Imaging of pediatric bone tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper.

Malignant primary bone tumors are uncommon in the pediatric population, accounting for 3%-5% of all pediatric malignancies. Osteosarcoma and Ewing sarcoma comprise 90% of malignant primary bone tumors in children and adolescents. This paper provides consensus-based recommendations for imaging in children with osteosarcoma and Ewing sarcoma at diagnosis, during therapy, and after therapy.

Correlation of histopathology and multi-modal magnetic resonance imaging in childhood osteosarcoma: Predicting tumor response to chemotherapy.

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. CONCLUSIONS: 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.

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning. We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response. 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 also developed a deep-learning architecture and trained it on the same data set. 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 then used the trained models to identify regions of interest on image-tiles generated from test whole slide images. The classification output is visualized as a tumor-prediction map, displaying the extent of viable and necrotic tumor in the slide image. Thus, we lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation. The proposed pipeline can also be adopted for other types of tumor.

COMPUTER AIDED IMAGE SEGMENTATION AND CLASSIFICATION FOR VIABLE AND NON-VIABLE TUMOR IDENTIFICATION IN OSTEOSARCOMA.

Osteosarcoma is one of the most common types of bone cancer in children. To gauge the extent of cancer treatment response in the patient after surgical resection, the H&E stained image slides are manually evaluated by pathologists to estimate the percentage of necrosis, a time consuming process prone to observer bias and inaccuracy. Digital image analysis is a potential method to automate this process, thus saving time and providing a more accurate evaluation. The slides are scanned in Aperio Scanscope, converted to digital Whole Slide Images (WSIs) and stored in SVS format. These are high resolution images, of the order of 109 pixels, allowing up to 40X magnification factor. This paper proposes an image segmentation and analysis technique for segmenting tumor and non-tumor regions in histopathological WSIs of osteosarcoma datasets. Our approach is a combination of pixel-based and object-based methods which utilize tumor properties such as nuclei cluster, density, and circularity to classify tumor regions as viable and non-viable. A K-Means clustering technique is used for tumor isolation using color normalization, followed by multi-threshold Otsu segmentation technique to further classify tumor region as viable and non-viable. Then a Flood-fill algorithm is applied to cluster similar pixels into cellular objects and compute cluster data for further analysis of regions under study. To the best of our knowledge this is the first comprehensive solution that is able to produce such a classification for Osteosarcoma cancer. The results are very conclusive in identifying viable and non-viable tumor regions. In our experiments, the accuracy of the discussed approach is 100% in viable tumor and coagulative necrosis identification while it is around 90% for fibrosis and acellular/hypocellular tumor osteoid, for all the sampled datasets used. We expect the developed software to lead to a significant increase in accuracy and decrease in inter-observer variability in assessment of necrosis by the pathologists and a reduction in the time spent by the pathologists in such assessments.

Chemotherapy for Generalized Infantile Myofibromatosis With Visceral Involvement.

Infantile myofibromatosis (IM) is most commonly limited to cutaneous lesions that resolve spontaneously. However, generalized IM with visceral involvement, which has a reported mortality rate as high as 73%, has been successfully treated with a combination of methotrexate and vinblastine. Here we report the further efficacy of low-dose methotrexate and vinblastine in 2 pediatric patients with IM and visceral involvement and review the literature describing chemotherapy for these patients.

Bone scintigraphy in osteosarcoma: a single institution experience.

Bone scintigraphy is a well-established method to evaluate for metastatic disease in osteosarcoma. We identified a patient who had a negative (cold) bone scan at skeletal relapse and consequently reviewed the frequency of cold scans in osteosarcoma at our institution. No cold scans were identified at diagnosis, and only 1 patient had a cold scan at skeletal recurrence. No correlation was identified between clinical outcomes and bone scan features, other than identification of metastatic disease. Patients with skeletal recurrence were all symptomatic, thus we suggest that bone scintigraphy is not indicated in routine postchemotherapy surveillance for patients with osteosarcoma.

Utility of apparent diffusion coefficient ratios in distinguishing common pediatric cerebellar tumors.

RATIONALE AND OBJECTIVES: The aim of this study was to identify clinically useful tumor/normal brain apparent diffusion coefficient (ADC) ratios for distinguishing common pediatric cerebellar tumors. MATERIALS AND METHODS: Review of medical records revealed 79 patients with cerebellar tumors who underwent preoperative magnetic resonance imaging, including diffusion-weighted imaging sequences, and surgery. There were 31 pilocytic astrocytomas, 27 medulloblastomas, 14 ependymomas, and seven atypical teratoid/rhabdoid tumors. ADC values were measured by placing regions of interest on the solid tumor and normal brain parenchyma by two reviewers. Tumor/normal brain ADC ratios were calculated. RESULTS: Mean ADC values of the pilocytic astrocytomas were greater than those of ependymomas, whose mean ADC values were greater than those of medulloblastomas and atypical teratoid/rhabdoid tumors. Using a tumor/normal brain ADC ratio threshold of 1.70 to distinguish pilocytic astrocytomas from ependymomas, sensitivity of 92% and specificity of 79% were achieved. A tumor/normal brain ADC ratio threshold of 1.20 enabled the sorting of ependymomas from medulloblastomas with sensitivity of 93% and specificity of 88%. CONCLUSIONS: Tumor/normal brain ADC ratios allow the distinguishing of common pediatric cerebellar tumors.

Superior pubic ramus fractures fixed with percutaneous screws: what predicts fixation failure?

OBJECTIVE: The purpose of this study is to present the early complications of percutaneous screw fixation of superior pubic ramus fractures and to present a new classification scheme for superior pubic ramus fractures. DESIGN: Retrospective. SETTING: Level 1 trauma center. PATIENTS: One hundred and twelve patients with pelvic fracture between the ages of 14 to 89 years underwent percutaneous screw fixation of 145 pubic ramus fractures. Eighty-one patients with 107 surgically repaired fractures were followed to fracture union. Follow-up averaged 9 months (range 2-52 months). One additional patient who sustained fixation failure 4 days after surgery was included to yield a study group of 82 patients with 108 surgically repaired ramus fractures. INTERVENTION: Patients underwent percutaneous screw fixation of a superior pubic ramus fracture. MAIN OUTCOME MEASUREMENTS: Superior pubic ramus fractures were classified according to a new scheme, the Nakatani system, which categorizes superior ramus fractures according to location with respect to the obturator foramen. Patient radiographs were examined for evidence of loss of reduction, defined as any motion at the ramus fracture site or hardware motion, after fracture surgery. RESULTS: Of the 82 patients followed to union or fixation failure, 12 (15%) had loss of reduction on follow-up radiographs. The average age of patients who lost reduction was 55 years. The most common mechanism of reduction loss was a collapse of the pubic ramus over the screw, with recurrence of an internal rotation deformity of the injured hemipelvis. Ten patients who lost reduction were women, and 11 had undergone ramus screw placement in retrograde fashion. No loss of reduction was seen in Zone III ramus fractures (those that involve the bone lateral to the obturator foramen). No patient sustained recognized neurologic, vascular, or urologic injury as a result of percutaneous screw fixation of a superior pubic ramus fracture. CONCLUSIONS: The prevalence of loss of reduction after percutaneous screw fixation of pubic ramus fractures is 15%. Loss of reduction is more common in elderly and female patients and in patients whose ramus screws are placed in a retrograde fashion. Also, loss of reduction appears to be more common in fractures medial to the lateral border of the obturator foramen.