Literature DB >> 29779936

Early Response Assessment on Mid-treatment Computed Tomography Predicts for Locoregional Recurrence in Oropharyngeal Cancer Patients Treated With Definitive Radiation Therapy.

Rafi Kabarriti1, N Patrik Brodin2, George Lundgren1, Nitin Ohri1, Wolfgang A Tomé2, Shalom Kalnicki1, Madhur K Garg3.   

Abstract

PURPOSE: To evaluate whether a response assessment using mid-treatment computed tomography (CT) scans during definitive radiation therapy (RT) for oropharyngeal head and neck cancer can predict for locoregional recurrence (LRR). METHODS AND MATERIALS: Head and neck cancer patients who receive RT at our institution undergo CT repeat scans at the 15th fraction, with treatment replanning in the case of an inadequate dose to gross disease or an increased dose to organs at risk. A retrospective cohort analysis was performed of 96 consecutive patients with oropharyngeal cancer treated from 2007 to 2015 with mid-treatment repeat CT scans available. The primary disease volume and involved lymph node volume were delineated on the pre- and mid-treatment CT scans. Univariable and multivariable Cox proportional hazards regression analyses were used to evaluate the efficacy of the mid-treatment reduction in tumor volume as a predictor of LRR. Risk stratification was performed by dichotomizing the patients into high- and low-risk groups according to the mid-treatment response and p16 status and smoking history.
RESULTS: With a median follow-up of 34 months, 14 patients experienced LRR. The median reduction in the total tumor volume was 18.7% (interquartile range 8.4%-30.9%). A reduction in total tumor volume greater than the median was an independent predictor of LRR (hazard ratio 0.22, 95% confidence interval 0.05-0.89; P = .020). The reduction in primary tumor volume was an even stronger predictor of LRR (hazard ratio 0.11, 95% confidence interval 0.02-0.57; P = .002). Stratifying patients into a high-risk group for those with a reduction in the total tumor volume at mid-treatment at or less than the median, p16 negative status, and smoking status of >10 pack-years and a low-risk group for those without these factors, we found a clear separation in Kaplan-Meier curves, with actuarial 3-year locoregional control, progression-free survival, and overall survival rates for the high-risk patients of 45.7%, 38.2%, and 71.8% compared with 90.7%, 70.6%, and 89.8% for low-risk patients, respectively (P ≤ .021 for all).
CONCLUSIONS: Our results have shown that the treatment response from an early assessment using mid-treatment CT scans is an independent predictor of LRR and can be used to effectively distinguish high- and low-risk patients, allowing for risk-adaptive treatment stratification at the midway point.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Year:  2018        PMID: 29779936     DOI: 10.1016/j.ijrobp.2018.03.059

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  3 in total

1.  Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer.

Authors:  Jia Wu; Michael F Gensheimer; Nasha Zhang; Meiying Guo; Rachel Liang; Carrie Zhang; Nancy Fischbein; Erqi L Pollom; Beth Beadle; Quynh-Thu Le; Ruijiang Li
Journal:  J Nucl Med       Date:  2019-08-16       Impact factor: 10.057

2.  Integrating Tumor and Nodal Imaging Characteristics at Baseline and Mid-Treatment Computed Tomography Scans to Predict Distant Metastasis in Oropharyngeal Cancer Treated With Concurrent Chemoradiotherapy.

Authors:  Jia Wu; Micheal F Gensheimer; Nasha Zhang; Fei Han; Rachel Liang; Yushen Qian; Carrie Zhang; Nancy Fischbein; Erqi L Pollom; Beth Beadle; Quynh-Thu Le; Ruijiang Li
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-03-30       Impact factor: 7.038

3.  Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network.

Authors:  Nicolette Taku; Kareem A Wahid; Lisanne V van Dijk; Jaakko Sahlsten; Joel Jaskari; Kimmo Kaski; Clifton D Fuller; Mohamed A Naser
Journal:  Clin Transl Radiat Oncol       Date:  2022-06-18
  3 in total

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