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3. Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence

Author

Jiang, Cheng, Bhattacharya, Abhishek, Linzey, Joseph, Joshi, Rushikesh S., Cha, Sung Jik, Srinivasan, Sudharsan, Alber, Daniel, Kondepudi, Akhil, Urias, Esteban, Pandian, Balaji, Al-Holou, Wajd, Sullivan, Steve, Thompson, B. Gregory, Heth, Jason, Freudiger, Chris, Khalsa, Siri, Pacione, Donato, Golfinos, John G., Camelo-Piragua, Sandra, Orringer, Daniel A., Lee, Honglak, and Hollon, Todd

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Background: Accurate diagnosis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative diagnosis can be challenging due to tumor diversity and lack of intraoperative pathology resources. Objective: To develop an independent and parallel intraoperative pathology workflow that can provide rapid and accurate skull base tumor diagnoses using label-free optical imaging and artificial intelligence. Method: We used a fiber laser-based, label-free, non-consumptive, high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of skull base tumor patients. SRH images were then used to train a convolutional neural network (CNN) model using three representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained CNN models were tested on a held-out, multicenter SRH dataset. Results: SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the three representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to identify tumor-normal margins and detect regions of microscopic tumor infiltration in whole-slide SRH images. Conclusion: SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making., Comment: Published as journal article

Published
2021
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5. SURG-07. DETECTION OF GLIOMA INFILTRATION AT THE TUMOR MARGIN USING QUANTITATIVE STIMULATED RAMAN SCATTERING MICROSCOPY

Author

Pekmezci, Melike, Morshed, Ramin, Chunduru, Pranathi, Pandian, Balaji, Young, Jacob, Villanueva-Meyer, Javier, Tihan, Tarik, Sloan, Emily, Aghi, Manish, Molinaro, Annette, Berger, Mitchel, and Hervey-Jumper, Shawn

Abstract

Abstract BACKGROUND Differentiating neoplastic tumor tissues from normal brain at the infiltrative tumor margin remains challenging. Stimulated Raman scattering microscopy (SRM) is a rapid, label-free technique that provides microscopic imaging of unprocessed tissues. However, it has never been studied as a tool to enhance extent of resection. METHODS In this single center study, patients with WHO II-IV gliomas undergoing surgical resection were included. Margin samples were taken with corresponding stereotactic coordinates. Each tumor margin sample was analyzed using (1) H&E, (2) SRM, and (3) immunohistochemical (IHC) stains for IDH1-R132H or p53. Specimens were imaged fresh with SRM, inked for orientation and placed in formalin for routine processing. Stained slides were scored by 3-neuro-pathologists using a semiquantitative 2-tiered scoring system for the presence or absence of residual tumor. SRM microscopy images were segmented into cellularity. The intraobserver measure of agreement between modalities for each pathologist was calculated using Cohen’s kappa. RESULTS A total of 31 patients and 179 margin specimens were acquired. Postoperative MRI confirmed that 169 (94%) of glioma margin sample coordinates were indeed at the tumor margins (10 of 179 were not true margins). All 10 samples which were not true margins were identified as having residual tumor by SRM. IHC semiquantitative scoring of margin specimen confirmed 72 of 128 samples (56%) had residual tumor. Similarly, H&E-scoring confirmed 82 of 169 samples (49%) and 82 of 167 samples (49%) by SRM-scoring had residual tumor. Intraobserver agreements between all 3 modalities confirmed agreement: IHC-SRM was near perfect (K-0.84, CI-0.750-0.94); IHC-H&E substantial, (K-0.67 CI-0.54-0.80); SRM-H&E agreement substantial (K-0.72 CI-0.62-0.83). Margin samples with residual tumor demonstrated higher tissue cellularity when compared with samples without tumor (p< 0.0001). CONCLUSIONS SRM allows for identification of residual microscopic disease at the infiltrative tumor margin and therefore may be a promising tool to enhance extent of resection.

Published
2020

8. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Author

Hollon, Todd C, Pandian, Balaji, Adapa, Arjun R, Urias, Esteban, Save, Akshay V, Khalsa, Siri Sahib S, Eichberg, Daniel G, D'Amico, Randy S, Farooq, Zia U, Lewis, Spencer, Petridis, Petros D, Marie, Tamara, Shah, Ashish H, Garton, Hugh JL, Maher, Cormac O, Heth, Jason A, McKean, Erin L, Sullivan, Stephen E, Hervey-Jumper, Shawn L, Patil, Parag G, Thompson, B Gregory, Sagher, Oren, McKhann, Guy M, Komotar, Ricardo J, Ivan, Michael E, Snuderl, Matija, Otten, Marc L, Johnson, Timothy D, Sisti, Michael B, Bruce, Jeffrey N, Muraszko, Karin M, Trautman, Jay, Freudiger, Christian W, Canoll, Peter, Lee, Honglak, Camelo-Piragua, Sandra, and Orringer, Daniel A

