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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 December 2025 | Viewed by 6007

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Special Issue Editor

Dr. Xia Jiang

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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 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

AI
machine learning
deep learning
Bayesian networks
causal learning
LLM
cancer

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Published Papers (6 papers)

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14 pages, 1609 KB

Open AccessArticle

A Deep Learning Framework for Classification of Neuroendocrine Neoplasm Whole Slide Images

by Amir Hadjifaradji, Michael Diaz-Stewart, Jenny Chu, David Farnell, David Schaeffer, Hossein Farahani, Ali Bashashati and Jonathan M. Loree

Cancers 2025, 17(18), 2991; https://doi.org/10.3390/cancers17182991 - 13 Sep 2025

Viewed by 816

Abstract

Background/Objectives: Neuroendocrine neoplasms (NENs) are uncommon neoplasms. Grading informs the prognosis and treatment decision of NENs and is determined by cell proliferation, which is measured by mitotic count and Ki-67 index. These measurements present challenges for pathologists as they suffer inter- and intra-observer variability and are cumbersome to quantify. To address these challenges, we developed a machine learning pipeline for identifying tumor areas, proliferating cells, and grading NENs. Methods: Our study includes 385 samples of gastroenteropancreatic NENs from across British Columbia with two stains (247 H&E and 138 Ki-67 images). Labels for these cases are at the patient-level, and there are 186 patients. We systematically investigated three settings for our study: H&E, H&E with Ki-67, and pathologist-reviewed and corrected cases. Results: Our H&E framework achieved a three-fold balanced accuracy of 77.5% in NEN grading. The H&E with Ki-67 framework yields a performance improvement to 83.0% on grading. We provide survival and multivariate analysis with a c-index of 0.65. Grade 1 NENs misclassified by the model were reviewed by a pathologist to assess reasons. Analysis of our AI-graded NENs for the subset of pathologist-assessed G1s demonstrated a significant (p-value = 0.007) survival difference amongst samples the algorithm assigned to a higher grade (n = 20; median survival 4.22 years) compared to concordant G1 samples (n = 60; median survival 10.13 years). Conclusions: Our model identifies NEN grades with high accuracy and identified some grade 1 tumors as prognostically unique, suggesting potential improvements to standard grading. Further studies are needed to determine if this discordant group is a different clinical entity. Full article

(This article belongs to the Special Issue AI-Based Applications in Cancers)

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25 pages, 2761 KB

Open AccessArticle

Leveraging Deep Learning, Grid Search, and Bayesian Networks to Predict Distant Recurrence of Breast Cancer

by Xia Jiang, Yijun Zhou, Alan Wells and Adam Brufsky

Cancers 2025, 17(15), 2515; https://doi.org/10.3390/cancers17152515 - 30 Jul 2025

Viewed by 1018

Abstract

Background: Unlike most cancers, breast cancer poses a persistent risk of distant recurrence—often years after initial treatment—making long-term risk stratification uniquely challenging. Current tools fall short in predicting late metastatic events, particularly for early-stage patients. Methods: We present an interpretable machine learning (ML) pipeline to predict distant recurrence-free survival at 5, 10, and 15 years, integrating Bayesian network-based causal feature selection, deep feed-forward neural network models (DNMs), and SHAP-based interpretation. Using electronic health record (EHR)-based clinical data from over 6000 patients, we first applied the Markov blanket and interactive risk factor learner (MBIL) to identify minimally sufficient predictor subsets. These were then used to train optimized DNM classifiers, with hyperparameters tuned via grid search and benchmarked against models from 10 traditional ML methods and models trained using all predictors. Results: Our best models achieved area under the curve (AUC) scores of 0.79, 0.83, and 0.89 for 5-, 10-, and 15-year predictions, respectively—substantially outperforming baselines. MBIL reduced input dimensionality by over 80% without sacrificing accuracy. Importantly, MBIL-selected features (e.g., nodal status, hormone receptor expression, tumor size) overlapped strongly with top SHAP contributors, reinforcing interpretability. Calibration plots further demonstrated close agreement between predicted probabilities and observed recurrence rates. The percentage performance improvement due to grid search ranged from 25.3% to 60%. Conclusions: This study demonstrates that combining causal selection, deep learning, and grid search improves prediction accuracy, transparency, and calibration for long-horizon breast cancer recurrence risk. The proposed framework is well-positioned for clinical use, especially to guide long-term follow-up and therapy decisions in early-stage patients. Full article

(This article belongs to the Special Issue AI-Based Applications in Cancers)

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30 pages, 11103 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1038

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 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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1026

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 (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, 3676 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1020

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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18 pages, 2427 KB

Open AccessSystematic Review

Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review

by Samanta Ortuño-Miquel, Reyes Roca, Cristina Alenda, Francisco Aranda, Natividad Martínez-Banaclocha, Sandra Amador and David Gil

Cancers 2025, 17(22), 3619; https://doi.org/10.3390/cancers17223619 - 10 Nov 2025

Viewed by 525

Abstract

Background/Objectives: Lung cancer (NSCLC), which accounts for approximately 85% of lung cancers, exhibits marked heterogeneity that complicates molecular characterization and treatment selection. Recent advances in deep learning (DL) have enabled the extraction of genomic-related morphological features directly from routine Hematoxylin and Eosin (H&E) histopathology, offering a potential complement to Next-Generation Sequencing (NGS) for precision oncology. This review aimed to evaluate how DL models have been applied to predict molecular alterations in NSCLC using H&E-stained slides. Methods: A systematic search following PRISMA 2020 guidelines was conducted across PubMed, Scopus, and Web of Science to identify studies published up to March 2025 that used DL models for mutation prediction in NSCLC. Eligible studies were screened, and data on model architectures, datasets, and performance metrics were extracted. Results: Sixteen studies met inclusion criteria. Most employed convolutional neural networks trained on publicly available datasets such as The Cancer Genome Atlas (TCGA) to infer key mutations including EGFR, KRAS, and TP53. Reported areas under the curve ranged from 0.65 to 0.95, demonstrating variable but promising predictive capability. Conclusions: DL-based histopathology shows strong potential for linking tissue morphology to molecular signatures in NSCLC. However, methodological heterogeneity, small sample sizes, and limited external validation constrain reproducibility and generalizability. Standardized protocols, larger multicenter cohorts, and transparent validation are needed before these models can be translated into clinical practice. Full article

(This article belongs to the Special Issue AI-Based Applications in Cancers)

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