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

Inter-observer variation in gross tumour volume delineation of oesophageal cancer on MR, CT and PET/CT

BACKGROUND

The aim of our study was to assess the inter-observer variability in delineation of the gross tumour volume (GTV) of oesophageal cancer on magnetic resonance (MR) in comparison to computed tomography (CT) and positron emission tomography and CT (PET/CT).

PATIENTS AND METHODS

Twenty-three consecutive patients with oesophageal cancer treated with chemo-radiotherapy were enrolled. All patients had PET/CT and MR imaging in treatment position. Five observers independently delineated the GTV on CT alone, MR alone, CT with co-registered MR, PET/CT alone and MR with co-registered PET/CT. Volumes of GTV were measured per patient and imaging modality. Inter-observer agreement, expressed in generalized conformity index (CIgen), volumetric conformity index (VCI), planar conformity index (PCI) and inter-delineation distance (IDD) were calculated per patient and imaging modality. Linear mixed models were used for statistical analysis.

RESULTS

GTV volume was significantly lower on MR (33.03 cm3) compared to CT (37.1 cm3; p = 0.002) and on PET/CT MR (35.2 cm3; p = 0.018) compared to PET/CT (39.1 cm3). The CIgen was lowest on CT (0.56) and highest on PET/CT MR (0.67). The difference in CIgen between MR (0.61) and CT was borderline significant (p = 0.048). The VCI was significantly higher on MR (0.71; p = 0.007) and on CT MR (0.71; p = 0.004) compared to CT (0.67). The PCI was significantly higher on CT MR (0.67; p = 0.031) compared to CT (0.64). The largest differences were observed in the cranio-caudal direction.

CONCLUSIONS

The highest inter-observer agreement was found for PET/CT MR and the lowest for CT. MR could reduce the difference in delineation between observers and provide additional information about the local extent of the tumour.

Advanced mathematical modeling for preciseestimation of CT energy spectrum using a calibration phantom

PURPOSE

This research aims to develop an advanced mathematical model using a CT calibration phantom to accurately estimate the CT energy spectrum in clinical settings, enhancing imaging quality and patient dose management.

METHODS

Data were collected from a CT scanner using a CT calibration phantom at various energy levels (80, 100, 120, and 135 kVp). The data was optimized to refine the energy spectrum model, followed by cross-validation with Monte Carlo simulations.

RESULTS

The developed model demonstrated high precision in estimating the CT energy spectrum at all tested energy levels, with R-squared values above 0.9738 and an R-squared value of 0.9829 at 100 kVp. The model also showed low Normalized Root Mean Square Deviation (NRMSD) ranging from 0.6698 % to 1.8745 %. The Mean Energy Difference (ΔE) between the estimated and simulated spectrum consistently remained under 0.01 keV. These results were comparable to recent studies, which reported higher NRMSD and ΔE.

CONCLUSIONS

This study presents a significantly improved model for estimating the CT energy spectrum, offering greater accuracy than existing models. Its strengths include high precision and the use of standard equipment and algorithmic values. While the current use of 13 plugs is adequate, incorporating plugs with varied densities could enhance accuracy. This model has potential for improving imaging quality and optimizing patient dosing in clinical applications. Future trends may include extending energy spectrum estimation to megavoltage domains and integrating technologies like EPID and MVCT for better dose distribution prediction in high-energy photon beam therapy.

Setup errors analysis in iterative kV CBCT: A clinical study of cervical cancer treated with Volumetric Modulated Arc Therapy

OBJECTIVE

This study aims to analyze setup errors in pelvic Volumetric Modulated Arc Therapy (VMAT) for patients with non-surgical primary cervical cancer, utilizing the onboard iterative kV cone beam CT (iCBCT) imaging system on the Varian Halcyon 2.0 ring gantry structure accelerator to enhance radiotherapy precision.

METHOD

We selected 132 cervical cancer patients who underwent VMAT with daily iCBCT imaging guidance. Before each treatment session, a registration method based on the bony structure was employed to acquire iCBCT images with the corresponding planning CT images. Following verification and adjustment of image registration results along the three axes (but not rotational), setup errors in the lateral (X-axis), longitudinal (Y-axis), and vertical (Z-axis) directions were recorded for each patient. Subsequently, we analyzed 3642 iCBCT image setup errors.

RESULTS

The mean setup errors for the X, Y, and Z axes were 4.50 ± 3.79 mm, 6.08 ± 6.30 mm, and 1.48 ± 2.23 mm, respectively. Before correction with iCBCT, setup margins based on the Van Herk formula for the X, Y, and Z axes were 6.28, 12.52, and 3.26 mm, respectively. In individuals aged 60 years and older, setup errors in the X and Y axes were significantly larger than those in the younger group (p < 0.05). Additionally, there is no significant linear correlation between setup errors and treatment fraction numbers.

CONCLUSION

Data analysis underscores the importance of precise Y-axis setup for cervical cancer patients undergoing VMAT. Radiotherapy centers without daily iCBCT should appropriately extend the planning target volume (PTV) along the Y-axis for cervical cancer patients receiving pelvic VMAT. Elderly patients exhibit significantly larger setup errors compared to younger counterparts. In conclusion, iCBCT-guided radiotherapy is recommended for cervical cancer patients undergoing VMAT to improve setup precision.

