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1. A Contemporary Approach to Intraoperative Evaluation in Neuropathology

Author

Becker, Nicole, Camelo-Piragua, Sandra, and Conway, Kyle S.

Subjects
Brain tumors -- Diagnosis -- Care and treatment, Patient monitoring -- Evaluation, Health
Abstract

* Context.--Although the basic principles of intraoperative diagnosis in surgical neuropathology have not changed in the last century, the last several decades have seen dramatic changes in tumor classification, terminology, molecular classification, and modalities used for intraoperative diagnosis. As many neuropathologic intraoperative diagnoses are performed by general surgical pathologists, awareness of these recent changes is important for the most accurate intraoperative diagnosis. Objective.--To describe recent changes in the practice of intraoperative surgical neuropathology, with an emphasis on new entities, tumor classification, and anticipated ancillary tests, including molecular testing. Data Sources.--The sources for this review include the fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System, primary literature on intraoperative diagnosis and newly described tumor entities, and the authors' clinical experience. Conclusions.--A significant majority of neuropathologic diagnoses require ancillary testing, including molecular analysis, for appropriate classification. Therefore, the primary goal for any neurosurgical intraoperative diagnosis is the identification of diagnostic tissue and the preservation of the appropriate tissue for molecular testing. The intraoperative pathologist should seek to place a tumor in the most accurate diagnostic category possible, but specific diagnosis at the time of an intraoperative diagnosis is often not possible. Many entities have seen adjustments to grading criteria, including the incorporation of molecular features into grading. Awareness of these changes can help to avoid overgrading or undergrading at the time of intraoperative evaluation. (Arch Pathol Lab Med. 2024;148:649-658; doi: 10.5858/arpa.2023-0097-RA), Nearly a century has passed since Louise Eisenhardt and Harvey Cushing published their experience in using the 'supravital technique,' or smear preparation, for the intraoperative diagnosis of brain tumors. (1) [...]

Published
2024
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2. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Author

Hollon, Todd C., Jiang, Cheng, Chowdury, Asadur, Nasir-Moin, Mustafa, Kondepudi, Akhil, Aabedi, Alexander, Adapa, Arjun, Al-Holou, Wajd, Heth, Jason, Sagher, Oren, Lowenstein, Pedro, Castro, Maria, Wadiura, Lisa Irina, Widhalm, Georg, Neuschmelting, Volker, Reinecke, David, von Spreckelsen, Niklas, Berger, Mitchel S., Hervey-Jumper, Shawn L., Golfinos, John G., Snuderl, Matija, Camelo-Piragua, Sandra, Freudiger, Christian, Lee, Honglak, and Orringer, Daniel A.

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

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma., Comment: Paper published in Nature Medicine

Published
2023
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3. OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Author

Jiang, Cheng, Chowdury, Asadur, Hou, Xinhai, Kondepudi, Akhil, Freudiger, Christian W., Conway, Kyle, Camelo-Piragua, Sandra, Orringer, Daniel A., Lee, Honglak, and Hollon, Todd C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support. Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data for end-to-end model development and validation. We provide a framework for patch-based whole slide SRH classification and inference using weak (i.e. patient-level) diagnostic labels. Finally, we benchmark two computer vision tasks: multiclass histologic brain tumor classification and patch-based contrastive representation learning. We hope OpenSRH will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support in order to improve the access, safety, and efficacy of cancer surgery in the era of precision medicine. Dataset access, code, and benchmarks are available at opensrh.mlins.org., Comment: Neural Information Processing Systems (NeurIPS) 2022 Datasets and Benchmarks Track

Published
2022

4. 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. 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

6. Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma

Author

Al-Holou, Wajd N., Wang, Hanxiao, Ravikumar, Visweswaran, Shankar, Sunita, Oneka, Morgan, Fehmi, Ziad, Verhaak, Roel GW, Kim, Hoon, Pratt, Drew, Camelo-Piragua, Sandra, Speers, Corey, Wahl, Daniel R, Hollon, Todd, Sagher, Oren, Heth, Jason A, Muraszko, Karin M., Lawrence, Theodore S., de Carvalho, Ana C, Mikkelsen, Tom, Rao, Arvind, and Rehemtulla, Alnawaz

Published
2023
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7. Polysomy is associated with poor outcome in 1p/19q codeleted oligodendroglial tumors

Author

Chen, Hui, Thomas, Cheddhi, Munoz, Felipe Andres, Alexandrescu, Sanda, Horbinski, Craig M, Olar, Adriana, McGuone, Declan, Camelo-Piragua, Sandra, Wang, Lu, Pentsova, Elena, Phillips, Joanna, Aldape, Kenneth, Chen, Wen, Iafrate, A John, Chi, Andrew S, Zagzag, David, Golfinos, John G, Placantonakis, Dimitris G, Rosenblum, Marc, Ohman-Strickland, Pamela, Hameed, Meera, and Snuderl, Matija

