Adaptive MRI-guided stereotactic body radiation therapy for locally advanced pancreatic cancer - A phase II study.

PURPOSE: Stereotactic body radiotherapy (SBRT) has emerged as a promising new modality for locally advanced pancreatic cancer (LAPC). The current study evaluated the efficacy and toxicity of SBRT in patients with LAPC (NCT03648632). METHODS: This prospective single institution phase II study recruited patients with histologically or cytologically proven adenocarcinoma of the pancreas after more than two months of combination chemotherapy with no sign of progressive disease. Patients were prescribed 50-60 Gy in 5-8 fractions. Patients were initially treated on a standard linac (n = 4). Since 2019, patients were treated using online magnetic resonance (MR) image-guidance on a 1.5 T MRI-linac, where the treatment plan was adapted to the anatomy of the day. The primary endpoint was resection rate. RESULTS: Twenty-eight patients were enrolled between August 2018 and March 2022. All patients had non-resectable disease at time of diagnosis. Median follow-up from inclusion was 28.3 months (95 % CI 24.0-NR). Median progression-free and overall survival from inclusion were 7.8 months (95 % CI 5.0-14.8) and 16.5 months (95 % CI 10.7-22.6), respectively. Six patients experienced grade III treatment-related adverse events (jaundice, nausea, vomiting and/or constipation). One of the initial four patients receiving treatment on a standard linac experienced a grade IV perforation of the duodenum. Six patients (21 %) underwent resection. A further one patient was offered resection but declined. CONCLUSION: This study demonstrates that SBRT in patients with LAPC was associated with promising overall survival and resection rates. Furthermore, SBRT was safe and well tolerated, with limited severe toxicities.

The influence of tumor volume on the risk of distant metastases in head and neck squamous cell carcinomas.

BACKGROUND AND PURPOSE: Distant metastases (DM) in head and neck squamous cell carcinomas (HNSCC) are in most circumstances non-curable. The TNM staging system is insufficient to predict the risk of DM. This study investigates if the DM risk can be predicted using a multivariate model including pre-treatment total tumor volume for both p16-positive oropharyngeal squamous cell carcinoma (OPSCC) and all other sites (other HNSCC). MATERIALS AND METHODS: The study includes patients with localized pharyngeal and laryngeal squamous cell carcinomas treated with primary radiotherapy from 2008-2017 from three head and neck cancer centers. Patients were identified in the Danish Head and Neck Cancer (DAHANCA) database. Total (nodal and primary) tumor volume (Gross Tumor Volume, GTV) was extracted from local treatment planning systems. The GTV was grouped by volume (cm3) in four intervals and included in a multivariate Cox proportional hazard regression controlled for pre-selected clinical values incl. stage. RESULTS: The study includes 2,865 patients, of which 321 (11 %) had DM post-treatment. The risk of DM was assessed in a multivariate model based on 2,751 patients (p16-positive OPSCC: 1,032; and other HNSCC: 1,719). There was a significant association between GTV and the risk of DM, and in tumor volumes ≥ 50 cm3 hazard ratios of 7.6 (2.5-23.4) for p16-positive OPSCC and 4.1 (2.3-7.2) in other HNSCC were observed. CONCLUSION: Tumor volume is an independent risk factor for DM. The addition of total tumor volume to a predictive model is important to identify subgroups of HNSCC patients at high risk of DM.

Larynx cancer survival model developed through open-source federated learning.

INTRODUCTION: Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. METHODS: Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, ß regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. RESULTS: The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. CONCLUSION: Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.