How rapid advances in imaging are defining the future of precision radiation oncology

Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis

BACKGROUND

Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns.

METHODS

In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists.

RESULTS

Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6).

CONCLUSION

Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.

Clinical research progress of callisperes® of drug-loaded microsphere arterial chemoembolisation in the treatment of solid tumors

The incidence and mortality of cancer is ever-increasing, which poses a significant challengesto human health and a substantial economic burden to patients. At present, chemotherapy is still a primary treatment for various cancers. However, chemotherapy kills tumors but also induces the related side effects, whichadversely impacting patient quality of life and exacerbating suffering. Therefore, there is an urgent need for new and effective treatments that can control tumor growth while reducing the side effects for patients. Arterial chemoembolization has been attracted much attentionwhich attributed to the advantage of ability to embolize tumor vessels to block blood and nutrition supplies. Thus, to achieve local tumor control, it has become an effective means of local tumor control and has been widely used in clinical practice. Despite its efficacy, conventional arterial chemoembolization techniques, limited by embolization materials, have been associated with incomplete embolization and suboptimal drug delivery outcomes. Gradually, researchers have shifted their attention to a new type of embolic material called CalliSperes® drug-eluting embolic bead (DEB). DEB can not only load high doses of drugs, but also has strong sustained drug release ability and good biocompatibility. The integration of DEBs with traditional arterial chemoembolization (DEB-TACE) promises targeted vascular embolization, mitigated tumor ischemia and hypoxia, and direct intravascular chemotherapy delivery. It can prevent cancer cell differentiation and accelerate their death, meanwhile, directly injecting chemotherapy drugs into the target blood vessels reduced the blood concentration of the whole body, thus reduced the toxic and side effects of chemotherapy. Furthermore, DEB-TACE's sustained drug release capability elevates local drug concentrations at the tumor site, amplifying its antitumor efficacy. Therefore, DEB-TACE has become a hot spot in clinical research worldwide. This review introduces the pathogenesis of solid tumors, the background of research and biological characteristics of DEB, and the action mechanism of DEB-TACE, as well as its clinical research in various solid tumors and future prospects. This review aims to provide new ideas for the treatment of DEB-TACE in various solid tumors.

Systematic Implementation of Effective Quality Assurance Processes for the Assessment of Radiation Target Volumes in Head and Neck Cancer

PURPOSE

Significant heterogeneity exists in clinical quality assurance (QA) practices within radiation oncology departments, with most chart rounds lacking prospective peer-reviewed contour evaluation. This has the potential to significantly affect patient outcomes, particularly for head and neck cancers (HNC) given the large variance in target volume delineation. With this understanding, we incorporated a prospective systematic peer contour-review process into our workflow for all patients with HNC. This study aims to assess the effectiveness of implementing prospective peer review into practice for our National Cancer Institute Designated Cancer Center and to report factors associated with contour modifications.

METHODS AND MATERIALS

Starting in November 2020, our department adopted a systematic QA process with real-time metrics, in which contours for all patients with HNC treated with radiation therapy were prospectively peer reviewed and graded. Contours were graded with green (unnecessary), yellow (minor), or red (major) colors based on the degree of peer-recommended modifications. Contours from November 2020 through September 2021 were included for analysis.

RESULTS

Three hundred sixty contours were included. Contour grades were made up of 89.7% green, 8.9% yellow, and 1.4% red grades. Physicians with >12 months of clinical experience were less likely to have contour changes requested than those with <12 months (8.3% vs 40.9%; P < .001). Contour grades were significantly associated with physician case load, with physicians presenting more than the median number of 50 cases having significantly less modifications requested than those presenting <50 (6.7% vs 13.3%; P = .013). Physicians working with a resident or fellow were less likely to have contour changes requested than those without a trainee (5.2% vs 12.6%; P = .039). Frequency of major modification requests significantly decreased over time after adoption of prospective peer contour review, with no red grades occurring >6 months after adoption.

CONCLUSIONS

This study highlights the importance of prospective peer contour-review implementation into systematic clinical QA processes for HNC. Physician experience proved to be the highest predictor of approved contours. A growth curve was demonstrated, with major modifications declining after prospective contour review implementation. Even within a high-volume academic practice with subspecialist attendings, >10% of patients had contour changes made as a direct result of prospective peer review.

A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.

Empowering PET: harnessing deep learning for improved clinical insight

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

The role of large language models in medical image processing: a narrative review

Background and Objective

The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation.

Methods

A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts, review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis.

Key Content and Findings

The review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI's impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain.

Conclusions

In conclusion, this review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.

Auto-segmentation of neck nodal metastases using self-distilled masked image transformer on longitudinal MR images

Objectives

Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T2-weighted (T2w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients.

Methods

This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T2w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients (ρ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P-values <0.05 were considered significant.

Results

No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm3, P = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm3, with a mean difference of 0.30 cm3. SMIT model and manually delineated tumor volume estimates were highly correlated (ρ = 0.84-0.96, P < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively.

Conclusions

The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC.

Advances in knowledge

First evaluation of auto-segmentation with SMIT using longitudinal T2w MRI in HPV+ OPSCC.