Subjects
Cancer, Clinical Research, Adolescent, Adult, Aged, Aged, 80 and over, Aneuploidy, Brain Neoplasms, Chemotherapy, Adjuvant, Child, Chromosomal Instability, Chromosome Deletion, Chromosomes, Human, Pair 1, Chromosomes, Human, Pair 19, Female, Humans, In Situ Hybridization, Fluorescence, Isocitrate Dehydrogenase, Male, Middle Aged, Neoadjuvant Therapy, Neurosurgical Procedures, Oligodendroglioma, Prognosis, Progression-Free Survival, Radiotherapy, Adjuvant, Survival Rate, Young Adult, 1p/19q codeletion, glioma, oligodendroglioma, polysomy, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

BackgroundChromosomal instability is associated with earlier progression in isocitrate dehydrogenase (IDH)-mutated astrocytomas. Here we evaluated the prognostic significance of polysomy in gliomas tested for 1p/19q status.MethodsWe analyzed 412 histologic oligodendroglial tumors with use of 1p/19q testing at 8 institutions from 1996 to 2013; fluorescence in situ hybridization (FISH) for 1p/19q was performed. Polysomy was defined as more than two 1q and 19p signals in cells. Tumors were divided into groups on the basis of their 1p/19q status and polysomy and were compared for progression-free survival (PFS) and overall survival (OS).ResultsIn our cohort, 333 tumors (81%) had 1p/19q loss; of these, 195 (59%) had concurrent polysomy and 138 (41%) lacked polysomy, 79 (19%) had 1p/19q maintenance; of these, 30 (38%) had concurrent polysomy and 49 (62%) lacked polysomy. In agreement with prior studies, the group with 1p/19q loss had significantly better PFS and OS than did the group with 1p/19q maintenance (P < 0.0001 each). Patients with 1p/19q loss and polysomy showed significantly shorter PFS survival than patients with 1p/19q codeletion only (P < 0.0001), but longer PFS and OS than patients with 1p/19q maintenance (P < 0.01 and P < 0.0001). There was no difference in survival between tumors with >30% polysomic cells and those with

Published
2019

9. Opposing Tumor-Promoting and -Suppressive Functions of Rictor/mTORC2 Signaling in Adult Glioma and Pediatric SHH Medulloblastoma

Author

Akgül, Seçkin, Li, Yinghua, Zheng, Siyuan, Kool, Marcel, Treisman, Daniel M, Li, Chaoyang, Wang, Yuan, Gröbner, Susanne, Ikenoue, Tsuneo, Shen, Yiping, Camelo-Piragua, Sandra, Tomasek, Gerald, Stark, Sebastian, Guduguntla, Vinay, Gusella, James F, Guan, Kun-Liang, Pfister, Stefan M, Verhaak, Roel GW, and Zhu, Yuan

Subjects
Pediatric, Neurosciences, Rare Diseases, Brain Cancer, Regenerative Medicine, Brain Disorders, Stem Cell Research - Nonembryonic - Human, Stem Cell Research - Nonembryonic - Non-Human, Cancer, Stem Cell Research, 1.1 Normal biological development and functioning, Underpinning research, Adult, Animals, Basic Helix-Loop-Helix Transcription Factors, Carcinogenesis, Cell Differentiation, Cell Proliferation, Child, Genome, Human, Glioma, Hedgehog Proteins, Humans, Mechanistic Target of Rapamycin Complex 2, Medulloblastoma, Mice, Mutation, Protein Binding, Proteolysis, Proto-Oncogene Proteins c-akt, Rapamycin-Insensitive Companion of mTOR Protein, Signal Transduction, Treatment Outcome, Tumor Suppressor Protein p53, Akt, PI3K, Pten, Rictor, glioblastoma, mTORC2, mammalian target of rapamycin complex 2, medulloblastoma, p53, phosphatidylinositol 3-kinase pathway, Biochemistry and Cell Biology, Medical Physiology
Abstract

Most human cancers arise from stem and progenitor cells by the sequential accumulation of genetic and epigenetic alterations, while cancer modeling typically requires simultaneous multiple oncogenic events. Here, we show that a single p53 mutation, despite causing no defect in the mouse brain, promoted neural stem and progenitor cells to spontaneously accumulate oncogenic alterations, including loss of multiple chromosomal (chr) regions syntenic to human chr10 containing Pten, forming malignant gliomas with PI3K/Akt activation. Rictor/mTORC2 loss inhibited Akt signaling, greatly delaying and reducing glioma formation by suppressing glioma precursors within the subventricular zone stem cell niche. Rictor/mTORC2 loss delayed timely differentiation of granule cell precursors (GCPs) during cerebellar development, promoting sustained GCP proliferation and medulloblastoma formation, which recapitulated critical features of TP53 mutant sonic hedgehog (SHH) medulloblastomas with GLI2 and/or N-MYC amplification. Our study demonstrates that Rictor/mTORC2 has opposing functions in neural stem cells and GCPs in the adult and the developing brain, promoting malignant gliomas and suppressing SHH-medulloblastoma formation, respectively.

