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Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling

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A special issue of Medical Sciences (ISSN 2076-3271).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5536

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Editors

Dr. Orazio Gambino

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Guest Editor

Department of Engineering, University of Palermo, 90133 Palermo, Italy
Interests: artificial intelligence; biomedical image processing; decision-making systems; DICOM standard; graphical user interface

Dr. Salvatore Contino

E-Mail Website
Guest Editor

Department of Engineering, University of Palermo, 90133 Palermo, Italy
Interests: image processing; deep learning; multimodal AI; cancer therapy; drug discovery; DICOM standard

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has transformed oncology by introducing powerful new methods for diagnosing, segmenting, and monitoring medical images across a wide spectrum of clinical applications. This Special Issue seeks original contributions that advance the integration of AI into oncologic imaging and related domains. Key areas of interest include radiomic analysis through multimodal deep neural networks, medical image segmentation for lesion detection, boundary delineation, treatment planning, as well as predictive modelling using machine learning, deep learning, and generative frameworks such as Vision–Language Models. These innovations enhance our capacity to interpret complex biomedical data, offering new insights into disease characterisation and supporting personalised therapeutic strategies. By highlighting current challenges and opportunities, this Special Issue aims to foster deeper integration of AI technologies within precision medicine.

Dr. Orazio Gambino
Dr. Salvatore Contino
Guest Editors

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Keywords

artificial intelligence
medical image segmentation
oncologic imaging
radiomics
deep learning
machine learning
predictive modelling
vision language models
multimodal imaging
precision medicine

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Published Papers (5 papers)

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Research

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17 pages, 3787 KB

Open AccessArticle

Human-in-the-Loop Enhances Machine Learning Inference in Intraoperative Optical Coherence Tomography Glioma Imaging

by Radik Zinatullin, Alexander Sovetsky, Artem Grishin, Elena Kiseleva, Liudmila Kukhnina, Svetlana Korikova, Alexander Matveyev, Vladimir Zaitsev, Konstantin Yashin and Lev Matveev

Med. Sci. 2026, 14(2), 263; https://doi.org/10.3390/medsci14020263 - 20 May 2026

Viewed by 501

Abstract

Background/Objectives: The integration of Artificial Intelligence (AI) into clinical workflows raises critical questions regarding decision-making responsibility, as fully autonomous systems inevitably carry a margin of error that can be fatal in high-stakes fields like surgery. This study addresses this challenge by evaluating a “Human-in-the-Loop” (HITL) workflow, using intraoperative Optical Coherence Tomography (OCT) for glioma detection. We aimed to determine if integrating Machine Learning (ML)-generated segmentation maps with human contextual analysis resolves the tension between automation and clinical responsibility, yielding superior diagnostic reliability compared to structural or quantitative imaging alone. Methods: We retrospectively analyzed 86 intraoperative OCT scans from 27 patients. Five neurosurgeons blindly assessed the data across three progressive levels of processing: (1) structural scans, (2) physics-based parametric maps, and (3) SVM-based generated segmentation maps. Crucially, the HITL inference performance on segmentation maps was benchmarked against “models-only” inference pipeline: a SVM and a state-of-the-art multimodal reasoning model, Gemini 3.1 Pro. To evaluate interpretability and the operator’s ability to confidently exercise their authority, we measured inter-rater consistency alongside diagnostic performance. Results: The results demonstrate that, while quantitative parametric maps improved Global Accuracy (87% [95% CI: 82–92%]) compared to structural scans (80% [95% CI: 73–86%]), they suffered from an “interpretability gap,” resulting in a moderate inter-rater consistency of 0.68 [95% CI: 0.59–0.78]. In contrast, the HITL approach using segmentation maps maximized consensus to 0.98 [95% CI: 0.95–1.00] and achieved the highest performance (Accuracy 94% [95% CI: 88–98%] and Sensitivity 98% [95% CI: 92–100%]). Compared to the standalone models, the HITL approach significantly outperformed the SVM baseline (Accuracy 84% [95% CI: 81–87%]; Sensitivity 83% [95% CI: 78–88%]). Furthermore, it surpassed the SOTA Gemini 3.1 Pro model (Accuracy 90% [95% CI: 83–95%]; Sensitivity 86% [95% CI: 74–95%]). While the HITL sensitivity demonstrated a definitive and statistically significant edge over the Gemini model, the accuracy improvement fell just slightly short of undisputed statistical significance due to overlapping confidence intervals. Conclusions: By utilizing their clinical domain knowledge of tumor invasion patterns and topological priors, surgeons effectively filtered algorithmic noise—overriding ML errors in 69% (9 out of 13) false positive cases that models alone could not resolve. This demonstrates exactly how and where HITL optimally utilizes human contextual intelligence to outperform autonomous “models-only” pipelines, confirming a human-ML synergy that augments the objectivity of machine learning with human domain knowledge. This paradigm ensures that the ultimate responsibility for diagnostic inference remains safely and practically in human hands. Open Data Initiative: To ensure essential reproducibility, enable independent multi-center validation and support open science, all examples of intraoperative in vivo OCT brain scans used in this study are made publicly available. To the best of our knowledge, this represents the first open-access data of its kind globally. Full article

