Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Wang, Xi; Li, Binbin

doi:10.3389/fgene.2021.624820

Cited by 20 publications

(12 citation statements)

References 100 publications

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“…One often discussed future trend was the capability of ML algorithms to combine multiple data sources [ 21 , 71 , 81 , 85 , 86 , 95 – 97 ], including biological and biochemical data, genomic information, socio-demographics, life-style risks, linkages to electronic health records, and other clinical information [ 9 , 22 , 23 , 34 , 68 ]. Multi-data ML (or multi-omic ML) is expected to inform and improve clinical workflows [ 21 , 22 , 36 , 42 , 68 , 98 , 99 ].…”

Section: Resultsmentioning

confidence: 99%

“…Mobile and wearables devices and sensors generate large volumes of yet-untapped health information, and are expected to democratize and contribute significantly to multi-omic ML, reducing costs and accessibility [ 35 , 57 , 63 , 69 , 96 , 100 ]. Yet, the actual application of multi-omic ML remains limited, with first efforts being reported in the field of oncology and cardiac imaging (e.g., combining echocardiographic and other clinical data for heart failure diagnosis; combining radiological tumour data with an array of physiological and genomic data) [ 69 , 95 ].…”

Section: Resultsmentioning

confidence: 99%

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Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

et al. 2023

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Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

et al. 2023

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“…Head and Neck (H&N) cancer is a collective term used to describe malignant tumors that develop in the mouth, nose, throat, or other head and neck areas. Early prediction and accurate diagnosis of the patient's survival risk (prognosis) can lower the mortality rate to 70% [27] of the H&N cancer patient. Furthermore, an accurate prognosis helps doctors better plan the treatments of patients [19].…”

Section: Introductionmentioning

confidence: 99%

TMSS: An End-to-End Transformer-Based Multimodal Network for Segmentation and Survival Prediction

Saeed

Sobirov

Majzoub

et al. 2022

Lecture Notes in Computer Science

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When oncologists estimate cancer patient survival, they rely on multimodal data. Even though some multimodal deep learning methods have been proposed in the literature, the majority rely on having two or more independent networks that share knowledge at a later stage in the overall model. On the other hand, oncologists do not do this in their analysis but rather fuse the information in their brain from multiple sources such as medical images and patient history. This work proposes a deep learning method that mimics oncologists' analytical behavior when quantifying cancer and estimating patient survival. We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival predication that leverages the superiority of transformers that lies in their abilities to handle different modalities. The model was trained and validated for segmentation and prognosis tasks on the training dataset from the HEad & NeCK TumOR segmentation and the outcome prediction in PET/CT images challenge (HECKTOR). We show that the proposed prognostic model significantly outperforms state-of-the-art methods with a concordance index of 0.763 ± 0.14 while achieving a comparable dice score of 0.772 ± 0.030 to a standalone segmentation model. The code is publicly available at https://t.ly/V-_W.

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Section: Introductionmentioning

confidence: 99%

“…Each year, 1.3 million people are diagnosed with head and neck (H&N) cancer worldwide on average [16]. However, the mortality rate can be lowered to 70% with early detection of H&N tumor [16]. Therefore, diagnosis and prognosis are the two primary practices involved in most medical treatment pipelines, especially for cancer-related diseases.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

Saeed¹,

Majzoub²,

Sobirov³

et al. 2022

Preprint

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Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Traditional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diagnosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient medical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and has the potential to create more accurate solutions. The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources. We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors using patients' clinical and imaging (CT and PET) data. Features from CT and PET scans are fused and then combined with patients' electronic health records for the prediction. The proposed model is trained and tested on 224 and 101 patient records respectively. Experimental results show that our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge. The full implementation based on PyTorch is available on https://github.com/numanai/BioMedIA-Hecktor2021.

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Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature

Cited by 20 publications

References 100 publications

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging

TMSS: An End-to-End Transformer-Based Multimodal Network for Segmentation and Survival Prediction

An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

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