ESTRO 2022 - Abstract Book

S1578

Abstract book

ESTRO 2022

Conclusion Radiomics can have a significant impact on the prediction of the pathological features of PCa; radiomic and clinical features, in particular the radiological features of PI-RADS category and EPE score, appear to complement each other with different roles in the prediction of different pathological features. Different types of RadF can be important in different contexts and should not be judged on an absolute utility scale.

PO-1773 Feasibility of a novel harmonization method for NSCLC multi-centric radiomic studies

A. Botti 1 , M. Bertolini 1 , V. Trojani 1 , N. Cucurachi 2 , M. Iori 1 , M. Galaverni 3 , C. Iotti 4 , P. Borghetti 5 , S. La Mattina 6 , N. Giaj Levra 7 , M. Sepulcri 8 , F. Iori 9 , P. Ciammella 10 1 AUSL - IRCCS Reggio Emilia, Medical Physics, Reggio Emilia, Italy; 2 Università di Modena e Reggio Emilia, Physics Department, Modena, Italy; 3 Azienda Ospedaliero-Universitaria di Parma, Radiation Therapy Department, Parma, Italy; 4 AUSL - IRCCS Reggio Emilia, Radiation Therapy Department, Reggio Emilia, Italy; 5 Spedali Civili di Brescia, Radiation Therapy Department, Brescia, Italy; 6 Spedali Civili di Brescia, Radiation Therapy, Reggio Emilia, Italy; 7 IRCCS Ospedale Sacro Cuore Don Calabria, Radiation Therapy, Verona, Italy; 8 Istituto Oncologico Veneto, Radiation Therapy, Padova, Italy; 9 Università degli studi di Modena e Reggio Emilia, Radiation Therapy, Modena, Italy; 10 AUSL - IRCCS Reggio Emilia, Radiation Therapy, Reggio Emilia, Italy Purpose or Objective This work aims to develop a predictive radiomic model using an innovative harmonization technique to evaluate patients affected by non-small cell lung cancer (NSCLC), using simulation CT and PET/CT. Materials and Methods 106 patients were enrolled from six centers within a research project. Radiomic biomarkers, calculated with pyRadiomics software, were evaluated in the volumes selected by the radiotherapy physicians using a validated protocol. Segmentations were placed in the contralateral healthy lung and shifted by 3 and 6 mm in 6 directions to assess the variability and robustness of the radiomic features. The resulting statistical data were used to create harmonized models according to an in-house method developed to reduce the bias caused by the different acquisition protocols used by the participating institutions. In addition, machine learning techniques capable of predicting the probability of overall disease progression at two years were evaluated. 68 patients from two centers were used in the training phase of the model, using a 10-fold cross-validation strategy. 38 patients from the other four centers were used for the external validation of the model. Results This harmonization method was able to make the feature distributions in the different centers comparable with each other. Out of the 4506 features from the three modalities, five were chosen using the LASSO technique as feature selection to construct the radiomic predictive models. The three models with the highest accuracy were linear SVM, quadratic SVM, and bagged trees. For the train dataset, the following AUC confidence intervals were obtained for linear SVM, quadratic SVM and bagged trees, respectively: [0.73 − 0.92], [0.75 − 0.96] and [0.73 − 0.90] for harmonized features; while for non-