Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study.

Objectives: To investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using 68 Ga-PSMA PET imaging as reference standard.Methods: In this IRB-approved ret...

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Publicado en:European Radiology Vol. 32; no. 3; pp. 1823 - 1833
Autores principales: Hinzpeter, Ricarda, Baumann, Livia, Guggenberger, Roman, Huellner, Martin, Alkadhi, Hatem, Baessler, Bettina
Formato: Journal Article
Publicado: Springer Nature Mar2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Mar2022
      vid: 32
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00330-021-08245-6
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        atl: Radiomics for detecting prostate cancer bone metastases invisible in CT: a proof-of-concept study.
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          Hinzpeter, Ricarda
          Baumann, Livia
          Guggenberger, Roman
          Huellner, Martin
          Alkadhi, Hatem
          Baessler, Bettina
        affil: Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistr. 100, CH-8091, Zurich, Switzerland
      sug:
        subj:
          Prostatic Neoplasms
          Gallium Radioisotopes
          Male
          Retrospective Design
          Aged
          Middle Age
          Tomography, X-Ray Computed
          Reproducibility of Results
          Aged: 65+ years
          Middle Aged: 45-64 years
          Male
      ab: Objectives: To investigate, in patients with metastatic prostate cancer, whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone using 68 Ga-PSMA PET imaging as reference standard.Methods: In this IRB-approved retrospective study, 67 patients (mean age 71 ± 7 years; range: 55-84 years) showing a total of 205 68 Ga-PSMA-positive prostate cancer bone metastases in the thoraco-lumbar spine and pelvic bone being invisible in CT were included. Metastases and 86 68 Ga-PSMA-negative bone volumes in the same body region were segmented and further post-processed. Intra- and inter-reader reproducibility was assessed, with ICCs < 0.90 being considered non-reproducible. To account for imbalances in the dataset, data augmentation was performed to achieve improved class balance and to avoid model overfitting. The dataset was split into training, test, and validation set. After a multi-step dimension reduction process and feature selection process, the 11 most important and independent features were selected for statistical analyses.Results: A gradient-boosted tree was trained on the selected 11 radiomic features in order to classify patients' bones into bone metastasis and normal bone using the training dataset. This trained model achieved a classification accuracy of 0.85 (95% confidence interval [CI]: 0.76-0.92, p < .001) with 78% sensitivity and 93% specificity. The tuned model was applied on the original, non-augmented dataset resulting in a classification accuracy of 0.90 (95% CI: 0.82-0.98) with 91% sensitivity and 88% specificity.Conclusion: Our proof-of-concept study indicates that radiomics may accurately differentiate unaffected bone from metastatic bone, being invisible by the human eye on CT.Key Points: • This proof-of-concept study showed that radiomics applied on CT images may accurately differentiate between bone metastases and metastatic-free bone in patients with prostate cancer. • Future promising applications include automatic bone segmentation, followed by a radiomics classifier, allowing for a screening-like approach in the detection of bone metastases.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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