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2. Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Author

Ho, David Joon, Agaram, Narasimhan P., Jean, Marc-Henri, Suser, Stephanie D., Chu, Cynthia, Vanderbilt, Chad M., Meyers, Paul A., Wexler, Leonard H., Healey, John H., Fuchs, Thomas J., and Hameed, Meera R.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Osteosarcoma is the most common primary bone cancer whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for predicting prognosis and further management of patients. Necrosis is routinely assessed post-chemotherapy from histology slides on resection specimens where necrosis ratio is defined as the ratio of necrotic tumor to overall tumor. Patients with necrosis ratio >=90% are known to have better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semi-quantitative and can have intra- and inter-observer variability. We propose an objective and reproducible deep learning-based approach to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images. We collected 103 osteosarcoma cases with 3134 WSIs to train our deep learning model, to validate necrosis ratio assessment, and to evaluate outcome prediction. We trained Deep Multi-Magnification Network to segment multiple tissue subtypes including viable tumor and necrotic tumor in pixel-level and to calculate case-level necrosis ratio from multiple WSIs. We showed necrosis ratio estimated by our segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts where mean absolute differences for Grades IV (100%), III (>=90%), and II (>=50% and <90%) necrosis response are 4.4%, 4.5%, and 17.8%, respectively. We successfully stratified patients to predict overall survival with p=10^-6 and progression-free survival with p=0.012. Our reproducible approach without variability enabled us to tune cutoff thresholds, specifically for our model and our data set, to 80% for OS and 60% for PFS. Our study indicates deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.

Published
2022

6. Clinical genomic profiling in the management of patients with soft tissue and bone sarcoma

Author

Gounder, Mrinal M, Agaram, Narasimhan P, Trabucco, Sally E, Robinson, Victoria, Ferraro, Richard A, Millis, Sherri Z, Krishnan, Anita, Lee, Jessica, Attia, Steven, Abida, Wassim, Drilon, Alexander, Chi, Ping, Angelo, Sandra P D’, Dickson, Mark A, Keohan, Mary Lou, Kelly, Ciara M, Agulnik, Mark, Chawla, Sant P, Choy, Edwin, Chugh, Rashmi, Meyer, Christian F, Myer, Parvathi A, Moore, Jessica L, Okimoto, Ross A, Pollock, Raphael E, Ravi, Vinod, Singh, Arun S, Somaiah, Neeta, Wagner, Andrew J, Healey, John H, Frampton, Garrett M, Venstrom, Jeffrey M, Ross, Jeffrey S, Ladanyi, Marc, Singer, Samuel, Brennan, Murray F, Schwartz, Gary K, Lazar, Alexander J, Thomas, David M, Maki, Robert G, Tap, William D, Ali, Siraj M, and Jin, Dexter X

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Biotechnology, Clinical Research, Human Genome, Genetic Testing, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Biomarkers, Tumor, Bone Neoplasms, Genomics, Humans, Mutation, Osteosarcoma, Prospective Studies, Sarcoma
Abstract

There are more than 70 distinct sarcomas, and this diversity complicates the development of precision-based therapeutics for these cancers. Prospective comprehensive genomic profiling could overcome this challenge by providing insight into sarcomas' molecular drivers. Through targeted panel sequencing of 7494 sarcomas representing 44 histologies, we identify highly recurrent and type-specific alterations that aid in diagnosis and treatment decisions. Sequencing could lead to refinement or reassignment of 10.5% of diagnoses. Nearly one-third of patients (31.7%) harbor potentially actionable alterations, including a significant proportion (2.6%) with kinase gene rearrangements; 3.9% have a tumor mutational burden ≥10 mut/Mb. We describe low frequencies of microsatellite instability (

Published
2022

8. Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

Author

Ho, David Joon, Agaram, Narasimhan P., Schueffler, Peter J., Vanderbilt, Chad M., Jean, Marc-Henri, Hameed, Meera R., and Fuchs, Thomas J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as measured by ratio of necrotic tumor area to overall tumor area is a known prognostic factor for overall survival. This assessment is currently done manually by pathologists by looking at glass slides under the microscope which may not be reproducible due to its subjective nature. Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One bottleneck for supervised learning is that large amounts of accurate annotations are required for training which is a time-consuming and expensive process. In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs. After an initial labeling step is done, annotators only need to correct mislabeled regions from previous segmentation predictions to improve the CNN model until the satisfactory predictions are achieved. Our experiments show that our CNN model trained by only 7 hours of annotation using DIaL can successfully estimate ratios of necrosis within expected inter-observer variation rate for non-standardized manual surgical pathology task., Comment: Accepted at MICCAI 2020

Published
2020
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11. Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets

Author

Nacev, Benjamin A., Sanchez-Vega, Francisco, Smith, Shaleigh A., Antonescu, Cristina R., Rosenbaum, Evan, Shi, Hongyu, Tang, Cerise, Socci, Nicholas D., Rana, Satshil, Gularte-Mérida, Rodrigo, Zehir, Ahmet, Gounder, Mrinal M., Bowler, Timothy G., Luthra, Anisha, Jadeja, Bhumika, Okada, Azusa, Strong, Jonathan A., Stoller, Jake, Chan, Jason E., Chi, Ping, D’Angelo, Sandra P., Dickson, Mark A., Kelly, Ciara M., Keohan, Mary Louise, Movva, Sujana, Thornton, Katherine, Meyers, Paul A., Wexler, Leonard H., Slotkin, Emily K., Glade Bender, Julia L., Shukla, Neerav N., Hensley, Martee L., Healey, John H., La Quaglia, Michael P., Alektiar, Kaled M., Crago, Aimee M., Yoon, Sam S., Untch, Brian R., Chiang, Sarah, Agaram, Narasimhan P., Hameed, Meera R., Berger, Michael F., Solit, David B., Schultz, Nikolaus, Ladanyi, Marc, Singer, Samuel, and Tap, William D.

Published
2022
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12. Pilot study of bempegaldesleukin in combination with nivolumab in patients with metastatic sarcoma

Author

D’Angelo, Sandra P., Richards, Allison L., Conley, Anthony P., Woo, Hyung Jun, Dickson, Mark A., Gounder, Mrinal, Kelly, Ciara, Keohan, Mary Louise, Movva, Sujana, Thornton, Katherine, Rosenbaum, Evan, Chi, Ping, Nacev, Benjamin, Chan, Jason E., Slotkin, Emily K., Kiesler, Hannah, Adamson, Travis, Ling, Lilan, Rao, Pavitra, Patel, Shreyaskumar, Livingston, Jonathan A., Singer, Samuel, Agaram, Narasimhan P., Antonescu, Cristina R., Koff, Andrew, Erinjeri, Joseph P., Hwang, Sinchun, Qin, Li-Xuan, Donoghue, Mark T. A., and Tap, William D.

Published
2022
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15. Validation of a digital pathology system including remote review during the COVID-19 pandemic

Author

Hanna, Matthew G., Reuter, Victor E., Ardon, Orly, Kim, David, Sirintrapun, Sahussapont Joseph, Schüffler, Peter J., Busam, Klaus J., Sauter, Jennifer L., Brogi, Edi, Tan, Lee K., Xu, Bin, Bale, Tejus, Agaram, Narasimhan P., Tang, Laura H., Ellenson, Lora H., Philip, John, Corsale, Lorraine, Stamelos, Evangelos, Friedlander, Maria A., Ntiamoah, Peter, Labasin, Marc, England, Christine, Klimstra, David S., and Hameed, Meera

Published
2020
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17. Supplementary Table S3 from PDGFRβ Signaling Cooperates with β-Catenin to Modulate c-Abl and Biologic Behavior of Desmoid-Type Fibromatosis

Author

Hu, Jia, primary, Hameed, Meera R., primary, Agaram, Narasimhan P., primary, Whiting, Karissa A., primary, Qin, Li-Xuan, primary, Villano, Anthony M., primary, O'Connor, Rachael B., primary, Rozenberg, Julian M., primary, Cohen, Sonia, primary, Prendergast, Katherine, primary, Kryeziu, Sara, primary, White, Richard L., primary, Posner, Mitchell C., primary, Socci, Nicholas D., primary, Gounder, Mrinal M., primary, Singer, Samuel, primary, and Crago, Aimee M., primary

Published
2024
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18. Data from PDGFRβ Signaling Cooperates with β-Catenin to Modulate c-Abl and Biologic Behavior of Desmoid-Type Fibromatosis

Author

Hu, Jia, primary, Hameed, Meera R., primary, Agaram, Narasimhan P., primary, Whiting, Karissa A., primary, Qin, Li-Xuan, primary, Villano, Anthony M., primary, O'Connor, Rachael B., primary, Rozenberg, Julian M., primary, Cohen, Sonia, primary, Prendergast, Katherine, primary, Kryeziu, Sara, primary, White, Richard L., primary, Posner, Mitchell C., primary, Socci, Nicholas D., primary, Gounder, Mrinal M., primary, Singer, Samuel, primary, and Crago, Aimee M., primary

Published
2024
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19. Supplementary Data 1 from PDGFRβ Signaling Cooperates with β-Catenin to Modulate c-Abl and Biologic Behavior of Desmoid-Type Fibromatosis

Author

Hu, Jia, primary, Hameed, Meera R., primary, Agaram, Narasimhan P., primary, Whiting, Karissa A., primary, Qin, Li-Xuan, primary, Villano, Anthony M., primary, O'Connor, Rachael B., primary, Rozenberg, Julian M., primary, Cohen, Sonia, primary, Prendergast, Katherine, primary, Kryeziu, Sara, primary, White, Richard L., primary, Posner, Mitchell C., primary, Socci, Nicholas D., primary, Gounder, Mrinal M., primary, Singer, Samuel, primary, and Crago, Aimee M., primary

Published
2024
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20. PDGFRβ signaling cooperates with β-catenin to modulate c-Abl and biologic behavior of desmoid-type fibromatosis