Published
2018

14. Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Author

Hollon, Todd, primary, Jiang, Cheng, additional, Chowdury, Asadur, additional, Nasir-Moin, Mustafa, additional, Kondepudi, Akhil, additional, Aabedi, Alexander, additional, Adapa, Arjun, additional, Al-Holou, Wajd, additional, Heth, Jason, additional, Sagher, Oren, additional, Lowenstein, Pedro, additional, Castro, Maria, additional, Wadiura, Lisa Irina, additional, Widhalm, Georg, additional, Neuschmelting, Volker, additional, Reinecke, David, additional, von Spreckelsen, Niklas, additional, Berger, Mitchel S., additional, Hervey-Jumper, Shawn L., additional, Golfinos, John G., additional, Snuderl, Matija, additional, Camelo-Piragua, Sandra, additional, Freudiger, Christian, additional, Lee, Honglak, additional, and Orringer, Daniel A., additional

Published
2023
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15. NIMG-30. AI-BASED MOLECULAR CLASSIFICATION OF DIFFUSE GLIOMAS USING RAPID, LABEL-FREE OPTICAL HISTOLOGY

Author

Hollon, Todd, primary, Jiang, Cheng, additional, Nasir-Moin, Mustafa, additional, Kondepudi, Akhil, additional, Chowdury, Asadur, additional, Al-Holou, Wajd, additional, Castro, Maria, additional, Lowenstein, Pedro, additional, Wadiura, Lisa Irina, additional, Widhalm, Georg, additional, Neuschmelting, Volker, additional, David, Reinecke, additional, von Spreckelsen, Niklas, additional, Berger, Mitchel S, additional, Golfinos, John, additional, Hervey-Jumper, Shawn L, additional, Camelo-Piragua, Sandra, additional, Lee, Honglak, additional, Freudiger, Christian, additional, and Orringer, Daniel, additional

Published
2022
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18. Mechanisms of Glioma Formation: Iterative Perivascular Glioma Growth and Invasion Leads to Tumor Progression, VEGF-Independent Vascularization, and Resistance to Antiangiogenic Therapy

Author

Baker, Gregory J., Yadav, Viveka Nand, Motsch, Sebastien, Koschmann, Carl, Calinescu, Anda-Alexandra, Mineharu, Yohei, Camelo-Piragua, Sandra Ines, Orringer, Daniel, Bannykh, Serguei, Nichols, Wesley S., deCarvalho, Ana C., Mikkelsen, Tom, Castro, Maria G., and Lowenstein, Pedro R.

Published
2014
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21. Abstract LB013: Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma

Author

Al-Holou, Wajd N., primary, Wang, Hanxiao, additional, Ravikumar, Visweswaran, additional, Oneka, Morgan, additional, Verhaak, Roel G., additional, Kim, Hoon, additional, Pratt, Drew, additional, Camelo-Piragua, Sandra, additional, Speers, Corey, additional, Wahl, Daniel, additional, Shankar, Sunita, additional, Hollon, Todd, additional, Sagher, Oren, additional, Heth, Jason, additional, Muraszko, Karin M., additional, Lawrence, Theodore S., additional, deCarvalho, Ana C., additional, Mikkelsen, Tom, additional, Rao, Arvind, additional, and Rehemtulla, Alnawaz, additional

Published
2022
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22. Transcranial histotripsy parameter study in primary and metastatic murine brain tumor models.

Author

Duclos, Sarah, Golin, Andrew, Fox, Adam, Chaudhary, Neeraj, Camelo-Piragua, Sandra, Pandey, Aditya, and Xu, Zhen

Subjects
BRAIN tumors, HEMATOXYLIN & eosin staining, TUMOR treatment, LABORATORY mice, METASTASIS, BIOLUMINESCENCE
Abstract

This study investigated the effect of various histotripsy dosages on tumor cell kill and associated bleeding in two murine brain tumor models (glioma [Gl261] and lung metastasis [LL/2-Luc2]). GL261 or LL/2-Luc2 cells were cultured and implanted into the brains of C57BL/6 mice. Histotripsy (1-cycle pulses, 5 Hz PRF, 30 MPa-P) was performed using a 1 MHz transducer for five different dosages for each cell line: 5, 20 or 200 pulses per location (PPL) at a single treatment point, or 5 or 10–20 PPL at multiple treatment points. MRI, bioluminescence imaging and histology were used to assess tumor ablation and treatment effects within 4–6 h post-treatment. All treatment groups resulted in a reduction of BLI intensity for the LL/2-Luc2 tumors, with significant signal reductions for the multi-point groups. The average pre-/post-treatment BLI flux (photons/s, ×108) for the different treatment groups were: 4.39/2.19 (5 PPL single-point), 5.49/1.80 (20 PPL single-point), 3.86/1.73 (200 PPL single-point), 2.44/1.11 (5 PPL multi-point) and 5.85/0.80 (10 PPL multi-point). MRI and H&E staining showed increased tumor damage and hemorrhagic effects with increasing histotripsy dose for both GL261 and LL/2-Luc2 tumors, but the increase in tumor damage was diminished beyond 10–20 PPL for single-point treatments and outweighed by increased hemorrhage. In general, hemorrhage was confined to be within 1 mm of the treatment boundary for all groups. Our results suggest that a lower number of histotripsy pulses at fewer focal locations can achieve substantial tumor kill while minimizing hemorrhage. [ABSTRACT FROM AUTHOR]

