Proton Image-guided Radiation Assignment for Therapeutic Escalation via Selection of locally advanced head and neck cancer patients [PIRATES]: A Phase I safety and feasibility trial of MRI-guided adaptive particle radiotherapy

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

BACKGROUND

Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches.

PURPOSE

To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).

METHODS

Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans.

RESULTS

The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set.

CONCLUSION

MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations

Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.

Dose escalation to hypoxic subvolumes in head and neck cancer: A randomized phase II study using dynamic [18F]FMISO PET/CT

BACKGROUND AND PURPOSE

Tumor hypoxia is a major cause of resistance to radiochemotherapy in locally advanced head-and-neck cancer (LASCCHN). We present results of a randomized phase II trial on hypoxia dose escalation (DE) in LASCCHN based on dynamic [¹⁸F]FMISO (dynFMISO) positron emission tomography (PET). The purpose was to confirm the prognostic value of hypoxia PET and assess feasibility, toxicity and efficacy of hypoxia-DE.

MATERIALS AND METHODS

Patients with LASCCHN underwent baseline dynFMISO PET/CT. Hypoxic volumes (HV) were derived from dynFMISO data. Patients with hypoxic tumors (HV>0) were randomized into standard radiotherapy (ST: 70Gy/35fx) or dose escalation (DE: 77Gy/35fx) to the HV. Patients with non-hypoxic tumors were treated with ST. After a minimum follow-up of 2 years, feasibility, acute/late toxicity and local control (LC) were analyzed.

RESULTS

The study was closed prematurely due to slow accrual. Between 2009 and 2017, 53 patients were enrolled, 39 (74%) had hypoxic tumors and were randomized into ST or DE. For non-hypoxic patients, 100% 5-year LC was observed compared to 74% in patients with hypoxic tumors (p=0.039). The difference in 5-year LC between DE (16/19) and ST (10/17) was 25%, p=0.150. No relevant differences related to acute and late toxicities between the groups were observed.

CONCLUSION

This study confirmed the prognostic value of hypoxia PET in LASCCHN for LC. Outcome after hypoxia DE appears promising and may support the concept of DE. Slow accrual and premature closure may partly be due to a high complexity of the study setup which needs to be considered for future multicenter trials.