The Additional Value of Ultrafast DCE-MRI to DWI-MRI and 18F-FDG-PET to Detect Occult Primary Head and Neck Squamous Cell Carcinoma

Martens, Roland M.; Stappen, Ruud van der; Koopman, Thomas; Comans, Emile F.I.; Zwezerijnen, Gerben J.C.; Vergeer, Marije R.; Leemans, C. René; Bree, Remco de; Boellaard, Ronald; Castelijns, Jonas A.; Graaf, Pim de

doi:10.3390/cancers12102826

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Adding more structural (T2-weighted) and functional (i.e. diffusion weighted imaging and 3D ultrafast dynamic contrast enhanced) MRI sequences could potentially improve manual and automated tumor delineations [ 34 , 35 ], where it would also be interesting to evaluate the added value of each MRI sequence to the overall performance of a network. Furthermore, the used conventional MRI sequences all consisted of 2D images, which were interpolated to make it usable for our 2.5D approach.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…The combined use of ultrafast-DCE, DWI and FDG-PET-CT yielded the highest sensitivity of 95%. 22 In another study, 3-dimensional volumetric MRI technique THRIVE was more sensitive to finding clinically occult primary than conventional techniques. The sensitivity (72.9%) and accuracy (71.2%) of 3D THRIVE for detecting primary tumours was higher than those of SE T1-weighted MRI (49.2% and 53.4%) or CT (36.4% and 46.4%).…”

Section: Contrast-enhanced Computed Tomography and Magnetic Resonance...mentioning

confidence: 96%

“…In a prospective study of 29 patients with clinically occult primary tumours, the addition of ultrafast‐dynamic contrast enhanced (DCE) MRI improved detection of the primary tumour. The combined use of ultrafast‐DCE, DWI and FDG‐PET‐CT yielded the highest sensitivity of 95% 22 …”

Section: Accuracy Of Imaging Modalities In the Detection Of Hnsccupmentioning

confidence: 99%

The radiological unknown primary of the head and neck: Recommendations for imaging strategies based on a systematic review

Madani,

Arain,

Awad

2023

Clinical Otolaryngology

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ObjectivesTo develop recommendations for the radiological investigation of clinically occult primary cancer in the head and neck.Design and SettingIn accordance with PRISMA guidelines, a search was performed on Medline, Embase and Cochrane library databases to investigate the efficacy of ultrasound guided Fine Needle Aspiration (US FNAC), contrast enhanced CT (CECT), magnetic resonance imaging (MRI) and 18F‐fluorodeoxyglucose PET‐CT (18F‐FDG PET‐CT) in the investigation of head and neck squamous cell carcinoma from an unknown primary (HNSCCUP) presenting with a metastatic cervical lymph node (s). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool and SIGN 50 guidelines were used to assess the risk of bias and quality of the included studies.ParticipantsAdult patients presenting with metastatic cervical lymph nodes from a HNSCCUP.Main Outcome measuresUtility of different imaging modalities (PET‐CT, MRI, CE CT and US FNAC in the management of HNSCCUP).ResultsTwenty‐eight studies met inclusion criteria; these were meta‐analyses, systematic reviews, prospective and retrospective studies.ConclusionsThe optimal imaging strategy involves utilisation of various imaging modalities. US FNAC can provide the initial diagnosis and HPV status of the occult primary tumour. CECT and MRI detect up to 44% of occult tumours and guide management. FDG PET‐CT is the most sensitive imaging modality for the detection of CUP and should be performed prior to panendoscopy.

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“…Finally, dynamic contrast-enhanced MRI perfusion imaging can be of value in detecting and quantifying the tissue microvascularity, providing characterization for HNC. This technique may help in assessing the histological grade of some tumours [ 69 ] and can improve detection of unknown primary tumours in addition to DWI and 18 FDG-PET/CT in patients presenting with cervical SCC lymph node metastasis [ 70 ]. Similarly, MRI spectroscopy can be applied, providing a molecular breakdown of biochemical composition and evaluating the presence of specific metabolites.…”

Section: Quantitative Diagnosismentioning

confidence: 99%

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

et al. 2021

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The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.

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The Additional Value of Ultrafast DCE-MRI to DWI-MRI and 18F-FDG-PET to Detect Occult Primary Head and Neck Squamous Cell Carcinoma

Cited by 11 publications

References 24 publications

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

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

The radiological unknown primary of the head and neck: Recommendations for imaging strategies based on a systematic review

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

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