Published
2023
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24. OTHR-01. SNO 2020 DIVERSITY SURVEY: DEFINING DEMOGRAPHICS, RACIAL BIASES, CAREER SUCCESS METRICS AND A PATH FORWARD FOR THE FIELD OF NEURO-ONCOLOGY

Author

Chukwueke, Ugonma, primary, Vera, Elizabeth, additional, Acquaye, Alvina, additional, Hervey-Jumper, Shawn, additional, Odia, Yazmin, additional, Klesse, Laura, additional, Dunbar, Erin, additional, Sharma, Akanksha, additional, Fonkem, Ekokobe, additional, Thomas, Alissa, additional, Werbowetski-Ogilvie, Tamra, additional, Camelo-Piragua, Sandra, additional, Gatson, Na Tosha, additional, de la Fuente, Macarena, additional, Armstrong, Terri, additional, Porter, Alyx, additional, and Jackson, Sadhana, additional

Published
2021
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25. SNO 2020 diversity survey: defining demographics, racial biases, career success metrics and a path forward for the field of neuro-oncology

Author

Chukwueke, Ugonma N, primary, Vera, Elizabeth, additional, Acquaye, Alvina, additional, Hervey-Jumper, Shawn L, additional, Odia, Yazmin, additional, Klesse, Laura J, additional, Dunbar, Erin, additional, Sharma, Akanksha, additional, Fonkem, Ekokobe, additional, Thomas, Alissa A, additional, Werbowetski-Ogilvie, Tamra E, additional, Camelo-Piragua, Sandra, additional, Gatson, Na Tosha N, additional, de la Fuente, Macarena I, additional, Armstrong, Terri S, additional, Porter, Alyx B, additional, and Jackson, Sadhana, additional

Published
2021
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28. ETMR-22. TITLE: DEFINING THE CLINICAL AND PROGNOSTIC LANDSCAPE OF EMBRYONAL TUMORS WITH MULTI-LAYERED ROSETTES (ETMRs), A RARE BRAIN TUMOR REGISTRY (RBTC) STUDY

Author

Khan, Sara, primary, Solano-Paez, Palma, additional, Suwal, Tannu, additional, Al-Karmi, Salma, additional, Lu, Mei, additional, Ho, Ben, additional, Fouladi, Maryam, additional, Leary, Sarah, additional, Levy, Jean M Mulcahy, additional, Lassaletta, Alvaro, additional, Rivas, Eloy, additional, Reddy, Alyssa, additional, Gillespie, G Yancey, additional, Gupta, Nalin, additional, Yalon-Oren, Michal, additional, Amariglio, Laura, additional, Nakamura, Hideo, additional, Wu, Kuo-Sheng, additional, Wong, Tai-Tong, additional, Ra, Young-Shin, additional, Spina, Milena La, additional, Emanuele, Policlinico Vittorio, additional, Massimi, Luca, additional, Buccoliero, Anna Maria, additional, Hansford, Jordan R, additional, Grundy, Richard G, additional, Adamek, Dariusz, additional, Fangusaro, Jason, additional, Scharnhorst, David, additional, Johnston, Donna, additional, Lafay-Cousin, Lucie, additional, Camelo-Piragua, Sandra, additional, Kabbara, Nabil, additional, Gajjar, Amar, additional, Boutarbouch, Mahjouba, additional, da Costa, Maria Joao Gil, additional, Hanson, Derek, additional, Wood, Paul, additional, Al-Hussaini, Maysa, additional, Amayiri, Nisreen, additional, Wang, Yin, additional, Catchpoole, Daniel, additional, Michaud, Jean, additional, Bendel, Anne E, additional, Ellezam, Benjamin, additional, Gerber, Nicholas, additional, Plant, Ashley, additional, Jeffery, Rubens, additional, Dunham, Christopher, additional, Moertel, Christopher, additional, Walter, Andrew, additional, Ziegler, David, additional, Dodgshun, Andrew, additional, Gottardo, Nicholas, additional, Demir, Ahmet, additional, Ramanujachar, Ramya, additional, Raabe, Eric, additional, Mary, Shago, additional, Dirks, Peter, additional, Taylor, Michael, additional, Eugene, Hwang, additional, Lindsey, Holly, additional, Tihan, Tarik, additional, Mette, Jorgensen, additional, Dahl, Christine, additional, Low, Sharon, additional, Smith, Amy, additional, Hazrati, Lili-Naz, additional, Kresak, Jesse, additional, Gino, Somers, additional, Tan, Enrica, additional, Morales, Andres, additional, Santa-Maria, Vicente, additional, Hawkins, Cynthia, additional, Bartels, Ute, additional, Stephens, Derek, additional, Nobusawa, Sumihito, additional, Dufour, Christelle, additional, Bourdeaut, Franck, additional, Andre, Nicolas, additional, Bouffet, Eric, additional, and Huang, Annie, additional

