Researchers from Johns Hopkins Medicine Developed a Machine Learning Model for Precise Osteosarcoma Necrosis Calculation

In the realm of oncology, assessing the effectiveness of chemotherapy on bone cancer patients is a critical determinant of prognosis. A research team at Johns Hopkins Medicine has recently pioneered a groundbreaking advancement in this field. They have successfully developed and trained a machine learning model to calculate percent necrosis (PN), a crucial metric indicating the extent of tumour death in patients with osteosarcoma. This innovative model demonstrates an impressive 85% accuracy compared to results obtained by a musculoskeletal pathologist. By removing a single outlier, accuracy soars to an astonishing 99%.

Traditionally, the process of calculating PN has been labor-intensive and reliant on extensive annotation data from musculoskeletal pathologists. Moreover, it suffers from low interobserver reliability, wherein two pathologists analyzing the same whole-slide images (WSIs) may arrive at different conclusions. Recognizing these challenges, the researchers highlighted the need for an alternative approach.

The team’s pursuit led them to develop a weakly supervised machine learning model that necessitates minimal annotation data for training. This innovative methodology implies that a musculoskeletal pathologist utilizing the model for PN calculation would only be required to provide partially annotated WSIs, substantially reducing the pathologist’s workload.

To construct this model, the team curated a comprehensive dataset, including WSIs, from the pathology archives of Johns Hopkins’ distinguished U.S. tertiary cancer center. This data exclusively comprised cases of intramedullary osteosarcoma, originating from the core of the bone, in patients who underwent both chemotherapy and surgery at the center between 2011 and 2021.

A musculoskeletal pathologist meticulously annotated three distinct tissue types on each collected WSIs: active tumor, necrotic tumor, and non-tumour tissue. Additionally, the pathologist estimated the PN for each patient. Armed with this invaluable information, the team embarked on the training phase.

The researchers explained the training process. They decided to train the model by teaching it to recognize image patterns. The WSIs were segregated into thousands of small patches and then divided into groups based on how the pathologist labeled them. Finally, these grouped patches were fed into the model for training. This approach was chosen to provide the model with a more robust frame of reference, avoiding the potential oversight that could occur by solely feeding it one large WSI.

Following training, the model and the musculoskeletal pathologist were presented with six WSIs to evaluate two osteosarcoma patients. The results were remarkable, with an 85% positive correlation between the model’s PN calculations and tissue labeling compared to the pathologist’s findings. The only caveat arose from occasional difficulties in properly identifying cartilage tissue, leading to an outlier due to an abundance of cartilage on one WSI. Upon its removal, the correlation skyrocketed to an impressive 99%.

Looking ahead, the team envisions incorporating cartilage tissue in the model’s training and expanding the scope of WSIs to encompass various types of osteosarcoma beyond intramedullary. This study represents a significant stride towards revolutionizing the evaluation of osteosarcoma treatment outcomes.

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Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.

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