ArticleCurrent oncology (Toronto, Ont.)2022
Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer.
Article in Current oncology (Toronto, Ont.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma.Diagnostics (Basel, Switzerland) · 2023Article
- Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.Insights into imaging · 2023Article
- Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?Diagnostics (Basel, Switzerland) · 2023Article
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- The Use of Cardiac Stereotactic Radiation Therapy (SBRT) to Manage Ventricular Tachycardia: A Case Report, Review of the Literature and Technical Notes.Journal of personalized medicine · 2022Article
- Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.In vivo (Athens, Greece)Article
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Authors and funding
20 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63-0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis.
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