Published
2020
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29. CSIG-20. L3MBTL3 SUPPRESSES MEDULLOBLASTOMA TUMORIGENESIS THROUGH MODULATION OF THE NOTCH/RBPJ SIGNALING PATHWAY

Author

Zhang, Honglai, primary, Xu, Tao, additional, Peabody, Claire, additional, Fernández, Ester Calvo, additional, Budhathoki, Rashmi, additional, Huang, Philip Chi-En, additional, Hughes, Elizabeth D, additional, Saunders, Thomas L, additional, Kuick, Rork, additional, Magnuson, Brian, additional, Camelo-Piragua, Sandra, additional, and Rual, Jean-François, 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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33. Automated histologic diagnosis of CNS tumors with machine learning

Author

Khalsa, Siri Sahib S, primary, Hollon, Todd C, additional, Adapa, Arjun, additional, Urias, Esteban, additional, Srinivasan, Sudharsan, additional, Jairath, Neil, additional, Szczepanski, Julianne, additional, Ouillette, Peter, additional, Camelo-Piragua, Sandra, additional, and Orringer, Daniel A, additional

Published
2020
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34. ATIM-44. A PHASE I FIRST-IN-HUMAN TRIAL OF TWO ADENOVIRAL VECTORS EXPRESSING HSV1-TK AND FLT3L FOR TREATING NEWLY DIAGNOSED RESECTABLE MALIGNANT GLIOMA: THERAPEUTIC REPROGRAMMING OF THE BRAIN IMMUNE SYSTEM

Author

Lowenstein, Pedro, primary, A Orringer, Daniel, additional, Sagher, Oren, additional, Heth, Jason, additional, Hervey-Jumper, Shawn, additional, Gerald Mammoser, Aaron, additional, Junck, Larry, additional, Leung, Denise, additional, Umemura, Yoshie, additional, Steven Lawrence, Theodore, additional, Miran Kim, Michelle, additional, Richard Wahl, Daniel, additional, McKeever, Paul, additional, Ines Camelo-Piragua, Sandra, additional, Lieberman, Andrew, additional, Venneti, Sriram, additional, Comba, Andrea, additional, Altshuler, David, additional, M Muraszko, Karin, additional, and Castro, Maria, additional

Published
2019
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35. MOESM1 of Plasmacytoid urothelial carcinoma: a rapid autopsy case report with unique clinicopathologic and genomic profile

Author

Simon, Caroline, Skala, Stephanie, Killen, Paul, Siddiqui, Javed, Xuhong Cao, Yuanyuan Qiao, Al-Ahmadie, Hikmat, Camelo-Piragua, Sandra, Jentzen, Jeffrey, Chinnaiyan, Arul, Saravana Dhanasekaran, Reichert, Zachery, and Rohit Mehra

Abstract

Additional file 1: Table S1. Primer sequences for amplification of coding exonic regions of the CDH1 gene.

Published
2019
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38. 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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39. Loss of AMPKα2 Impairs Hedgehog-Driven Medulloblastoma Tumorigenesis

Author

Zhang, Honglai, primary, Kuick, Rork, additional, Park, Sung-Soo, additional, Peabody, Claire, additional, Yoon, Justin, additional, Fernández, Ester, additional, Wang, Junying, additional, Thomas, Dafydd, additional, Viollet, Benoit, additional, Inoki, Ken, additional, Camelo-Piragua, Sandra, additional, and Rual, Jean-François, additional

Published
2018
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40. Alternative lengthening of telomeres, ATRX loss and H3‐K27M mutations in histologically defined pilocytic astrocytoma with anaplasia

Author

Rodriguez, Fausto J., primary, Brosnan‐Cashman, Jacqueline A., additional, Allen, Sariah J., additional, Vizcaino, M. Adelita, additional, Giannini, Caterina, additional, Camelo‐Piragua, Sandra, additional, Webb, Milad, additional, Matsushita, Marcus, additional, Wadhwani, Nitin, additional, Tabbarah, Abeer, additional, Hamideh, Dima, additional, Jiang, Liqun, additional, Chen, Liam, additional, Arvanitis, Leonidas D., additional, Alnajar, Hussein H., additional, Barber, John R., additional, Rodríguez‐Velasco, Alicia, additional, Orr, Brent, additional, and Heaphy, Christopher M., additional

Published
2018
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41. 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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43. 5th International Symposium on Focused Ultrasound