(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)

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19 pages, 1807 KB

Open AccessArticle

Drawing the Line: From U-Net-Based Glioblastoma Segmentation to Machine Learning-Driven Survival Prediction

by Costin Chirica, Bogdan-Ionuț Dobrovăț, Sabina-Ioana Chirica, Oriana-Maria Onicescu, Andreea Rotundu, Emilia-Adriana Marciuc, Laura-Elena Cucu, Daniela Pomohaci, Răzvan-Constantin Anghel, Mihaela-Roxana Popescu, Alexandra Maștaleru, Danisia Haba and Maria Magdalena Leon

Med. Sci. 2026, 14(1), 119; https://doi.org/10.3390/medsci14010119 - 3 Mar 2026

Cited by 1 | Viewed by 1663

Abstract

Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)

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21 pages, 3100 KB

Open AccessArticle

A Lightweight Cross-Gated Dual-Branch Attention Network for Colon and Lung Cancer Diagnosis from Histopathological Images

by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Sergio Octavio Rosales-Aguayo, Jesús Ezequiel Molinar-Solís, Sonia Espinoza-Morales and René Gudiño-Venegas

Med. Sci. 2025, 13(4), 286; https://doi.org/10.3390/medsci13040286 - 26 Nov 2025

Cited by 2 | Viewed by 1225

Abstract

Background/Objectives: Accurate histopathological classification of lung and colon tissues remains difficult due to subtle morphological overlap between benign and malignant regions. Deep learning approaches have advanced diagnostic precision, yet models often lack interpretability or require complex multi-stage pipelines. This study aimed to develop an end-to-end dual-branch attention network capable of achieving high accuracy while preserving computational efficiency and transparency. Methods: The architecture integrates EfficientNetV2-B0 and MobileNetV3-Small backbones through a cross-gated fusion mechanism that adaptively balances global context and fine structural details. Efficient channel attention and generalized mean pooling enhance discriminative learning without external feature extraction or optimization stages. Results: The network achieved 99.84% accuracy, precision, recall, and F1-score, with an MCC of 0.998. Grad-CAM maps showed strong spatial correspondence with diagnostically relevant histological structures. Conclusions: The end-to-end framework enables the reliable, interpretable, and computationally efficient classification of lung and colon histopathology and has potential applicability to computer-assisted diagnostic workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)

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16 pages, 3443 KB

Open AccessArticle

Automated Detection and Grading of Renal Cell Carcinoma in Histopathological Images via Efficient Attention Transformer Network

by Hissa Al-kuwari, Belqes Alshami, Aisha Al-Khinji, Adnan Haider and Muhammad Arsalan

