Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients

Fei, Baowei; Lu, Guolan; Wang, Xu; Zhang, Hongzheng; Little, James V.; Patel, Mihir R.; Griffith, Christopher C.; El-Diery, Mark W.; Chen, Amy Y.

doi:10.1117/1.jbo.22.8.086009

Cited by 103 publications

(88 citation statements)

References 44 publications

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“…Panasyuk et al were one of the first to demonstrate that HSI may be used to guide surgery in vivo by using HSI to distinguish tumour tissue from normal breast and other tissues in an experimental rat breast tumour model . Others have also evaluated the effectiveness of HSI to examine tumour resection margins in ex vivo surgical specimens in the operating room including patients undergoing head and neck cancer , colon , and skin cancer surgery and in vivo fluorescence‐based HSI systems have been reported for guiding urological and brain tumour surgery .…”

Section: Discussionmentioning

confidence: 99%

Intraoperative multispectral and hyperspectral label‐free imaging: A systematic review of in vivo clinical studies

Shapey

Xie

Nabavi

et al. 2019

Journal of Biophotonics

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Multispectral and hyperspectral imaging (HSI) are emerging optical imaging techniques with the potential to transform the way surgery is performed but it is not clear whether current systems are capable of delivering real-time tissue characterization and surgical guidance. We conducted a systematic review of surgical in vivo label-free multispectral and HSI systems that have been assessed intraoperatively in adult patients, published over a 10-year period to May 2018. We analysed 13 studies including 7 different HSI systems. Current in-vivo HSI systems generate an intraoperative tissue oxygenation map or enable tumour detection. Intraoperative tissue oxygenation measurements may help to predict those patients at risk of postoperative complications and in-vivo intraoperative tissue characterization may be performed with high specificity and sensitivity. All systems utilized a line-scanning or wavelength-scanning method but the spectral range and number of spectral bands employed varied significantly between studies and according to the system's clinical aim. The time to acquire a hyperspectral cube dataset ranged between 5 and 30 seconds. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of intraoperative in-vivo label-free HSI but further work is needed to fully integrate it into the current surgical workflow.

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Section: Discussionmentioning

confidence: 99%

Intraoperative multispectral and hyperspectral label‐free imaging: A systematic review of in vivo clinical studies

Shapey

Xie

Nabavi

et al. 2019

Journal of Biophotonics

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“…7,8 Three tissue samples were collected from each patient: a sample of the tumor, a normal tissue sample, and a sample at the tumor-normal interface. Tissues were kept cold and imaged fresh.…”

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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“…7,8 From these 21 patients, a total of 60 excised tissue samples were collected. Three tissue samples were collected from each patient, i.e ., a sample of the tumor, a normal tissue sample, and a sample at the tumor-normal interface.…”

Section: Methodsmentioning

confidence: 99%

“…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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Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients

Cited by 103 publications

References 44 publications

Intraoperative multispectral and hyperspectral label‐free imaging: A systematic review of in vivo clinical studies

Intraoperative multispectral and hyperspectral label‐free imaging: A systematic review of in vivo clinical studies

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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