scholarly journals Impact of primary tumor volume on local control after definitive radiotherapy for head and neck cancer

Head & Neck ◽  
2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
William M. Mendenhall ◽  
Anthony A. Mancuso ◽  
Primoz Strojan ◽  
Jonathan J. Beitler ◽  
Carlos Suarez ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Local Control ◽  
Neck Cancer ◽  
Primary Tumor ◽  
Tumor Volume ◽  
Definitive Radiotherapy ◽  
Primary Tumor Volume

scholarly journals Primary tumor volume delineation in head and neck cancer: missing the tip of the iceberg?

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Olgun Elicin ◽  
Dario Terribilini ◽  
Mohamed Shelan ◽  
Werner Volken ◽  
Etienne Mathier ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Primary Tumor ◽  
Tumor Volume ◽  
Primary Tumor Volume ◽  
Volume Delineation

Semi-automated primary tumor volume measurements by dynamic contrast-enhanced MRI in patients with head and neck cancer

Head & Neck ◽  
2012 ◽  
Vol 35 (4) ◽  
pp. 521-526 ◽  
Author(s):  
Wouter L. Lodder ◽  
Kenneth G. A. Gilhuijs ◽  
Charlotte A. H. Lange ◽  
Frank A. Pameijer ◽  
Alfons J. M. Balm ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Primary Tumor ◽  
Tumor Volume ◽  
Dynamic Contrast Enhanced ◽  
Primary Tumor Volume ◽  
Contrast Enhanced ◽  
Volume Measurements ◽  
Contrast Enhanced Mri

scholarly journals Feasibility, SAR Distribution, and Clinical Outcome upon Reirradiation and Deep Hyperthermia Using the Hypercollar3D in Head and Neck Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6149
Author(s):  
Michiel Kroesen ◽  
Netteke van Holthe ◽  
Kemal Sumser ◽  
Dana Chitu ◽  
Rene Vernhout ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Clinical Outcome ◽  
Head And Neck ◽  
Local Control ◽  
Neck Cancer ◽  
Tumor Volume ◽  
Complete Response ◽  
Primary Treatment ◽  
The Novel ◽  
Deep Hyperthermia

(1) Background: Head and neck cancer (HNC) patients with recurrent or second primary (SP) tumors in previously irradiated areas represent a clinical challenge. Definitive or postoperative reirradiation with or without sensitizing therapy, like chemotherapy, should be considered. As an alternative to chemotherapy, hyperthermia has shown to be a potent sensitizer of radiotherapy in clinical studies in the primary treatment of HNC. At our institution, we developed the Hypercollar3D, as the successor to the Hypercollar, to enable improved application of hyperthermia for deeply located HNC. In this study, we report on the feasibility and clinical outcome of patients treated with the Hypercollar3D as an adjuvant to reirradiation in recurrent or SP HNC patients; (2) Methods: We retrospectively analyzed all patients with a recurrent or SP HNC treated with reirradiation combined with hyperthermia using the Hypercollar3D between 2014 and 2018. Data on patients, tumors, and treatments were collected. Follow-up data on disease specific outcomes as well as acute and late toxicity were collected. Data were analyzed using Kaplan Meier analyses; (3) Results: Twenty-two patients with recurrent or SP HNC were included. The average mean estimated applied cfSAR to the tumor volume for the last 17 patients was 80.5 W/kg. Therefore, the novel Hypercollar3D deposits 55% more energy at the target than our previous Hypercollar applicator. In patients treated with definitive thermoradiotherapy a complete response rate of 81.8% (9/11) was observed at 12 weeks following radiotherapy. Two-year local control (LC) and overall survival (OS) were 36.4% (95% CI 17.4–55.7%) and 54.6% (95% CI 32.1–72.4%), respectively. Patients with an interval longer than 24 months from their previous radiotherapy course had an LC of 66.7% (95% CI 37.5–84.6%), whereas patients with a time interval shorter than 24 months had an LC of 14.3% (95% CI 0.7–46.5%) at 18 months (p = 0.01). Cumulative grade 3 or higher toxicity was 39.2% (95% CI 16.0–61.9%); (4) Conclusions: Reirradiation combined with deep hyperthermia in HNC patients using the novel Hypercollar3D is feasible and deposits an average cfSAR of 80.5 W/kg in the tumor volume. The treatment results in high complete response rates at 12 weeks post-treatment. Local control and local toxicity rates were comparable to those reported for recurrent or SP HNC. To further optimize the hyperthermia treatment in the future, temperature feedback is warranted to apply heat at the maximum tolerable dose without toxicity. These data support further research in hyperthermia as an adjuvant to radiotherapy, both in the recurrent as well as in the primary treatment of HNC patients.

Tumor Volume Predicts Local Control in Head and Neck Cancer

2012 ◽  
Vol 147 (2_suppl) ◽  
pp. P70-P70
Author(s):  
Mohamed-Shaji Mansuri ◽  
Tom Alun-Jones ◽  
Albert Pace-Balzan
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Local Control ◽  
Neck Cancer ◽  
Tumor Volume

scholarly journals Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jens P.E. Schouten ◽  
Samantha Noteboom ◽  
Roland M. Martens ◽  
Steven W. Mes ◽  
C. René Leemans ◽  
...  
Keyword(s):  
Head And Neck ◽  
Treatment Planning ◽  
Primary Tumor ◽  
Tumor Volume ◽  
Automatic Segmentation ◽  
Neck Region ◽  
Primary Tumors ◽  
Head And Neck Region ◽  
Primary Tumor Volume ◽  
Mri Sequences

Abstract Background  Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.

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