Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

Bos, Paula; Brekel, Michiel W. M. van den; Gouw, Zeno A.R.; Waktola, Selam; Aerts, Hugo J.W.L.; Beets‐Tan, Regina G. H.; Castelijns, Jonas A.; Jasperse, Bas

doi:10.1002/hed.26505

Cited by 36 publications

(61 citation statements)

References 34 publications

(67 reference statements)

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“…The multivariate regression model had good predictive performance (AUC = 0.87) and indicated that the most significant factors associated with HPV status were tumor subsite, alcohol intake, and D t of LN. In particular, tumors located in the tonsil, compared to those located in the base of the tongue, were associated with a higher risk of HPV positivity according to a recent investigation of Bos et al [44], while moderate or heavy alcoholic patients and increased D t values of lymphadenopathy were associated with a lower risk of HPV-positive OPSCC [45,46]. Some potential limitations of our study need to be acknowledged.…”

Section: Discussionmentioning

confidence: 64%

Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study

Piludu

Marzi

Gangemi

et al. 2021

JCM

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The aim of this paper is to define the pre-treatment radiological characteristics of oropharyngeal squamous cell carcinoma (OPSCC) using morphological and non-morphological magnetic resonance imaging (MRI), based on HPV status, in a single-institution cohort. In total, 100 patients affected by OPSCC were prospectively enrolled in the present study. All patients underwent 1.5T MR with standard sequences, including diffusion-weighted imaging with and intravoxel incoherent motion (IVIM-DWI) technique and a dynamic contrast-enhanced (DCE) MRI. For all patients, human papillomavirus (HPV) status was available. No statistically significant differences in the volume of primary tumors (PTs) and lymph nodes (LNs) were observed based on HPV status. When comparing the two patient groups, no significant differences were found for the PT radiologic characteristics (presence of well-defined borders, exophytic growth, ulceration, and necrosis) and LN morphology (solid/cystic/necrotic). Tumor subsite, smoking status, and alcohol intake significantly differed based on HPV status, as well as ADC and Dt values of both PTs and LNs. We detected no significant difference in DCE-MRI parameters by HPV status. Based on a multivariate logistic regression model, the combination of clinical factors, such as tumor subsite and alcohol habits, with the perfusion-free diffusion coefficient Dt of LNs, may help to accurately discriminate OPSCC by HPV status.

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Section: Discussionmentioning

confidence: 64%

Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study

Piludu

Marzi

Gangemi

et al. 2021

JCM

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“…There is an increasing interest in the literature about differences on the tumors depending on their HPV status. According to Bos et al [20], HPV positive tumors present on MRI post contrast with rounder shapes, lower maximum intensity values, and texture homogeneity. One strength of our work is that we include both HPV positive and HPV negative tumors in the training set, making the networks able to segment both subtypes of OPSCC.…”

Section: Discussionmentioning

confidence: 99%

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning

Outeiral

Bos

Al‐Mamgani

et al. 2021

Physics and Imaging in Radiation Oncology

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Background and purpose: Segmentation of oropharyngeal squamous cell carcinoma (OPSCC) is needed for radiotherapy planning. We aimed to segment the primary tumor for OPSCC on MRI using convolutional neural networks (CNNs). We investigated the effect of multiple MRI sequences as input and we proposed a semiautomatic approach for tumor segmentation that is expected to save time in the clinic. Materials and methods: We included 171 OPSCC patients retrospectively from 2010 until 2015. For all patients the following MRI sequences were available: T1-weighted, T2-weighted and 3D T1-weighted after gadolinium injection. We trained a 3D UNet using the entire images and images with reduced context, considering only information within clipboxes around the tumor. We compared the performance using different combinations of MRI sequences as input. Finally, a semi-automatic approach by two human observers defining clipboxes around the tumor was tested. Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The 3D UNet trained with full context and all sequences as input yielded a median Dice of 0.55, HD of 8.7 mm and MSD of 2.7 mm. Combining all MRI sequences was better than using single sequences. The semiautomatic approach with all sequences as input yielded significantly better performance (p < 0.001): a median Dice of 0.74, HD of 4.6 mm and MSD of 1.2 mm. Conclusion:Reducing the amount of context around the tumor and combining multiple MRI sequences improved the segmentation performance. A semi-automatic approach was accurate and clinically feasible.

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“…In future, computer-assistant methods can be explored and put into application. One type of research utilizes both MRI image features and clinical information such as smoking history, age and other factors to automatically predict the existence of HPV in patients suffering from oropharyngeal squamous cell carcinoma ( Bos et al, 2021 ). Relations between clinical characteristics and HPV status are also analyzed as the existence of HPV is closely related to cancer prognosis.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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Clinical variables and magnetic resonance imaging‐based radiomics predict human papillomavirus status of oropharyngeal cancer

Cited by 36 publications

References 34 publications

Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study

Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study

Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

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