Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Abstract

Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging-and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics-and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.

Diagnostic Accuracy of 128-Slice Single-Source CT for the Detection of Dislocated Bucket Handle Meniscal Tears in the Setting of an Acute Knee Trauma—Correlation with MRI and Arthroscopy

“…A growing number of novel deep-learning-based and machine-learning-based algorithms offer exciting opportunities for improving diagnostic performances and a perspective on future goals and objectives in musculoskeletal radiology for various tasks [ 31 , 32 ]. Thus far, research mostly focuses on MRI rather than on CT imaging.…”

Preoperative prediction of residual back pain after vertebral augmentation for osteoporotic vertebral compression fractures: Initial application of a radiomics score based nomogram

“…Its enormous potential in oncological imaging has been demonstrated by extensive radiomics evaluations (22). In skeletal diseases, although some studies have found that radiomics can evaluate the heterogeneity in femoral and spinal microarchitecture (23)(24)(25), compared with the number of published studies about oncologic radiomics, musculoskeletal radiomics investigations are less frequently reported (26). In the present study, we investigated, for the first time, the use of radiomics features to preoperatively predict RBP risk in OVCF patients receiving PKP.…”

A radiomics approach to the diagnosis of femoroacetabular impingement

“…The assumption of radiomics is that image features quantify crucial information regarding pathologic conditions through intra-region heterogeneity (19). Several studies have used radiomics to evaluate musculoskeletal diseases of soft tissue and bone (23). However, to our knowledge no previous work has investigated the use of radiomics to diagnose FAI (24).…”