Impact of pretreatment second look 18FDG-PET/CT on stage and treatment changes in head and neck cancer

Eliçin, Olgun; Vollnberg, Bernd; Shelan, Mohamed; Riggenbach, Elena; Bojaxhiu, Beat; Mathier, Etienne; Giger, Roland; Aebersold, Daniel M.; Klaeser, Bernd

doi:10.1016/j.ctro.2021.08.007

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…Tissue changes from previous local therapies strongly impede target volume delineation in recurrent head and neck cancer. Combining multi-modality imaging including CT, diffusionweighted MRI and positron emission tomography (PET)/ CT can be crucial to mitigate uncertainties in contouring [32,33]. One patient with type E local failure developed tumor recurrence within 5 mm of the CTV.…”

Section: Discussionmentioning

confidence: 99%

Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial

et al. 2022

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Background Carbon ion radiotherapy (CIRT) yields biophysical advantages compared to photons but randomized studies for the reirradiation setting are pending. The aim of the current project was to evaluate potential clinical benefits and drawbacks of CIRT compared to volumetric modulated arc therapy (VMAT) in recurrent head and neck cancer. Methods Dose-volume parameters and local failure patterns of CIRT compared to VMAT were evaluate in 16 patients from the randomized CARE trial on head and neck cancer reirradiation. Results Despite an increased target dose, CIRT resulted in significantly reduced organ at risk (OAR) dose across all patients (− 8.7% Dmean). The dose-volume benefits were most pronounced in the brainstem (− 20.7% Dmax) and the optic chiasma (− 13.0% Dmax). The most frequent local failure was type E (extraneous; 50%), followed type B (peripheral; 33%) and type A (central; 17%). In one patient with type A biological and/or dosimetric failure after CIRT, mMKM dose recalculation revealed reduced target coverage. Conclusions CIRT resulted in highly improved critical OAR sparing compared to VMAT across all head and neck cancer reirradiation scenarios despite an increased prescription dose. Local failure pattern analysis revealed further potential CIRT specific clinical benefits and potential pitfalls with regard to image-guidance and biological dose-optimization.

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Section: Discussionmentioning

confidence: 99%

Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial

et al. 2022

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“…The radiological lymph node metastasis (RLNM) was determined in MRI by two maxillofacial surgeons (L.Z.N. and Y.L., with more than 10 years of experience) blinded to the patients' clinical outcomes, according to the radiological diagnostic criteria reported by Yoon et al 15 The radiological criteria had been reported for the diagnosis of CLNM in patients with head and neck squamous cell carcinoma (HNSCC) 16,17 . The detailed diagnostic criteria for RLNM were presented in Data S1, Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…and Y.L., with more than 10 years of experience) blinded to the patients' clinical outcomes, according to the radiological diagnostic criteria reported by Yoon et al 15 The radiological criteria had been reported for the diagnosis of CLNM in patients with head and neck squamous cell carcinoma (HNSCC). 16,17 The detailed diagnostic criteria for RLNM were presented in Data S1, Supporting Information. Preoperative clinical T-staging was determined on the basis of the 8th edition of American Joint Committee on Cancer (AJCC) guidelines.…”

Section: Mri With Obvious Motion Artifacts and Metal Artifacts;mentioning

confidence: 99%

The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients

Liu,

Zhang,

Xiong

et al. 2023

Head & Neck

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BackgroundThe purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients.MethodsA total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1‐weighted images (T1WI) and FS‐T2WI (fat‐suppressed T2‐weighted images), group II consisted of patients with T1WI, FS‐T2WI, and contrast enhanced MRI (CE‐MRI), group III consisted of patients with T1WI, FS‐T2WI, and T2‐weighted images (T2WI), group IV consisted of patients with T1WI, FS‐T2WI, CE‐MRI, and T2WI, group V consisted of patients with T1WI, FS‐T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS‐T2WI, CE‐MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group.ResultsThe machine learning model in group IV including T1WI, FS‐T2WI, T2WI, and CE‐MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE‐MRI performed better than the models without CE‐MRI(I vs. II, III vs. IV, V vs. VI).ConclusionsThe radiomics machine learning models based on CE‐MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.

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Impact of pretreatment second look 18FDG-PET/CT on stage and treatment changes in head and neck cancer

Cited by 2 publications

References 27 publications

Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial

Ways to unravel the clinical potential of carbon ions for head and neck cancer reirradiation: dosimetric comparison and local failure pattern analysis as part of the prospective randomized CARE trial

The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients

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