Subjects
Humans, Brain Neoplasms, Monitoring, Intraoperative, Spectrum Analysis, Raman, Probability, Algorithms, Computer Systems, Image Processing, Computer-Assisted, Clinical Trials as Topic, Deep Learning, Neural Networks, Computer, Monitoring, Intraoperative, Spectrum Analysis, Raman, Image Processing, Computer-Assisted, Neural Networks, Computer, Medical and Health Sciences, Immunology
Abstract

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

Published
2020

11. Confirmation and characterization of the first case of acetolactate synthase (ALS)‐inhibitor resistance in Japanese brome (Bromus japonicus) in the US.

Author

Adari, Manogna Devi, Pandian, Balaji Aravindhan, Gaines, Todd A., Prasad, PV Vara, and Jugulam, Mithila

Subjects
ACETOLACTATE synthase, HERBICIDE resistance, BROMEGRASSES, WHEAT, WINTER wheat, WEED control
Abstract

BACKGROUND: Japanese brome (Bromus japonicus Thumb.) is one of the problematic annual weeds in winter wheat (Triticum aestivum L.) and is generally controlled by acetolactate synthase (ALS) inhibitors. Repeated use of the ALS inhibitor propoxycarbazone‐Na resulted in the evolution of resistance to this herbicide in three B. japonicus populations, i.e., R1, R2, and R3 in Kansas (KS). However, the level of resistance and mechanism conferring resistance in these populations is unknown. The objectives of this research were to (i) evaluate the level of resistance to propoxycarbazone‐Na in R1, R2, and R3 in comparison with a known susceptible population (S1), (ii) investigate the mechanism of resistance involved in conferring ALS‐inhibitor resistance, and (iii) investigate the cross‐resistance to other ALS inhibitors. RESULTS: Dose–response (0 to 16x; x = 44 g ai ha−1 of propoxycarbazone‐Na) assay indicated 167, 125, and 667‐fold resistance in R1, R2 and R3 populations, respectively, compared to S1 population. ALS gene sequencing confirmed the mutations resulting in amino acid substitutions, i.e., Pro‐197‐Thr (R3, R1)/Ser (R2, R1) bestowing resistance to these ALS inhibitors. Such amino acid substitutions also showed differential cross‐resistance to sulfosulfuron, mesosulfuron‐methyl, pyroxsulam, and imazamox among resistant populations. Pretreatment with malathion (a cytochrome P450 enzyme‐inhibitor) followed by imazamox treatment suggested cross‐resistance to this herbicide possibly via metabolism only in R3 population. CONCLUSION: Overall, these results confirm the first case of target‐site based resistance to ALS inhibitors in B. japonicus in the US, highlighting the need for exploring herbicides with alternative modes of action to enhance weed control in winter wheat. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Data from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Author

Hollon, Todd C., primary, Lewis, Spencer, primary, Pandian, Balaji, primary, Niknafs, Yashar S., primary, Garrard, Mia R., primary, Garton, Hugh, primary, Maher, Cormac O., primary, McFadden, Kathryn, primary, Snuderl, Matija, primary, Lieberman, Andrew P., primary, Muraszko, Karin, primary, Camelo-Piragua, Sandra, primary, and Orringer, Daniel A., primary

Published
2023
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17. Table S2 from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Author

Hollon, Todd C., primary, Lewis, Spencer, primary, Pandian, Balaji, primary, Niknafs, Yashar S., primary, Garrard, Mia R., primary, Garton, Hugh, primary, Maher, Cormac O., primary, McFadden, Kathryn, primary, Snuderl, Matija, primary, Lieberman, Andrew P., primary, Muraszko, Karin, primary, Camelo-Piragua, Sandra, primary, and Orringer, Daniel A., primary

Published
2023
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18. Supplementary Figure 1 from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Author

Hollon, Todd C., primary, Lewis, Spencer, primary, Pandian, Balaji, primary, Niknafs, Yashar S., primary, Garrard, Mia R., primary, Garton, Hugh, primary, Maher, Cormac O., primary, McFadden, Kathryn, primary, Snuderl, Matija, primary, Lieberman, Andrew P., primary, Muraszko, Karin, primary, Camelo-Piragua, Sandra, primary, and Orringer, Daniel A., primary

Published
2023
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19. Supplementary Figure 2 from Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Author

Hollon, Todd C., primary, Lewis, Spencer, primary, Pandian, Balaji, primary, Niknafs, Yashar S., primary, Garrard, Mia R., primary, Garton, Hugh, primary, Maher, Cormac O., primary, McFadden, Kathryn, primary, Snuderl, Matija, primary, Lieberman, Andrew P., primary, Muraszko, Karin, primary, Camelo-Piragua, Sandra, primary, and Orringer, Daniel A., primary

