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These limitations include the fact that the histological estimation is typically available only after 10 weeks of pre-operative chemotherapy potentially too late to make meaningful adjustments in patient therapy. Limitations, therefore, in the predictive capacity of histological interpretation of necrosis and radiological interpretation of response suggest the opportunity to optimize both methods toward the development of an effective biomarker. Non-invasive imaging with CT-PET and both diffusion-weighted and dynamic contrast-enhanced MRI might also be employed as biomarkers of therapy, but each are variably successful in predicting tumor response in high-grade osteosarcoma.

It is clear from these data that limitations exist in the current approach to treating patients with osteosarcoma and while fundamentally this includes the need for novel therapies it also emphasizes the need for an effective indicator of treatment response to allow greater opportunity for personalized treatments. However, patient outcomes were not significantly improved by randomized therapy in the EURAMOS-1 trial, leading some to challenge the validity of necrosis as a predictor in this disease.

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Repeated international series emphasized the value of necrosis in predicting treatment outcome, and led to an extensive international study (EURAMOS-1) randomizing patients with high-grade resectable osteosarcoma to therapy based on tumor necrosis. For high-grade osteosarcoma, tumor necrosis in response to pre-operative therapy has been a highly significant prognostic indicator for four decades. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist.Įxamination of resected cancer specimens after delivery of chemotherapy allows pathologists to interpret the responsiveness of patient tumors and successfully influences patient outcomes by stratifying treatment administration to individual patient risk. The Cancer Imaging Archive.įunding: This work was supported by Cancer Prevention and Research Institute of Texas (CPRIT) award RP150164 (URL: ). Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment.

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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Data are available on The Cancer Imaging Archive (TCIA): Leavey, P., Sengupta, A., Rakheja, D., Daescu, O., Arunachalam, H. Received: Accepted: JanuPublished: April 17, 2019Ĭopyright: © 2019 Arunachalam et al. University of California Riverside, UNITED STATES (2019) Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models. The proposed pipeline can also be adopted for other types of tumor.Ĭitation: Arunachalam HB, Mishra R, Daescu O, Cederberg K, Rakheja D, Sengupta A, et al. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. We then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. We computed the receiver-operator characteristic for discrimination of non-tumor from tumor followed by conditional discrimination of necrotic from viable tumor and found our models performing exceptionally well. We also developed a deep-learning architecture and trained it on the same data set.

With the goal of labeling the diverse regions of the digitized tissue into viable tumor, necrotic tumor, and non-tumor, we trained 13 machine-learning models and selected the top performing one (a Support Vector Machine) based on reported accuracy. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma.

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