Enhancement of Radiation Sensitivity by Cathepsin L Suppression in Colon Carcinoma Cells

Cancer is one of the main causes of death globally. Radiotherapy/Radiation therapy (RT) is one of the most common and effective cancer treatments. RT utilizes high-energy radiation to damage the DNA of cancer cells, leading to their death or impairing their proliferation. However, radiation resistance remains a significant challenge in cancer treatment, limiting its efficacy. Emerging evidence suggests that cathepsin L (cath L) contributes to radiation resistance through multiple mechanisms. In this study, we investigated the role of cath L, a member of the cysteine cathepsins (caths) in radiation sensitivity, and the potential reduction in radiation resistance by using the specific cath L inhibitor (Z-FY(tBu)DMK) or by knocking out cath L with CRISPR/Cas9 in colon carcinoma cells (caco-2). Cells were treated with different doses of radiation (2, 4, 6, 8, and 10), dose rate 3 Gy/min. In addition, the study conducted protein expression analysis by western blot and immunofluorescence assay, cytotoxicity MTT, and apoptosis assays. The results demonstrated that cath L was upregulated in response to radiation treatment, compared to non-irradiated cells. In addition, inhibiting or knocking out cath L led to increased radiosensitivity in contrast to the negative control group. This may indicate a reduced ability of cancer cells to recover from radiation-induced DNA damage, resulting in enhanced cell death. These findings highlight the possibility of targeting cath L as a therapeutic strategy to enhance the effectiveness of RT. Further studies are needed to elucidate the underlying molecular mechanisms and to assess the translational implications of cath L knockout in clinical settings. Ultimately, these findings may contribute to the development of novel treatment approaches for improving outcomes of RT in cancer patients.

Systematic Review and Meta-analysis of the Association Between Radiation Therapy Treatment Volume and Patient Outcomes

PURPOSE

Evidence of a volume-outcome association in cancer surgery has shaped the centralization of cancer services; however, it is unknown whether a similar association exists for radiation therapy. The objective of this study was to determine the association between radiation therapy treatment volume and patient outcomes.

METHODS AND MATERIALS

This systematic review and meta-analysis included studies that compared outcomes of patients who underwent definitive radiation therapy at high-volume radiation therapy facilities (HVRFs) versus low-volume facilities (LVRFs). The systematic review used Ovid MEDLINE and Embase. For the meta-analysis, a random effects model was used. Absolute effects and hazard ratios (HRs) were used to compare patient outcomes.

RESULTS

The search identified 20 studies assessing the association between radiation therapy volume and patient outcomes. Seven of the studies looked at head and neck cancers (HNCs). The remaining studies covered cervical (4), prostate (4), bladder (3), lung (2), anal (2), esophageal (1), brain (2), liver (1), and pancreatic cancer (1). The meta-analysis demonstrated that HVRFs were associated with a lower chance of death compared with LVRFs (pooled HR, 0.90; 95% CI, 0.87- 0.94). HNCs had the strongest evidence of a volume-outcome association for both nasopharyngeal cancer (pooled HR, 0.74; 95% CI, 0.62-0.89) and nonnasopharyngeal HNC subsites (pooled HR, 0.80; 95% CI, 0.75-0.84), followed by prostate cancer (pooled HR, 0.92; 95% CI, 0.86-0.98). The remaining cancer types showed weak evidence of an association. The results also demonstrate that some centers defined as HVRFs are undertaking very few procedures per annum (<5 radiation therapy cases per year).

CONCLUSIONS

An association between radiation therapy treatment volume and patient outcomes exists for most cancer types. Centralization of radiation therapy services should be considered for cancer types with the strongest volume-outcome association, but the effect on equitable access to services needs to be explicitly considered.

Implementation of a retrofit MRI simulator for radiation therapy planning

Magnetic resonance imaging (MRI) is being integrated into routine radiation therapy (RT) planning workflows. To reap the benefits of this imaging modality, patient positioning, image acquisition parameters and a quality assurance programme must be considered for accurate use. This paper will report on the implementation of a retrofit MRI Simulator for RT planning, demonstrating an economical, resource efficient solution to improve the accuracy of MRI in this setting.

Dose optimization of 2D X-ray image acquisition protocols in image-guided radiotherapy

PURPOSE

In contemporary radiotherapy, patient positioning accuracy relies on kV imaging. This study aims at optimizing planar kV image acquisition protocols regarding patient dose without degrading image quality.

MATERIALS AND METHODS

An image quality test-object was placed in-between PMMA plates, suitably arranged to model head or pelvis. Constructed phantoms were imaged using default protocols, the resultant image quality was assessed and the corresponding radiation dose was measured. The process was repeated using numerous kV/mAs combinations to identify those acquisition settings providing images at lower dose than the default protocols but without deterioration in image quality. Default and dose-optimized protocols were then tested on an anthropomorphic phantom and on 51 patients during two successive treatment sessions. Image quality was independently assessed by two readers. Organ and effective doses were estimated using a Monte Carlo simulation software.

RESULTS

Low-contrast detectability exhibited a stronger dependence on kV/mAs settings, compared to high-contrast resolution. Dose-optimized protocols resulted in significant dose reductions (anteroposterior-head 48.0 %, lateral-head 30.0 %, anteroposterior-pelvis 28.4 %, lateral-pelvis 27.0 %) compared to the default ones, without compromising image quality. Optimized protocols decreased effective doses by 54 % and 29.6 % in head and pelvic acquisitions, respectively. Regarding image quality, anthropomorphic and patient images acquired using the dose-optimized protocols were subjectively evaluated equivalent to those obtained with the corresponding default settings, indicating that the proposed protocols may be routinely used.

CONCLUSIONS

Given the potentially large number of radiotherapy fractions and the pertinent image acquisitions, dose-optimized protocols could significantly reduce patient dose associated with planar imaging without compromising positioning accuracy.

