Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images

Fujima, Noriyuki; Arasa, Vanessa Carlota Andreu; Meibom, Sara K.; Mercier, Gustavo; Truong, Minh Tam; Hirata, Kenji; Yasuda, Kôichi; Kano, Satoshi; Homma, Akihiro; Kudo, Kohsuke; Sakai, Osamu

doi:10.1186/s12885-021-08599-6

Cited by 16 publications

(12 citation statements)

References 28 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%

“…15 Recently, CNNs have been successfully applied in image classification tasks 16,17 and showed the potential of predicting complications and prognostic outcomes in HNC. [18][19][20] For OPSCC specifically, Cheng et al 21 and Fujima et al 22 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.…”

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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“…92 It is possible that AI techniques could aid risk stratification following pre-treatment 18 F FDG PET-CT in future. 93 …”

Section: Pre-treatment Structural Imaging With Ct and Mrimentioning

confidence: 99%

Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment

Touska

Connor

2022

The British Journal of Radiology

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The clinical behaviour and outcomes of patients with oropharyngeal cancer (OPC) may be dichotomised according to their association with human papilloma virus (HPV) infection. Patients with HPV-associated disease (HPV +OPC) have a distinct demographic profile, clinical phenotype and demonstrate considerably better responses to chemo-radiotherapy. This has led to a reappraisal of staging and treatment strategies for HPV +OPC, which are underpinned by radiological data. Structural modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI) can provide accurate staging information. These can be combined with ultrasound-guided tissue sampling and functional techniques (such as diffusion-weighted MRI and 18F-FDG PET-CT) to monitor response to treatment, derive prognostic information, and to identify individuals who might benefit from intensification or deintensification strategies. Furthermore, advanced MRI techniques, such as intravoxel incoherent motion and perfusion MRI as well as application of artificial intelligence and radiomic techniques, have shown promise in treatment response monitoring and prognostication. The following review will consider the contemporary role and knowledge on imaging in HPV +OPC.

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“…However, its clinical application is restricted due to its dependence on manual segmentation and handcrafted features [9]. Deep learning-based methods includes algorithms and techniques that identify more complex patterns than radiomics in large image data sets without handcrafted feature extraction, and they have been employed in various medical image fields [10][11][12] as well as H&N cancer outcome prediction [13][14][15][16]. In our method, we select Autoencoders as the basic architecture for image feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Guo

Biase

et al. 2022

Lecture Notes in Computer Science

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Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high-or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.

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Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images

Cited by 16 publications

References 28 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

Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment

Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

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