ESTRO 2020 Abstract Book

S587 ESTRO 2020

the proposed constraints: Dmean ≤37 Gy and V40 ≤50%. For patients with overload, we realized an optimized treatment plan to try to respect the constraints. Results For the planning plan, mean Dmean and V40 were 35 Gy [28-44] and 42% [9-96] respectively. Dose constraints were respected in 39 cases (66.1%) and in 38 cases (64.4%) for Dmean and V40 respectively. A total of 23 optimized plans were realized. After optimization, mean Dmean and V40 were 34 Gy [28-44] and 39% [9-91] respectively. Dose constraints were respected in 52 cases (88%) for Dmean and in 49 cases (83%) for V40. Conclusion Our study results show that the proposed dose constraints (Dmean ≤37 Gy and V40 ≤50%) are suitable for routine use : they can be respected in more than 80% of cases. For the remaining 20% of cases, the bladder volume should be checked and an intensity modulated radiotherapy discussed each time the overrun is important. PO-1111 Pathological lymph node staging for intermediate-risk rectal cancer patients A. Biche 1 , A. Choudhury 1 , L. Wee 1 , A. Dekker 1 , J. Van Soest 1 , M. Berbee 1 1 Department of Radiation Oncology MAASTRO- GROW School for Oncology and Developmental Biology- Maastricht University Medical Centre+- Maastricht- the Netherlands, MAASTRO clinic, Maastricht, The Netherlands Purpose or Objective Pre-operative radiotherapy and chemo-radiotherapy have been shown to reduce the risk of local recurrences after total mesorectal excision (TME) surgery. These neoadjuvant treatment strategies do not appear to improve survival and may negatively affect the quality of life after treatment. Hence, it is crucial to accurately select the patients who will or will not benefit from neoadjuvant therapy. Under-staging of the tumor and lymph nodes may lead to the omission of a beneficial pre- treatment, whereas over-staging may cause unnecessary morbidity. This study investigates the predictive value of radiomic features derived from pretreatment CT images for pathological lymph node (pN) staging for rectal cancer patients. Material and Methods A retrospective study of 64 diagnosed colorectal cancer patients treated with short-course (5x5Gy) radiotherapy followed by total mesorectal excision (TME) surgery within ten days between 2007 to 2015. The regions of interest (ROI) were delineated manually by an experience rectal radiologist. Radiomics features extraction was implemented using the Ontology-guided Radiomics Analysis Workflow (O-RAW) software. A total of 105 radiomic features extracted from each segmented ROI (Mesorectum and gross tumor volume of the primary disease (GTVp1) ) of pretreatment CT images were analyzed. Missing clinical information was imputed using the multivariate imputations by chained equations (MICE) package in R. Principal component analyses (PCA) was employed for dimensionality reduction after all features with a correlation coefficient above 0.7 have been excluded. Repeated (50) 5-fold cross-validation decision tree models are used to classify patients based on their pN status (Negative or Positive). The area under the receiver operating characteristic curve (AUC) is used to measures the performance of these models. Results Figure 1 shows the developed trees from the meserectum and tumor data, respectively. The decision tree used three radiomics mesorectum information to make a decision. However, just two variables are used for the tumor information and one for the clinical.

Figure 2 shows the mean AUCs and confidence intervals of the trees discriminating abilities on the mesorectum, tumor, and clinical data are 0.62 (0.61-0.64), 0.59 (0.58- 0.60), and 0.58 (0.57-0.60) respectively.

Conclusion We observed that mesorectum radiomics features have a statistically significant higher discriminating ability compared to the tumor (p-value = 0.004) and clinical (p- value = 0.003) informations despite the lower than optimal prediction accuracy. However, there was no difference between the tumor and clinical data (p-value = 0.429). Our study highlights the potential benefit of a non-invasive and cost-effective radiomics for precision medicine, which could enhance the efficiency and efficacy of cancer care. The optimal performance of these models with further research and increased sample size is the next step for this project in addition to external validation. PO-1112 Total neoadjuvant therapy in high risk rectal cancer: a feasibility pilot study E. Palazzari 1 , A. Buonadonna 2 , A. Lauretta 3 , F. Navarria 4 , E. Ongaro 2 , L. Foltran 2 , R. Innocente 4 , C. Belluco 3 , P. Ubiali 5 , V. Canzonieri 6 , M. Urbani 7 , C. Colombo 8 , G. Bertola 3 , A. De Paoli 4 1 CRO IRCCS Aviano National Cancer Institute, Radiation Oncology Dept, Aviano PN, Italy ; 2 CRO IRCCS Aviano National Cancer Institute, Medical Oncology Dept, Aviano PN, Italy ; 3 CRO IRCCS Aviano National Cancer Institute, Surgical Oncology Dept, Aviano PN, Italy ; 4 CRO IRCCS Aviano National Cancer Institute, Radiation Oncology Dept, Aviano PN, Italy ; 5 Santa Maria degli Angeli Hospital, Surgery Dept, Pordenone, Italy ; 6 CRO IRCCS Aviano National Cancer Institute, Oncologic Pathology Dept, Aviano PN, Italy ; 7 CRO IRCCS Aviano National Cancer Institute, Oncologic Radiology Dept, Aviano PN, Italy ; 8 CRO IRCCS Aviano National Cancer Institute, Nuclear Medicine Dept, Aviano PN, Italy