We also seek contributions on multimodal AI algorithms that integrate digital pathology, radiology, genomics, and electronic medical records to generate comprehensive prognostic insights, facilitate patient monitoring, follow-up care, and long-term outcome predictions.

Additionally, we invite discussions on the ethical, legal, and practical implications of deploying AI in cancer care. Submissions that address data privacy, algorithmic bias, and the integration of AI with existing clinical workflows are highly valued.

This Special Issue aspires to present a diverse collection of pioneering research that showcases the transformative impact of AI, particularly generative AI and deep learning, in oncology, fostering innovation and collaboration among researchers, clinicians, and technologists in the fight against cancer

I look forward to receiving your contributions.

Dr. Wei Wu
Guest Editor

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

  • cancer genomics
  • artificial intelligence
  • machine learning
  • deep learning
  • tumor microenvironment
  • graph-based convolutional neural network
  • computational cancer biology

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Published Papers (6 papers)

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21 pages, 963 KB  
Article
Digital Pathology with AI for Cervical Biopsies: Diagnostic Accuracy at the CIN2+ Threshold
by Anja Kristin Andreassen, Elin Mortensen, Roy Stenbro, Øistein Sørensen and Sveinung Wergeland Sørbye
Cancers 2025, 17(23), 3808; https://doi.org/10.3390/cancers17233808 - 27 Nov 2025
Viewed by 1005
Abstract
Background/Objectives: Histopathologic grading of cervical biopsies is subject to interobserver variability, particularly at the CIN2+ treatment threshold. We evaluated a deep learning system (EagleEye) for detecting CIN2+ (CIN2, CIN3, ACIS, invasive carcinoma) on hematoxylin–eosin (H&E) whole-slide images (WSIs) and compared its performance [...] Read more.
Background/Objectives: Histopathologic grading of cervical biopsies is subject to interobserver variability, particularly at the CIN2+ treatment threshold. We evaluated a deep learning system (EagleEye) for detecting CIN2+ (CIN2, CIN3, ACIS, invasive carcinoma) on hematoxylin–eosin (H&E) whole-slide images (WSIs) and compared its performance with independent pathologists, including an AI-assisted workflow. Spatial correspondence with p16 staining was preliminarily assessed. Methods: Ninety-nine archived cervical punch biopsies from a single university hospital, originally diagnosed as Normal (n = 19), CIN1 (n = 20), CIN2 (n = 20), CIN3 (n = 20), or adenocarcinoma in situ (ACIS; n = 20), were digitized in a deliberately spectrum-balanced design. The original sign-out (P1), a second gynecologic pathologist (P2, microscope and digital), EagleEye alone (EE), and an AI-assisted read (EE + P2) served as diagnostic conditions. Outcomes were dichotomized as <CIN2 vs. CIN2+. Agreement was evaluated using Cohen’s κ and sensitivity/specificity (95% CI) under pre-specified internal reference standards. In 30 cases with prior p16 staining, visual correspondence between AI heatmaps/tiles and p16-positive epithelial domains was recorded. Results: Agreement between P1 and EagleEye was moderate (κ = 0.67), while P2 showed high internal consistency (κ = 0.86) and good agreement with P1 (κ = 0.78). Using P1 as reference, EagleEye detected CIN2+ with 93.3% sensitivity and 71.8% specificity. When the AI-assisted consensus (EE + P2) was used as an augmented internal comparator, P1 showed 83.8% sensitivity and 100% specificity, indicating that the human-in-the-loop workflow identified additional CIN2+ cases that had been signed out as <CIN2 by P1, particularly near the CIN1/CIN2 boundary. In a subset of cases originally classified as CIN3 by P1, EagleEye flagged squamous cell carcinoma (SCC); several of these were confirmed as SCC by expert review (EE + P2). In ACIS, EagleEye under-called relative to pathologists but improved after adjudication. p16-to-AI spatial correspondence ≥70% was observed in 73.3% of evaluated cases. Conclusions: In this single-centre, spectrum-balanced cohort, EagleEye achieved high CIN2+ sensitivity and substantial agreement with expert readers in a human-in-the-loop workflow. The main added value was internal case-finding of treatment-relevant lesions when AI assistance was available, while final diagnoses remained with the pathologist. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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18 pages, 1070 KB  
Article
Predicting Toxicities and Survival Outcomes in De Novo Metastatic Hormone-Sensitive Prostate Cancer Using Clinical Features, Routine Blood Tests and Their Early Variations
by Giuseppe Salfi, Martino Pedrani, Amos Colombo, Lorenzo Ruinelli, Daniele Brenna, Chiara Maria Agrippina Clerici, Giovanna Pecoraro, Sara Merler, Caroline-Claudia Erhart, Marialuisa Puglisi, Fabio Turco, Luigi Tortola, Ursula Vogl, Silke Gillessen and Ricardo Pereira Mestre
Cancers 2025, 17(23), 3806; https://doi.org/10.3390/cancers17233806 - 27 Nov 2025
Viewed by 810
Abstract