Unleashing novel horizons in advanced prostate cancer treatment: investigating the potential of prostate specific membrane antigen-targeted nanomedicine-based combination therapy

Prostate cancer (PCa) is a prevalent malignancy with increasing incidence in middle-aged and older men. Despite various treatment options, advanced metastatic PCa remains challenging with poor prognosis and limited effective therapies. Nanomedicine, with its targeted drug delivery capabilities, has emerged as a promising approach to enhance treatment efficacy and reduce adverse effects. Prostate-specific membrane antigen (PSMA) stands as one of the most distinctive and highly selective biomarkers for PCa, exhibiting robust expression in PCa cells. In this review, we explore the applications of PSMA-targeted nanomedicines in advanced PCa management. Our primary objective is to bridge the gap between cutting-edge nanomedicine research and clinical practice, making it accessible to the medical community. We discuss mainstream treatment strategies for advanced PCa, including chemotherapy, radiotherapy, and immunotherapy, in the context of PSMA-targeted nanomedicines. Additionally, we elucidate novel treatment concepts such as photodynamic and photothermal therapies, along with nano-theragnostics. We present the content in a clear and accessible manner, appealing to general physicians, including those with limited backgrounds in biochemistry and bioengineering. The review emphasizes the potential benefits of PSMA-targeted nanomedicines in enhancing treatment efficiency and improving patient outcomes. While the use of PSMA-targeted nano-drug delivery has demonstrated promising results, further investigation is required to comprehend the precise mechanisms of action, pharmacotoxicity, and long-term outcomes. By meticulous optimization of the combination of nanomedicines and PSMA ligands, a novel horizon of PSMA-targeted nanomedicine-based combination therapy could bring renewed hope for patients with advanced PCa.

Radiotherapy in the management of rare gastrointestinal cancers: A systematic review

The aim of this analysis is to assess radiotherapy's role and technical aspects in an array of rare gastrointestinal (GI) cancers for adult patients. Collection data pertaining to radiotherapy and digestive rare cancers were sourced from Medline, EMBASE, and Cochrane Library. Preoperative chemoradiotherapy improved outcomes for patients with esophageal undifferentiated carcinoma compared with esophageal salivary gland types of carcinomas. For rare gastric epithelial carcinoma, perioperative chemotherapy is the common treatment. Adjuvant chemoradiotherapy showed no benefice compared with adjuvant chemotherapy for duodenal adenocarcinoma. Small bowel sarcomas respond well to radiotherapy. By analogy to anal squamous cell carcinoma, exclusive chemoradiotherapy provided better outcomes for patients with rectal squamous cell carcinoma. For anal adenocarcinoma, neoadjuvant chemoradiotherapy, followed by radical surgery, was the most effective regimen. For pancreatic neuroendocrine tumors, chemoradiotherapy can be a suitable option as postoperative or exclusive for unresectable/borderline disease. The stereotactic body radiotherapy (SBRT) is a promising approach for hepatobiliary malignancy. Radiotherapy is a valuable option in gastrointestinal stromal tumors (GIST) for palliative intent, tyrosine kinase inhibitors (TKIs) resistant disease, and unresectable or residual disease. Involved field (IF) radiotherapy for digestive lymphoma provides good results, especially for gastric extranodal marginal zone lymphoma (MALT). In conclusion, radiotherapy is not an uncommon indication in this context. A multidisciplinary approach is needed for better management of digestive rare cancers.

What do we want? Training! When do we want it? Now? A training needs analysis for adaptive radiotherapy for therapeutic radiographers

INTRODUCTION

Therapeutic radiographers (TRs) have adapted to the changing requirements and demands of the oncology service and in response to advanced techniques such as on-line adaptive MRI-guided radiotherapy (MRIgRT). The skills required for MRIgRT would benefit many TRs not just those involved in this technique. This study presents the results of a training needs analysis (TNA) for the required MRIgRT skills in readiness for training TRs for current and future practice.

METHODS

A UK-based TNA was used to ask TRs about their knowledge and experience with essential skills required for MRIgRT based on previous investigations into the topic. A five-point Likert scale was used for each of the skills and the difference in values were used to calculate the training need for current and future practice.

RESULTS

261 responses were received (n = 261). The skill rated the most important to current practice was CBCT/CT matching and/or fusion. The current highest priority needs were radiotherapy planning and radiotherapy dosimetry. The skill rated the most important to future practice was CBCT/CT matching and/or fusion. The future highest priority needs were MRI acquisition and MRI Contouring. Over 50% of participants wanted training or additional training in all skills. There was an increase in all values for skills investigated from current to future roles.

CONCLUSION

Although the examined skills were viewed as important to current roles, the future training needs, both overall and high priority, were different compared to current roles. As the 'future' of radiotherapy can arrive rapidly, it is essential that training is delivered appropriately and timely. Before this can occur, there must be investigations into the method and delivery of this training.

IMPLICATIONS FOR PRACTICE

Role development. Education changes for therapeutic radiographers.

The Role of MRI and PET/CT in Radiotherapy Target Volume Determination in Gastrointestinal Cancers-Review of the Literature

Positron emission tomography with computed tomography (PET/CT) and magnetic resonance imaging (MRI) could improve accuracy in target volume determination for gastrointestinal cancers. A systematic search of the PubMed database was performed, focusing on studies published within the last 20 years. Articles were considered eligible for the review if they included patients with anal canal, esophageal, rectal or pancreatic cancer, as well as PET/CT or MRI for radiotherapy treatment planning, and if they reported interobserver variability or changes in treatment planning volume due to different imaging modalities or correlation between the imaging modality and histopathologic specimen. The search of the literature retrieved 1396 articles. We retrieved six articles from an additional search of the reference lists of related articles. Forty-one studies were included in the final review. PET/CT seems indispensable for target volume determination of pathological lymph nodes in esophageal and anal canal cancer. MRI seems appropriate for the delineation of primary tumors in the pelvis as rectal and anal canal cancer. Delineation of the target volumes for radiotherapy of pancreatic cancer remains challenging, and additional studies are needed.

