Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

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

doi:10.1016/j.ajpath.2022.12.004

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…A promising future direction will include automated viable and necrotic histopathologic tumor assessment using artificial intelligence based on machine learning, 10,11 and a potential real-time radiomic approach based on a model to predict tumor response to chemotherapy using multi-modal MRI sequences which could be established by direct correlation between histopathology and MRI. 12 Current efforts to develop such a model by Teo et al 12 have utilized histology performed using the current “gold standard.” Methods like what is provided in the current study would be able to provide an objective and accurate histopathologic measurement of treatment effect so to aid future model development.…”

Section: Discussionmentioning

confidence: 99%

Manual and Semi-Automated Measurement and Calculation of Osteosarcoma Treatment Effect Using Whole Slide Image and Qupath

He,

Weng

et al. 2023

Pediatr Dev Pathol

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Introduction: In osteosarcoma, the most significant indicator of prognosis is the histologic changes related to tumor response to preoperative chemotherapy, such as necrosis. We have developed a method to measure the osteosarcoma treatment effect using whole slide image (WSI) with an open-source digital image analytical software Qupath. Materials and Methods: In Qupath, each osteosarcoma case was treated as a project. All H&E slides from the entire representative slice of osteosarcoma were scanned into WSIs and imported into a project in Qupath. The regions of tumor and tumor necrosis were annotated, and their areas were measured in Qupath. In order to measure the osteosarcoma treatment effect, we needed to calculate the percentage of total necrosis area over total tumor area. We developed a tool that can automatically extract all values of tumor and necrosis areas from a Qupath project into an Excel file, sum these values for necrosis and whole tumor respectively, and calculate necrosis/tumor percentage. Conclusion: Our method that combines WSI with Qupath can provide an objective measurement to facilitate pathologist’s assessment of osteosarcoma response to treatment. The proposed approach can also be used for other types of tumors that have clinical need for post-treatment response assessment.

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Section: Discussionmentioning

confidence: 99%

Manual and Semi-Automated Measurement and Calculation of Osteosarcoma Treatment Effect Using Whole Slide Image and Qupath

He,

Weng

et al. 2023

Pediatr Dev Pathol

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“…Primary bone tumor histology can present a diagnostic challenge related to cellular heterogeneity, and ML models can be trained to assist with identification of tumor based on histologic imaging 58,59 . In addition, ML technology can assist in other specific aspects of histologic evaluation in primary bone tumors, such as identification and quantification of mitotic figure detection 60 , histologic chemotherapy response in osteosarcoma with outcome prediction 61 , and identification of sarcoma subtype 62 on histopathology. Leveraging the ability of ML to learn and classify digitized images of pathology slides can improve the diagnostic ability of pathologists who may be unfamiliar with musculoskeletal pathology and better direct patient treatment or even clarify further the spectra of evolving sarcoma 63 .…”

Section: Applications Of Ai In Orthopaedic Oncologymentioning

confidence: 99%

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Rizk,

Gonzalez,

Galoaa

et al. 2024

JBJS Reviews

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» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

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“…For instance, Jiang et al [ 66 ] designed deep learning models for prognosis and IDH mutation status prediction in grade 2 gliomas. Ho et al [ 78 ] devised a deep learning network for assessing the necrosis ratio in osteosarcoma WSIs. This innovative technique allows for objective and reproducible evaluations and aids in patient stratification for survival prediction.…”

Section: Deep Learning With Whole Slide Images In Studies On Cancer P...mentioning

confidence: 99%

“…Head and Neck CancerZhang et al[77] Bone Cancer Ho et al[78] Oral Cancer Shaban et al[79] Multiple Cancers Shao et al[80], Cheerla and Gevaert[81], Fu et al[82], Jiang et al[83] …”

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confidence: 99%

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

Lee

2023

Bioengineering

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This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review’s findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

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Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Cited by 5 publications

References 26 publications

Manual and Semi-Automated Measurement and Calculation of Osteosarcoma Treatment Effect Using Whole Slide Image and Qupath

Manual and Semi-Automated Measurement and Calculation of Osteosarcoma Treatment Effect Using Whole Slide Image and Qupath

Machine Learning–Assisted Decision Making in Orthopaedic Oncology

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

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