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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Radiomics for bone tumour diagnosis and management.

V Molinari1, S Gitto2, F Serpi3

  • 1Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy.

Clinical Radiology
|October 10, 2025
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Summary
This summary is machine-generated.

Radiomics, an advanced imaging analysis, enhances bone cancer diagnosis and management by extracting quantitative features from medical scans. This technique improves accuracy in differentiating tumors and predicting treatment responses, aiding personalized patient care.

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Area of Science:

  • Medical Imaging
  • Oncology
  • Data Science

Background:

  • Radiomics offers advanced quantitative analysis of medical images.
  • It is particularly valuable in bone oncology for detailed tumor characterization.
  • Visual inspection of bone tumors can be limited in assessing complex characteristics.

Purpose of the Study:

  • To explore the utility of radiomics in bone oncology for diagnosis and management.
  • To highlight radiomics' role in improving diagnostic accuracy and predicting treatment outcomes.
  • To discuss the potential of radiomics in personalizing treatment strategies and monitoring disease progression.

Main Methods:

  • Utilizing machine learning algorithms to extract quantitative radiomic features (texture, shape, intensity).
  • Analyzing complex patterns within medical imaging data.
  • Integrating radiomic features with clinical data for enhanced predictive models.

Main Results:

  • Radiomics improves accuracy in differentiating benign from malignant bone lesions and metastases.
  • It aids in identifying bone tumor histotypes and predicting tumor grade.
  • Radiomics predicts response to neoadjuvant therapy and monitors disease progression/recurrence effectively.

Conclusions:

  • Radiomics significantly enhances the diagnostic and prognostic capabilities in bone oncology.
  • It supports personalized treatment planning and monitoring of malignant bone tumors.
  • Further standardization and reproducibility efforts are needed for widespread clinical adoption.