Artificial Intelligence in CT and MR Imaging for Oncological Applications

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use, Perceptions of and Barriers to Implementation

AIMS

Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors.

MATERIALS AND METHODS

The Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters.

RESULTS

In total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country.

CONCLUSION

Careful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

A Decade of Prospective Peer Review: Impact on Safety Culture and Lessons Learned in a Multicenter Radiation Medicine Department

PURPOSE

Quality assurance (QA) is critical to the success of radiation therapy (RT) for patients with cancer and affects clinical outcomes. We report longitudinal findings of a prospective peer review evaluation system implemented at a major academic health system as part of RT QA during a 10-year period.

METHODS AND MATERIALS

All cases treated within our department undergo prospective multidisciplinary peer review and are assigned a grade (A, B, or C). "A" cases require no changes, "B" cases require minor modification, and "C" cases require major modification before treatment planning. The z-ratio test for the significance of the difference between the 5-year baseline (2012-2016) and follow-up (2017-2021) period was used to compare grades between the 2 periods. A 2-tailed P value <.05 was considered significant.

RESULTS

Of the 20,069 cases, 15,659 (78%) were curative and were analyzed. The fraction of A cases decreased from 74.8% (baseline) to 64.5% (follow-up), whereas B cases increased from 19.4% to 35.4% and C cases decreased from 5.8% to 0.1%. Of the 9 treatment locations, the main hospital site had a higher percentage of A grades relative to community locations in the baseline (78.6% vs 67.8%; P < .002) and follow-up (66.9% vs 62.3%; P < .002) periods. There was a decrease in the percentage of A cases from the baseline to the follow-up period regardless of plan type (complex vs intermediate vs simple). There was a decrease in the percentage of A cases among specialists from baseline to follow-up (78.2% to 67.7%; P < .002) and among generalists from baseline to follow-up (69.7% to 61.7%; P < .002).

CONCLUSIONS

Our 10-year experience in contour peer review identified increased opportunities in improving treatment plan quality over time. The drop in A scores and rise in B scores suggests increased scrutiny and findings-based improvements over time, whereas the drop in C scores indicates amelioration of "major failures" addressed in the startup years. Peer review rounds upstream of treatment planning provide valuable RT QA and should be considered by other departments to enhance the quality and consistency of RT.

Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases

Background

Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).

Methods

A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC).

Results

Most defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively.

Conclusion

Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.

Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network

BACKGROUND

Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning.

PURPOSE

In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images.

METHODS

The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis.

RESULTS

In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods.

CONCLUSION

The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

The Role of Positron Emission Tomography and Computed Tomographic (PET/CT) Imaging for Radiation Therapy Planning: A Literature Review

PET/CT is revolutionising radiotherapy treatment planning in many cancer sites. While its utility has been confirmed in some cancer sites, and is used in routine clinical practice, it is still at an experimental stage in many other cancer sites. This review discusses the utility of PET/CT in cancer sites where the role of PET/CT has been established in cases such as head and neck, cervix, brain, and lung cancers, as well as cancer sites where the role of PET/CT is still under investigation such as uterine, ovarian, and prostate cancers. Finally, the review touches on PET/CT utilisation in Africa.

Evaluation of a New, Highly Flexible Radiofrequency Coil for MR Simulation of Patients Undergoing External Beam Radiation Therapy

PURPOSE

To evaluate the performance of a new, highly flexible radiofrequency (RF) coil system for imaging patients undergoing MR simulation.

METHODS

Volumetric phantom and in vivo images were acquired with a commercially available and prototype RF coil set. Phantom evaluation was performed using a silicone-filled humanoid phantom of the head and shoulders. In vivo assessment was performed in five healthy and six patient subjects. Phantom data included T1-weighted volumetric imaging, while in vivo acquisitions included both T1- and T2-weighted volumetric imaging. Signal to noise ratio (SNR) and uniformity metrics were calculated in the phantom data, while SNR values were calculated in vivo. Statistical significance was tested by means of a non-parametric analysis of variance test.

RESULTS

At a threshold of p = 0.05, differences in measured SNR distributions within the entire phantom volume were statistically different in two of the three paired coil set comparisons. Differences in per slice average SNR between the two coil sets were all statistically significant, as well as differences in per slice image uniformity. For patients, SNRs within the entire imaging volume were statistically significantly different in four of the nine comparisons and seven of the nine comparisons performed on the per slice average SNR values. For healthy subjects, SNRs within the entire imaging volume were statistically significantly different in seven of the nine comparisons and eight of the nine comparisons when per slice average SNR was tested.

CONCLUSIONS

Phantom and in vivo results demonstrate that image quality obtained from the novel flexible RF coil set was similar or improved over the conventional coil system. The results also demonstrate that image quality is impacted by the specific coil configurations used for imaging and should be matched appropriately to the anatomic site imaged to ensure optimal and reproducible image quality.