Published
2023
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21. Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence

Author

Jiang, Cheng, primary, Bhattacharya, Abhishek, additional, Linzey, Joseph R., additional, Joshi, Rushikesh S., additional, Cha, Sung Jik, additional, Srinivasan, Sudharsan, additional, Alber, Daniel, additional, Kondepudi, Akhil, additional, Urias, Esteban, additional, Pandian, Balaji, additional, Al-Holou, Wajd N., additional, Sullivan, Stephen E., additional, Thompson, B. Gregory, additional, Heth, Jason A., additional, Freudiger, Christian W., additional, Khalsa, Siri Sahib S., additional, Pacione, Donato R., additional, Golfinos, John G., additional, Camelo-Piragua, Sandra, additional, Orringer, Daniel A., additional, Lee, Honglak, additional, and Hollon, Todd C., additional

Published
2022
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25. Use of the MyProstateScore Test to Rule Out Clinically Significant Cancer: Validation of a Straightforward Clinical Testing Approach

Author

Tosoian, Jeffrey J., primary, Trock, Bruce J., additional, Morgan, Todd M., additional, Salami, Simpa S., additional, Tomlins, Scott A., additional, Spratt, Daniel E., additional, Siddiqui, Javed, additional, Kunju, Lakshmi P., additional, Botbyl, Rachel, additional, Chopra, Zoey, additional, Pandian, Balaji, additional, Eyrich, Nicholas W., additional, Longton, Gary, additional, Zheng, Yingye, additional, Palapattu, Ganesh S., additional, Wei, John T., additional, Niknafs, Yashar S., additional, and Chinnaiyan, Arul M., additional

Published
2021
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26. Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence.

Author

Cheng Jiang, Bhattacharya, Abhishek, Linzey, Joseph R., Joshi, Rushikesh S., Sung Jik Cha, Srinivasan, Sudharsan, Alber, Daniel, Kondepudi, Akhil, Urias, Esteban, Pandian, Balaji, Al-Holou, Wajd N., Sullivan, Stephen E., Thompson, B. Gregory, Heth, Jason A., Freudiger, Christian W., Khalsa, Siri Sahib S., Pacione, Donato R., Golfinos, John G., Camelo-Piragua, Sandra, and Orringer, Daniel A.

Published
2022
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27. Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology

Author

Morshed, Ramin, primary, Chunduru, Pranathi, additional, Pandian, Balaji, additional, Young, Jacob, additional, Villanueva-Meyer, Javier, additional, Tihan, Tarik, additional, Sloan, Emily, additional, Aghi, Manish, additional, Molinaro, Annette, additional, Berger, Mitchel, additional, Hervey-Jumper, Shawn, additional, and Pekmezci, Melike, additional

Published
2020
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28. Characterization, Genetic Analyses, and Identification of QTLs Conferring Metabolic Resistance to a 4-Hydroxyphenylpyruvate Dioxygenase Inhibitor in Sorghum (Sorghum bicolor)

Author

Pandian, Balaji Aravindhan, primary, Varanasi, Aruna, additional, Vennapusa, Amaranatha R., additional, Sathishraj, Rajendran, additional, Lin, Guifang, additional, Zhao, Mingxia, additional, Tunnell, Madison, additional, Tesso, Tesfaye, additional, Liu, Sanzhen, additional, Prasad, P. V. Vara, additional, and Jugulam, Mithila, additional

Published
2020
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32. Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks

Author

Hollon, Todd C, primary, Pandian, Balaji, additional, Urias, Esteban, additional, Save, Akshay V, additional, Adapa, Arjun R, additional, Srinivasan, Sudharsan, additional, Jairath, Neil K, additional, Farooq, Zia, additional, Marie, Tamara, additional, Al-Holou, Wajd N, additional, Eddy, Karen, additional, Heth, Jason A, additional, Khalsa, Siri Sahib S, additional, Conway, Kyle, additional, Sagher, Oren, additional, Bruce, Jeffrey N, additional, Canoll, Peter, additional, Freudiger, Christian W, additional, Camelo-Piragua, Sandra, additional, Lee, Honglak, additional, and Orringer, Daniel A, additional

Published
2020
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34. Current status and prospects of herbicide‐resistant grain sorghum (Sorghum bicolor).

