Efficacy of FDG-PET for defining gross tumor volume of head and neck cancer

Kajitani, C.; Asakawa, Isao; Uto, Fumiaki; Katayama, E.; Inoue, Kohei; Tamamoto, Tetsuro; Shirone, Norihisa; Okamoto, Hideyuki; Kirita, Tadaaki; Hasegawa, Masatoshi

doi:10.1093/jrr/rrs131

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…Early prospective studies of PET in patients being considered for primary radiation therapy demonstrated new information over that included in the anatomic imaging alone in a high fraction of cases that resulted in alterations in therapy 7 , 8 . A recent retrospective study found that PET provided greater sensitivity than CT in identifying the GTV of the primary tumour in patients with squamous cell carcinomas of the head and neck, and that determination of the GTV combining data from PET and CT was superior to GTV based upon contrast-enhanced CT alone 9 . The use of PET data can also conceivably decrease the observed large variation in target volume delineation in radiation treatment planning by more easily distinguishing between tumour and non-malignant soft tissue 10 …”

Section: Discussionmentioning

confidence: 99%

Overall and disease-free survival outcomes of patients receiving intensity-modulated radiation therapy (IMRT) with PET-CT-based planning for cancers of the head and neck

Franc

DeLemos²,

Jones

2015

J Radiother Pract

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IntroductionCombined modality treatment regimens have provided modest gains in locoregional control rates of cancers of the head and neck (HNC), and intensity-modulated radiation therapy (IMRT) has gained widespread use. The methodology for determining contours of the gross tumour volume (GTV) in the radiation treatment plan is often based on combined anatomic and metabolic data from positron emission tomography–computed tomography (PET-CT). This study aimed to retrospectively evaluate the overall survival and disease-free survival outcomes of patients with HNC who received definitive IMRT with or without chemotherapy, planned with PET-CT.Materials and MethodsA total of 1,200 patients underwent treatment for HNC during the study period, from 1 January 2002 to 31 December 2010. Of those, 261 cases had evaluable data that met the inclusion criteria for the study. The incidence and timing of locoregional recurrence, distant metastatic disease, new primary malignancies and death were evaluated retrospectively. Overall and disease-free survival (survival to time of first recurrence) were determined by the life table method. Incidence of distance metastatic disease and additional cancers were also studied.ResultsMedian follow-up from treatment initiation was 26·4 months (range 1·2–84·7 months). Overall survival and disease-free survival rates were 0·883 and 0·791, respectively, at 1 year; 0·793 and 0·688, respectively, at 2 years; and 0·732 and 0·619, respectively, at 3 years. The cumulative risk of recurrence was 22·6% at 3 years following definitive IMRT and the median time to recurrence was 345 days. There was an overall low incidence of distant metastatic disease (3·07%) and additional cancers (8·05%).ConclusionOverall and disease-free survival outcomes of a large cohort of HNC patients treated with definitive IMRT radiotherapy following treatment planning with PET-CT shows a similar high level of disease control and mortality rate as previously published outcome studies of shorter terms and/or smaller numbers of patients.

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Section: Discussionmentioning

confidence: 99%

Overall and disease-free survival outcomes of patients receiving intensity-modulated radiation therapy (IMRT) with PET-CT-based planning for cancers of the head and neck

Franc

DeLemos²,

Jones

2015

J Radiother Pract

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“…Despite differences across imaging modalities in the above studies, the median or mean agreement between CNNs using multimodality input and the expert's ground truth is above 70%. Previous studies conclude that there are considerable interobserver variations in manual HNC target volume delineations [4][5][6][7]17]. In Bird et al [17], the Dice agreement between five clinicians (three radiation oncologists and two radiologists) was only 56% when delineating the GTV in CT images.…”

Section: Comparison To Previous Workmentioning

confidence: 97%

“…Radiotherapy (RT) with concurrent chemotherapy is the preferred curative treatment option for inoperable head and using combined PET/CT instead of CT [4][5][6][7]. Despite this, considerable interobserver variations still occur.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

Moe

Groendahl

Tomić

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed the applicability of convolutional neural networks (CNNs) for fully automatic delineation of the GTV from FDG-PET/CT images of patients with head and neck cancer (HNC). CNN models were compared to manual GTV delineations made by experienced specialists. New structure-based performance metrics were introduced to enable in-depth assessment of auto-delineation of multiple malignant structures in individual patients. Methods U-Net CNN models were trained and evaluated on images and manual GTV delineations from 197 HNC patients. The dataset was split into training, validation and test cohorts (n= 142, n = 15 and n = 40, respectively). The Dice score, surface distance metrics and the new structure-based metrics were used for model evaluation. Additionally, auto-delineations were manually assessed by an oncologist for 15 randomly selected patients in the test cohort. Results The mean Dice scores of the auto-delineations were 55%, 69% and 71% for the CT-based, PET-based and PET/CT-based CNN models, respectively. The PET signal was essential for delineating all structures. Models based on PET/CT images identified 86% of the true GTV structures, whereas models built solely on CT images identified only 55% of the true structures. The oncologist reported very high-quality auto-delineations for 14 out of the 15 randomly selected patients. Conclusions CNNs provided high-quality auto-delineations for HNC using multimodality PET/CT. The introduced structure-wise evaluation metrics provided valuable information on CNN model strengths and weaknesses for multi-structure auto-delineation.

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“…In patients with oropharyngeal cancer and tongue cancer, the corresponding sensitivities were 71% and 63%, respectively. 32 Schinagl and colleagues 33 studied 5 different segmentation methods for FDG-PET target volume definition in 78 head and neck cancer patients, including visual determination, applying a fixed SUV threshold of 2.5, using a fixed threshold of 40% and 50% of the maximum signal intensity, and applying an adaptive threshold based on the signal-to-background ratio. The GTVs were analyzed comparatively.…”

Section: Effect Of Pet/computed Tomography On Gross Tumor Volume Defimentioning

confidence: 99%

Radiotherapy Planning

Truong

Kovalchuk

2015

PET Clinics

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Incorporation of PET/computed tomography (CT) in radiotherapy planning plays a critical role in assisting gross tumor volume delineation for radiotherapy planning and delivery. As radiotherapy techniques evolve to become more conformal with the increasing use of intensity-modulated radiotherapy and stereotactic body radiotherapy, whereby sharp dose gradients exist between the target and adjacent normal tissue, accurate contouring of tumor targets is vital for the success of radiotherapy to achieve cure and locoregional control. This article outlines the integration of PET/CT into radiotherapy planning for head and neck, lung, and other solid tumors.

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Efficacy of FDG-PET for defining gross tumor volume of head and neck cancer

Cited by 9 publications

References 27 publications

Overall and disease-free survival outcomes of patients receiving intensity-modulated radiation therapy (IMRT) with PET-CT-based planning for cancers of the head and neck

Overall and disease-free survival outcomes of patients receiving intensity-modulated radiation therapy (IMRT) with PET-CT-based planning for cancers of the head and neck

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

Radiotherapy Planning

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