Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques

A conceptual framework for cautious escalation of anticancer treatment: How to optimize overall benefit and obviate the need for de-escalation trials

BACKGROUND

The developmental workflow of the currently performed phase 1, 2 and 3 cancer trial stages lacks essential information required for the determination of the optimal efficacy threshold of new anticancer regimens. Due to this there is a serious risk of overdosing and/or treating for an unnecessary long time, leading to excess toxicity and a higher financial burden for society. But often post-approval de-escalation trials for dose-optimization and treatment de-intensification are not performed due to failing resources and time. Therefore, the developmental workflow needs to be restructured toward cautious systemic cancer treatment escalation, in order to guarantee optimal efficacy and sustainability.

METHODS

In this manuscript we discuss opportunities to produce the information needed for cautious escalation, based on models of cancer growth and cancer kill kinetics as well as exploratory biomarkers, for the purpose of designing the optimal phase 3 superiority trial. Subsequently, we compare the sample size needed for a phase 3 superiority trial, followed by a necessary de-escalation trial with the sample size needed for a multi-arm phase 3 trial with intervention arms of differing intensity. All essential items are structured within a Framework for Cautious Escalation (FCE). The discussion uses illustrations from the breast cancer setting, but aims to be applicable for all cancers.

RESULTS

The FCE is a promising model of clinical development in oncology to prevent overtreatment and associated issues, especially with regard to the number of repetitive treatment cycles. It will hopefully increase the relevance and success rate of clinical trials, to deliver improved patient-centric outcomes.

Nanoprobe-based molecular imaging for tumor stratification

The responses of patients to tumor therapies vary due to tumor heterogeneity. Tumor stratification has been attracting increasing attention for accurately distinguishing between responders to treatment and non-responders. Nanoprobes with unique physical and chemical properties have great potential for patient stratification. This review begins by describing the features and design principles of nanoprobes that can visualize specific cell types and biomarkers and release inflammatory factors during or before tumor treatment. Then, we focus on the recent advancements in using nanoprobes to stratify various therapeutic modalities, including chemotherapy, radiotherapy (RT), photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), ferroptosis, and immunotherapy. The main challenges and perspectives of nanoprobes in cancer stratification are also discussed to facilitate probe development and clinical applications.

Coupling Lipid Labeling and Click Chemistry Enables Isolation of Extracellular Vesicles for Noninvasive Detection of Oncogenic Gene Alterations

Well-preserved molecular cargo in circulating extracellular vesicles (EVs) offers an ideal material for detecting oncogenic gene alterations in cancer patients, providing a noninvasive diagnostic solution for detection of disease status and monitoring treatment response. Therefore, technologies that conveniently isolate EVs with sufficient efficiency are desperately needed. Here, a lipid labeling and click chemistry-based EV capture platform ("Click Beads"), which is ideal for EV message ribonucleic acid (mRNA) assays due to its efficient, convenient, and rapid purification of EVs, enabling downstream molecular quantification using reverse transcription digital polymerase chain reaction (RT-dPCR) is described and demonstrated. Ewing sarcoma protein (EWS) gene rearrangements and kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutation status are detected and quantified using EVs isolated by Click Beads and matched with those identified in biopsy specimens from Ewing sarcoma or pancreatic cancer patients. Moreover, the quantification of gene alterations can be used for monitoring treatment responses and disease progression.

Computed Tomography as a Characterization Tool for Engineered Scaffolds with Biomedical Applications

The ever-growing field of materials with applications in the biomedical field holds great promise regarding the design and fabrication of devices with specific characteristics, especially scaffolds with personalized geometry and architecture. The continuous technological development pushes the limits of innovation in obtaining adequate scaffolds and establishing their characteristics and performance. To this end, computed tomography (CT) proved to be a reliable, nondestructive, high-performance machine, enabling visualization and structure analysis at submicronic resolutions. CT allows both qualitative and quantitative data of the 3D model, offering an overall image of its specific architectural features and reliable numerical data for rigorous analyses. The precise engineering of scaffolds consists in the fabrication of objects with well-defined morphometric parameters (e.g., shape, porosity, wall thickness) and in their performance validation through thorough control over their behavior (in situ visualization, degradation, new tissue formation, wear, etc.). This review is focused on the use of CT in biomaterial science with the aim of qualitatively and quantitatively assessing the scaffolds’ features and monitoring their behavior following in vivo or in vitro experiments. Furthermore, the paper presents the benefits and limitations regarding the employment of this technique when engineering materials with applications in the biomedical field.

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT

Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT.

