ArticleLa Radiologia medica2020
CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.
Article in La Radiologia medica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Construction of a predictive model for radiation proctitis after radical radiotherapy for cervical cancer based on MRI radiomics.Abdominal radiology (New York) · 2026Article
- Identifying the optimal time point for adaptive re-planning in prostate cancer radiotherapy to minimise rectal toxicity using normal tissue imaging biomarkers.Physics and imaging in radiation oncology · 2025Article
- Precision medicine in prostate cancer: individualized treatment through radiomics, genomics, and biomarkers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Biomarkers in Localized Prostate Cancer: From Diagnosis to Treatment.International journal of molecular sciences · 2025Review
- Predictive Factors for Gastrointestinal and Genitourinary Toxicities in Prostate Cancer External Beam Radiotherapy: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.Journal of applied clinical medical physics · 2025Article
- Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Vocal Cord Dysfunction in Nonlaryngeal Head and Neck Cancer After Chemoradiation Therapy: Predictive Modeling Using CT Radiomics and Machine Learning.BioMed research international · 2025Article
- Imaging the post-treatment pelvis with gynecologic cancers.Abdominal radiology (New York) · 2024Review
- The Application of Radiomics and AI to Molecular Imaging for Prostate Cancer.Journal of personalized medicine · 2024Review
- Article
- Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison.Physical and engineering sciences in medicine · 2023Article
- Article
- Integration between Novel Imaging Technologies and Modern Radiotherapy Techniques: How the Eye Drove the Chisel.Cancers · 2022Review
- Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model.Journal of personalized medicine · 2022Article
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRadiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters.
methodsIn this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic.
resultsSixty-four prostate cancer patients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively.
conclusionsWe have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.
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