IMRT – Biomarkers for dose escalation, dose de-escalation and personalized medicine in radiotherapy for head and neck cancer

Risk Stratification for Trial Enrichment Considering Loco-Regional Failure in Head and Neck Cancer: UICC8 Versus Purpose-Built Failure-Type Specific Risk Prediction Model

BACKGROUND

A previously published failure-type specific risk model showed good performance in the original cohort.

AIM

to validate the model and separate patients with high- and low-risk loco-regional failure (LRF).

GOAL

to identify patients potentially suitable for treatment intensification trials.

METHODS

Validation data: 756 patients from two institutions (different countries). Predictive performance was evaluated by Brier scores and AUCs. Discriminatory performance was compared to Union for International Cancer Control (UICC) staging (versions 7 and 8).

RESULTS

The model's 3-year AUC for LRF was 65%, significantly better than UICC7 staging, but no significant difference to UICC8. Model-based risk stratification and UICC8 both identified high-risk patient groups with 3-year LRF ≈30%. The population mean was 18%.

CONCLUSIONS

The model performed well on a group level. UICC8 staging performed equally well. Although developed for the endpoint of OS, an improvement from UICC version 7 to version 8 was evident also for the prediction of LRF.

End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning

Background

Foundation models (FMs) show promise in medical AI by learning flexible features from large datasets, potentially surpassing handcrafted radiomics. Outcome prediction of head and neck squamous cell carcinomas (HNSCC) with FMs using routine imaging remains unexplored.

Purpose

To evaluate end-to-end FM-based multiple instance learning (MIL) for 2-year overall survival (OS), locoregional control (LRC), and freedom from distant metastasis (FFDM) prediction and risk group stratification using pretreatment CT scans in HNSCC.

Materials and Methods

We analyzed data of 2485 patients from three retrospective HNSCC cohorts (RADCURE, HN1, HN-PET-CT), treated between 2004 and 2017 with available pre-treatment CTs and primary gross tumor volume (GTVp) segmentations. The RADCURE cohort was split into training (n=1464) and test (N=606), with HN1 (n=131) and HN-PET-CT (n=284) as additional test cohorts. FM-based MIL models (2D, multiview and 3D) for 2-year endpoint prediction and risk stratification wre evaluated based on area under the receiver operator curve (AUROC) and Kaplan-Meier (KM) with hazard ratios (HR), compared with radiomics and assessed for multimodal enhancement with clinical baselines.

Results

2D MIL models achieved 2-year test AUROCs of 0.75-0.84 (OS), 0.66-0.75 (LRC) and 0.71-0.78 (FFDM), outperforming multiview and 3D MIL (AUROCs: 0.50-0.77, p≥0.15) and comparable or superior to radiomics (AUROCs: 0.64-0.74, p≥0.012). Significant stratification was observed (HRs: 2.14-4.77, p≤0.039). Multimodal enhancement of 2-year OS/FFDM (AUROCs: 0.82-0.87, p≤0.018) was observed for patients without human papilloma virus positive (HPV+) tumors.

Conclusion

FM-based MIL demonstrates promise in HNSCC risk prediction, showing similar or superior performance to radiomics and enhancing clinical baselines in non-HPV+ patients.

Ex vivo radiation sensitivity assessment for individual head and neck cancer patients using deep learning-based automated nuclei and DNA damage foci detection

Introduction

Tumor biopsy tissue response to ex vivo irradiation is potentially an interesting biomarker for in vivo tumor response, therefore, for treatment personalization. Tumor response ex vivo can be characterized by DNA damage response, expressed by the large-scale presence of DNA damage foci in tumor nuclei. Currently, characterizing tumor nuclei and DNA damage foci is a manual process that takes hours per patient and is subjective to inter-observer variability, which is not feasible in for clinical decision making. Therefore, our goal was to develop a method to automatically segment nuclei and DNA damage foci in tumor tissue samples treated with radiation ex vivo to characterize the DNA damage response, as potential biomarker for in vivo radio-sensitivity.

Methods

Oral cavity tumor tissue of 21 patients was irradiated ex vivo (5 or 0 Gy), fixated 2 h post-radiation, and used to develop our method for automated nuclei and 53BP1 foci segmentation. The segmentation model used both deep learning and conventional image-analysis techniques. The training (22 %), validation (22 %), and test set (56 %) consisted of thousands of manually segmented nuclei and foci. The segmentations and number of foci per nucleus in the test set were compared to their ground truths.

Results

The automatic nuclei and foci segmentations were highly accurate (Dice = 0.901 and Dice = 0.749, respectively). An excellent correlation (R2 = 0.802) was observed for the foci per nucleus that outperformed reported inter-observation variation. The analysis took ∼ 8 s per image.

Conclusion

This model can replace manual foci analysis for ex vivo irradiation of head-and-neck squamous cell carcinoma tissue, reduces the image-analysis time from hours to minutes, avoids the problem of inter-observer variability, enables assessment of multiple images or conditions, and provides additional information about the foci size. Thereby, it allows for reliable and rapid ex vivo radio-sensitivity assessment, as potential biomarker for response in vivo and treatment personalization.