Author

Abounader, Roger, Abraham, Christopher, Adema, Gosse, Agrawal, Punit, Airan, Raag, Aleman, Dionne, Alexander, Phillip, Alkins, Ryan, Alnazeer, Moez, Altman, Michael, Aly, Amirah, Amaral, Joao G., Amrahli, Maral, Amraoui, Sana, Andarawewa, Kumari, Andriyakhina, Yulia, Angstadt, Mary, Ankou, Bénédicte, Arias, Ana C., Arvanitis, Costas, Asadnia, Kiana, Aubert, Isabelle, Aubry, Jean-Francois, Aurup, Christian, Bader, Kenneth, Badr, Lena, Baek, Hongchae, Barbato, Gaetano, Beccaria, Kevin, Bellorofonte, Carlo, Benson, Lee, Bernus, Olivier, Berriet, Rémi, Bertolina, Jim, Beskin, Viktoriya, Bessière, Francis, Bethune, Allison, Bezzi, Mario, Bond, Aaron, Bonomo, Guido, Borowsky, Alexander, Borys, Nicolas, Böttcher, Joachim, Bouley, Donna, Bour, Pierre, Bourekas, Eric, Brenin, David, Brokman, Omer, Brosh, Inbar, Buckner, Andrew, Bullock, Timothy, Cafarelli, Andrea, Cahill, Jessica, Camarena, Francisco, Camelo-Piragua, Sandra, Campbell, Benjamin, Campbell, Fiona, Cannata, Jon, Canney, Michael, Carlson, Roy, Carneiro, Antonio, Carpentier, Alexandre, Catheline, Stefan, Cavin, Ian, Cesana, Claudio, Chabok, Hamid R., Chamanara, Marzieh, Chang, Jin H., Chang, Won S., Changizi, Barbara, Chapelon, Jean Y., Chaplin, Vandiver, Chapman, Martin, Chaudhary, Neeraj, Chaussy, Christian, Chen, Cherry, Chen, Johnny, Chen, Wohsing, Chen, Xiaoming, Chevalier, Philippe, Chiou, George, Chisholm, Alexander, Christofferson, Ivy, Chung, Hyun H., Ciuti, Gastone, Clement, Gregory, Cooper, Mark, Corea, Joseph, Corso, Cristiano, Cosman, Josh, Coughlin, Dezba, Crake, Calum, Cunitz, Bryan, Curiel, Laura, Curley, Colleen T., Czarnota, Gregory, Dababou, Susan, Dallapiazza, Robert, de Bever, Joshua, de Jager, Bram, de Ruiter, Joost, de Senneville, Baudouin D., Deckers, Roel, Delattre, Jean-Yves, den Brok, Martijn, Dhanaliwala, Ali, Diodato, Alessandro, Dixon, Adam, Donner, Elizabeth, Downs, Matthew, Du, Zhongmin, Dubois, Rémi, Dupre, Aurelien, Eikelenboom, Dylan, Elias, W. J., Ellens, Nicholas, Endre, Ruby, Eran, Ayelet, Erasmus, Hans-Peter, Everstine, Ashli, Farahani, Keyvan, Farrer, Alexis, Farry, Justin, Federau, Christian, Feng, Xue, Ferrer, Cyril, Ferrera, Vincent, Fishman, Paul, Foley, Jessica, Frenkel, Victor, Fütterer, Jurgen, Gach, H. M., Gandhi, Dheeraj, Gertner, Michael, Goldsher, Dorit, Gorgone, Alessandro, Greillier, Paul, Griesenauer, Rebekah, Grissom, William, Grondin, Julien, Guha, Chandan, Gulati, Amitabh, Gullapalli, Rao, Guo, Sijia, Gupta, Samit, Gurm, Hitinder, Gwinn, Ryder, Hadley, Rock, Haïssaguerre, Michel, Hammoud, Dima, Hananel, Arik, Hargrove, Amelia, Hatch, Robert, Haworth, Kevin, Hazan, Eilon, He, Ye, Heemels, Maurice, Heerschap, Arend, Hilas, Elaine, Hoang-Xuan, Khe, Hocini, Mélèze, Hodaie, Mojgan, Hofmann, Denis, Holland, Christy, Hoogenboom, Martijn, Hopyan, Sevan, Hossack, John, Houdouin, Alexandre, Hsu, Po-Hung, Hu, Jim, Hurwitz, Mark, Huss, Diane, Hwang, Chang-il, Hwang, Joo H., Idbaih, Ahmed, Ikeuchi, Masahiko, Ingham, Elizabeth, Ives, Kimberly, Izumi, Masashi, Jackson-Lewis, Vernice, Janát-Amsbury, Margit, Jang, Kee W., Jedruszczuk, Kathleen, Jiménez-Gambín, Sergio, Jiménez, Noé, Johnson, Sara, Jonathan, Sumeeth, Joy, Joyce, Jung, Hyun H., Jung, Na Y., Kahn, Itamar, Kamimura, Hermes, Kamrava, Seyed K., Kang, Jeeun, Kang, Kook J., Kang, Soo Y., Kao, Yi-tzu, Katti, Prateek, Kawasaki, Motohiro, Kaye, Elena, Keupp, Jochen, Kim, AeRang, Kim, Harry, Kim, Hyun-Chul, Kim, Hyuncheol, Kim, Hyungmin, Kim, Min S., Kim, Namho, Kiyasu, Katsuhito, Kneepkens, Esther, Knopp, Michael, Kobus, Thiele, Koral, Korgun, Kreider, Wayne, Krishna, Vibhor, Krug, Roland, Krupa, Steve, Kuo, Chia-Chun, Kwiecinski, Wojciech, Lacoste, Romain, Lam, Heather, Lamberti-Pasculli, Maria, Lang, Brian, Larner, James, Larrabee, Zachary, Leach, J. K., LeBlang, Suzanne, Leclercq, Delphine, Lee, Hak