In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

Potential role of functional imaging in predicting outcome for patients treated with carbon ion therapy: a review

OBJECTIVE

Carbon ion radiation therapy (CIRT) is an emerging radiation technique with advantageous physical and radiobiologic properties compared to conventional radiotherapy (RT) providing better response in case of radioresistant and hypoxic tumors. Our aim is to critically review if functional imaging techniques could play a role in predicting outcome of CIRT-treated tumors, as already proven for conventional RT.

METHODS

14 studies, concerning Magnetic resonance imaging (MRI) and Positron Emission Tomography (PET), were selected after a comprehensive search on multiple electronic databases from January 2000 to March 2020.

RESULTS

MRI studies (n = 5) focused on diffusion-weighted MRI and, even though quantitative parameters were the same in all studies (apparent diffusion coefficient, ADC), results were not univocal, probably due to different imaging acquisition protocols and tumoral histology. For PET studies (n = 9), different tracers were used such as [¹⁸F]FDG and other uncommon tracers ([¹¹C]MET, [¹⁸F]FLT), with a relevant heterogeneity regarding parameters used for outcome assessment.

CONCLUSION

No conclusion can be drawn on the predictive value of functional imaging in CIRT-treated tumors. A standardization of image acquisition, multi-institutional large trials and external validations are needed in order to establish the prognostic value of functional imaging in CIRT and to guide clinical practice.

ADVANCES IN KNOWLEDGE

Emerging studies focused on functional imaging's role in predicting CIRT outcome. Due to the heterogeneity of images acquisition and studies, results are conflicting and prospective large studies with imaging standardized protocol are needed.

Contemporary radiotherapy: present and future

Oncology care is increasingly a multidisciplinary endeavour, and radiation therapy continues to have a key role across the disease spectrum in nearly every cancer. However, the field of radiation oncology is still one of the most poorly understood of the cancer disciplines. In this Review, we attempt to summarise and contextualise developments within the field of radiation oncology for the non-radiation oncologist. We discuss advancements in treatment technologies and imaging, followed by an overview of the interplay with advancements in systemic therapy and surgical techniques. Finally, we review new frontiers in radiation oncology, including advances within the metastatic disease continuum, reirradiation, and emerging types of radiation therapy.

18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer

Purpose

Head and neck cancers radiotherapy (RT) is associated with inevitable injury to parotid glands and subsequent xerostomia. We investigated the utility of SUV derived from ¹⁸FDG-PET to develop metabolic imaging biomarkers (MIBs) of RT-related parotid injury.

Methods

Data for oropharyngeal cancer (OPC) patients treated with RT at our institution between 2005 and 2015 with available planning computed tomography (CT), dose grid, pre- & first post-RT ¹⁸FDG-PET-CT scans, and physician-reported xerostomia assessment at 3-6 months post-RT (Xero 3-6 ms) per CTCAE, was retrieved, following an IRB approval. A CT-CT deformable image co-registration followed by voxel-by-voxel resampling of pre & post-RT ¹⁸FDG activity and dose grid were performed. Ipsilateral (Ipsi) and contralateral (contra) parotid glands were sub-segmented based on the received dose in 5 Gy increments, i.e. 0-5 Gy, 5-10 Gy sub-volumes, etc. Median and dose-weighted SUV were extracted from whole parotid volumes and sub-volumes on pre- & post-RT PET scans, using in-house code that runs on MATLAB. Wilcoxon signed-rank and Kruskal-Wallis tests were used to test differences pre- and post-RT.

Results

432 parotid glands, belonging to 108 OPC patients treated with RT, were sub-segmented & analyzed. Xero 3-6 ms was reported as: non-severe (78.7%) and severe (21.3%). SUV- median values were significantly reduced post-RT, irrespective of laterality (p = 0.02). A similar pattern was observed in parotid sub-volumes, especially ipsi parotid gland sub-volumes receiving doses 10-50 Gy (p < 0.05). Kruskal-Wallis test showed a significantly higher mean RT dose in the contra parotid in the patients with more severe Xero 3-6mo (p = 0.03). Multiple logistic regression showed a combined clinical-dosimetric-metabolic imaging model could predict the severity of Xero 3-6mo; AUC = 0.78 (95%CI: 0.66-0.85; p < 0.0001).

Conclusion

We sought to quantify pre- and post-RT ¹⁸FDG-PET metrics of parotid glands in patients with OPC. Temporal dynamics of PET-derived metrics can potentially serve as MIBs of RT-related xerostomia in concert with clinical and dosimetric variables.

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges

The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.