International Expert-Based Consensus Definition, Staging Criteria, and Minimum Data Elements for Osteoradionecrosis of the Jaw: An Inter-Disciplinary Modified Delphi Study.

Purpose: Osteoradionecrosis of the jaw (ORNJ) is a severe iatrogenic disease characterized by bone death after radiation therapy (RT) to the head and neck. With over 9 published definitions and at least 16 diagnostic/staging systems, the true incidence and severity of ORNJ are obscured by lack of a standard for disease definition and severity assessment, leading to inaccurate estimation of incidence, reporting ambiguity, and likely under-diagnosis worldwide. This study aimed to achieve consensus on an explicit definition and phenotype of ORNJ and related precursor states through data standardization to facilitate effective diagnosis, monitoring, and multidisciplinary management of ORNJ. Methods: The ORAL Consortium comprised 69 international experts, including representatives from medical, surgical, radiation oncology, and oral/dental disciplines. Using a web-based modified Delphi technique, panelists classified descriptive cases using existing staging systems, reviewed systems for feature extraction and specification, and iteratively classified cases based on clinical/imaging feature combinations. Results: The Consortium ORNJ definition was developed in alignment with SNOMED-CT terminology and recent ISOO-MASCC-ASCO guideline recommendations. Case review using existing ORNJ staging systems showed high rates of inability to classify (up to 76%). Ten consensus statements and nine minimum data elements (MDEs) were outlined for prospective collection and classification of precursor/ORNJ stages. Conclusion: This study provides an international, consensus-based definition and MDE foundation for standardized ORNJ reporting in cancer survivors treated with RT. Head and neck surgeons, radiation, surgical, medical oncologists, and dental specialists should adopt MDEs to enable scalable health information exchange and analytics. Work is underway to develop both a human- and machine-readable knowledge representation for ORNJ (i.e., ontology) and multidisciplinary resources for dissemination to improve ORNJ reporting in academic and community practice settings.

Xevinapant plus radiotherapy in resected, high-risk, cisplatin-ineligible LA SCCHN: the phase III XRay Vision study design.

There is a significant unmet need and lack of treatment options for patients with resected, high-risk, cisplatin-ineligible locally advanced squamous cell carcinoma of the head and neck (LA SCCHN). Xevinapant, a first-in-class, potent, oral, small-molecule IAP inhibitor, is thought to restore cancer cell sensitivity to chemotherapy and radiotherapy in clinical and preclinical studies. We describe the design of XRay Vision (NCT05386550), an international, randomized, double-blind, phase III study. Approximately 700 patients with resected, high-risk, cisplatin-ineligible LA SCCHN will be randomized 1:1 to receive 6 cycles of xevinapant or placebo, in combination with radiotherapy for the first 3 cycles. The primary end point is disease-free survival, and secondary end points include overall survival, health-related quality of life, and safety.

Squamous cell carcinoma is the most common form of head and neck cancer (SCCHN) and includes cancers of the lips, mouth, throat, tongue and voice box. It is called 'locally advanced' when the cancer has spread to nearby areas but not to other parts of the body. Few treatment options are available for people with locally advanced SCCHN who have had surgery and are unable to receive a type of chemotherapy called cisplatin. Xevinapant is being developed as a possible new type of cancer treatment. It is a liquid that is taken by mouth or given through a feeding tube. Adding xevinapant to the standard treatment ­ called radiotherapy ­ aims to make radiotherapy more effective against the cancer. Researchers have started a large, international, phase III study called XRay Vision to see if adding xevinapant to radiotherapy can help stop the cancer from coming back after surgery and help people live longer. Clinical Trial Registration: NCT05386550 (ClinicalTrials.gov).

The Next Chapter in Immunotherapy and Radiation Combination Therapy: Cancer-Specific Perspectives.

Immunotherapeutic agents have revolutionized cancer treatment over the past decade. However, most patients fail to respond to immunotherapy alone. A growing body of preclinical studies highlights the potential for synergy between radiation therapy and immunotherapy, but the outcomes of clinical studies have been mixed. This review summarizes the current state of immunotherapy and radiation combination therapy across cancers, highlighting existing challenges and promising areas for future investigation.

Multicenter Evaluation of Radiation and Immune Checkpoint Inhibitor Therapy in Mucosal Melanoma and Review of Recent Literature.