Author

Pandian, Balaji A, Sexton‐Bowser, Sarah, Prasad, PV Vara, and Jugulam, Mithila

Subjects
SORGHUM, WEEDS, WEED control, HERBICIDE resistance, CROP losses, GROWING season
Abstract

Grain sorghum is a versatile crop, which can thrive under limited water and other inputs. However, crop loss from weed infestation continues to be a major constraint in grain sorghum production. Particularly, post‐emergence grass weed control is a great challenge in grain sorghum due to the lack of herbicide options. Unlike in other major crops, such as maize or soybean, herbicide‐resistant sorghum technology that can facilitate weed control throughout crop growing season is not available to growers yet. The development of herbicide‐resistant sorghum can have potential to improve weed management, including post‐emergence grass weed control. One of the major concerns in the development of such technology in sorghum is escape of resistance traits into weedy relatives of sorghum (e.g. shattercane and johnsongrass). This review focuses on sources of herbicide resistance in sorghum, the status of the development of herbicide‐resistant sorghum technologies, overview of breeding methods, and limitations in the development of such sorghum technology as well as economic benefits for sorghum growers. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2022
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37. Near Real-Time Intraoperative Brain Tumor Diagnosis Using Stimulated Raman Histology and Deep Neural Networks

Author

Hollon, Todd C, primary, Pandian, Balaji, additional, Khalsa, Siri Sahib Singh, additional, D'Amico, Randy, additional, Sisti, Michael B, additional, Bruce, Jeffrey N, additional, Muraszko, Karin M, additional, Thompson, Gregory B, additional, Freudiger, Christian, additional, Hervey-Jumper, Shawn L, additional, McKhann, Guy M, additional, Camelo-Piragua, Sandra, additional, Canoll, Peter D, additional, Lee, Honglak, additional, and Orringer, Daniel A, additional

Published
2019
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38. Rapid, label-free detection of diffuse glioma recurrence using intraoperative stimulated Raman histology and deep neural networks.

Author

Hollon, Todd C, Pandian, Balaji, Urias, Esteban, Save, Akshay V, Adapa, Arjun R, Srinivasan, Sudharsan, Jairath, Neil K, Farooq, Zia, Marie, Tamara, Al-Holou, Wajd N, Eddy, Karen, Heth, Jason A, Khalsa, Siri Sahib S, Conway, Kyle, Sagher, Oren, Bruce, Jeffrey N, Canoll, Peter, Freudiger, Christian W, Camelo-Piragua, Sandra, and Lee, Honglak

Published
2021
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39. NIMG-33. MULTICENTER, PROSPECTIVE VALIDATION OF AUTOMATED INTRAOPERATIVE NEUROPATHOLOGY USING STIMULATED RAMAN HISTOLOGY AND CONVOLUTIONAL NEURAL NETWORKS

Author

Hollon, Todd, primary, Pandian, Balaji, additional, Heth, Jason, additional, Sagher, Oren, additional, Maher, Cormac, additional, Sullivan, Steve, additional, Garton, Hugh, additional, Thompson, Greg, additional, Save, Akshay, additional, Marie, Tamara, additional, Boyett, Deborah, additional, Petridis, Petros, additional, McKhann, Guy, additional, Muraszko, Karin, additional, Bruce, Jeffrey, additional, Camelo-Piragua, Sandra, additional, Canoll, Peter, additional, and Orringer, Daniel, additional

Published
2018
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43. Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Author

Hollon, Todd C., primary, Lewis, Spencer, additional, Pandian, Balaji, additional, Niknafs, Yashar S., additional, Garrard, Mia R., additional, Garton, Hugh, additional, Maher, Cormac O., additional, McFadden, Kathryn, additional, Snuderl, Matija, additional, Lieberman, Andrew P., additional, Muraszko, Karin, additional, Camelo-Piragua, Sandra, additional, and Orringer, Daniel A., additional

Published
2018
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46. Gender Disparity in Referral Patterns of Malignant Pleural Effusions

Author

Foote, Darci, primary, Burke, Christopher, additional, Pandian, Balaji, additional, Banks, Sarah, additional, Haug, Karlie, additional, Hipp, Michael, additional, Zhao, Lili, additional, Smola, Brian, additional, Roh, Michael, additional, Carrott, Philip, additional, Lynch, William, additional, Chang, Andrew, additional, Lin, Jules, additional, and Reddy, Rishindra, additional

Published
2017
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50. Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy

Author

Orringer, Daniel A., primary, Pandian, Balaji, additional, Niknafs, Yashar S., additional, Hollon, Todd C., additional, Boyle, Julianne, additional, Lewis, Spencer, additional, Garrard, Mia, additional, Hervey-Jumper, Shawn L., additional, Garton, Hugh J. L., additional, Maher, Cormac O., additional, Heth, Jason A., additional, Sagher, Oren, additional, Wilkinson, D. Andrew, additional, Snuderl, Matija, additional, Venneti, Sriram, additional, Ramkissoon, Shakti H., additional, McFadden, Kathryn A., additional, Fisher-Hubbard, Amanda, additional, Lieberman, Andrew P., additional, Johnson, Timothy D., additional, Xie, X. Sunney, additional, Trautman, Jay K., additional, Freudiger, Christian W., additional, and Camelo-Piragua, Sandra, additional

Published
2017
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