Lessons and Opportunities for Biomarker-Driven Radiation Personalization in Head and Neck Cancer

Head and neck cancer is notoriously challenging to treat in part because it constitutes an anatomically and biologically diverse group of cancers with heterogeneous prognoses. While treatment can be associated with significant late toxicities, recurrence is often difficult to salvage with poor survival rates and functional morbidity.^1,2 Thus, achieving tumor control and cure at the initial diagnosis is the highest priority. Given the differing outcome expectations (even within a specific sub-site like oropharyngeal carcinoma), there has been growing interest in personalizing treatment: de-escalation in selected cancers to decrease the risk of late toxicity without compromising oncologic outcomes, and intensification for more aggressive cancers to improve oncologic outcomes without causing undue toxicity. This risk stratification is increasingly accomplished using biomarkers, which can represent molecular, clinicopathologic, and/or radiologic data. In this review, we will focus on biomarker-driven radiotherapy dose personalization with emphasis on oropharyngeal and nasopharyngeal carcinoma. This radiation personalization is largely performed on the population level by identifying patients with good prognosis via traditional clinicopathologic factors, although there are emerging studies supporting inter-tumor and intra-tumor level personalization via imaging and molecular biomarkers.

Ex Vivo Functional Assay for Evaluating Treatment Response in Tumor Tissue of Head and Neck Squamous Cell Carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) displays a large heterogeneity in treatment response, and consequently in patient prognosis. Despite extensive efforts, no clinically validated model is available to predict tumor response. Here we describe a functional test for predicting tumor response to radiation and chemotherapy on the level of the individual patient.

METHODS

Resection material of 17 primary HNSCC patients was cultured ex vivo, irradiated or cisplatin-treated, after which the effect on tumor cell vitality was analyzed several days after treatment.

RESULTS

Ionizing radiation (IR) affected tumor cell growth and viability with a clear dose-response relationship, and marked heterogeneity between tumors was observed. After a single dose of 5Gy, proliferation in IR-sensitive tumors dropped below 30% of the untreated level, while IR-resistant tumors maintained at least 60% of proliferation. IR-sensitive tumors showed on average a twofold increase in apoptosis, as well as an increased number and size of DNA damage foci after treatment. No differences in the homologous recombination (HR) proficiency between IR-sensitive and -resistant tumors were detected. Cisplatin caused a decrease in proliferation, as well as induction of apoptosis, again with marked variation between the samples.

CONCLUSIONS

Our functional ex vivo assay discriminated between IR-sensitive and IR-resistant HNSCC tumors, and may also be suitable for predicting response to cisplatin. Its predictive value is currently under investigation in a prospective clinical study.

Delineation uncertainties of tumour volumes on MRI of head and neck cancer patients

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Role of target delineation uncertainties in head and neck cancer patients.

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Knowing contouring variations for MRI allows better adaptation of MRLinac for H&N cancers.

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An interobserver variation for GTV among 8 observers was below 2 mm using MRI.

•

Variability between observers might improve using other imaging modalities.

Background

During the last decade, radiotherapy using MR Linac has gone from research to clinical implementation for different cancer locations. For head and neck cancer (HNC), target delineation based only on MR images is not yet standard, and the utilisation of MRI instead of PET/CT in radiotherapy planning is not well established. We aimed to analyse the inter-observer variation (IOV) in delineating GTV (gross tumour volume) on MR images only for patients with HNC.

Material/methods

32 HNC patients from two independent departments were included. Four clinical oncologists from Denmark and four radiation oncologists from Australia had independently contoured primary tumour GTVs (GTV-T) and nodal GTVs (GTV-N) on T2-weighted MR images obtained at the time of treatment planning. Observers were provided with sets of images, delineation guidelines and patient synopsis. Simultaneous truth and performance level estimation (STAPLE) reference volumes were generated for each structure using all observer contours. The IOV was assessed using the DICE Similarity Coefficient (DSC) and mean absolute surface distance (MASD).

Results

32 GTV-Ts and 68 GTV-Ns were contoured per observer. The median MASD for GTV-Ts and GTV-Ns across all patients was 0.17 cm (range 0.08–0.39 cm) and 0.07 cm (range 0.04–0.33 cm), respectively. Median DSC relative to a STAPLE volume for GTV-Ts and GTV-Ns across all patients were 0.73 and 0.76, respectively. A significant correlation was seen between median DSCs and median volumes of GTV-Ts (Spearman correlation coefficient 0.76, p < 0.001) and of GTV-Ns (Spearman correlation coefficient 0.55, p < 0.001).

Conclusion

Contouring GTVs in patients with HNC on MRI showed that the median IOV for GTV-T and GTV-N was below 2 mm, based on observes from two separate radiation departments. However, there are still specific regions in tumours that are difficult to resolve as either malignant tissue or oedema that potentially could be improved by further training in MR-only delineation.

