Kamal Jnawali1, Bhargava Chinni2, Vikram Dogra2, Navalgund Rao3. 1. Rochester Institute of Technology, Rochester, NY, USA. kj5500@rit.edu. 2. University of Rochester, Rochester, NY, USA. 3. Rochester Institute of Technology, Rochester, NY, USA.
Abstract
PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tissue-stained glass slide under a microscope. METHOD: Multispectral photoacoustic (MPA) specimen imaging has proven successful in differentiating photoacoustic (PA) signal characteristics between a histopathology-defined cancer region and normal tissue. This is mainly due to its ability to efficiently map oxyhemoglobin and deoxyhemoglobin contents from MPA images and key features for cancer detection. A fully automated deep learning algorithm is purposed, which learns to detect the presence of malignant tissue in freshly excised ex vivo human thyroid and prostate tissue specimens using the three-dimensional MPA dataset. The proposed automated deep learning model consisted of the convolutional neural network architecture, which extracts spatially colocated features, and a softmax function, which detects thyroid and prostate cancer tissue at once. This is one of the first deep learning models, to the best of our knowledge, to detect the presence of cancer in excised thyroid and prostate tissue of humans at once based on PA imaging. RESULT: The area under the curve (AUC) was used as a metric to evaluate the predictive performance of the classifier. The proposed model detected the cancer tissue with the AUC of 0.96, which is very promising. CONCLUSION: This model is an improvement over the previous work using machine learning and deep learning algorithms. This model may have immediate application in cancer screening of the numerous sliced specimens that result from thyroidectomy and prostatectomy. Since the instrument that was used to capture the ex vivo PA images is now being developed for in vivo use, this model may also prove to be a starting point for in vivo PA image analysis for cancer diagnosis.
PURPOSE: In the case of multispecimen study to locate cancer regions, such as in thyroidectomy and prostatectomy, a significant labor-intensive processing is required at a high cost. Pathology diagnosis is usually done by a pathologist observing tissue-stained glass slide under a microscope. METHOD: Multispectral photoacoustic (MPA) specimen imaging has proven successful in differentiating photoacoustic (PA) signal characteristics between a histopathology-defined cancer region and normal tissue. This is mainly due to its ability to efficiently map oxyhemoglobin and deoxyhemoglobin contents from MPA images and key features for cancer detection. A fully automated deep learning algorithm is purposed, which learns to detect the presence of malignant tissue in freshly excised ex vivo human thyroid and prostate tissue specimens using the three-dimensional MPA dataset. The proposed automated deep learning model consisted of the convolutional neural network architecture, which extracts spatially colocated features, and a softmax function, which detects thyroid and prostate cancer tissue at once. This is one of the first deep learning models, to the best of our knowledge, to detect the presence of cancer in excised thyroid and prostate tissue of humans at once based on PA imaging. RESULT: The area under the curve (AUC) was used as a metric to evaluate the predictive performance of the classifier. The proposed model detected the cancer tissue with the AUC of 0.96, which is very promising. CONCLUSION: This model is an improvement over the previous work using machine learning and deep learning algorithms. This model may have immediate application in cancer screening of the numerous sliced specimens that result from thyroidectomy and prostatectomy. Since the instrument that was used to capture the ex vivo PA images is now being developed for in vivo use, this model may also prove to be a starting point for in vivo PA image analysis for cancer diagnosis.
Entities:
Keywords:
3D CNN; Cancer tissue detection; Classification; Computer-aided diagnosis; Deep learning; Machine learning; Prostate; Thyroid; Transfer learning
Authors: Vikram S Dogra; Bhargava K Chinni; Keerthi S Valluru; Jacob Moalem; Ellen J Giampoli; Katie Evans; Navalgund A Rao Journal: AJR Am J Roentgenol Date: 2014-06 Impact factor: 3.959
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