Background: Conventional prognostic factors are typically assessed at diagnosis in metastatic hormone-sensitive prostate cancer (mHSPC). However, variations in vital signs and laboratory parameters occur during systemic treatment and may predict patients’ prognosis and anticipate organ-specific toxicity development. Methods: This single-center retrospective study included [...] Read more.
Background: Conventional prognostic factors are typically assessed at diagnosis in metastatic hormone-sensitive prostate cancer (mHSPC). However, variations in vital signs and laboratory parameters occur during systemic treatment and may predict patients’ prognosis and anticipate organ-specific toxicity development. Methods: This single-center retrospective study included 363 patients with de novo mHSPC treated between 2014 and 2023. Clinical and laboratory data were systematically collected from the hospital data warehouse, from treatment initiation through the following seven months. Variations in vital parameters and blood test results were graded using CTCAE V5.0 (dynamic variables). Cox regression analyses were performed to explore the impact of dynamic variables on progression-free survival (PFS) and overall survival (OS). Machine learning (ML) models (Support Vector Classifier, Random Forest, and LGBM Classifier) were developed to predict single organ-specific toxicities and to identify good and poor responders based on 7-month PSA levels, PFS and OS. We compared ML model performance when trained only on baseline factors (static models) with those integrating variables generated by vital sign and blood test monitoring within 3 and 7 months from treatment start (dynamic models). Results: Dynamic model failed to improve the prediction of single organ-specific toxicities. Univariable Cox analysis revealed that the development of hematological, liver, and kidney-related toxicity, as well as the development of electrolyte disturbances within 3 or 7 months, was associated with shorter PFS (p = 0.011, 0.007, 0.174, and 0.02, respectively) and/or OS (p = 0.001, 0.099, 0.012, and 0.001, respectively). In multivariable Cox analysis, increasing alkaline phosphatase levels (HR = 1.93, p = 0.009), decreasing albumin (HR = 1.92, p = 0.008) and development of hyponatremia (HR = 1.79, p = 0.033) were associated with a shorter OS. The combination of static and dynamic variables significantly improved the ability of ML models to identify poor responders (shorter PFS: AUC range 0.91–0.94 vs. 0.79–0.89). Conclusions: The integration of conventional prognostic factors with the detection of significant changes in vital signs and blood tests occurring early during systemic treatment in patients with de novo mHSPC may enhance patient stratification and improve prediction of survival outcomes. Multicenter validation studies are needed to confirm these results. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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11 pages, 829 KB  
Article
Artificial Intelligence for Lymph Node Detection and Malignancy Prediction in Endoscopic Ultrasound: A Multicenter Study
by Belén Agudo Castillo, Miguel Mascarenhas Saraiva, António Miguel Martins Pinto da Costa, João Ferreira, Miguel Martins, Francisco Mendes, Pedro Cardoso, Joana Mota, Maria João Almeida, João Afonso, Tiago Ribeiro, Marcos Eduardo Lera dos Santos, Matheus de Carvalho, María Morís, Ana García García de Paredes, Daniel de la Iglesia García, Carlos Estebam Fernández-Zarza, Ana Pérez González, Khoon-Sheng Kok, Jessica Widmer, Uzma D. Siddiqui, Grace E. Kim, Susana Lopes, Pedro Moutinho Ribeiro, Filipe Vilas-Boas, Eduardo Hourneaux de Moura, Guilherme Macedo and Mariano González-Haba Ruizadd Show full author list remove Hide full author list
Cancers 2025, 17(21), 3398; https://doi.org/10.3390/cancers17213398 - 22 Oct 2025
Viewed by 1071
Abstract
Background/Objectives: Endoscopic ultrasound (EUS) is crucial for lymph node (LN) characterization, playing a key role in oncological staging and treatment guidance. EUS criteria for predicting malignancy are imprecise, and histologic diagnosis may have limitations. This multicenter study aimed to evaluate the effectiveness [...] Read more.
Background/Objectives: Endoscopic ultrasound (EUS) is crucial for lymph node (LN) characterization, playing a key role in oncological staging and treatment guidance. EUS criteria for predicting malignancy are imprecise, and histologic diagnosis may have limitations. This multicenter study aimed to evaluate the effectiveness of a novel artificial intelligence (AI)–based system in predicting LN malignancy from EUS images. Methods: This multicenter study included EUS images from nine centers. Lesions were labeled (“malignant” or “benign”) and delimited with bounding boxes. Definitive diagnoses were based on cytology/biopsy or surgical specimens and, if negative, a minimum six-month clinical follow-up. A convolutional neural network (CNN) was developed using the YOLO (You Only Look Once) architecture, incorporating both detection and classification modules. Results: A total of 59,992 images from 82 EUS procedures were analyzed. The CNN distinguished malignant from benign lymph nodes with a sensitivity of 98.8% (95% CI: 98.5–99.2%), specificity of 99.0% (95% CI: 98.3–99.7%), and precision of 99.0% (95% CI: 98.4–99.7%). The negative and positive predictive values for malignancy were 98.8% and 99.0%, respectively. Overall diagnostic accuracy was 98.3% (95% CI: 97.6–99.1%). Conclusions: This is the first study evaluating the performance of deep learning systems for LN assessment using EUS imaging. Our AI-powered imaging model shows excellent detection and classification capabilities, emphasizing its potential to provide a valuable tool to refine LN evaluation with EUS, ultimately supporting more tailored, efficient patient care. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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14 pages, 2889 KB  
