[The importance of radiology in bone sarcoma diagnostics : Initial and advanced diagnostics]

Delay in Diagnosis and Treatment of Primary Bone Tumors during COVID-19 Pandemic in Poland

BACKGROUND

The COVID-19 pandemic has affected all of the medical specialties, including orthopedic oncology. Therefore, the aim of the study was to assess how it influenced the diagnostic and therapeutic processes for patients with bone neoplasms.

METHODS

We evaluated 87 patients treated due for bone neoplasms before (Group I, n = 36) and during the COVID-19 pandemic (Group II, n = 51). A delay in diagnosis was defined as the period between the initial clinical symptoms and the date of referral to an oncology center. The patients from Group II were asked to complete a short questionnaire regarding the COVID-19 pandemic.

RESULTS

The median general delay in diagnosis before the pandemic was 7 months, while during the pandemic, it was 10 months (p = 0.728). The biopsy delay was lower in the pre-pandemic group: median-6.5 vs. 12 days (p = 0.025). The patients from Group II were diagnosed with larger tumors compared to those in Group I: the median values were 75 vs. 56 mm (p = 0.025), respectively. After an X-ray examination, the bone neoplasms were suspected more frequently in the Group II: 63% vs. 44% cases (p = 0.024), respectively. In Group II, 20 (60.8%) cases of SARS-CoV-2 infection were reported, however, no respiratory failure cases were noticed.

CONCLUSION

The pandemic affected the diagnostic process of primary bone tumors, resulting in delays in performing biopsies. During the pandemic, the patients reported larger diameters of their bone lesions.

[Conventional epiphyseal chondrosarcoma of childhood and adolescence: a case report]

INTRODUCTION

Conventional chondrosarcoma is the second most common primary malignant bone tumor and usually occurs at older adult ages. It is rare in childhood and adolescence.

CASE HISTORY

This case report presents the treatment course of a 13-year-old boy with a symptomatic chondrogenic tumor of the right distal femur. Histopathologically, an epiphyseal intermediate-grade chondrosarcoma (G2) was diagnosed.

DISCUSSION

Based on the following case, potential radiological and histopathological differential diagnoses, such as chondroblastoma or chondroblastic osteosarcoma, are discussed against the background of current standards in orthopedic oncology.

Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors

Objectives

To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors.

Materials and Methods

In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical characteristics extracted by three musculoskeletal radiologists with ten years of experience. SHapley Additive exPlanations (SHAP) was used to identify the most essential features for the classification of bone tumors. The diagnostic performance of the RF models was quantified using receiver operating characteristic (ROC) curves.

Results

The features extracted by the three radiologists had a satisfactory agreement and the minimum intraclass correlation coefficient (ICC) was 0.761 (CI: 0.686-0.824, P < .001). The binary and tertiary models were built to classify tumors as benign, malignant, or intermediate based on the imaging and clinical features from 627 and 796 patients. The AUC of the binary (19 variables) and tertiary (22 variables) models were 0.97 and 0.94, respectively. The accuracy of binary and tertiary models were 94.71% and 82.77%, respectively. In descending order, the most important features influencing classification in the binary model were margin, cortex involvement, and the pattern of bone destruction, and the most important features in the tertiary model were margin, high-density components, and cortex involvement.

Conclusions

This study developed interpretable models to classify bone tumors with great performance. These should allow radiographers to identify imaging features that are important for the classification of bone tumors in the clinical setting.