J., Lee, Jong-Hwan, Lehericy, Stéphane, Leighton, Wan, Leung, Steven, Lewis, Bobbi, Lewis, Matthew, Li, Dawei, Linn, Sabine, Lipsman, Nir, Liu, Hao-Li, Liu, Jingfei, Lopes, M. B., Lotz, Jeff, Lu, Xin, Lundt, Jonathan, Luo, Xi, Lustgarten, Lior, Lustig, Micheal, Macoskey, Jonathan, Madore, Bruno, Maev, Roman, Magat, Julie, Maimbourg, Guillaume, Maimon, Noam, Mainprize, Todd, Malayer, Jerry, Maples, Danny, Marquet, Fabrice, Marrocchio, Cristina, Marx, Mike, Mastorakos, Panagiotis, Mauri, Giovanni, McLean, Hailey, McMichael, John, Mead, Brian P., Melodelima, David, Melot-Dusseau, Sandrine, Menciassi, Arianna, Merrill, Robb, Meyer, Joshua, Midiri, Massimo, Miga, Michael, Migliore, Ilaria G., Miller, Eric, Minalga, Emilee, Moon, Hyungwon, Moore, David, Mourad, Pierre, Mouratidis, Petros, Mueller, Michael, Mugler, John, Muller, Sébastien, Namba, Hirofumi, Naor, Omer, Nassar, Maria, Nazai, Navid, Negron, Karina, Negussie, Ayele, Nguyen, Thai-Son, Nicolay, Klaas, Nikolaeva, Anastasia V., Oetgen, Matthew, Olive, Kenneth, Olumolade, Oluyemi, Orsi, Franco, Owens, Gabe, Ozilgen, Arda, Padegimas, Linas, Palermo, Carmine, Pan, Chia-Hsin, Pandey, Aditya, Papadakis, Georgios, Park, Chang K., Park, Sang M., Parker, Jonathon, Parvizi, Mohammad H., Pascal-Tenorio, Aurea, Patel, Janish, Patz, Sam, Payen, Thomas, Perich, Eloi, Pernot, Mathieu, Perol, David, Perry, James, Pillarisetty, Venu, Pioche, Mathieu, Pizzuto, Matthew, Plaksin, Michael, Plata, Juan, Price, Karl, Prince, Jessica, Przedborski, Serge, Quinones-Hinojosa, Alfredo, Ramachandran, Akhilesh, Ranjan, Ashish, Ravikumar, Vinod, Reichenbach, Juergen, Repasky, Elizabeth, Rezai, Ali, Ritter, Philippe, Rivoire, Michel, Rochman, Carrie, Rosenberg, Jarrett, Rosnitskiy, Pavel B., Ruiz, Antonio, Sahgal, Arjun, Samiotaki, Gesthimani, Sanghvi, Narendra, Santin, Mathieu D., Santos, Domiciano, Sasaki, Noboru, Sastra, Steve, Schade, George, Schall, Jeffrey, Schlesinger, Ilana, Schmitt, Paul, Schwaab, Julia, Scionti, Stephen, Scipione, Roberto, Scoarughi, Gian L., Scott, Serena, Sebeke, Lukas, Seifabadi, Reza, Seo, Jai, Sesenoglu-Laird, Ozge, Shah, Binit, Shahriari, Kian, Shaikh, Sumbul, Shea, Jill, Shi, Jiaqi, Shim, Jenny, Shinkov, Alexander, Shuman, Jillian, Silvestrini, Matthew, Sim, Changbeom, Sin, Vivian, Sinai, Alon, Singh, Manoj, Sinilshchikov, Ilya, Skalina, Karin, Slingluff, Craig, So, Po-Wah, Solomon, Stephen, Son, Keon H., Sperling, Scott, Stein, Ruben, Stein, Sherman, Stevens, Aaron, Stimec, Jennifer, Storm, Gert, Straube, William, Suelmann, Britt, Sutton, Jonathan, Svedin, Bryant, Takemasa, Ryuichi, Takiguchi, Mitsuyoshi, Tam, Emily, Tan, Jeremy, Tang, Xinyan, Tanter, Mickael, Tebebi, Pamela, Tehrani, Seruz, Temple, Michael, Teofilovic, Dejan, ter Haar, Gail, Terzi, Marina E., Thueroff, Stefan, Timbie, Kelsie, Tognarelli, Selene, Tretbar, Steffen, Trudeau, Maureen, Tsai, Yi-Chieh, Tsysar, Sergey A., Tucci, Samantha, Tuveson, David, Ushida, Takahiro, Vaessen, Paul, Vaillant, Fanny, Van Arsdell, Glen, van Breugel, Johanna, Van der Jeugd, Anneke, Van der Wall, Elsken, van Diest, Paul, van Stralen, Marijn, Varano, Gianluca, Velat, Manuela, Vidal-Jove, Joan, Vigna, Paolo D., Vignot, Alexandre, Vincenot, Jeremy, Vykhodtseva, Natalia, Wang, Bin, Wang, Han, Wang, Kevin, Wang, Qi, Wang, Qingguo, Wang, Shengping, Wang, Yak-Nam, Wang, Zhaorui, Wardlow, Rachel, Warren, Amy, Waszczak, Barbara, Watson, Katherine, Webb, Taylor, Wei-Bin, Shen, Wei, Kuo-Chen, Weiss, Steffen, Weissler, Yoni, Werner, Beat, Wesseling, Pieter, Williams, Noelle, Wilson, Emmanuel, Wintermark, Max, Witkamp, Arjen, Wong, Carlos, Wu, Jing-Fu, Wydra, Adrian, Xu, Alexis, Xu, Doudou, Xu, Su, Yang, Georgiana, Yang, Nai-Yi, Yao, Chen, Yarowsky, Paul, Ye, Patrick P., Yuldashev, Petr, Zaaroor, Menashe, Zachiu, Cornel, Zahos, Peter, Zangos, Stephan, Zhang, Dandan, Zhang, Hua, Zhang, Jimin, Zhang, Junhai, Zhang, Xi, Zhao, Li, Zhong, Pei, Zhuo, Jiachen, Zidowitz, Stephan, Zinke, Wolf, Zorgani, Ali, and Aerospace and Ocean Engineering

