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A Special Issue of Cancers (ISSN 2072-6694) belonging to the section "Cancer Therapy".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4815

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Dr. He C. Wang

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Radiation Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
Interests: radiation oncology

Special Issue Information

Dear Colleagues,

Radiation oncology has achieved remarkable advancements in cancer care, with image-assisted techniques emerging as a cornerstone for achieving effective and high-precision treatment. By integrating medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), four-dimensional CT (4DCT), dual-energy CT (DECT), and optical surface imaging, radiation therapy can be tailored to the unique anatomical and physiological characteristics of each patient. These technologies enable accurate tumor localization and delineation, provide advanced image guidance through precise positioning and real-time motion tracking, and facilitate adaptive dose delivery, therefore minimizing damage to surrounding healthy tissues while enhancing treatment efficacy. Furthermore, the addition of artificial intelligence (AI) techniques and radiomics promotes efficient and standardized radiotherapy, while also offering predictive insights into treatment optimization and tumor response. As the field progresses, image-assisted high-precision radiation therapy will be poised to improve survival rates and reduce treatment-related complications, marking a significant leap toward personalized oncology care.

Dr. He C. Wang
Guest Editor

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Keywords

medical imaging
radiation oncology
artificial intelligence
radiomics
adaptation
tumor response

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Published Papers (3 papers)

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Research

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10 pages, 733 KB

Open AccessArticle

Generalization of the Conformity Index for Multi-Target Radiotherapy Plans

by Yong Sang, Jun Dang, Jianan Wu, Yanling Wu, Enzhuo Quan and Jianrong Dai

Cancers 2026, 18(3), 426; https://doi.org/10.3390/cancers18030426 - 28 Jan 2026

Viewed by 1072

Abstract

Background and Purpose: Based on the distortion of the current conformity index (CI) formula in handling multi-target plans, the

V_{T V}

parameter in the current CI formula has been redefined to more accurately calculate the CI value of multi-target plans, providing a [...] Read more.

Background and Purpose: Based on the distortion of the current conformity index (CI) formula in handling multi-target plans, the

V_{T V}

parameter in the current CI formula has been redefined to more accurately calculate the CI value of multi-target plans, providing a reference for clinical applications. Methods and Materials: Considering the limitations of the current

V_{T V}

calculation formula in CI proposed by van’t Riet and Paddick, a new

V_{T V}

has been defined to better reflect the true conformity of the target volume in multi-target planning. We selected 15 breast cancer (BC) plans with PTVsc and PTVcw as the target volumes, and 15 nasopharyngeal carcinoma (NPC) plans with PTVp, PTVn, PTVrpn, PTV1, and PTV2 as target volumes.

V_{T V}^{n e w}

and

C I_{n e w}

were calculated using the proposed formulas, while

V_{T V}^{o l d}

and

C I_{o l d}

were calculated using traditional formulas based on van’t Riet and Paddick. A paired, two-tailed Wilcoxon signed-rank test was conducted to compare

V_{T V}^{n e w}

with

V_{T V}^{o l d}

, and

C I_{n e w}

with

C I_{o l d}

across all target volumes. Pearson’s correlation analysis was performed between

C I_{n e w}

and

C I_{o l d}

. Results: For BC, the

V_{T V}

values calculated by the two methods for PTVsc and PTVcw showed statistically significant differences; the values calculated in this study were significantly lower than those calculated by van’t Riet and Paddick (p < 0.05). Consequently, the

C I_{n e w}

values for BC were significantly higher than

C I_{o l d}

. These results were consistent with those for PTVp, PTVn, PTVrpn, and PTV2 in NPC. For PTV1 in NPC, the results calculated by the two formulas were identical. Conclusions: The new

V_{T V}

calculation formula eliminates the influence of dose spillage from adjacent targets, retaining only the prescription dose range of the specific target under analysis. This makes the calculated CI more reflective of true conformity compared to traditional formulas. We recommend using the proposed formula to calculate CI values for multi-target plans such as those for BC and NPC. Full article

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

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20 pages, 2956 KB

Open AccessArticle

Tumor Microenvironment: Insights from Multiparametric MRI in Pancreatic Ductal Adenocarcinoma

by Ramesh Paudyal, James Russell, H. Carl Lekaye, Joseph O. Deasy, John L. Humm, Muhammad Awais, Saad Nadeem, Richard K. G. Do, Eileen M. O’Reilly, Lawrence H. Schwartz and Amita Shukla-Dave

Cancers 2026, 18(2), 273; https://doi.org/10.3390/cancers18020273 - 15 Jan 2026

Cited by 1 | Viewed by 1221

Abstract

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

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

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Review

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47 pages, 4949 KB

Open AccessReview

Artificial Intelligence in Image Assisted Radiation Oncology

by He Wang, Yao Zhao, Xinru Chen, Brigid McDonald, Yunxiang Li, Jiacheng Xie, Dong Joo Rhee, Tze Yee Lim, Tucker J. Netherton, Jack Phan, Michael T. Spiotto and Mu-Han Lin

Cancers 2026, 18(11), 1715; https://doi.org/10.3390/cancers18111715 - 25 May 2026

Cited by 1 | Viewed by 1798

Abstract

Advanced imaging is the cornerstone of modern radiation oncology, contributing to each phase of patient care, from diagnosis and treatment planning to delivery and follow-up. It has evolved from providing purely geometric guidance to enabling biological and dynamic precision, capturing detailed spatial and functional information about tumors and surrounding tissues. This progress has also generated vast amounts of complex data that remain largely underexplored. AI-based methods have shown promises to unlock the potential of these data, ensuring quality and standardization while extracting previously inaccessible insights. AI-driven tools can enhance accuracy, efficiency, and personalization of radiation oncology through precision diagnosis, automated segmentation, adaptive treatment planning, real-time image guidance, and predictive response assessment. In this review, we conducted a systematic bibliometric analysis of relevant literature published in the last decade and explored current advancements in AI and radiomics applications across radiation oncology. We also addressed ongoing challenges, such as data heterogeneity, model interpretability, and clinical implementation, and discussed future directions for integrating AI-powered imaging solutions into routine practice to advance precision cancer care. Full article

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

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