Decreasing treatment burden in HPV-related OPSCC: A systematic review of clinical trials

INTRODUCTION

Favorable outcomes are observed after treatment with standard chemoradiotherapy (CRT) for Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC) patients. The consistent growing interest on treatment-related toxicity burden, potentially jeopardizing survivors' quality of life, led clinicians to investigate possible de-escalation strategies.

MATERIALS AND METHODS

A comprehensive systematic literature search of clinical trials was performed through the EMBASE database to provide an overview of the de-escalation strategies spectrum. Additionally, hand searching and clinicaltrials.gov were also used.

RESULTS

Herein, we report and discuss different approaches to de-escalation of therapy, with respect to both local and systemic strategies.

CONCLUSIONS

Several promising de-escalation experiences have been published. However, while further evidence is awaited, no changes in the management nor deviation from the standard of care should be made outside of clinical trials.

[Standardized and individualized diagnosis and treatment of oral squamous cell carcinoma: opportunities and challenges]

How to improve the effects of treatment on patients with oral squamous cell carcinoma (OSCC) has always been the focus of clinical and basic studies. The standardized diagnosis and treatment of malignant tumors aim to improve the effects of treatment, and individualized treatment based on standardized diagnosis and treatment may further improve these effects. On the basis of the existing guidelines for the diagnosis and treatment of patients with OSCC, this study explored the opportunities and challenges of standardized and individualized diagnosis and treatment of OSCC. These challenges and opportunities were related to the updates of clinical and pathological staging system, surgical margins, and neck dissection in patients with OSCC at early stage and preoperative induction therapy and postoperative adjuvant treatment in patients with advanced OSCC. This study also shared ideas on clinical studies of OSCC to optimize the existing treatment schemes, improve the treatment effects, and enhance the guidelines.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases

OBJECTIVES

To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.

MATERIALS AND METHODS

A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models.

RESULTS

Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10^-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10^-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).

CONCLUSION

Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.

Different oral cancer scenarios to personalize targeted therapy: Boron Neutron Capture Therapy translational studies

Boron neutron capture therapy (BNCT) is a targeted therapy, which consists of preferential accumulation of boron carriers in tumor followed by neutron irradiation. Each oral cancer patient has different risks of developing one or more carcinomas and/or oral mucositis induced after treatment. Our group proposed the hamster oral cancer model to study the efficacy of BNCT and associated mucositis. Translational studies are essential to the advancement of novel boron delivery agents and targeted strategies. Herein, we review our work in the hamster model in which we studied BNCT induced mucositis using three different cancerization protocols, mimicking three different clinical scenarios. The BNCT-induced mucositis increases with the aggressiveness of the carcinogenesis protocol employed, suggesting that the study of different oral cancer patient scenarios would help to develop personalized therapies.

Technical Assessment of an Automated Treatment Planning on Dose Escalation of Pancreas Stereotactic Body Radiotherapy

BACKGROUND

Stereotactic body radiotherapy has been suggested to provide high rates of local control for locally advanced pancreatic cancer. However, the close proximity of highly radiosensitive normal tissues usually causes the labor-intensive planning process and may impede further escalation of the prescription dose.

PURPOSE

The present study aims to evaluate the consistency and efficiency of Pinnacle Auto-Planning for pancreas stereotactic body radiotherapy with original prescription and escalated prescription.

METHODS

Twenty-four patients with pancreatic cancer treated with stereotactic body radiotherapy were studied retrospectively. The prescription is 40 Gy over 5 consecutive fractions. Most of patients (n = 21) also had 3 other different dose-level targets (6 Gy/fraction, 5 Gy/fraction, and 4 Gy/fraction). Two types of plans were generated by Pinnacle Auto-Planning with the original prescription (8 Gy/fraction, 6 Gy/fraction, 5 Gy/fraction, and 4 Gy/fraction) and escalated prescription (9 Gy/fraction, 7 Gy/fraction, 6 Gy/fraction, and 5 Gy/fraction), respectively. The same Auto-Planning template, including beam geometry, intensity-modulated radiotherapy objectives and intensity-modulated radiotherapy optimization parameters, were utilized for all the auto-plans in each prescription group. The intensity-modulated radiotherapy objectives do not include any manually created structures. Dosimetric parameters including percentage volume of PTV receiving 100% of the prescription dose, percentage volume of PTV receiving 93% of the prescription dose, and consistency of the dose-volume histograms of the target volumes were assessed. Dmax and D1 cc of highly radiosensitive organs were also evaluated.

RESULTS

For all the pancreas stereotactic body radiotherapy plans with the original or escalated prescriptions, auto-plans met institutional dose constraints for critical organs, such as the duodenum, small intestine, and stomach. Furthermore, auto-plans resulted in acceptable planning target volume coverage for all targets with different prescription levels. All the plans were generated in a one-attempt manner, and very little human intervention is necessary to achieve such plan quality.

CONCLUSIONS

Pinnacle3 Auto-Planning consistently and efficiently generate acceptable treatment plans for multitarget pancreas stereotactic body radiotherapy with or without dose escalation and may play a more important role in treatment planning in the future.