ESTRO 2023 - Abstract Book

S1908

Digital Posters

ESTRO 2023

T2-w and axial diffusion using multiple b-values. Apparent diffusion coefficient (ADC) maps were calculated. Subsequently, all subjects received total doses between 74 and 80 Gy, delivered in 2 Gy fractions. Then, patients were followed up by means of clinical examination and PSA level analysis every 6 months for at least 4 years. A total of 20 patients suffered biochemical recurrence, defined according to the Phoenix criteria, within this delay. In order to simulate tumour response to radiotherapy with our mechanistic model, 206 2D digital tissues representing the 206 patients of the cohort were built. In depth, tumour volume and average T2-w and ADC were extracted from the corresponding pre-treatment images and then used to initialise the virtual tissues. An initial prostate-specific vascular density of 3.8% was supposed for every digital tissue. The radiotherapy protocol administered to each patient of the cohort was simulated on the corresponding digital tissue and the number of virtual tumour cells at the end of treatment was obtained. Then, two 4 year biochemical predictions were performed using the logistic regression model (5-fold cross-validation, 1000 repetitions). Prediction 1 was made from tumour and treatment parameters (Gleason score, PSA level, T stage, hormonotherapy prescription and total dose). Prediction 2 was performed from these features, plus the number of virtual tumour cells at the end of treatment obtained after simulation. Results Results for the two biochemical recurrence predictions are presented in Fig. 1. A significantly higher (p-value ≤ 0.0001, given by a Wilcoxon signed-rank test) AUC was obtained for Prediction 2, which incorporated the simulation-based marker (median of 0.78 vs. 0.75).

Fig 1. AUC of 4 year biochemical recurrence predictions. Prediction 1 was made from tumour and treatment parameters. Prediction 2 incorporated our simulation-based marker. Conclusion The use of simulation-based markers results in significantly better biochemical recurrence predictions after standard irradiation schedules in the context of prostate cancer.

PO-2121 Determination of prognostic factors in 18F-FDG PET/CT of relapsed/refractory Hodgkin lymphoma

M. Jajroudi 1 , P. Geramifar 2 , S. Eslami 3,4

1 Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran, Medical Informatics, Mashhad, Iran Islamic Republic of; 2 Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran, Nuclear Medicine, Tehran, Iran Islamic Republic of; 3 Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran, Medical informatics, Mashhad, Iran Islamic Republic of; 4 Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, Medical informatics, Amsterdam, The Netherlands Purpose or Objective 18F-FDG-PET/CT imaging is extremely useful in identifying patients with poor prognosis at the time of Hodgkin lymphoma (HL) diagnosis, during and at the end of treatment in. Despite being a highly curable cancer by first line therapy, refractory/ relapsed occurs in number of patients. Knowing the effective parameters related to salvage treatment response helps to choose the effective treatment for a given patient and avoid losing time. The aim of the study is to build a prognostic model using decision tree, a data mining algorithm that predicts and represents the target value by generating rules, and assess response to salvage treatment in HL patients. Materials and Methods Sixty-four HL patients [median age: 36, female/male: 26/38] with confirmed Refractory/Relapsed histopathology being candidate for salvage chemotherapy were included. The explored demographic and clinical variables includes age, gender, the presence of B symptoms, ECOG scale, pathologic subtype, Deauville score, tumor site, tumor stage, previous radiotherapy. Moreover image-based semi-quantitative parameters including lesion maximum standardized uptake value (SUVmax), lesion to mediastinal SUVmax (SUVMAXM), lesion to liver SUVmax (SUVMAXL), SUVpeak, lesion to mediastinal SUVpeak (SUVPM), lesion to liver SUVpeak (SUVPL), maximal transverse diameter, and SUVmean, MTV, and TLG (with 40%,