Randomized Phase II Study of Physiologic MRI-Directed Adaptive Radiation Boost in Poor Prognosis Head and Neck Cancer

Neoadjuvant Nivolumab Plus Chemotherapy Followed by Response-Stratified Chemoradiation Therapy in HPV-Negative Head and Neck Cancer: The DEPEND Phase 2 Nonrandomized Clinical Trial

Importance

Neoadjuvant immunotherapy in human papillomavirus (HPV)-negative locoregionally advanced (LA) head and neck squamous cell carcinoma (HNSCC) appears promising, yet its role in nonsurgical treatment for head and neck cancer remains undefined. Neoadjuvant nivolumab plus chemotherapy followed by response-stratified de-escalated chemoradiation therapy (CRT) in HPV-negative LA stage IVa/b HNSCC may improve treatment efficacy while reducing treatment-related toxic effects.

Objective

To determine the deep response rate and tolerability of neoadjuvant nivolumab plus chemotherapy followed by response-stratified CRT in nonvirally mediated stage IVa/b HNSCC.

Design, Setting, and Participants

In this investigator-initiated phase 2 nonrandomized clinical trial conducted at a single academic center, patients with stage IVa/b (American Joint Committee on Cancer Tumor Classification, 8th edition) HPV-negative LA HNSCC were enrolled between 2019 and 2022. Data were analyzed from February 2023 to January 2024.

Interventions

The DEPEND trial evaluated neoadjuvant nivolumab plus carboplatin and paclitaxel, followed by response-stratified CRT. Patients with 50% or greater reduction per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 received de-escalated CRT to 66 Gy with elimination of elective nodal volumes; patients with less than 50% reduction received standard CRT to 70 to 75 Gy. Adjuvant nivolumab was administered for 9 cycles.

Main Outcomes and Measures

The primary end point was deep response rate (DRR; 50% or greater shrinkage per RECIST version 1.1) following neoadjuvant nivolumab plus chemotherapy. Secondary end points included progression-free survival (PFS), overall survival (OS), locoregional control, and distant control. Exploratory end points included acute toxic effects in patients who received response-adapted de-escalated CRT.

Results

Of 36 included patients, 28 (78%) were male, and the median (range) age was 58.9 (27-77) years. All patients started treatment and were available for analysis. The median (range) follow-up was 20 (13-40) months. The primary end point was met, with a DRR following neoadjuvant nivolumab/chemotherapy of 53% (95% CI, 35-70). The objective response rate was 86% (95% CI, 71-95). A total of 19 received de-escalated CRT and 16 received standard CRT. PFS and OS at 2 years were 66% (95% CI, 34-76) and 73% (95% CI, 52-86), respectively. The most common treatment-emergent adverse events for de-escalated and standard CRT were mucositis (14 of 19 [74%] and 15 of 16 [94%], respectively), radiation dermatitis (13 of 19 [68%] and 14 of 16 [88%], respectively), and dry mouth (7 of 19 [37%] and 10 of 16 [63%], respectively).

Conclusions and Relevance

In this phase 2 nonrandomized clinical trial, neoadjuvant nivolumab/chemotherapy led to deep responses in 53% of patients with HPV-negative LA stage IVa/b HNSCC, and response-adapted de-escalated CRT led to favorable survival with lower acute toxic effects among deep responders.

Trial Registration

ClinicalTrials.gov Identifier: NCT03944915.

Emerging Radiotherapy Technologies for Head and Neck Squamous Cell Carcinoma: Challenges and Opportunities in the Era of Immunotherapy

Radiotherapy (RT) is an integral component in the multidisciplinary management of patients with head and neck squamous cell carcinoma (HNSCC). Significant advances have been made toward optimizing tumor control and toxicity profiles of RT for HNSCC in the past two decades. The development of intensity modulated radiotherapy (IMRT) and concurrent chemotherapy established the standard of care for most patients with locally advanced HNSCC around the turn of the century. More recently, selective dose escalation to the most radioresistant part of tumor and avoidance of the most critical substructures of organs at risk, often guided by functional imaging, allowed even further improvement in the therapeutic ratio of IMRT. Other highly conformal RT modalities, including intensity modulated proton therapy (IMPT) and stereotactic body radiotherapy (SBRT) are being increasingly utilized, although there are gaps in our understanding of the normal tissue complication probabilities and their relative biological effectiveness. There is renewed interest in spatially fractionated radiotherapy (SFRT), such as GRID and LATTICE radiotherapy, in both palliative and definitive settings. The emergence of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of patients with recurrent and metastatic HNSCC. Novel RT modalities, including IMPT, SBRT, and SFRT, have the potential to reduce lymphopenia and immune suppression, stimulate anti-tumor immunity, and synergize with ICIs. The next frontier in the treatment of HNSCC may lie in the exploration of combined modality treatment with new RT technologies and ICIs.

Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers

This study explores the predictive utility of multi-time point, multi-modality quantitative imaging biomarkers (QIBs) and clinical factors in patients with poor-prognosis head and neck cancers (HNCs) using interpretable machine learning. We examined 93 patients with p16 + oropharyngeal squamous cell carcinoma or locally advanced p16- HNCs enrolled in a phase II adaptive radiation dose escalation trial. FDG-PET and multiparametric MRI scans were conducted before radiation therapy and at the 10th fraction (2 weeks). A survival network analyzed MRI and PET-derived biomarkers such as gross tumor volume (GTV), blood volume (BV), and metabolic tumor volume (MTV50), along with clinical factors to predict local (LF) and distant failures (DF). Feature attributions and interactions were assessed using Expected Gradients (EG) and Expected Hessian (EH). Through rigorous cross-validation, the model for predicting LF, incorporating biomarkers like p16 status and radiation boost, achieved a c-index of 0.758. Similarly, the DF prediction model showed a c-index of 0.695. The analysis of feature attributions and interactions enhanced understanding of important features and complex factor interplays, potentially guiding more personalized and intensified treatment approaches for HNC patients.

Conference Report: Review of Clinical Implementation of Advanced Quantitative Imaging Techniques for Personalized Radiotherapy

The topic of quantitative imaging in radiation therapy was presented as a "Masterclass" at the 2023 annual meeting of the American Society of Radiation Oncology (ASTRO). Dual-energy computed tomography (CT) and single-positron computed tomography were reviewed in detail as the first portion of the meeting session, with data showing utility in many aspects of radiation oncology including treatment planning and dose response. Positron emission tomography/CT scans evaluating the functional volume of lung tissue so as to provide optimal avoidance of healthy lungs were presented second. Advanced brain imaging was then discussed in the context of different forms of magnetic resonance scanning methods as the third area noted with significant discussion of ongoing research programs. Quantitative image analysis was presented to provide clinical utility for the analysis of patients with head and neck cancer. Finally, quality assurance was reviewed for different forms of quantitative imaging given the critical nature of imaging when numerical valuation, not just relative contrast, plays a crucial role in clinical process and decision-making. Conclusions and thoughts are shared in the conclusion, noting strong data supporting the use of quantitative imaging in radiation therapy going forward and that more studies are needed to move the field forward.

Prognostic value of pretreatment radiological MRI variables and dynamic contrast-enhanced MRI on radiotherapy treatment outcome in laryngeal and hypopharyngeal tumors

Background

This study aimed to determine the prognostic value of radiological magnetic resonance imaging (MRI) variables and dynamic contrast enhanced (DCE)-MRI for local control (LC), disease control (DC), and overall survival (OS) in laryngeal and hypopharyngeal cancer patients after radiotherapy.

Methods

320 patients treated with radiotherapy were retrospectively included. Pretreatment MRIs were evaluated for the following anatomical tumor characteristics: cartilage invasion, extralaryngeal spread, and involvement of the anterior commissure, pre-epiglottic space, and paralaryngeal space.Pretreatment DCE-MRI was available in 89 patients. The median and 95th percentile of the 60-second area under the contrast-distribution-curve (AUC60median and AUC60p95) were determined in the tumor volume.

Results

Univariable log-rank test determined that extralaryngeal spread, tumor volume and T-stage were prognostic for worse LC, DC, and OS. A low AUC60p95 (<31.7 mmol·s/L) and thyroid cartilage invasion were prognostic for worse OS.In multivariable analysis, a Cox proportional hazard model showed that a AUC60p95 ≥ 31.7 mmol·s/L was prognostic for better OS (HR=0.25, P<.001). Tumor volume was prognostic for DC (HR=3.42, P<.001) and OS (HR=3.27, P<.001). No anatomical MRI variables were significantly prognostic for LC, DC, or OS in multivariable analysis when corrected for confounders.

Conclusion

Low pretreatment AUC60p95 is prognostic for a worse OS, suggesting that poor tumor perfusion leads to worse survival. Large tumor volume is also prognostic for worse DC and OS. Anatomical MRI parameters are not prognostic for any of the evaluated treatment outcomes when corrected for confounders like age, T-stage, N-stage, and tumor volume.

Quality and Safety Considerations for Adaptive Radiation Therapy: An ASTRO White Paper

PURPOSE

Adaptive radiation therapy (ART) is the latest topic in a series of white papers published by the American Society for Radiation Oncology addressing quality processes and patient safety. ART widens the therapeutic index by improving the precision of radiation dose to targets, allowing for dose escalation and/or minimization of dose to normal tissue. ART is performed via offline or online methods; offline ART is the process of replanning a patient's treatment plan between fractions, whereas online ART involves plan adjustment with the patient on the treatment table. This is achieved with in-room imaging capable of assessing anatomic changes and the ability to reoptimize the treatment plan rapidly during the treatment session. Although ART has occurred in its simplest forms in clinical practice for decades, recent technological developments have enabled more clinical applications of ART. With increased clinical prevalence, compressed timelines, and the associated complexity of ART, quality and safety considerations are an important focus area.

METHODS

The American Society for Radiation Oncology convened an interdisciplinary task force to provide expert consensus on key workflows and processes for ART. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale, from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters selecting "strongly agree" or "agree" indicated consensus. Content not meeting this threshold was removed or revised.

