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Image Assisted High Precision Radiation Oncology

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

Deadline for manuscript submissions: 25 July 2026 | Viewed by 1902

Special Issue Editors


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Guest Editor
Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 47405, USA
Interests: image-guided radiation therapy

E-Mail Website
Guest Editor
Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 47405, USA
Interests: radiation oncology; medical physics

Special Issue Information

Dear Colleagues,

Modern radiation therapy has evolved into a discipline deeply reliant on advanced imaging technologies to ensure precision, safety, and personalization. This Special Issue aims to showcase the current state and future directions of image-assisted high-precision cancer radiation therapy, focusing on how the integration of multi-modal imaging—CT, MRI, PET, ultrasound, and surface imaging—has transformed treatment planning, delivery, adaptation, and verification. Emphasis will be placed on innovations in IGRT, adaptive therapy, radiomics, motion management, and the role of AI in image interpretation and automation. We welcome original research, technical notes, and reviews that span from preclinical developments to clinical implementation across various cancer types and treatment modalities.

Dr. Christopher Njeh
Dr. Senthamizhchelvan Srinivasan
Guest Editors

Manuscript Submission Information

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Keywords

  • image-guided radiation therapy (IGRT)
  • MR-guided radiation therapy (MRgRT)
  • adaptive radiation therapy (ART)
  • radiomics and AI in imaging
  • multi-modal imaging (CT, MRI, PET)
  • treatment planning and verification
  • real-time tracking and motion management
  • stereotactic radiotherapy (SBRT/SRS)
  • imaging biomarkers
  • dose accumulation and image registration

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Published Papers (2 papers)

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Review

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13 pages, 256 KB  
Review
AI in High-Frequency Micro-Ultrasound: Advancing Prostate Imaging from Segmentation to Cancer Detection
by Ludovica Cella, Marco Paciotti, Pier Paolo Avolio, Vittorio Fasulo, Andrea Piccolini, Rebecca Canneto, Giacomo Cavadini, Luca Di Stefano, Alberto Saita, Paolo Casale, Massimo Lazzeri, Nicolò Maria Buffi and Giovanni Lughezzani
Cancers 2026, 18(4), 665; https://doi.org/10.3390/cancers18040665 - 18 Feb 2026
Viewed by 764
Abstract
Background/Objective: High-frequency micro-ultrasound (micro-US) offers real-time, high-resolution imaging for prostate cancer. Although artificial intelligence (AI) has shown potential in enhancing micro-US interpretation, a comprehensive review of this emerging field is currently missing. This review synthesizes current evidence on AI applied to ExactVu 29 [...] Read more.
Background/Objective: High-frequency micro-ultrasound (micro-US) offers real-time, high-resolution imaging for prostate cancer. Although artificial intelligence (AI) has shown potential in enhancing micro-US interpretation, a comprehensive review of this emerging field is currently missing. This review synthesizes current evidence on AI applied to ExactVu 29 MHz micro-US for prostate cancer. Methods: PubMed/MEDLINE, Embase, Scopus, Web of Science and the Cochrane Library were searched up to December 2025. Studies were included if they applied machine learning or deep learning directly to 29 MHz micro-US data and reported quantitative performance metrics. Results: Ten studies met the inclusion criteria: six on prostate cancer detection, three on prostate segmentation and one on micro-US–histopathology registration. Detection models ranged from classical quantitative ultrasound machine learning to deep architectures using self-supervision, transformers, multiple-instance learning, ensemble calibration and 3D segmentation-based pipelines. Among core-level models for clinically significant cancer, area under the receiver operating characteristic curve (AUROC) values clustered around 0.76–0.81; one lesion-level framework reported an AUROC of 0.92, though at a non-comparable analytical unit. Segmentation studies achieved accurate prostate delineation (Dice similarity coefficient ≈ 0.94), and a single study demonstrated high-precision 3D registration to whole-mount histopathology (Dice similarity coefficient 0.97 and landmark error < 3 mm). All studies evaluated AI on previously acquired data, without real-time clinical implementation. Conclusions: AI for micro-US shows promising and reproducible early results across detection, segmentation and registration, but evidence is still limited. In view of the potential of AI to optimize micro-US utilization and its related advantages, additional efforts are warranted to achieve clinical adoption. Full article
(This article belongs to the Special Issue Image Assisted High Precision Radiation Oncology)

Other

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11 pages, 1596 KB  
Systematic Review
Conventionally Fractionated Radiotherapy (CFRT) Versus Stereotactic Body Radiotherapy (SBRT) for Locally Advanced Pancreatic Cancer: A Systematic Review and Meta-Analysis of Comparative Studies
by Giampaolo Montesi, Marcin Miszczyk, Rita Marina Niespolo, Giorgia Capezzali, Francesco Cellini, Nunziata D’Abbiero, Michele Fiore, Domenico Genovesi, Mariangela La Macchia, Marco Lupattelli, Giovanna Mantello, Fabio Matrone, Luca Nicosia, Nicola Simoni, Pierfrancesco Franco and Francesca De Felice
Cancers 2026, 18(6), 971; https://doi.org/10.3390/cancers18060971 - 17 Mar 2026
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Abstract
Background: Stereotactic body radiotherapy (SBRT) has gained increasing interest in the treatment of locally advanced pancreatic cancer (LAPC), although its effectiveness has not been defined in randomized trials. This systematic review and meta-analysis aimed to compare clinical outcomes and treatment-related toxicity between SBRT [...] Read more.
Background: Stereotactic body radiotherapy (SBRT) has gained increasing interest in the treatment of locally advanced pancreatic cancer (LAPC), although its effectiveness has not been defined in randomized trials. This systematic review and meta-analysis aimed to compare clinical outcomes and treatment-related toxicity between SBRT and CFRT in LAPC. Methods: This analysis was performed in accordance with PRISMA guidelines (PROSPERO: CRD420251128943). MEDLINE and Scopus were searched for comparative studies published between January 2015 and July 2025. Five retrospective studies comprising 768 patients fulfilled the eligibility criteria. Pooled hazard ratios (HRs) were calculated for overall survival (OS) and progression-free survival (PFS), while risk ratios (RRs) were estimated for severe (grade ≥ 3) acute toxicity using random-effects models. Study quality was evaluated using the ROBINS-I tool. Results: No significant OS or PFS differences were observed between SBRT and CFRT. SBRT was associated with a lower incidence of severe acute toxicity. The overall risk of bias across studies was moderate. Conclusions: SBRT appears to achieve survival outcomes comparable to CFRT with a favorable acute toxicity profile in patients with LAPC. Nevertheless, the current evidence is limited by retrospective designs and heterogeneity, highlighting the need for prospective randomized trials to define the role of SBRT in this setting. Full article
(This article belongs to the Special Issue Image Assisted High Precision Radiation Oncology)
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