Purpose: Optimal integration of local therapy and systemic immune therapy for patients with mucosal melanoma (MM) is uncertain. We evaluated treatment patterns and outcomes following radiation therapy (RT) in combination with immune checkpoint inhibition (ICI) in MM. Methods and Materials: Thirty-seven patients with localized (n = 32, 87%) or node-positive (n = 5, 14%) MM were treated across 4 institutions with RT to the primary tumor with or without oncologic resection (n = 28, 76%) and ICI from 2012 to 2020. Recurrence rates were estimated using cumulative incidence in the presence of the competing risk of death. Results: Mucosal sites were head/neck (n = 29, 78%), vaginal (n = 7, 19%), and anorectal (n = 1, 3%). Patients received ICI prior to or concurrent with RT (n = 14, 38%), following RT (n = 5, 14%), or at recurrence (n = 18, 49%). The objective response rate for evaluable patients was 31% for ICI as initial treatment (95% CI, 11%-59%) and 19% for ICI at recurrence (95% CI, 4%-46%). Median follow-up was 26 months for living patients; median overall survival (OS) was 54 months (95% CI, 31 months-not reached). Two-year OS was 85%; distant metastasis-free survival 44%. The 2-year cumulative incidence of local recurrence (LR) was 26% (95% CI, 13%-41%). For 9 patients with unresectable disease, 2-year OS was 88% (95% CI, 35%-98%); LR was 25% (95% CI, 3%-58%). For 5 patients with positive nodes at diagnosis, 2-year OS was 100%; LR was 0%. Conclusions: High rates of local control were achieved with RT with or without oncologic resection and ICI for localized and locally advanced MM. In particular, favorable local control was possible even for patients with unresectable or node-positive disease. Although risk of distant failure remains high, patients with MM may benefit from aggressive local therapy including RT in the setting of immunotherapy treatment.

A multicenter real-world analysis of risk factors, therapeutics, and outcomes of patients with metastatic basal cell carcinoma.

BACKGROUND: Metastatic basal cell carcinoma (mBCC) is rare and there are limited data regarding patient and tumor risk factors, optimal treatments, and disease prognosis. OBJECTIVE: To assess patient and tumor characteristics, therapeutics, and outcomes of mBCC stratified by location of metastasis. METHODS: Retrospective cohort study of 53 patients with mBCC treated at 4 large academic centers in Boston, Massachusetts; Philadelphia, Pennsylvania; and Cleveland, Ohio between January 1, 2005 and December 31, 2021. RESULTS: A total of 53 patients with mBCC were identified across 4 centers, 22 (42%) of whom had mBCC with spread limited to lymph nodes and 31 (58%) patients with distant organ spread (with or without lymph node involvement). Overall, half (n = 11) of patients with nodal metastasis achieved complete remission of disease, compared with just 1 (3%) patient with distant metastasis. The 5-year survival for nodal and distant metastatic patients was 89.3% and 61.0%, respectively. LIMITATIONS: Small sample size due to disease rarity. CONCLUSIONS AND RELEVANCE: Patients with nodal disease are more likely to have disease remission whereas patients with distant metastasis are more likely to have persistent disease and die from their disease. However, 5-year survival rates exceed 50%, even for stage IV disease.

AGuIX nanoparticle-nanobody bioconjugates to target immune checkpoint receptors.

This article presents bioconjugates combining nanoparticles (AGuIX) with nanobodies (VHH) targeting Programmed Death-Ligand 1 (PD-L1, A12 VHH) and Cluster of Differentiation 47 (CD47, A4 VHH) for active tumor targeting. AGuIX nanoparticles offer theranostic capabilities and an efficient biodistribution/pharmacokinetic profile (BD/PK), while VHH's reduced size (15 kDa) allows efficient tumor penetration. Site-selective sortagging and click chemistry were compared for bioconjugation. While both methods yielded bioconjugates with similar functionality, click chemistry demonstrated higher yield and could be used for the conjugation of various VHH. The specific targeting of AGuIX@VHH has been demonstrated in both in vitro and ex vivo settings, paving the way for combined targeted immunotherapies, radiotherapy, and cancer imaging.

Preexisting tumor-resident T cells with cytotoxic potential associate with response to neoadjuvant anti-PD-1 in head and neck cancer.