SUMMARY

Establishing and maintaining an adaptive program requires a team-based approach, appropriately trained and credentialed specialists, significant resources, specialized technology, and implementation time. A comprehensive quality assurance program must be developed, using established guidance, to make sure all forms of ART are performed in a safe and effective manner. Patient safety when delivering ART is everyone's responsibility, and professional organizations, regulators, vendors, and end users must demonstrate a clear commitment to working together to deliver the highest levels of quality and safety.

Spatial correlation between in vivo imaging and immunohistochemical biomarkers: A methodological study

In this study, we present a method that enables voxel-by-voxel comparison of in vivo imaging to immunohistochemistry (IHC) biomarkers. As a proof of concept, we investigated the spatial correlation between dynamic contrast enhanced (DCE-)CT parameters and IHC biomarkers Ki-67 (proliferation), HIF-1α (hypoxia), and CD45 (immune cells). 54 whole-mount tumor slices of 15 laryngeal and hypopharyngeal carcinomas were immunohistochemically stained and digitized. Heatmaps of biomarker positivity were created and registered to DCE-CT parameter maps. The adiabatic approximation to the tissue homogeneity model was used to fit the following DCE parameters: Ktrans (transfer constant), Ve (extravascular and extracellular space), and Vi (intravascular space). Both IHC and DCE maps were downsampled to 4 × 4 × 3 mm[3] voxels. The mean values per tumor were used to calculate the between-subject correlations between parameters. For the within-subject (spatial) correlation, values of all voxels within a tumor were compared using the repeated measures correlation (rrm). No between-subject correlations were found between IHC biomarkers and DCE parameters, whereas we found multiple significant within-subject correlations: Ve and Ki-67 (rrm = -0.17, P < .001), Ve and HIF-1α (rrm = -0.12, P < .001), Ktrans and CD45 (rrm = 0.13, P < .001), Vi and CD45 (rrm = 0.16, P < .001), and Vi and Ki-67 (rrm = 0.08, P = .003). The strongest correlation was found between IHC biomarkers Ki-67 and HIF-1α (rrm = 0.35, P < .001). This study shows the technical feasibility of determining the 3 dimensional spatial correlation between histopathological biomarker heatmaps and in vivo imaging. It also shows that between-subject correlations do not reflect within-subject correlations of parameters.

Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery

Background and purpose

Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS).

Materials and methods

Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients.

Results

Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model.

Conclusions

A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.

Adaptive radiotherapy for head and neck cancer: Pitfalls and possibilities from the radiation oncologist's point of view

BACKGROUND

Patients with head and neck cancer (HNC) may experience substantial anatomical changes during the course of radiotherapy treatment. The implementation of adaptive radiotherapy (ART) proves effective in managing the consequent impact on the planned dose distribution.

METHODS

This narrative literature review comprehensively discusses the diverse strategies of ART in HNC and the documented dosimetric and clinical advantages associated with these approaches, while also addressing the current challenges for integration of ART into clinical practice.

RESULTS AND CONCLUSION

Although based on mainly non-randomized and retrospective trials, there is accumulating evidence that ART has the potential to reduce toxicity and improve quality of life and tumor control in HNC patients treated with RT. However, several questions remain regarding accurate patient selection, the ideal frequency and timing of replanning, and the appropriate way for image registration and dose calculation. Well-designed randomized prospective trials, with a predetermined protocol for both image registration and dose summation, are urgently needed to further investigate the dosimetric and clinical benefits of ART.

Diffusion MRI correlation with p16 status and prediction for tumor progression in locally advanced head and neck cancer

Purpose

To investigate p16 effects on diffusion image metrics and associations with tumor progression in patients with locally advanced head and neck cancers.

Methods

Diffusion images pretreatment and after 20 Gy (2wk) of RT were analyzed in patients with cT4/N3 p16+ oropharynx cancer (OPSCC) (N=51) and locoregionally advanced head and neck squamous cell carcinoma (LAHNSCC) (N=28), enrolled onto a prospective adaptive RT trial. Mean ADC values, subvolumes with ADC <1.2 um2/ms (TVLADC), and peak values of low (µL) and high (µH) components of ADC histograms in primary and total nodal gross tumor volumes were analyzed for prediction of freedom from local, distant, or any progression (FFLP, FFDP or FFLRDP) using multivariate Cox proportional-hazards model with clinical factors. P value with false discovery control <0.05 was considered as significant.

Results

With a mean follow up of 36 months, 18 of LAHNSCC patients and 16 of p16+ OPSCC patients had progression. After adjusting for p16, small µL and ADC values, and large TVLADC of primary tumors pre-RT were significantly associated with superior FFLRDP, FFLP and FFDP in the LAHNSCC (p<0.05), but no diffusion metrics were significant in p16+ oropharynx cancers. Post ad hoc analysis of the p16+ OPSCC only showed that large TVLADC of the total nodal burden pre-RT was significantly associated with inferior FFDP (p=0.05).

Conclusion

ADC metrics were associated with different progression patterns in the LAHNSCC and p16+ OPSCC, possibly explained by differences in cancer biology and morphology. A deep understanding of ADC metrics is warranted to establish imaging biomarkers for adaptive RT in HNSCC.

The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation

Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.