ESTRO 38 Abstract book

S384 ESTRO 38

spread patterns of glioblastoma will help to define the clinical target volume for radiation therapy. Material and Methods After receiving IRB approval form MD Anderson, Houston Teys, the clinical MR imaging of 1250 cases of glioma were studied for 10 years using an anatomic software program developed by the authors (Anatom-e, Houston Texas). The final 3 years of the project was devoted to reorganizing venous anatomy to make it responsive to clinically relevant consultations and data mining. This was accomplished by developing deformable anatomic templates (DAT) which could overlay the venous territory maps on the clinical scans. The system also contained a drawing tool which embed the tumors shape into a DAT template. The software was also able to store and compare the shape and location of a single glioma between exams or across a group of tumors. Results The highly variable MR appearance of glial tumors and their complicated spread patterns strongly infer the existence of previously undescribed perivenous compartment in the cerebral hemisphers.

Purpose or Objective To define a dose–response relationship for alopecia when using conventionally fractionated VMAT. Material and Methods The scalp was defined as a region of interest. At the moment of the end of RT and during the follow-up, the areas of scalp where alopecia developed were defined. Grade of alopecia was assessed according to CTCAE version 4.0. A treatment planning system-based dosimetric evaluation of areas of acute alopecia was performed. The following dosimetric parameters were registered for the whole scalp and the areas of alopecia: dose received by 0.1 cc (D 0.1cc ), mean D (D mean ), volumes that received 16, 20, 25, 30, 35, 40 and 43 Gy (V 16 Gy, V 20 Gy, V 25 Gy, V 30 Gy, V 35 Gy, V 40 Gy and V 43 Gy ). Receiver operating characteristics (ROC) curve analysis was used to identify dosimetric parameters associated with high risk of acute and chronic hair loss. Time from end of radiotherapy to alopecia recovery was analyzed. Kaplan Meier analysis and Cox regression analysis were performed in order to assess clinical and dosimetric factors impacting on the recovery probability. Results A total of 101 patients were included in the study. All the patients received a limited-volume RT with conventionally fractionated VMAT technique. 5 patients who were treated for deep tumors did not develop any area of alopecia. Their scalp received very low doses (mean D mean 3.1 Gy; mean D 0.1cc 19.7 Gy). At the end of RT, 96/101 patients developed acute alopecia (G1 only n=11; G2 only n=52; G1+G2 n=33). The whole scalp of the patients with G1 and G2 alopecia received mean D mean equal to 10.6 Gy and 11.8 Gy with mean D 0,1 cc equal to 40.2 and 47.3 Gy, respectively. D 0.1cc , D mean , V 16 Gy, V 20 Gy, V 25 Gy, V 30 Gy, V 35 Gy, V 40 Gy and V 43 Gy were significantly different if compared with the same parameters of patients who did not developed acute alopecia (p<0.05, Mann-whitney test). Mean D mean in areas of alopecia G1 and alopecia G2 at end of RT was 16.5 Gy and 20.3 Gy, respectively (p<0.05, Mann-whitney test). Mean D 0,1 cc in areas of alopecia G1 and alopecia G2 at end of RT was 33.4 Gy and 44.6 Gy, respectively (p<0.05, Mann-whitney test). Trichological follow-up was available for 74/101 patients. 65 patients (92.8%) had an intact scalp. Median time to recover was 5,9 months. The actuarial rate of hair recovery was 87.0% and 98.1% at 9, and 18 months after the end of RT. At ROC curve analysis, V 16 Gy ≥ 16.7 cc and V 20 Gy ≥ 5.2 cc were the strongest predictors of risk of acute alopecia, whereas V 40 Gy ≥ 5.4 cc and V 43 Gy ≥ 2.2 cc were the strongest predictors of risk of chronic alopecia. Kaplan Meier analysis and Cox regression showed that age, D 0.1cc , D mean , V 30 Gy, V 35 Gy, V 40 Gy and V 43 Gy are related to recovery probability. Conclusion By treating patients with this scalp-sparing approach, in most patients complete recovery from alopecia was obtained within some months. By maintaining the doses lower than the dosimetric cut-off values we identified, further reduction of the risk of alopecia may occur. PO-0751 Neutrophil lymphocyte ratio and Platelet lymphocyte ratio as a prognostic factor in brain metastases A. Niiya 1 , K. Murakami 2 , R. Kobayashi 2 , K. Toyofuku 2 , E. Nishimura 3 , M. Kato 2 , Y. Ozawa 4 , H. Shinjo 4 , K. Miyaura 2 , M. Morota 1 , T. Serizawa 5 , Y. Ito 2 , A. Imai 4 , Y. Kagami 2 1 Showa University Koto Toyosu Hospital, Radiation Oncology, Koto-ku, Japan ; 2 Showa University Hospital, Radiation Oncology, Shinagawa-ku, Japan ; 3 Showa University Hospital, Radiology, Shinagawa-ku, Japan ; 4 Showa University Fujigaoka Hospital, Radiation Oncology, Yokohama, Japan ; 5 Tokyo Gamma Unit Center- Tsukiji Neurological Clinic, Neurosurgery, Chuo- ku, Japan

The only natural anatomic structure of the brain that is compatible with the shape and location of gliomas, regardless of their histology, is venous anatomy. The otherwise inexplicable tumor spread of certain multicentric gliomas to the contralateral hemisphere reflects perivenous spread along the deep venous network. Multicentric gliomas with corresponding venous maps will be presented. For example, we analyzed the case of a multicentric GBM (Figure 1) that spreads along the inferior choroidal vein to the basal vein and then crosses the midline along the peduncular vein Conclusion DAT is capable of correctly explaining the complicated spread patterns of glioblastoma. It seems that the superficial and deep peri-venous compartments are responsible for the spread patterns of glioblastoma. Clinical imaging of gliomas strongly supports the existence of perivenous space which is capable of storing and spreading glial tumor cells throughout the brain. This new concept can be used to develop a new classification system for glial tumors and can be used to predict tumor growth PO-0750 VMAT for CNS Tumors and alopecia:results of an observational study and new constraints for the SCALP S. Scoccianti 1 , R. Grassi 1 , P. Marco 1 , F. Terziani 1 , G. Simontacchi 1 , C. Talamonti 2 , G. Caramia 1 , M. Lo Russo 1 , M.A. Teriaca 1 , E. Scoccimarro 1 , C. Saieva 3 , L. Cosi 1 , S. Pallotta 2 , L. Livi 1 1 Azienda Ospedaliera Universitaria Careggi, Radiation Oncology, Firenze, Italy ; 2 Azienda Ospedaliera Universitaria Careggi, Medical Physics Unit, Firenze, Italy ; 3 Istituto per lo Studio- la Prevenzione e la rete Oncologica, SC Epidemiology of Risk Factors and Lifestyles, Florence, Italy