About 50% of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) experience recurrences after definitive therapy. The presurgical administration of anti-programmed cell death protein 1 (PD-1) immunotherapy results in substantial pathologic tumor responses (pTR) within the tumor microenvironment (TME). However, the mechanisms underlying the dynamics of antitumor T cells upon neoadjuvant PD-1 blockade remain unresolved, and approaches to increase pathologic responses are lacking. In a phase 2 trial (NCT02296684), we observed that 45% of patients treated with two doses of neoadjuvant pembrolizumab experienced marked pTRs (≥50%). Single-cell analysis of 17,158 CD8+ T cells from 14 tumor biopsies, including 6 matched pre-post neoadjuvant treatment, revealed that responding tumors had clonally expanded putative tumor-specific exhausted CD8+ tumor-infiltrating lymphocytes (TILs) with a tissue-resident memory program, characterized by high cytotoxic potential (CTX+) and ZNF683 expression, within the baseline TME. Pathologic responses after 5 weeks of PD-1 blockade were consistent with activation of preexisting CTX+ZNF683+CD8+ TILs, paralleling loss of viable tumor and associated tumor antigens. Response was associated with high numbers of CD103+PD-1+CD8+ T cells infiltrating pretreatment lesions, whereas revival of nonexhausted persisting clones and clonal replacement were modest. By contrast, nonresponder baseline TME exhibited a relative absence of ZNF683+CTX+ TILs and subsequent accumulation of highly exhausted clones. In HNSCC, revival of preexisting ZNF683+CTX+ TILs is a major mechanism of response in the immediate postneoadjuvant setting.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.

Importance: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use. Objective: To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes. Design, Setting, and Participants: For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023. Exposure: C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC. Main Outcomes and Measures: Overall survival and treatment toxicity outcomes of HNSCC. Results: The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia. Conclusions and Relevance: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Radiotherapy Dose in Patients Receiving Immunotherapy.

There is significant rationale for combining radiation therapy (RT) and immuno-oncology (IO) agents, but the optimal radiation parameters are unknown. This review summarizes key trials in the RT and IO space with a focus on RT dose. Very low RT doses solely modulate the tumor immune microenvironment, intermediate doses both modulate the tumor immune microenvironment and kill some fraction of tumor cells, and ablative doses eliminate the majority of target tumor cells and also possess immunomodulatory effects. Ablative RT doses may have high toxicity if targets are adjacent to radiosensitive normal organs. The majority of completed trials have been conducted in the setting of metastatic disease and direct RT to a single lesion with the goal of generating systemic antitumor immunity termed the abscopal effect. Unfortunately, reliable generation of an abscopal effect has proved elusive over a range of radiation doses. Newer trials are exploring the effects of delivering RT to all or most sites of metastatic disease, with dose personalization based on the number and location of lesions. Additional directions include testing RT and IO in earlier stages of disease, sometimes in further combination with chemotherapy and surgery, where lower doses of RT may still contribute substantially to pathologic responses.

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.

BACKGROUND: Pretreatment identification of pathological extranodal extension (ENE) would guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated oropharyngeal carcinoma but is diagnostically challenging. ECOG-ACRIN Cancer Research Group E3311 was a multicentre trial wherein patients with HPV-associated oropharyngeal carcinoma were treated surgically and assigned to a pathological risk-based adjuvant strategy of observation, radiation, or concurrent chemoradiation. Despite protocol exclusion of patients with overt radiographic ENE, more than 30% had pathological ENE and required postoperative chemoradiation. We aimed to evaluate a CT-based deep learning algorithm for prediction of ENE in E3311, a diagnostically challenging cohort wherein algorithm use would be impactful in guiding decision-making. METHODS: For this retrospective evaluation of deep learning algorithm performance, we obtained pretreatment CTs and corresponding surgical pathology reports from the multicentre, randomised de-escalation trial E3311. All enrolled patients on E3311 required pretreatment and diagnostic head and neck imaging; patients with radiographically overt ENE were excluded per study protocol. The lymph node with largest short-axis diameter and up to two additional nodes were segmented on each scan and annotated for ENE per pathology reports. Deep learning algorithm performance for ENE prediction was compared with four board-certified head and neck radiologists. The primary endpoint was the area under the curve (AUC) of the receiver operating characteristic. FINDINGS: From 178 collected scans, 313 nodes were annotated: 71 (23%) with ENE in general, 39 (13%) with ENE larger than 1 mm ENE. The deep learning algorithm AUC for ENE classification was 0·86 (95% CI 0·82-0·90), outperforming all readers (p<0·0001 for each). Among radiologists, there was high variability in specificity (43-86%) and sensitivity (45-96%) with poor inter-reader agreement (κ 0·32). Matching the algorithm specificity to that of the reader with highest AUC (R2, false positive rate 22%) yielded improved sensitivity to 75% (+ 13%). Setting the algorithm false positive rate to 30% yielded 90% sensitivity. The algorithm showed improved performance compared with radiologists for ENE larger than 1 mm (p<0·0001) and in nodes with short-axis diameter 1 cm or larger. INTERPRETATION: The deep learning algorithm outperformed experts in predicting pathological ENE on a challenging cohort of patients with HPV-associated oropharyngeal carcinoma from a randomised clinical trial. Deep learning algorithms should be evaluated prospectively as a treatment selection tool. FUNDING: ECOG-ACRIN Cancer Research Group and the National Cancer Institute of the US National Institutes of Health.

