Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma

Wahid, Kareem A.; He, Renjie; Dede, Cem; Mohamed, Abdallah S.R.; Abobakr, Moamen; Dijk, Lisanne V. van; Fuller, Clifton D.; Naser, Mohamed A.

doi:10.1007/978-3-030-98253-9_28

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…MLL2 models trained by using image data only showed better prediction performance for most endpoints than traditional radiomics models in both internal and external test sets. In other relevant studies, CNNs were already used to acquire highly representative image features from PET-with/without CT-images, which showed good prediction ability for OS, 21 local failure, 22 and PFS 25,45 of OPSCC. Pang et al proposed an advanced combination of training loss with oversampling to train a 3D ResNet18 based on pre-treatment CT and GTV, which achieved the state-of -art AUCs of 0.91, 0.78, and 0.70 for DMFS, LRC, and OS prediction in HNC patients, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the winner in HECKTOR 2021 challenge 23 used FDG-PET/CT images, GTVt contours, and clinical parameters together to build a DenseNet 24 for (progression-free survival) PFS prediction. 25 Multi-label learning is a technique that predicts all the relevant labels for a given example by exploiting the label correlations and the input feature information. 26 Its advantage over single-label learning is that it can capture the correlations between different labels and exploit them for better prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Guo

Zhai

et al. 2023

Medical Physics

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BackgroundPersonalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high‐risk patients who may be suitable to receive de‐escalation or intensified treatment approaches.PurposeTo develop a deep learning (DL)‐based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).MethodsTwo patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre‐treatment CT‐scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2‐year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis‐free survival (DMFS), disease‐specific survival (DSS), overall survival (OS), and disease‐free survival (DFS). We proposed DL outcome prediction models with the multi‐label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT‐scans.ResultsThe multi‐label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2‐year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2‐year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low‐risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set.ConclusionMLL models demonstrated better discriminative ability for all 2‐year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Guo

Zhai

et al. 2023

Medical Physics

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“…For example, while delineation of tumor volumes is both imperative and time-consuming, researchers such as Huang et al in 2018 [57] and Moe et al in 2021 [58] have utilized PET/CT-based convolutional neural networks (CNNs) to perform high-quality and precise automated tumor delineations. PET/CT CNNs have also demonstrated utility in improving patient prognostication [59,60] and predicting individual response to chemotherapy [61]. The ability of AI tools to improve both the speed and quality of diagnostic interpretations maximizes the utility of PET/CT for both patients and providers and will continue to do so as advances in AI techniques continue to be made.…”

Section: Recent Advances Conclusion and Future Directionsmentioning

confidence: 99%

Role of PET/CT in Oropharyngeal Cancers

Avery

Joshi

Mehra

et al. 2023

Cancers

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Oropharyngeal squamous cell carcinoma (OPSCC) comprises cancers of the tonsils, tongue base, soft palate, and uvula. The staging of oropharyngeal cancers varies depending upon the presence or absence of human papillomavirus (HPV)-directed pathogenesis. The incidence of HPV-associated oropharyngeal cancer (HPV + OPSCC) is expected to continue to rise over the coming decades. PET/CT is a useful modality for the diagnosis, staging, and follow up of patients with oropharyngeal cancers undergoing treatment and surveillance.

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“…Deep learning (DL) has found wide success in a variety of domains for RT-related medical imaging applications such as target and OAR segmentation ( 6 – 11 ) and outcome prediction ( 12 , 13 ). One less routinely studied domain is synthetic image generation, i.e., mapping an input image to an output image.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy

Wahid

El-Habashy

et al. 2022

Front. Oncol.

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BackgroundQuick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images.MethodsWe used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and 6mMRI as output. Eighteen image sets were used to test model performance. Similarity metrics, including the mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were calculated between normalized synthetic 6mMRI and ground-truth 6mMRI for all test cases. In addition, a previously trained OAR DL auto-segmentation model was used to segment the right parotid gland, left parotid gland, and mandible on all test case images. Dice similarity coefficients (DSC) were calculated between 2mMRI and either ground-truth 6mMRI or synthetic 6mMRI for each OAR; two one-sided t-tests were applied between the ground-truth and synthetic 6mMRI to determine equivalence. Finally, a visual Turing test using paired ground-truth and synthetic 6mMRI was performed using three clinician observers; the percentage of images that were correctly identified was compared to random chance using proportion equivalence tests.ResultsThe median similarity metrics across the whole images were 0.19, 0.93, and 33.14 for MSE, SSIM, and PSNR, respectively. The median of DSCs comparing ground-truth vs. synthetic 6mMRI auto-segmented OARs were 0.86 vs. 0.85, 0.84 vs. 0.84, and 0.82 vs. 0.85 for the right parotid gland, left parotid gland, and mandible, respectively (equivalence p<0.05 for all OARs). The percent of images correctly identified was equivalent to chance (p<0.05 for all observers).ConclusionsUsing 2mMRI inputs, we demonstrate that DL-generated synthetic 6mMRI outputs have high similarity to ground-truth 6mMRI, but further improvements can be made. Our study facilitates the clinical incorporation of synthetic MRI in MRI-guided radiotherapy.

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Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma

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Cited by 7 publications

References 19 publications

CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

CT‐based deep multi‐label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Role of PET/CT in Oropharyngeal Cancers

Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy

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