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

Deadline for manuscript submissions: 25 March 2027 | Viewed by 3939

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Editors

Dr. Christopher Njeh

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Guest Editor

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

Dr. Senthamizhchelvan Srinivasan

E-Mail Website
Guest Editor

Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 47405, USA
Interests: radiation oncology; medical physics
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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

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-anonymized 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

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 (4 papers)

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Research

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16 pages, 4266 KB

Open AccessArticle

A Phantom Investigation of the Robustness and Adaptability of a Novel PET LINAC During Lung Cancer Biology-Guided Radiotherapy (BgRT) Characterized by Unplanned Perturbations in the Tumor Trajectory

by Roland Teboh, Offormata Osunkwor and Brett Lewis

Cancers 2026, 18(11), 1763; https://doi.org/10.3390/cancers18111763 - 28 May 2026

Viewed by 457

Abstract

This novel biology-guided radiotherapy system (BgRT), the RefleXion X1 SCINTIX™ system, uses annihilation photons produced from a PET-avid tumor to plan and then subsequently guide the delivery of beamlets of radiation to the tumor, tracking the dose delivered in real time. The current BgRT system is cleared by the U.S. Food and Drug Administration (FDA) to treat tumors in the lung or bone characterized by periodic or non-periodic motion, respectively. In this work, we assessed the robustness and adaptability of the dose tracking capability under tumor motion perturbation. We designed a phantom study based on a lung case planned under periodic tracking for four-pass (4-Pass) BgRT and then introduced transient translational shifts to the phantom motion continuously under treatment. By comparing the dose delivered under a normal periodic sinusoidal tumor motion to the dose delivered under a perturbed sinusoidal delivery, with a simple perturbation situation characterized by a perturbation introduced at the end of the second pass of the 4-Pass delivery and a more complex situation with perturbation introduced at each of the four passes of the 4-Pass delivery, we assessed the robustness and adaptability of the RefleXion X1 SCINTIX™ system in delivering the correct dose under such circumstances. Full article

(This article belongs to the Special Issue Image Assisted High Precision Radiation Oncology)

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Review

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17 pages, 793 KB

Open AccessReview

Adaptive Radiotherapy in the Era of Image Guidance and Artificial Intelligence: A Critical Review

by Senthamizhchelvan Srinivasan and Christopher F. Njeh

Cancers 2026, 18(14), 2281; https://doi.org/10.3390/cancers18142281 - 16 Jul 2026

Viewed by 599

Abstract

Adaptive radiotherapy (ART) transforms radiotherapy from a static treatment plan into a monitored, feedback-driven process that can respond to anatomic and biologic change. This critical narrative review synthesizes foundational physics literature, consensus reports, prospective studies, randomized trials, implementation reports, and ART-specific artificial-intelligence validation studies, with emphasis on image guidance, evidence quality, and clinical actionability. Further the review separates image guidance, reduced margins, gating, tracking, plan-of-the-day selection, offline replanning, and online reoptimization because these related interventions have different evidence bases. ART is most compelling when anatomic change is frequent, dosimetrically important, measurable, and not adequately managed by population margins or conventional image guidance. Quantitative evidence is strongest for dosimetric recovery, organ-at-risk sparing, workflow feasibility, and selected toxicity reduction; evidence for survival improvement remains limited and heterogeneous. Offline ART has mature roles for progressive or systematic change, whereas online MR-guided and cone-beam CT-guided workflows address daily anatomy through same-session contour review, plan selection or reoptimization, and rapid quality assurance. Artificial intelligence can accelerate segmentation, synthetic CT generation, planning, and quality assurance, but clinically meaningful validation requires geometric, dosimetric, time, failure-rate, bias, and post-update monitoring. ART should be deployed through explicit adaptation triggers, validated dose-accumulation methods, independent quality management, and prospective endpoints connecting adaptive decisions to toxicity, tumor control, workflow efficiency, cost, access, and equity. Full article

(This article belongs to the Special Issue Image Assisted High Precision Radiation Oncology)

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13 pages, 256 KB

Open AccessReview

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

Cited by 1 | Viewed by 1321

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 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

Open AccessSystematic 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 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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