RESUMO
There are various treatment modalities for prostate cancer, which has a high incidence. In this study, it is aimed to make predictions with machine learning in order to determine the optimal treatment option for prostate cancer patients. The study included 88 male patients diagnosed with prostate cancer. Independent variables were determined as Gleason scores, biopsy, PSA, SUVmax, and age. Prostate cancer treatments, which are dependent variables, were determined as hormone therapy(n = 30), radiotherapy(n = 28) and radiotherapy + hormone therapy(n = 30). Machine learning was carried out in the Python with SVM, RF, DT, ETC and XGBoost. Metrics such as accuracy, ROC curve, and AUC were used to evaluate the performance of multi-class predictions. The model with the highest number of successful predictions was the XGBoost. False negative rates for hormone therapy, radiotherapy, and radiotherapy + hormone therapy treatments were, respectively, 12.5, 33.3, and 0%. The accuracy values were computed as 0.61, 0.83, 0.83, 0.72 and 0.89 for SVM, RF, DT, ETC and XGBoost, respectively. The three features that had the greatest influence on the treatment model prediction for prostate cancer with XGBoost were biopsy, Gleason score (3 + 3), and PSA level, respectively. According to the AUC, ROC and accuracy, it was determined that the XGBoost was the model that made the best estimation of prostate cancer treatment. Among the variables biopsy, Gleason score, and PSA level are identified as key variables in prediction of treatment.
RESUMO
AIM: This study was aimed to re-determine the radiation dose rate emitted from the patients who underwent bone scintigraphy. MATERIAL AND METHODS: A mean of 20.87±2.54 mCi 99mTc-MDP was injected into patients. A GM counter was used to measure dose rates in 3 different periods, at intervals of 25, 50, 100, 150, and 200 cm from the patient's anterior for head, thorax, abdomen, and pelvis levels. Measurements were used to determine patient-induced environmental doses and radiation doses to personnel/patient relatives. RESULTS AND DISCUSSION: There were strong correlations between mean dose rate (mRh-1mCi-1) and time at all regions and distances. The received dose for staff was calculated between a range of 0.01-0.02 mSv/mCi per patient. The total dose to be received by the companion was estimated to be between 0.019-0.039 and 0.011-0.022 mSv for public and personal vehicle transportation, respectively. The radiation dose exposed by nurses (4th, 6th, and 8th hours after injection) was found to be 0.012-0.064, 0.006-0.038, and 0.002-0.018 mSv/- patient, respectively. CONCLUSION: The fact that the doses of personnel and patient relatives in the study were below the legal limits shows that the study was carried out within a safe range. However, in terms of radiation protection, it is necessary to limit the time spent with the patient as much as possible and increase the distance. Since the dangers of low radiation dosages are unknown, there is a need to inform the patient's relatives and staff about the potential risks.