Potential Molecular Mechanisms behind the Ultra-High Dose Rate "FLASH" Effect

FLASH radiotherapy, or the delivery of a dose at an ultra-high dose rate (>40 Gy/s), has recently emerged as a promising tool to enhance the therapeutic index in cancer treatment. The remarkable sparing of normal tissues and equivalent tumor control by FLASH irradiation compared to conventional dose rate irradiation—the FLASH effect—has already been demonstrated in several preclinical models and even in a first patient with T-cell cutaneous lymphoma. However, the biological mechanisms responsible for the differential effect produced by FLASH irradiation in normal and cancer cells remain to be elucidated. This is of great importance because a good understanding of the underlying radiobiological mechanisms and characterization of the specific beam parameters is required for a successful clinical translation of FLASH radiotherapy. In this review, we summarize the FLASH investigations performed so far and critically evaluate the current hypotheses explaining the FLASH effect, including oxygen depletion, the production of reactive oxygen species, and an altered immune response. We also propose a new theory that assumes an important role of mitochondria in mediating the normal tissue and tumor response to FLASH dose rates.

Modulation of Reactive Oxygen Species in Cancers: Recent Advances

Oxidation-reduction reactions played a significant role in the chemical evolution of life forms on oxygenated earth. Cellular respiration is dependent on such redox reactions, and any imbalance leads to the accumulation of reactive oxygen species (ROS), resulting in both chronic and acute illnesses. According to the International Agency for Research on Cancer (IARC), by 2040, the global burden of new cancer cases is expected to be around 27.5 million, with 16.3 million cancer deaths due to an increase in risk factors such as unhealthy lifestyle, environmental factors, aberrant gene mutations, and resistance to therapies. ROS play an important role in cellular signalling, but they can cause severe damage to tissues when present at higher levels. Elevated and chronic levels of ROS are pertinent in carcinogenesis, while several therapeutic strategies rely on altering cellular ROS to eliminate tumour cells as they are more susceptible to ROS-induced damage than normal cells. Given this selective targeting potential, therapies that can effectively modulate ROS levels have been the focus of intense research in recent years. The current review describes biologically relevant ROS, its origins in solid and haematological cancers, and the current status of evolving antioxidant and pro-oxidant therapies in cancers.

Images and images: Current roles of therapeutic radiographers

INTRODUCTION

Therapeutic radiography is a small profession and has adapted in response to advanced techniques. An increase in on-line adaptive MRI-guided radiotherapy (MRIgRT) will require role extension for therapeutic radiographers (TRs). This study will investigate the current role description for TRs and the activities they currently undertake with regards to MRIgRT.

METHOD

A training needs analysis was used to ask TRs about their current roles and responsibilities and essential skills required for MRIgRT. For the purposes of this paper, the authors present the results from the demographics of the individual, their current job title with roles and responsibilities, and experience with decision making and image assessment. Descriptive statistics was used to analyse the data.

RESULTS

261 responses were received (n = 261). Only 28% of job titles listed contained the protected title of 'therapeutic radiographer'. Advanced clinical practice roles were expressed by participants indicating that if a service need is presented, emerging roles will be created. Variation existed across the standardised roles of TRs and this discrepancy could present challenges when training for MRIgRT. TRs are pivotal in image verification and recognition on a standard linac, and skills developed there can be transferred to MRIgRT. Decision making is crucial for adaptive techniques and there are many skills within their current scope of practice that are indispensable for the MRIgRT.

CONCLUSION

It has been demonstrated that TRs have a range of roles that cover vast areas of the oncology pathway and so it is important that TRs are recognised so the pivotal role they play is understood by all. TRs have extensive soft-tissue IGRT knowledge and experience, aiding the evolution of decision-making skills and application of off-protocol judgments, the basis of MRIgRT.

IMPLICATIONS FOR PRACTICE

Role development and changes in education for therapeutic radiographers.

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications

Objective. In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline. Approach. A deep learning model is proposed that denoises low precision (∼106 simulated particles) dose distributions to produce high precision (109 simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well. Main results. The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average. Significance. This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

Radiomics-guided radiation therapy: opportunities and challenges

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

Effect of Supine vs Prone Breast Radiotherapy on Acute Toxic Effects of the Skin Among Women With Large Breast Size: A Randomized Clinical Trial

Importance

Women with large breast size treated with adjuvant breast radiotherapy (RT) have a high rate of acute toxic effects of the skin. Breast RT in the prone position is one strategy that may decrease these toxic effects.

Objective

To determine if breast RT in the prone position reduces acute toxic effects of the skin when compared with treatment in the supine position.

Design, Setting, and Participants

This phase 3, multicenter, single-blind randomized clinical trial accrued patients from 5 centers across Canada from April 2013 to March 2018 to compare acute toxic effects of breast RT for women with large breast size (bra band ≥40 in and/or ≥D cup) in the prone vs supine positions. A total of 378 patients were referred for adjuvant RT and underwent randomization. Seven patients randomized to supine position were excluded (5 declined treatment and 2 withdrew consent), and 14 patients randomized to prone position were excluded (4 declined treatment, 3 had unacceptable cardiac dose, and 7 were unable to tolerate being prone). Data were analyzed from April 2019 through September 2020.

Interventions

Patients were randomized to RT in the supine or prone position. From April 2013 until June 2016, all patients (n = 167) received 50 Gy in 25 fractions (extended fractionation) with or without boost (range, 10-16 Gy). After trial amendment in June 2016, the majority of patients (177 of 190 [93.2%]) received the hypofractionation regimen of 42.5 Gy in 16 fractions.

Main Outcomes and Measures

Main outcome was moist desquamation (desquamation).

Results

Of the 357 women (mean [SD] age, 61 [9.9] years) included in the analysis, 182 (51.0%) were treated in the supine position and 175 (49.0%) in prone. There was statistically significantly more desquamation in patients treated in the supine position compared with prone (72 of 182 [39.6%] patients vs 47 of 175 [26.9%] patients; OR, 1.78; 95% CI, 1.24-2.56; P = .002), which was confirmed on multivariable analysis (OR, 1.99; 95% CI, 1.48-2.66; P < .001), along with other independent factors: use of boost (OR, 2.71; 95% CI, 1.95-3.77; P < .001), extended fractionation (OR, 2.85; 95% CI, 1.41-5.79; P = .004), and bra size (OR, 2.56; 95% CI, 1.50-4.37; P < .001).

