A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone

Giant cell tumor of bone in the pediatric population: a retrospective study highlighting cases of metaphyseal only location and increased local recurrence rates in skeletally immature patients

OBJECTIVE

To describe the presentation of giant cell tumors (GCT) of the bone in the pediatric population to (1) improve the differential diagnosis of pediatric bone tumors and (2) identify the origin of GCT. Understanding the origin of bone tumors assists in establishing appropriate diagnoses and recommending treatment options. This is particularly important in children, where evaluating the need for invasive procedures is balanced with the desire to avoid overtreatment. GCT have historically been considered epiphyseal lesions with potential metaphyseal extension. Therefore, GCT may be inappropriately excluded from the differential diagnosis of metaphyseal lesions in the skeletally immature.

MATERIALS AND METHODS

We identified 14 patients from 1981 to 2021 at a single institution who had histologic confirmation of GCT and were less than 18 years old at diagnosis. Patient characteristics, tumor location, surgical treatment, and local recurrence rates were collected.

RESULTS AND CONCLUSIONS

Ten (71%) patients were female. Eleven (78.6%) were epiphysiometaphyseal (1 epiphyseal, 4 metaphyseal, 6 epiphysiometaphyseal). Five patients had an open adjacent physis, of which three (60%) had tumors confined solely to the metaphysis. Of the five patients with open physis, four (80%) developed local recurrence while only one patient (11%) with a closed physis had local recurrence (p value = 0.0023). Our results illustrate that for the skeletally immature, GCT can (and in our results more commonly did) occur in the metaphyseal location. These findings suggest that GCT should be included in the differential diagnosis of primary metaphyseal-only lesions in the skeletally immature.

Research status and progress of radiomics in bone and soft tissue tumors: A review

Bone and soft tissue tumors are diverse, accompanying by complex histological components and significantly divergent biological behaviors. It is a challenge to address the demand for qualitative imaging as traditional imaging is restricted to the detection of anatomical structures and aberrant signals. With the improvement of digitalization in hospitals and medical centers, the introduction of electronic medical records and easier access to large amounts of information coupled with the improved computational power, traditional medicine has evolved into the combination of human brain, minimal data, and artificial intelligence. Scholars are committed to mining deeper levels of imaging data, and radiomics is worthy of promotion. Radiomics extracts subvisual quantitative features, analyzes them based on medical images, and quantifies tumor heterogeneity by outlining the region of interest and modeling. Two observers separately examined PubMed, Web of Science and CNKI to find existing studies, case reports, and clinical guidelines about research status and progress of radiomics in bone and soft tissue tumors from January 2010 to February 2023. When evaluating the literature, factors such as patient age, medical history, and severity of the condition will be considered. This narrative review summarizes the application and progress of radiomics in bone and soft tissue tumors.

Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning

BACKGROUND

The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning.

METHODS

Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves.

RESULTS

Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively.

CONCLUSIONS

The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.

A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers

PURPOSE

To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature.

METHODS

We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented.

RESULTS

Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present.

CONCLUSION

The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.

Emerging trends and focus of giant cell tumor of bone research from 2001-2021: A visualization research

Giant cell tumor of bone is a highly invasive benign tumor with a high postoperative recurrence rate.

OBJECTIVE

This study aims to analyze the research hotspots and trends of global research on giant cell tumor of bone in the past 20 years, to provide a reference for relevant personnel in this field to carry out academic research.

METHODS

The literature related to giant cell tumor of bone from 2001 to 2021 was retrieved from the Web of Science. The bibliometrics research method and VOS Viewer were used to extract and analyze the keywords of the journal authors' research institutions, and the research status and development direction in the recent 20 years were visualized.

RESULTS

A total of 2063 articles were included. The number of global publications is increasing every year. The United States contributes the most to global research, with the most citations and the highest H-index. The journal Clinical Orthopaedics and Related Research published the most articles on this issue. "Denosumab" and "h3f3a" will get more attention and be the next popular hotspot in the future.

CONCLUSION

The study of giant cell tumor of bone is a hot spot of continuous development and has an important contribution to human health.

Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach

The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOIentire, VOIedge, and VOIcore) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models' performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOIcore-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making.