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.

Purpose: Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes. Materials and Methods: 899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression. Results: DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r ß 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis. Conclusion: We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC. SUMMARY STATEMENT: In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

Inhibiting the inhibitors: Development of the IAP inhibitor xevinapant for the treatment of locally advanced squamous cell carcinoma of the head and neck.

Standard of care for patients with locally advanced squamous cell carcinoma of the head and neck (LA SCCHN) is surgery followed by chemoradiotherapy (CRT) or definitive CRT. However, approximately 50 % of patients with LA SCCHN develop disease recurrence or metastasis within 2 years of completing treatment, and the outcome for these patients is poor. Despite this, the current treatment landscape for LA SCCHN has remained relatively unchanged for more than 2 decades, and novel treatment options are urgently required. One of the key causes of disease recurrence is treatment resistance, which commonly occurs due to cancer cells' ability to evade apoptosis. Evasion of apoptosis has been in part attributed to the overexpression of inhibitor of apoptosis proteins (IAPs). IAPs, including X-linked IAP (XIAP) and cellular IAP 1 and 2 (cIAP1/2), are a class of proteins that regulate apoptosis induced by intrinsic and extrinsic apoptotic pathways. IAPs have been shown to be overexpressed in SCCHN, are associated with poor clinical outcomes, and are, therefore, a rational therapeutic target. To date, several IAP inhibitors have been investigated; however, only xevinapant, a potent, oral, small-molecule IAP inhibitor, has shown clinical proof of concept when combined with CRT. Specifically, xevinapant demonstrated superior efficacy in combination with CRT vs placebo + CRT in a randomized, double-blind, phase 2 trial in patients with unresected LA SCCHN. Here, we describe the current treatment landscape in LA SCCHN and provide the rationale for targeting IAPs and the clinical data reported for xevinapant.

Diagnostic performance of computed tomography features in detecting oropharyngeal squamous cell carcinoma extranodal extension.

OBJECTIVES: Accurate pre-treatment imaging determination of extranodal extension (ENE) could facilitate the selection of appropriate initial therapy for HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). Small studies have associated 7 CT features with ENE with varied results and agreement. This article seeks to determine the replicable diagnostic performance of these CT features for ENE. METHODS: Five expert academic head/neck neuroradiologists from 5 institutions evaluate a single academic cancer center cohort of 75 consecutive HPV + OPSCC patients. In a web-based virtual laboratory for imaging research and education, the experts performed training on 7 published CT features associated with ENE and then independently identified the "single most (if any) suspicious" lymph node and presence/absence of each of the features. Inter-rater agreement was assessed using percentage agreement, Gwet's AC1, and Fleiss' kappa. Sensitivity, specificity, and positive and negative predictive values were calculated for each CT feature based on histologic ENE. RESULTS: All 5 raters identified the same node in 52 cases (69%). In 15 cases (20%), at least one rater selected a node and at least one rater did not. In 8 cases (11%), all raters selected a node, but at least one rater selected a different node. Percentage agreement and Gwet's AC1 coefficients were > 0.80 for lesion identification, matted/conglomerated nodes, and central necrosis. Fleiss' kappa was always < 0.6. CT sensitivity for histologically confirmed ENE ranged 0.18-0.94, specificity 0.41-0.88, PPV 0.26-0.36, and NPV 0.78-0.96. CONCLUSIONS: Previously described CT features appear to have poor reproducibility among expert head/neck neuroradiologists and poor predictive value for histologic ENE. KEY POINTS: • Previously described CT imaging features appear to have poor reproducibility among expert head and neck subspecialized neuroradiologists as well as poor predictive value for histologic ENE. • Although it may still be appropriate to comment on the presence or absence of these CT features in imaging reports, the evidence indicates that caution is warranted when incorporating these features into clinical decision-making regarding the likelihood of ENE.