Conclusions and Relevance

This randomized clinical trial confirms that treatment in the prone position decreases desquamation in women with large breast size receiving adjuvant RT. It also shows increased toxic effects using an RT boost and conventional fractionation.

Trial Registration

ClinicalTrials.gov Identifier: NCT01815476.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT‐MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT‐MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT‐MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t‐test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT‐MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t‐test showed that the displacements along the z‐axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior‐posterior and superior‐inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t‐test showed significant deviations from zero for C1 on the anterior‐posterior and roll sides. For C2, there was a significant deviation from zero for the left‐right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Heterogeneous Iron Oxide/Dysprosium Oxide Nanoparticles Target Liver for Precise Magnetic Resonance Imaging of Liver Fibrosis

Challenges remain in precisely diagnosing the progress of liver fibrosis in a noninvasive way. We here synthesized small (4 nm) heterogeneous iron oxide/dysprosium oxide nanoparticles (IO-DyO NPs) as a contrast agent (CA) for magnetic resonance imaging (MRI) to precisely diagnose liver fibrosis in vivo at both 7.0 and 9.4 T field strength. Our IO-DyO NPs can target the liver and show an increased T₂ relaxivity along with an increase of magnetic field strength. At a ultrahigh magnetic field, IO-DyO NPs can significantly improve spatial/temporal image resolution and signal-to-noise ratio of the liver and precisely distinguish the early and moderate liver fibrosis stages. Our IO-DyO NP-based MRI diagnosis can exactly match biopsy (a gold standard for liver fibrosis diagnosis in the clinic) but avoid the invasiveness of biopsy. Moreover, our IO-DyO NPs show satisfactory biosafety in vitro and in vivo. This work illustrates an advanced T₂ CA used in ultrahigh-field MRI (UHFMRI) for the precise diagnosis of liver fibrosis via a noninvasive means.

Multi-parametric PET/MRI for enhanced tumor characterization of patients with cervical cancer

Aim

The concept of personalized medicine has brought increased awareness to the importance of inter- and intra-tumor heterogeneity for cancer treatment. The aim of this study was to explore simultaneous multi-parametric PET/MRI prior to chemoradiotherapy for cervical cancer for characterization of tumors and tumor heterogeneity.

Methods

Ten patients with histologically proven primary cervical cancer were examined with multi-parametric 68Ga-NODAGA-E[c(RGDyK)]2-PET/MRI for radiation treatment planning after diagnostic 18F-FDG-PET/CT. Standardized uptake values (SUV) of RGD and FDG, diffusion weighted MRI and the derived apparent diffusion coefficient (ADC), and pharmacokinetic maps obtained from dynamic contrast-enhanced MRI with the Tofts model (iAUC60, Ktrans, ve, and kep) were included in the analysis. The spatial relation between functional imaging parameters in tumors was examined by a correlation analysis and joint histograms at the voxel level. The ability of multi-parametric imaging to identify tumor tissue classes was explored using an unsupervised 3D Gaussian mixture model-based cluster analysis.

Results

Functional MRI and PET of cervical cancers appeared heterogeneous both between patients and spatially within the tumors, and the relations between parameters varied strongly within the patient cohort. The strongest spatial correlation was observed between FDG uptake and ADC (median r =  − 0.7). There was moderate voxel-wise correlation between RGD and FDG uptake, and weak correlations between all other modalities. Distinct relations between the ADC and RGD uptake as well as the ADC and FDG uptake were apparent in joint histograms. A cluster analysis using the combination of ADC, FDG and RGD uptake suggested tissue classes which could potentially relate to tumor sub-volumes.

Conclusion

A multi-parametric PET/MRI examination of patients with cervical cancer integrated with treatment planning and including estimation of angiogenesis and glucose metabolism as well as MRI diffusion and perfusion parameters is feasible. A combined analysis of functional imaging parameters indicates a potential of multi-parametric PET/MRI to contribute to a better characterization of tumor heterogeneity than the modalities alone. However, the study is based on small patient numbers and further studies are needed prior to the future design of individually adapted treatment approaches based on multi-parametric functional imaging.

High-dimensional role of AI and machine learning in cancer research

The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).

Rapid Progress in Intelligent Radiotherapy and Future Implementation

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.

[Oncological surgery in the interdisciplinary context-On the way to personalized medicine]

Oncological surgery is a discipline which closely interacts with other clinical partners and remains in many cases the cornerstone of a curative treatment of solid tumors. Due to the progress in the field of systemic tumor treatment as well as innovations in surgical techniques, the indications in oncological surgery are also changing, such as extended indications for patients with oligometastatic disease. Surgery of metastases has long been established for colorectal cancer and is being further tested for other entities, such as pancreatic and gastric cancer, within randomized controlled clinical trials (e.g. RENAISSANCE and METAPANC). A new challenge is the handling of a clinical complete remission after total neoadjuvant therapy, for example in locally advanced rectal cancer or in esophageal cancer. Here, organ and function preservation are increasingly propagated but should only be performed within clinical trials until stratification enables the identification of patients in whom this concept is oncologically safe. The personalized use of oncological surgery is dependent on the patient, the tumor and on the total multimodal concept.

