Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Müller, Susan; Wang, Xu; Chen, Amy Y.; Chen, Zhuo Georgia; Fei, Baowei

doi:10.1002/jbio.201700078

Cited by 32 publications

(28 citation statements)

References 41 publications

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“…By increasing the block size to 10×10 pixels, we reach a higher sensitivity and (E5-1650v4, 3.60 GHz), versus 6 hours and 1 minute (≈ 900.000 data samples) when the 6×6-pixel block-based processing is adopted. Lastly, Lu et al 7 apply the block-based approach for squamous displasia detection on 34 mice, reaching an AUC of 86%, sensitivity of 79%, and specificity of 79%, which are outperformed by our system on human data when employing linear SVM.…”

Section: Resultsmentioning

confidence: 76%

“…The block-based approach is a powerful solution for increasing the robustness to the spectral noise, while reducing the redundancy between the neighboring pixels and decreasing the computation load. 7 For each patient, the ground truth is extracted from the corresponding annotated H&E section, outlined by an experienced pathologist, and registered with the RGB image of the specimen by selecting control points and using a non-rigid registration algorithm. Figure 2 shows an example of (a) the labeled pathology slide, (b) ground truth registered with the RGB image and (c) the segmented ground-truth mask, used for block labeling.…”

Section: Feature Extractionmentioning

confidence: 99%

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Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Manni

Sommen

Zinger

et al. 2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

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de With, "Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging," ABSTRACT Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machinelearning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.

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Section: Resultsmentioning

confidence: 76%

Section: Feature Extractionmentioning

confidence: 99%

Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Manni

Sommen

Zinger

et al. 2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

View full text Add to dashboard Cite

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“…The histological images serve as the ground truth for the experiment, as shown in Figure 2, but registration is necessary to create gold-standard masks for HSI. 13–15 …”

Section: Methodsmentioning

confidence: 99%

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Halicek

Little

Wang

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

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One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.

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“…7,9,11,12 The hypercube contains 91 spectral bands, ranging from 450 to 900 nm with a 5 nm spectral sampling interval.…”

Section: Methodsmentioning

confidence: 99%

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Halicek

Little

Wang

et al. 2018

Optical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018

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Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

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Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis

Cited by 32 publications

References 41 publications

Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

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