Author

Hu, Jia, primary, Hameed, Meera R., additional, Agaram, Narasimhan P., additional, Whiting, Karissa A., additional, Qin, Li-Xuan, additional, Villano, Anthony M., additional, O'Connor, Rachael B., additional, Rozenberg, Julian M., additional, Cohen, Sonia, additional, Prendergast, Katherine, additional, Kryeziu, Sara, additional, White, Richard L., additional, Posner, Mitchell C., additional, Socci, Nicholas D., additional, Gounder, Mrinal M., additional, Singer, Samuel, additional, and Crago, Aimee M., additional

Published
2023
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22. Implementation of Digital Pathology Offers Clinical and Operational Increase in Efficiency and Cost Savings

Author

Hanna, Matthew G., Reuter, Victor E., Samboy, Jennifer, England, Christine, Corsale, Lorraine, Fine, Samson W., Agaram, Narasimhan P., Stamelos, Evangelos, Yagi, Yukako, Hameed, Meera, Klimstra, David S., and Sirintrapun, S. Joseph

Subjects
Workflow software -- Economic aspects, Return on investment -- Economic aspects, Surgical clinics -- Economic aspects, Surgery, Benchmarking, Workflow software, Return on investment, Health, Memorial Sloan-Kettering Cancer Center
Abstract

Context.--Digital pathology (DP) implementations vary in scale, based on aims of intended operation. Few laboratories have completed a full-scale DP implementation, which may be due to high overhead costs that disrupt the traditional pathology workflow. Neither standardized criteria nor benchmark data have yet been published showing practical return on investment after implementing a DP platform. Objective.--To provide benchmark data and practical metrics to support operational efficiency and cost savings in a large academic center. Design.--Metrics reviewed include archived pathology asset retrieval; ancillary test request for recurrent/meta-static disease; cost analysis and turnaround time (TAT); and DP experience survey. Results.--Glass slide requests from the department slide archive and an off-site surgery center showed a 93% and 97% decrease, respectively. Ancillary immunohistochemical orders, compared in 2014 (52%)--before whole slide images (WSIs) were available in the laboratory information system--and 2017 (21%) showed $114 000/y in anticipated savings. Comprehensive comparative cost analysis showed a 5-year $1.3 million savings. Surgical resection cases with prior WSIs showed a 1-day decrease in TAT. A DP experience survey showed 80% of respondents agreed WSIs improved their clinical sign-out experience. Conclusions.--Implementing a DP operation showed a noteworthy increase in efficiency and operational utility. Digital pathology deployments and operations may be gauged by the following metrics: number of glass slide requests as WSIs become available, decrease in confirmatory testing for patients with metastatic/recurrent disease, long-term decrease in off-site pathology asset costs, and faster TAT. Other departments may use our benchmark data and metrics to enhance patient care and demonstrate return on investment to justify adoption of DP. doi: 10.5858/arpa.2018-0514-OA, Applications for digital pathology (DP) have seen increased growth; however, few departments have successfully implemented a full-scale operation of a DP platform. Digital pathology has tremendous potential to disrupt the [...]

Published
2019
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36. Data from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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37. Supplementary Figure 2 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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38. Supplementary Figure 6 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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39. Supplementary Data from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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40. Supplementary Figure 3 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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41. Supplementary Figure 5 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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42. Supplementary Figure 1 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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43. Supplementary Figure 4 from Rb and p53-Deficient Myxofibrosarcoma and Undifferentiated Pleomorphic Sarcoma Require Skp2 for Survival

Author

Li, George Z., primary, Okada, Tomoyo, primary, Kim, Young-Mi, primary, Agaram, Narasimhan P., primary, Sanchez-Vega, Francisco, primary, Shen, Yawei, primary, Tsubokawa, Norifumi, primary, Rios, Jordan, primary, Martin, Axel S., primary, Dickson, Mark A., primary, Qin, Li-Xuan, primary, Socci, Nicholas D., primary, and Singer, Samuel, primary

Published
2023
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46. Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Author

Ho, David J., primary, Agaram, Narasimhan P., additional, Jean, Marc-Henri, additional, Suser, Stephanie D., additional, Chu, Cynthia, additional, Vanderbilt, Chad M., additional, Meyers, Paul A., additional, Wexler, Leonard H., additional, Healey, John H., additional, Fuchs, Thomas J., additional, and Hameed, Meera R., additional

Published
2022
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49. Evolving classification of rhabdomyosarcoma

Author

Agaram, Narasimhan P.

Subjects
Histology, Forkhead Box Protein O1, Rhabdomyosarcoma, Biomarkers, Tumor, Humans, Soft Tissue Neoplasms, General Medicine, Article, MyoD Protein, Pathology and Forensic Medicine
Abstract

Rhabdomyosarcomas comprise the single largest category of soft tissue sarcomas in children and adolescents in the United States, occurring in 4.5 million persons aged less than 20 years. Based on the clinic-pathologic features and genetic abnormalities identified, the rhabdomyosarcomas are classified into embryonal, alveolar, spindle cell / sclerosing and pleomorphic subtypes. Each subtype shows distinctive morphology and has characteristic genetic abnormalities. This review discusses the evolution of the classification of rhabdomyosarcoma to the present day, along with a discussion of key histomorpholgic and genetic features of each subtype and the diagnostic approach to these tumors.

Published
2021

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