Article
Ensuring Reproducibility and Deploying Models with the Image2Radiomics Framework: An Evaluation of Image Processing on PanNET Model Performance
by Florent Tixier, Felipe Lopez-Ramirez, Emir A. Syailendra, Alejandra Blanco, Ammar A. Javed, Linda C. Chu, Satomi Kawamoto and Elliot K. Fishman
Cancers 2025, 17(15), 2552; https://doi.org/10.3390/cancers17152552 - 1 Aug 2025
Cited by 1 | Viewed by 1031
Abstract
Background/Objectives: To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. Methods: A [...] Read more.
Background/Objectives: To evaluate the importance of image processing in a previously validated model for detecting pancreatic neuroendocrine tumors (PanNETs) and to introduce Image2Radiomics, a new framework that ensures reproducibility of the image processing pipeline and facilitates the deployment of radiomics models. Methods: A previously validated model for identifying PanNETs from CT images served as the reference. Radiomics features were re-extracted using Image2Radiomics and compared to those from the original model using performance metrics. The impact of nine alterations to the image processing pipeline was evaluated. Prediction discrepancies were quantified using the mean ± SD of absolute differences in PanNET probability and the percentage of classification disagreement. Results: The reference model was successfully replicated with Image2Radiomics, achieving a Cohen’s kappa coefficient of 1. Alterations to the image processing pipeline led to reductions in model performance, with AUC dropping from 0.87 to 0.71 when image windowing was removed. Prediction disagreements were observed in up to 45% of patients. Even minor changes, such as switching the library used for spatial resampling, resulted in up to 21% disagreement. Conclusions: Reproducing image processing pipelines remains challenging and limits the clinical deployment of radiomics models. While this study is limited to one model and imaging modality, the findings underscore a common risk in radiomics reproducibility. The Image2Radiomics framework addresses this issue by allowing researchers to define and share complete processing pipelines in a standardized way, improving reproducibility and facilitating model deployment in clinical and multicenter settings. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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15 pages, 606 KB  
Systematic Review
Artificial Intelligence for Risk–Benefit Assessment in Hepatopancreatobiliary Oncologic Surgery: A Systematic Review of Current Applications and Future Directions on Behalf of TROGSS—The Robotic Global Surgical Society
by Aman Goyal, Michail Koutentakis, Jason Park, Christian A. Macias, Isaac Ballard, Shen Hong Law, Abhirami Babu, Ehlena Chien Ai Lau, Mathew Mendoza, Susana V. J. Acosta, Adel Abou-Mrad, Luigi Marano and Rodolfo J. Oviedo
Cancers 2025, 17(20), 3292; https://doi.org/10.3390/cancers17203292 - 11 Oct 2025
Viewed by 1164
Abstract
Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its [...] Read more.
Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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21 pages, 1245 KB  
Perspective
Artificial Intelligence–Driven Computational Approaches in the Development of Anticancer Drugs
by Pankaj Garg, Gargi Singhal, Prakash Kulkarni, David Horne, Ravi Salgia and Sharad S. Singhal
Cancers 2024, 16(22), 3884; https://doi.org/10.3390/cancers16223884 - 20 Nov 2024
Cited by 27 | Viewed by 6660
Abstract
The integration of AI has revolutionized cancer drug development, transforming the landscape of drug discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided drug design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, cancer drug design has been [...] Read more.
The integration of AI has revolutionized cancer drug development, transforming the landscape of drug discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided drug design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, cancer drug design has been a complex, resource-intensive process, but AI introduces new opportunities to accelerate discovery, reduce costs, and optimize efficiency. This manuscript delves into the transformative applications of AI-driven methodologies in predicting and developing anticancer drugs, critically evaluating their potential to reshape the future of cancer therapeutics while addressing their challenges and limitations. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence-Based Approaches in Cancer Diagnosis, Treatment and Prognosis)
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