ESTRO 2023 - Abstract Book

S1880

Digital Posters

ESTRO 2023

Materials and Methods Our study involved data from 20 low-risk prostate cancer patients. For each patient, the planning CT (pCT) image and fractional cone-beam CT (CBCT) images were imported into RayStation 10A (RaySearch Laboratories, Stockholm, Sweden), along with the manual contours delineated on the images. DIR was then performed using the pCT image as the reference image and the fractional CBCT images as the target images. Various DVF-based metrics, such as the minimum and maximum DVF magnitude, as well as the DSC which measures the overlap between the deformed pCT contours and the manual CBCT contours were obtained from RayStation. Using the extracted DVF-based metrics as features, machine learning was done to predict DSC. Analysis was done on four sets of data, i.e. 1) prostate only, 2) bladder only, 3) rectum only and 4) all the organs combined. The first three sets have the same total number of examples (761) while the last set has three times as many (2283). Three different models, linear regression (LR), Nu Support Vector Regression (NuSVR) and Random Forest Regressor (RFR) were tested. To achieve the best performance for NuSVR and RFR, the hyperparameters were optimised using MAE through 10-fold validation. The models with the optimal hyperparameters were then used to predict the test set. To evaluate the model performance, 10-fold validation was applied and the average of the mean absolute error (MAE) were computed. As LR did not involve hyperparameter tuning, the inner loop was absent in its training pipeline. Similar to NuSVR and RFR, 10-fold cross validation was used for the model evaluation of LR.

Results

The average MAE with their standard deviation were tabulated in Table 1 , for all three models and all four datasets. Overall, RFR showed the best performance, while LR and NuSVR had similar performances. The lowest average MAE achieved was 0.045 while the highest was 0.072. Conclusion This study demonstrated the potential of several machine learning models in predicting DSC using DVF-based metrics. For a reliable clinical translation, further analysis on the robustness of these models to uncertainties could be done through quantification of prediction interval.

PO-2097 Acute toxicity prediction after breast radiotherapy using machine-learning and spectrophotometry

S. Cilla 1 , C. Romano 1 , G. Macchia 2 , M. Boccardi 2 , D. Pezzulla 2 , M. Buwenge 3 , A. Di Castelnuovo 4 , F. Bracone 5 , A. De Curtis 5 , C. Cerletti 5 , L. Iacoviello 6 , M.B. Donati 5 , F. Deodato 2 , A.G. Morganti 7 1 Gemelli Molise Hospital – Università Cattolica del Sacro Cuore, Medical Physics Unit, Campobasso, Italy; 2 Gemelli Molise Hospital – Università Cattolica del Sacro Cuore, Radiation Oncology Unit, Campobasso, Italy; 3 IRCCS Azienda Ospedaliero- Universitaria di Bologna, Radiation Oncology, Bologna, Italy; 4 Mediterranea Cardiocentro, Department of Epidemiology and Prevention, Napoli, Italy; 5 IRCCS NEUROMED, Department of Epidemiology and Prevention, Pozzilli, Italy; 6 EPIMED Research Center, University of Insubria, Department of Medicine and Surgery, Varese, Italy; 7 Alma Mater Studiorum, Bologna University, Department of Experimental, Diagnostic, and Specialty Medicine - DIMES, Bologna, Italy Purpose or Objective Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity. Materials and Methods One hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient’s dataset was split into a training and validation set used for model development and cross-validation (75%/25% split). Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin prediction purposes. Results Thirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥ 2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05). Classification performances reported precision, recall and F1-values