Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N=224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N=101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed.

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PO-0967 Prediction of treatment outcome for head and neck cancers using radiomics of PET/CT images

Radiotherapy and Oncology ◽

10.1016/s0167-8140(19)31387-8 ◽

2019 ◽

Vol 133 ◽

pp. S526

Author(s):

A. Rosvoll Groendahl ◽

A.D. Midtfjord ◽

G.S. Elvatun Rakh Langberg ◽

O. Tomic ◽

U.G. Indahl ◽

...

Keyword(s):

Treatment Outcome ◽

Head And Neck ◽

Ct Images ◽

Head And Neck Cancers ◽

Pet Ct

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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

10.1101/2021.10.14.21264958 ◽

2021 ◽

Author(s):

Kareem A. Wahid ◽

Renjie He ◽

Cem Dede ◽

Abdallah Sherif Radwan Mohamed ◽

Moamen Abobakr Abdelaal ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Deep Learning ◽

Clinical Data ◽

Cross Validation ◽

External Validation ◽

Ct Images ◽

Tumor Segmentation ◽

Learning Framework ◽

Pet Ct ◽

Additional Input

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC patients. Through internal validation (10-fold cross-validation) based on a large set of training data provided by the 2021 HECKTOR Challenge, we achieve a mean C-index of 0.855 +- 0.060 and 0.650 +- 0.074 when observed events are and are not included in the C-index calculation, respectively. Ensemble approaches applied to cross-validation folds yield C-index values up to 0.698 in the independent test set (external validation). Importantly, the value of the added segmentation mask is underscored in both internal and external validation by an improvement of the C-index when compared to models that do not utilize the segmentation mask. These promising results highlight the utility of including segmentation masks as additional input channels in deep learning pipelines for clinical outcome prediction in HNSCC.

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Fully Automated Delineation of Gross Tumor Volume for Head and Neck Cancer on PET-CT Using Deep Learning: A Dual-Center Study

Contrast Media & Molecular Imaging ◽

10.1155/2018/8923028 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 14

Author(s):

Bin Huang ◽

Zhewei Chen ◽

Po-Man Wu ◽

Yufeng Ye ◽

Shi-Ting Feng ◽

...

Keyword(s):

Head And Neck Cancer ◽

Deep Learning ◽

Head And Neck ◽

Neck Cancer ◽

Gold Standard ◽

Gross Tumor Volume ◽

Tumor Volume ◽

Ct Images ◽

Dice Similarity Coefficient ◽

Pet Ct

Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

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