AIMC Topic: Bone Neoplasms

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The preoperative machine learning algorithm for extremity metastatic disease can predict 90-day and 1-year survival: An external validation study.

Journal of surgical oncology
BACKGROUND: The prediction of survival is valuable to optimize treatment of metastatic long-bone disease. The Skeletal Oncology Research Group (SORG) machine-learning (ML) algorithm has been previously developed and internally validated. The purpose ...

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Radiology
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and cla...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BMC medical imaging
BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data.

Scientific reports
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmen...

A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

European radiology
OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion.

Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer.

European journal of nuclear medicine and molecular imaging
PURPOSE: We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

Contrast media & molecular imaging
This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer...

Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.

Radiology
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bo...