Application of H2N-Fe3O4 Nanoparticles for Prostate Cancer Magnetic Resonance Imaging in an Animal Model

This paper presents the efficacy of a contrast agent based on H2N-Fe3O4 nanoparticles for the detection of prostate cancer in an animal model using a preclinical 9.4 T MRI system. The relaxivities r1 and r2 of the nanoparticles were 6.31 mM-1s-1 and 8.33 mM-1s-1, respectively. Nanoparticles injected in a concentration of 2 mg Fe/mL decreased the tumor-relative T1 relaxation across all animals from 100 to 76 ± 26, 85 ± 27, 89 ± 20, and 97 ± 16 12 min 1 h, 2 h, and 24 h post injection, respectively. The corresponding T1 decrease in muscle tissues was 90 ± 20, 94 ± 23, 99 ± 12, and 99 ± 14. The relative T2 changes in the tumor were 82 ± 17, 89 ± 19, 97 ± 14, and 99 ± 8 12 min, 1 h, 2 h, and 24 h post injection, respectively, while, for muscle tissues, these values were 95 ± 11, 95 ± 8, 97 ± 6, and 95 ± 10 at the corresponding time points. The differences in the relative T1 and T2 were only significant 12 min after injection (p < 0.05), although a decrease was visible at each time point, but it was statistically insignificant (p > 0.05). The results showed the potential application of H2N-Fe3O4 nanoparticles as contrast agents for enhanced prostate cancer MRI.

Ferroptosis in radiation-induced brain injury: roles and clinical implications

Radiation-induced brain injury (RBI) presents a significant challenge for patients undergoing radiation therapy for head, neck, and intracranial tumors. This review aims to elucidate the role of ferroptosis in RBI and its therapeutic implications. Specifically, we explore how ferroptosis can enhance the sensitivity of tumor cells to radiation while also examining strategies to mitigate radiation-induced damage to normal brain tissues. By investigating the mechanisms through which radiation increases cellular reactive oxygen species (ROS) and initiates ferroptosis, we aim to develop targeted therapeutic strategies that maximize treatment efficacy and minimize neurotoxicity. The review highlights key regulatory factors in the ferroptosis pathway, including glutathione peroxidase 4 (GPX4), cystine/glutamate antiporter system Xc- (System Xc-), nuclear factor erythroid 2-related factor 2 (NRF2), Acyl-CoA synthetase long-chain family member 4 (ACSL4), and others, and their interactions in the context of RBI. Furthermore, we discuss the clinical implications of modulating ferroptosis in radiation therapy, emphasizing the potential for selective induction of ferroptosis in tumor cells and inhibition in healthy cells. The development of advanced diagnostic tools and therapeutic strategies targeting ferroptosis offers a promising avenue for enhancing the safety and efficacy of radiation therapy, underscoring the need for further research in this burgeoning field.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance

Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.

Patterns of practice of image guided particle therapy for cranio-spinal irradiation: A site specific multi-institutional survey of European Particle Therapy Network

PURPOSE

To investigate the current practice patterns in image-guided particle therapy (IGPT) for cranio-spinal irradiation (CSI).

METHODS

A multi-institutional survey was distributed to European particle therapy centres to analyse all aspects of IGPT. Based on the survey results, a Delphi consensus analysis was developed to define minimum requirements and optimal workflow for clinical practice. The centres participating in the institutional survey were invited to join the Delphi process.

RESULTS

Eleven centres participated in the survey. Imaging for treatment planning was rather similar among the centres with Computed Tomography (CT) being the main modality. For positioning verification, 2D IGPT was more commonly used than 3D IGPT. Two centres performed routinely imaging for plan adaptation, by the rest ad hoc. Eight centres participated in the Delphi consensus analysis. The full consensus was reached on the use of CT imaging without contrast for treatment planning and the role of magnetic resonance imaging (MRI) in target and organs-at-risk delineation. There was an agreement on the necessity to perform patient position verification and correction before each isocentre. The most important outcome was the clear need for standardization and harmonization of the workflow.

CONCLUSION

There were differences in CSI IGPT clinical practice among the European particle therapy centres. Moreover, the optimal workflow as identified by experts was not yet reached. There is a strong need for consensus guidelines. The state-of-the-art imaging technology and protocols need to be implemented into clinical practice to improve the quality of IGPT for CSI.

Cone beam CT doses in radiotherapy patient positioning in Finland-prostate treatments

Imaging parameters, frequencies and resulting patient organ doses in treatments of prostate cancer were assessed in Finnish radiotherapy centres. Based on a questionnaire to the clinics, Monte Carlo method was used to estimate organ doses in International Commission on Radiological Protection standard phantom for prostate, bladder, rectum and femoral head. The results show that doses from cone beam computed tomography imaging have reduced compared to earlier studies and are between 3.6 and 34.5 mGy per image for the above-mentioned organs and for normal sized patients. There still is room for further optimization of the patient exposure, as many centres use the default imaging parameters, and the length of the imaged region may not be optimal for the purpose.

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.

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.

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.

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.

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.

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.

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.

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.