DNA Damage Clustering after Ionizing Radiation and Consequences in the Processing of Chromatin Breaks

Charged-particle radiotherapy (CPRT) utilizing low and high linear energy transfer (low-/high-LET) ionizing radiation (IR) is a promising cancer treatment modality having unique physical energy deposition properties. CPRT enables focused delivery of a desired dose to the tumor, thus achieving a better tumor control and reduced normal tissue toxicity. It increases the overall radiation tolerance and the chances of survival for the patient. Further improvements in CPRT are expected from a better understanding of the mechanisms governing the biological effects of IR and their dependence on LET. There is increasing evidence that high-LET IR induces more complex and even clustered DNA double-strand breaks (DSBs) that are extremely consequential to cellular homeostasis, and which represent a considerable threat to genomic integrity. However, from the perspective of cancer management, the same DSB characteristics underpin the expected therapeutic benefit and are central to the rationale guiding current efforts for increased implementation of heavy ions (HI) in radiotherapy. Here, we review the specific cellular DNA damage responses (DDR) elicited by high-LET IR and compare them to those of low-LET IR. We emphasize differences in the forms of DSBs induced and their impact on DDR. Moreover, we analyze how the distinct initial forms of DSBs modulate the interplay between DSB repair pathways through the activation of DNA end resection. We postulate that at complex DSBs and DSB clusters, increased DNA end resection orchestrates an increased engagement of resection-dependent repair pathways. Furthermore, we summarize evidence that after exposure to high-LET IR, error-prone processes outcompete high fidelity homologous recombination (HR) through mechanisms that remain to be elucidated. Finally, we review the high-LET dependence of specific DDR-related post-translational modifications and the induction of apoptosis in cancer cells. We believe that in-depth characterization of the biological effects that are specific to high-LET IR will help to establish predictive and prognostic signatures for use in future individualized therapeutic strategies, and will enhance the prospects for the development of effective countermeasures for improved radiation protection during space travel.

RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity

Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R² for the published models on test data was very poor at 0.01 (range: −0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: −0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.

The role of feature-based radiomics for predicting response and radiation injury after stereotactic radiation therapy for brain metastases: A critical review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

Introduction

differential diagnosis of tumor recurrence and radiation injury after stereotactic radiotherapy (SRT) is challenging. The advances in imaging techniques and feature-based radiomics could aid to discriminate radionecrosis from progression.

Methods

we performed a systematic review of current literature, key references were obtained from a PubMed query. Data extraction was performed by 3 researchers and disagreements were resolved with a discussion among the authors.

Results

we identified 15 retrospective series, one prospective trial, one critical review and one editorial paper. Radiomics involves a wide range of imaging features referred to necrotic regions, rate of contrast-enhancing area or the measure of edema surrounding the metastases. Features were mainly defined through a multistep extraction/reduction/selection process and a final validation and comparison.

Conclusions

feature-based radiomics has an optimal potential to accurately predict response and radionecrosis after SRT of BM and facilitate differential diagnosis. Further validation studies are eagerly awaited to confirm radiomics reliability.

Adaptive radiation therapy: When, how and what are the benefits that literature provides?

PURPOSE

To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with.

MATERIALS AND METHODS

An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review.

RESULTS

The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning.

CONCLUSION

ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.

Adapting for Adaptive Radiotherapy (ART): The need to evolve our roles as Therapeutic Radiographers

OBJECTIVES

4D Adaptive Radiotherapy (4D-ART) has been stated as the future baseline standard-of-care for technical radiotherapy. Its goal is to optimise radiation dose received by 'adapting' to changes 'seen' in each individual patient, for each treatment delivery (fraction), throughout each treatment delivery. The drive for technological developments to achieve this is ongoing. To enhance the potential benefits, we should consider other aspects of the processes needed: How do changes in clinical practices and processes affect the role of the Therapeutic Radiographer? The aim is to raise the need to explore questions of Therapeutic Radiographers roles and responsibilities within 4D-ART.

KEY FINDINGS

Moving from using current predictive strategies (such as plan-of-the-day) to being able to dynamically adapt (real-time/4D-ART) for patient changes requires rapid clinical judgements to be made. The question becomes 'who makes these decisions'? Currently Therapeutic Radiographers maybe ideally placed for this. Dynamically adaptive radiotherapy requires Radiographers to have clinical decisions-making skills and authorities within the multi-professional team (MPT). It is not sufficient to train radiographers in the 'how' to use 4D-ART techniques and technologies; the ability to make good clinical judgments comes from understanding the principles supporting this concept by understanding the 'why'.

CONCLUSION

To support future service needs and ongoing developments within ART, Radiographer's roles need to adapt and evolve, as well as the way their role is perceived within the MPT. We need to provide Radiographers with the education required, abilities and authorities to act.

IMPLICATIONS FOR PRACTICE

Role revision is required to include greater responsibility for clinical decision making for implementing 4D-ART practices.

