ArticleOMICS journal of radiology2016
Patient Specific Characteristics Are an Important Factor That Determines the Risk of Acute Grade ≥ 2 Rectal Toxicity in Patients Treated for Prostate Cancer with IMRT and Daily Image Guidance Based on Implanted Gold Markers.
Article in OMICS journal of radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.Frontiers in oncology · 2020Review
- Strategies for prediction and mitigation of radiation-induced liver toxicity.Journal of radiation research · 2018Article
- Modeling of Acute Rectal Toxicity to Compare Two Patient Positioning Methods for Prostate Cancer Radiotherapy: Can Toxicity Modeling be Used for Quality Assurance?OMICS journal of radiology · 2018Article
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Authors and funding
8 authors.
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Abstract
aimTo model acute rectal toxicity in Intensity Modulated Radiation Therapy (IMRT) for prostate cancer using dosimetry and patient specific characteristics.
methodsA database of 79 prostate cancer patients treated with image guided IMRT was used to fit parameters of Lyman-Kutcher-Burman (LKB) and logistic regression Normal Tissue Complications Probability (NTCP) models to acute grade ≥ 2 rectal toxicities. We used a univariate regression model to find the dosimetric index which was most correlated with toxicity and a multivariate logistic regression model with machine learning algorithm to integrate dosimetry with patient specific characteristics. We used Receiver Operating Characteristics (ROC) analysis and the area under the ROC curve (AUC) to quantify the predictive power of models.
resultsSixteen patients (20.3%) developed acute grade≥2 rectal toxicity. Our best estimate (95% confidence interval) of LKB model parameters for acute rectal toxicity are exponent n=0.13 (0.1-0.16), slope m=0.09 (0.08-0.11), and threshold dose TD50=56.8 (53.7-59.9) Gy. The best dosimetric indices in the univariate logistic regression NTCP model were D25% and V50Gy. The best AUC of dosimetry only modeling was 0.67 (0.54, 0.8). In the multivariate logistic regression two patient specific variables were particularly strongly correlated with acute rectal toxicity, the use of statin drugs and PSA level prior to IMRT, while two additional variables, age and diabetes were weakly correlated. The AUC of the logistic regression NTCP model improved to 0.88 (0.8, 0.96) when patient specific characteristics were included. In a group of 79 patients, 40 took Statins and 39 did not. Among patients who took statins, (4/40)=10% developed acute grade ≥2 rectal toxicity, compared to (12/39)=30.8% who did not take statins (p=0.03). The average and standard deviation of PSA distribution for patients with acute rectal toxicity was
conclusionsPatient specific characteristics strongly influence the likelihood of acute grade ≥ 2 rectal toxicity in radiation therapy for prostate cancer.
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