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Cancers
Special Issues
AI-Based Applications in Cancers
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AI-Based Applications in Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 798

Special Issue Editor

Dr. Xia Jiang

E-Mail Website
Guest Editor
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
Interests: metastatic breast cancer

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI) technologies is transforming oncology, reshaping how cancer is detected, diagnosed, and treated. This Special Issue, "AI-Based Applications in Cancers", highlights the growing impact of AI across the cancer care continuum—from early detection and risk prediction to precision therapy and real-time monitoring. Leveraging machine learning, deep learning, Bayesian networks, causal learning, large language models (LLMs), and other data-driven approaches, researchers and clinicians can now analyze complex biomedical data with greater accuracy and efficiency. AI applications in radiology, pathology, genomics, and clinical decision-making are enabling more personalized, timely, and effective interventions. At the same time, the integration of AI into clinical practice presents key challenges, including data quality, model performance, training efficiency, transparency, and ethical considerations. This Special Issue brings together cutting-edge research and multidisciplinary perspectives to showcase the latest innovations at the intersection of AI and cancer care. By fostering collaboration between computational scientists and healthcare professionals, we aim to advance the development and responsible deployment of AI tools that improve patient outcomes. We invite authors to contribute original research papers to this Special Issue as we explore the opportunities and challenges of harnessing AI to address one of the most critical health issues of our time.

Dr. Xia Jiang
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 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

  • AI
  • machine learning
  • deep learning
  • Bayesian networks
  • causal learning
  • LLM
  • cancer

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Published Papers (3 papers)

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Research

30 pages, 11103 KiB  
Article
Histological Image Classification Between Follicular Lymphoma and Reactive Lymphoid Tissue Using Deep Learning and Explainable Artificial Intelligence (XAI)
by Joaquim Carreras, Haruka Ikoma, Yara Yukie Kikuti, Shunsuke Nagase, Atsushi Ito, Makoto Orita, Sakura Tomita, Yuki Tanigaki, Naoya Nakamura and Yohei Masugi
Cancers 2025, 17(15), 2428; https://doi.org/10.3390/cancers17152428 - 22 Jul 2025
Viewed by 81
Abstract
Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a [...] Read more.
Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging. Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&E). Explainable artificial intelligence (XAI) methods were used for interpretability. Results: The series included 1,004,509 follicular lymphoma and 490,506 reactive lymphoid tissue image-patches at 224 × 244 × 3, and was partitioned into training (70%), validation (10%), and testing (20%) sets. The performance of the training (training and validation sets) had an accuracy of 99.81%. In the testing set, the performance metrics achieved an accuracy of 99.80% at the image-patch level for follicular lymphoma. The other performance parameters were precision (99.8%), recall (99.8%), false positive rate (0.35%), specificity (99.7%), and F1 score (99.9%). Interpretability was analyzed using three methods: grad-CAM, image LIME, and occlusion sensitivity. Additionally, hybrid partitioning was performed to avoid information leakage using a patient-level independent validation set that confirmed high classification performance. Conclusions: Narrow artificial intelligence (AI) can perform differential diagnosis between follicular lymphoma and reactive lymphoma tissue, but it is task-specific and operates within limited constraints. The trained ResNet convolutional neural network (CNN) may be used as transfer learning for larger series of cases and lymphoma diagnoses in the future. Full article
(This article belongs to the Special Issue AI-Based Applications in Cancers)
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16 pages, 2108 KiB  
Article
Decoding the JAK-STAT Axis in Colorectal Cancer with AI-HOPE-JAK-STAT: A Conversational Artificial Intelligence Approach to Clinical–Genomic Integration
by Ei-Wen Yang, Brigette Waldrup and Enrique Velazquez-Villarreal
Cancers 2025, 17(14), 2376; https://doi.org/10.3390/cancers17142376 - 17 Jul 2025
Viewed by 234
Abstract
Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC [...] Read more.
Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology. Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site. Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p < 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p = 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p = 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I–III showed superior survival compared to Stage IV cases (p = 0.00001), reinforcing stage as a dominant clinical determinant. Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts. Full article
(This article belongs to the Special Issue AI-Based Applications in Cancers)
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16 pages, 3666 KiB  
Article
Bridging the Gap Between Accuracy and Efficiency in AI-Based Breast Cancer Diagnosis from Histopathological Data
by Kuldashbay Avazov, Sabina Umirzakova, Akmalbek Abdusalomov, Zavqiddin Temirov, Rashid Nasimov, Abror Buriboev, Lola Safarova Ulmasovna, Cheolwon Lee and Heung Seok Jeon
Cancers 2025, 17(13), 2159; https://doi.org/10.3390/cancers17132159 - 26 Jun 2025
Viewed by 291
Abstract
Breast cancer continues to be a leading cause of cancer death in women globally [...] Full article
(This article belongs to the Special Issue AI-Based Applications in Cancers)
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