Advances in Biomimetic Nanoparticles for Targeted Cancer Therapy and Diagnosis

Biomimetic nanoparticles have recently emerged as a novel drug delivery platform to improve drug biocompatibility and specificity at the desired disease site, especially the tumour microenvironment. Conventional nanoparticles often encounter rapid clearance by the immune system and have poor drug-targeting effects. The rapid development of nanotechnology provides an opportunity to integrate different types of biomaterials onto the surface of nanoparticles, which enables them to mimic the natural biological features and functions of the cells. This mimicry strategy favours the escape of biomimetic nanoparticles from clearance by the immune system and reduces potential toxic side effects. Despite the rapid development in this field, not much has progressed to the clinical stage. Thus, there is an urgent need to develop biomimetic-based nanomedicine to produce a highly specific and effective drug delivery system, especially for malignant tumours, which can be used for clinical purposes. Here, the recent developments for various types of biomimetic nanoparticles are discussed, along with their applications for cancer imaging and treatments.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

Image-guided, intensity-modulated radiotherapy for the treatment of diffuse-type tenosynovial giant cell tumor of the knee: Case report and review of the literature

RATIONALE

Tenosynovial giant cell tumor (TGCT) is a neoplastic, inflammatory disease with a benign but aggressive course that often presents as localized (TGCT-L) and diffuse (TGCT-D) forms based on the growth pattern and clinical behavior. For TGCT-L, simple excision of the diseased synovial tissue is the preferred treatment option, while for TGCT-D, adequate synovectomy is usually tricky but is essential. However, approximately 44% of TGCT-D cases will relapse after surgery alone. Thus, the optimal treatment strategy in patients with TGCT-D is evolving, and standalone surgical resection can no longer be regarded as the only treatment. The previous studies have shown that postoperative adjuvant radiotherapy can reduce recurrence in TGCT, especially in patients with incomplete synovectomy.

PATIENT CONCERNS

In the first case, a 54-year-old male presented with recurrent pain and swelling of the right knee with a protracted disease course (≥10 years). The other patient is a 64-year-old male who developed swelling, pain, abnormal bending, and limited movement of the left knee without obvious inducement.

DIAGNOSES

Clinical and imaging examinations can provide a definitive diagnosis, and pathology is the gold standard. TGCT-D was confirmed by postoperative pathology. After the operation, the patients underwent an MRI re-examination and showed that the lesions of the knee were not completely resected.

INTERVENTIONS

Arthroscopic synovectomy was performed on the patients, and postoperative pathology was confirmed as TGCT-D. Because of incomplete synovectomy, the 2 cases received image-guided, intensity-modulated radiotherapy (IG-IMRT) after the operation.

OUTCOMES

The follow-up time was 1 year, no evidence of disease progression was found in MRI. No obvious adverse effects associated with radiotherapy were detected during the follow-up period.

LESSONS

These cases and reviews illustrate the necessity of radiotherapy for TGCT-D and that IG-IMRT is a safe and effective method for treating TGCT-D of the knee.

Imaging in head and neck cancers: Update for non-radiologist

Head and neck cancer (HNC) is the fifth most frequent cancer worldwide and associated with significant morbidity. Along with clinical examination and endoscopic evaluation, imaging plays an important role in pre- and posttherapeutic evaluation of patients with HNC. Cross-sectional Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography / computed tomography (PET/CT) are routinely used in the assessment of these patients. This review provides an overview of the various cross-sectional imaging modalities used in the evaluation of HNC and will give a short summary of the latest imaging technologies regarding head and neck cancer diagnosis.

Improved adaptive radiotherapy to adjust for anatomical alterations during curative treatment for locally advanced lung cancer

Anatomical changes during chemoradiation for lung cancer may decrease dose to the target or increase dose to organs at risk. To assess our ability to identify clinically significant anatomical alterations, we followed 67 lung cancer patients by daily cone-beam CT scans to ensure correct patient positioning and observe anatomical alterations. We also re-calculated the original dose distribution on a planned control CT scan obtained halfway during the treatment course to identify anatomical changes that potentially affected doses to the target or organs at risk. Of 66 patients who completed the treatment, 12 patients needed adaptation, two patients were adapted twice. We conclude that daily cone-beam CT and routines at the treatment machine discover relevant anatomical changes during curative radiotherapy for patients with lung cancer without additional imaging.

Proton path reconstruction for pCT using neural networks

The Most Likely Path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography (pCT). However, while this method accounts for small-angle Multiple Coulomb Scattering (MCS) and energy loss, inelastic nuclear interactions play an influential role in a significant number of proton paths. By applying cuts based on energy and direction, tracks influenced by nuclear interactions are largely discarded from the MLP analysis. In this work we propose a new method to estimate the proton paths based on a Deep Neural Network (DNN). Through this approach, estimates of proton paths equivalent to MLP predictions have been achieved in the case where only MCS occurs, together with an increased accuracy when nuclear interactions are present. Moreover, our tests indicate that the DNN algorithm can be considerably faster than the MLP algorithm.

Trimodality PET/CT/MRI and Radiotherapy: A Mini-Review

Computed tomography (CT) has revolutionized external radiotherapy by making it possible to visualize and segment the tumors and the organs at risk in a three-dimensional way. However, if CT is a now a standard, it presents some limitations, notably concerning tumor characterization and delineation. Its association with functional and anatomical images, that are positron emission tomography (PET) and magnetic resonance imaging (MRI), surpasses its limits. This association can be in the form of a trimodality PET/CT/MRI. The objective of this mini-review is to describe the process of performing this PET/CT/MRI trimodality for radiotherapy and its potential clinical applications. Trimodality can be performed in two ways, either a PET/MRI fused to a planning CT (possibly with a pseudo-CT generated from the MRI for the planning), or a PET/CT fused to an MRI and then registered to a planning CT (possibly the CT of PET/CT if calibrated for radiotherapy). These examinations should be performed in the treatment position, and in the second case, a patient transfer system can be used between the PET/CT and MRI to limit movement. If trimodality requires adapted equipment, notably compatible MRI equipment with high-performance dedicated coils, it allows the advantages of the three techniques to be combined with a synergistic effect while limiting their disadvantages when carried out separately. Trimodality is already possible in clinical routine and can have a high clinical impact and good inter-observer agreement, notably for head and neck cancers, brain tumor, prostate cancer, cervical cancer.