Med. Sci. 2025, 13(4), 257; https://doi.org/10.3390/medsci13040257 - 1 Nov 2025

Cited by 3 | Viewed by 1128

Abstract

Background: Renal Cell Carcinoma (RCC) is the most common type of kidney cancer and requires accurate histopathological grading for effective prognosis and treatment planning. However, manual grading is time-consuming, subjective, and susceptible to inter-observer variability. Objective: This study proposes EAT-Net (Efficient Attention Transformer Network), a dual-stream deep learning model designed to automate and enhance RCC grade classification from histopathological images. Method: EAT-Net integrates EfficientNetB0 for local feature extraction and a Vision Transformer (ViT) stream for capturing global contextual dependencies. The architecture incorporates Squeeze-and-Excitation (SE) modules to recalibrate feature maps, improving focus on informative regions. The model was trained and evaluated on two publicly available datasets, KMC-RENAL and RCCG-Net. Standard preprocessing was applied, and the model’s performance was assessed using accuracy, precision, recall, and F1-score. Results: EAT-Net achieved superior results compared to state-of-the-art models, with an accuracy of 92.25%, precision of 92.15%, recall of 92.12%, and F1-score of 92.25%. Ablation studies demonstrated the complementary value of the EfficientNet and ViT streams. Additionally, Grad-CAM visualizations confirmed that the model focuses on diagnostically relevant areas, supporting its interpretability and clinical relevance. Conclusion: EAT-Net offers an accurate, and explainable framework for RCC grading. Its lightweight architecture and high performance make it well-suited for clinical deployment in digital pathology workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)

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23 pages, 1079 KB

Open AccessSystematic Review

MRI-Based Radiomics and Artificial Intelligence for Prediction of Recurrence and Prognostic Outcomes in Oral Tongue Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis

by Carlos M. Ardila, Eliana Pineda-Vélez, Anny M. Vivares-Builes and Alejandro I. Díaz-Laclaustra

Med. Sci. 2026, 14(2), 332; https://doi.org/10.3390/medsci14020332 - 19 Jun 2026

Viewed by 179

Abstract

Background/Objectives: Oral tongue squamous cell carcinoma (OTSCC) remains clinically challenging because conventional clinicopathological markers do not fully explain variability in recurrence and survival. This systematic review and functional meta-synthesis aimed to identify and critically appraise studies using preoperative magnetic resonance imaging (MRI)-based radiomics, artificial intelligence (AI), deep learning, or quantitative MRI-derived models to predict recurrence and prognostic outcomes in OTSCC. Methods: PubMed, Scopus, and Embase were searched from inception to March 2026. Eligible studies included prognostic model investigations in adults with OTSCC or primary tongue cancer without reported base-of-tongue/oropharyngeal involvement, undergoing preoperative MRI and surgery, with recurrence- or survival-related follow-up. The primary synthesis was a functional meta-synthesis; pooling was not performed because studies were not sufficiently comparable. Results: Seven retrospective studies were included, with a summed descriptive sample of 1287 participants. The evidence base was heterogeneous in MRI sequences, segmentation workflows, model architecture, validation strategy, and endpoint definition. Functional meta-synthesis identified four domains: direct recurrence-oriented modeling, broader prognostic stratification, reported incremental or complementary value over clinical frameworks, and translational maturity/technical implementation. Several studies reported associations between MRI-derived signatures and recurrence- or survival-related outcomes, but findings were interpreted narratively because of differences in primary endpoints, imaging features, model design, validation methods, and outcome definitions. Most studies were judged at high overall risk of bias, and certainty of evidence ranged from low to very low. Conclusions: MRI-based radiomics and AI show preliminary promise for prognostic stratification in OTSCC, particularly recurrence-related risk refinement, but current evidence remains limited by retrospective design, heterogeneity, sparse external validation, and low certainty. Full article

(This article belongs to the Special Issue Artificial Intelligence in Oncologic Imaging: Advances in Medical Image Segmentation and Predictive Modelling)

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