Abstract:

Background. Osteosarcoma, which is the most common malignant pediatric bone cancer, remains dependent on an imprecise systemic treatment largely unchanged in 30 years. In this study, we correlated histopathology with magnetic resonance imaging (MRI), used the correlation to extract MRI-specific features representative of tumor necrosis, and subsequently developed a novel classification model for predicting tumor response to neoadjuvant chemotherapy in pediatric patients with osteosarcoma using multi-modal MRI. The model could ultimately serve as a testable biomarker for a high-risk malignancy without successful precision treatments.

Methods. Patients with newly diagnosed high-grade appendicular osteosarcoma were enrolled in a single-center observational study, wherein patients underwent pre-surgical evaluation using both conventional MRI (post-contrast T1-weighted with fat saturation, pre-contrast T1-weighted, and short inversion-time inversion recovery (STIR)) and advanced MRI (diffusion weighted (DW) and dynamic contrast enhanced (DCE)). A classification model was established based on a direct correlation between histopathology and MRI, which was achieved through histologic-MR image co-registration and subsequent extraction of MR image features for identifying histologic tumor necrosis. By operating on the MR image features, tumor necrosis was estimated from different combinations of MR images using a multi-feature fuzzy clustering technique together with a weighted majority ruling. Tumor necrosis calculated from MR images, for either an MRI plane of interest or whole tumor volume, was compared to pathologist-estimated necrosis and necrosis quantified from digitized histologic section images using a previously described deep learning classification method.

Results. 15 patients were enrolled, of whom two withdrew, one became ineligible, and two were subjected to inadequate pre-surgical imaging. MRI sequences of n = 10 patients were subsequently used for classification model development. Different MR image features, depending on the modality of MRI, were shown to be significant in distinguishing necrosis from viable tumor. The scales at which MR image features optimally signified tumor necrosis were different as well depending on the MR image type. Conventional MRI was shown capable of differentiating necrosis from viable tumor with an accuracy averaging above 90%. Conventional MRI was equally effective as DWI in distinguishing necrotic from viable tumor regions. The accuracy of tumor necrosis prediction by conventional MRI improved to above 95% when DCE-MRI was added into consideration. Volume-based tumor necrosis estimations tended to be lower than those evaluated on an MRI plane of interest.

Conclusion. The study has shown a proof-of-principle model for interpreting chemotherapeutic response using multi-modal MRI for patients with high-grade osteosarcoma. The model will continue to be evaluated as MR image features indicative of tumor response are now computable for the disease prior to surgery.

Data set:

In this data set, we share prospectively collected magnetic resonance imaging (MRI) sequences and histopathology whole slide images for 10 patients (from 10 to 20 years of age) with osteosarcoma. Patients participated in an observational IRB-approved cohort study consenting to the investigating team to study tumor necrosis in digitized histology of resected tumor specimens by machine learning and to merge MRI with histology to develop computational algorithms for accurately predicting tumor necrosis by MRI. Patients received treatment (chemotherapy and surgery) according to institutional standards of care. No information developed in this study informed any clinical decisions for patients. All shared images have been stripped of any patient identifying information.

The following MRI sequences are shared: i) post-contrast T1-weighted with fat saturation, ii) pre-contrast T1-weighted, iii) short inversion-time inversion recovery (STIR), iv) diffusion weighted (DW), and v) dynamic contrast enhanced (DCE). The MRI sequences were acquired at the time of pre-operative imaging performed after week 10 of chemotherapy. MRI was performed on a 3-Tesla scanner (Magnetom Skyra, Siemens Healthcare, Erlangen, Germany), and all MR images were acquired in the coronal plane.

The histopathology data set consists of digitized H&E-stained whole slide images of resected osteosarcoma. Histology glass slides were digitally scanned using Aperio AT Turbo Scanning System (Leica Biosystems, Vista, CA).

Recommended software for viewing the image files:
Aperio ImageScope for SVS files;
RadiAnt DICOM Viewer or 3D Slicer for DICOM files.