Subjects
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Abstract

Published version

Published
2016

46. 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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47. PDCT-14. CLINICALLY INTEGRATED SEQUENCING SIGNIFICANTLY ALTERS THERAPY IN CHILDREN AND YOUNG ADULTS WITH HIGH-RISK GLIAL BRAIN TUMORS

Author

Koschmann, Carl, primary, Wu, Yi-Mi, additional, Kumar, Chandan, additional, Lonigro, Robert, additional, Vats, Pankaj, additional, Kasaian, Katayoon, additional, Cieslik, Marcin, additional, Cao, Xuhong, additional, Frank, Kevin, additional, Prensner, John R, additional, Zureick, Andrew, additional, Everett, Jessica, additional, Anderson, Bailey, additional, Mullan, Brendan, additional, Marini, Bernard, additional, Camelo-Piragua, Sandra, additional, Vennneti, Sriram, additional, Keever, Paul Mc, additional, McFadden, Kathryn A, additional, Lieberman, Andrew, additional, Leonard, Marcia, additional, Maher, Cormac O, additional, Garton, Hugh J L, additional, Muraszko, Karin, additional, Robertson, Patricia, additional, Robinson, Dan, additional, Chinnaiyan, Arul, additional, and Mody, Rajen, additional

Published
2017
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50. External and internal influences on muscle pathology

Author

McKeever, Paul E., Camelo-Piragua, Sandra, and Dowling, James

Subjects
Ischemia, Health
Abstract

Three cases of different types of neuromuscular diseases demonstrate different muscle responses to external stress or intrinsic muscle abnormalities. The first muscle biopsy shows stenosis of its vessels causing acute muscle ischemia, stress from an external vascular disease. The muscle response is similar to the cellular necrosis seen in primary muscle diseases (myopathies), but the histologic pattern is more focal than most myopathies. The second muscle biopsy demonstrates the effects of external motor nerve injury or disease causing groups of muscle fibers to atrophy. If a nerve reinnervates the muscle, it changes the fiber types in distinct patterns. The third muscle biopsy shows an intrinsic muscle abnormality causes chronic failure of the muscle fibers to thrive and repeated attempts by the fibers to regenerate, stimulating other tissue repair processes, like fibrosis, to change the muscle. Depending on the etiologic factor, muscle will respond to internal and external influences in different manners. (Arch Pathol Lab Med. 2012; 136:927-934; doi: 10.5858/arpa.2012-0232-CR), The diagnosis of muscle disease is a specialized discipline. However, that does not mean that muscle tissue responds entirely different from the way other tissues in the body respond to [...]

Published
2012
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