Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy:Large-Scale Observational Cohort

Purpose: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORNI-IV) and grade IV (ORNIV) after RT (+/- chemotherapy) in patients with HNC.Methods and Materials: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.Results: Of 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORNI-IV primary endpoint) and 5% (n = 65) ORNIV (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D30% and pre-RT dental extraction as independent predictors for both ORNI-IV and ORNIV best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUCvalidation = 0.75 [0.69-0.82]) and 0.81 (AUCvalidation = 0.82 [0.74-0.89]), respectively.Conclusions: This study presented NTCP models based on mandible bone D30% and pre-RT dental extraction that predict ORNI-IV and ORNIV (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORNI-IV risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN. (C) 2021 The Author(s). Published by Elsevier Inc.</p

Prospective Validation of Diffusion-Weighted MRI as a Biomarker of Tumor Response and Oncologic Outcomes in Head and Neck Cancer: Results From an Observational Biomarker Pre-Qualification Study

PURPOSE: To determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT). METHODS: HNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. RESULTS: Eighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10-3 mm2/s vs. (1.37 ± 0.22) × 10-3 mm2/s, p \u3c 0.0001), while patients with non-CR had no significant increase (p \u3e 0.05). RPA identified GTV-P delta (Δ)ADCmean \u3c 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADCmean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p \u3c 0.0001 for both). CONCLUSION: ΔADCmean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse

Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation

Measurement(s) Image Segmentation • hepatocellular carcinoma Technology Type(s) Neural Network • Multiphasic Computed Tomography of the Abdomen Sample Characteristic - Organism multiphasic CT of the abdomen Sample Characteristic - Location contiguous United States of Americ