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:10.1101/2021.10.14.21264958

Cited by 6 publications

(7 citation statements)

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“…The best result (0.694, rank 2) was obtained with the Image+Clinical consensus model. The team also participated in Task 3 were they used ground-truth mask as an additional input channel to the same network [58], achieving the first rank with C-index of 0.70.…”

Section: Results: Reporting Of Challenge Outcomementioning

confidence: 99%

“…Individual participants' papers reporting their methods and results were submitted to the challenge organizers. Reviews were organized by the organizers and the papers of the participants are published in the LNCS challenges proceedings [60,1,52,56,59,12,53,62,21,32,43,37,54,6,67,16,9,48,49,58,40,51,17,46,65,39,33,45,27]. When participating in multiple tasks, participants could submit one or multiple papers.…”

Section: Introduction: Research Contextmentioning

confidence: 99%

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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Andrearczyk

Oreiller

Jreige

et al. 2021

Lecture Notes in Computer Science

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This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects. equal contribution equal contribution

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Section: Results: Reporting Of Challenge Outcomementioning

confidence: 99%

Section: Introduction: Research Contextmentioning

confidence: 99%

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Andrearczyk

Oreiller

Jreige

et al. 2021

Lecture Notes in Computer Science

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“…proposed fully automated CNNs‐based models using (positron emission tomography) PET images for the prediction of OS and local tumor control, respectively. 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 …”

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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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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“…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)(7)(8)(9)(10)(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 High-Resolution MRI from Low-Resolution MRI for Use in Head and Neck Cancer Adaptive Radiotherapy

Wahid

El-Habashy

et al. 2022

Preprint

Self Cite

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Background: Quick, low contrast resolution magnetic resonance imaging (MRI) scans 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-resolution synthetic images from low-resolution images. Methods: We 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 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. Results: The 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 0.84 vs. 0.83, 0.82 vs. 0.82, and 0.80 vs. 0.83 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). Conclusions: Using 2mMRI inputs, we demonstrate that DL-generated synthetic 6mMRI outputs have high similarity to ground-truth 6mMRI. 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

Cited by 6 publications

References 10 publications

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

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

Deep-Learning-Based Generation of Synthetic High-Resolution MRI from Low-Resolution MRI for Use in Head and Neck Cancer Adaptive Radiotherapy

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