Gastrointestinal stromal tumours (GIST) in adolescents and young adults (AYA)

abstracts

Annals of Oncology Conclusions: Imatinib has high response rates in both the NA and PA settings. This study suggests the maximum reduction in PTV can be achieved after greater than 12mths of treatment. For patients responding to NA Imatinib who have ongoing concerns regarding resectability, a more prolonged course of treatment may further reduce PTV to facilitate an R0 resection. Legal entity responsible for the study: Sharath Gangadhara. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

Gastrointestinal stromal tumours (GIST) in adolescents and young adults (AYA)

N.S. Ijzerman1, C. Drabbe2, D. Den Hollander2, M. Mohammadi3, H. van Boven4, I.M.E. Desar2, H. Gelderblom3, D.J. Gru¨nhagen5, A.K.L. Reyners6, R.H.J. Mathijssen7, N. Steeghs1, W.T.A. van der Graaf1 1 Department of Medical Oncology, Netherlands Cancer Institute/Antoni van Leeuwenhoek hospital (NKI-AVL), Amsterdam, Netherlands, 2Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands, 3Department of Medical Oncology, Leiden University Medical Center, Leiden, Netherlands, 4Department of Pathology, Netherlands Cancer Institute/Antoni van Leeuwenhoek hospital (NKIAVL), Amsterdam, Netherlands, 5Department of Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands, 6Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands, 7Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands Background: GIST in older adults and in children are well-known entities, but this is not the case for AYA patients with GIST. Typically, GIST in children (70% female) rarely show mutations in KIT or platelet-derived growth factor receptor (PDGFR) (<15%), are often Succinate Dehydrogenase (SDH) deficient, are almost always located in the stomach (90%) and have a relatively indolent course of disease. The typical adult GIST (about 50% female) has mutations in KIT (75%) or PDGFR (15%), stomach as main location and an overall 5-years survival of 65% (SEER data). As data on AYA with GIST are very limited, we aimed to study the clinical and genetic characteristics and outcome of this specific group. Methods: AYA GIST patients (18-40 years at diagnosis) diagnosed between 2009-2019 and registered in the Dutch GIST Registry (DGR) were included. Patients without mutations in KIT/PDGFR/BRAF and SDH deficiency (by immunohistochemistry) were considered quadruple wildtype (WT). Overall survival (OS) was estimated using Kaplan-Meier method. Furthermore, two subgroups were compared: 18-29 years vs. 30-40 years (Chi-square, Fisher’s exact, Mann-Whitney U test). Results: From 1011 patients in the DGR, 52 AYA patients (5%) were identified: 54% male, median age 35 years. Main primary tumor locations were stomach (46%) and small intestine (46%). Four AYA patients had a known genetic predisposition: 2 Neurofibromatosis 1 (NF1), 1 Carney Triad, 1 KIT exon 11 germline mutation. GIST genetic profiles were reported as KIT mutation 64%, PDGFR mutation 6%, KIT/ PDGFR WT 6%, quadruple WT 8%, SDH deficient 6% and NF1 associated 4%. At diagnosis, 42% had high-risk GIST and 13% metastatic disease. With a median follow-up of 43 months (0-113), median OS for all patients was 8.9 years with a 5-year survival of 85%. No significant differences were found between the two subgroups with regard to gender, location, size, morphology, risk classification and mutation status. Conclusions: GIST presenting at AYA age is rare. AYA GIST differ from the wellknown paediatric GIST, but are also not fully similar to the typical adult GIST. In our series a remarkable high percentage of small intestine GIST and high-risk tumours were observed, 30% non-KIT/PDGFR mutations and a relatively good survival. Legal entity responsible for the study: The Netherlands Cancer Institute. Funding: An unrestricted research grant for the Dutch GIST Registry was received from Novartis, Bayer and Pfizer. Disclosure: I.M.E. Desar: Research grant / Funding (institution): Novartis; Advisory / Consultancy, advisory board: Eisai; Advisory / Consultancy, advisory board: Lilly. R.H.J. Mathijssen: Research grant / Funding (institution), Travel / Accommodation / Expenses: Astellas; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): Boehringer; Research grant / Funding (institution): Cristal Therapeutics; Honoraria (institution), Research grant / Funding (institution): Novartis; Research grant / Funding (institution): Pamgene; Research grant / Funding (institution), Travel / Accommodation / Expenses: Pfizer; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Sanofi; Honoraria (institution): Servier. N. Steeghs: Research grant / Funding (institution): AstraZeneca/MedImmune; Research grant / Funding (institution): Bayer; Research grant / Funding (institution): Bristol-Myers Squibb; Research grant / Funding (institution): Novartis; Research grant / Funding (institution): GlaxoSmithKline; Research grant / Funding (institution): Pfizer; Research grant / Funding (institution): Roche; Research grant / Funding (institution): Genentech/Roche; Research grant / Funding (institution): Boehringer Ingelheim; Research grant / Funding (institution): Blueprint Medicines; Research grant / Funding (institution): AB science; Research grant / Funding (institution): Deciphera; Research grant / Funding (institution): Genentech; Research grant / Funding (institution): Merck Sharp & Dohme; Research grant / Funding (institution): Amgen; Research grant / Funding (institution): Lilly; Research grant / Funding (institution): Incyte; Research grant / Funding (institution): Merus. W.T.A. van der Graaf: Research grant / Funding (institution): Novartis; Advisory / Consultancy: Bayer. All other authors have declared no conflicts of interest.

Volume 30 | Supplement 5 | October 2019

Radiomics improves response evaluation for desmoid tumours treated with chemotherapy

Crombe1, M. Kind1, I.L. Ray-Coquard2, N. Isambert3, C.M. Chevreau4, T. Andre5, C. Lebbe6, A. Le Cesne7, E. Bompas8, S. Piperno-Neumann9, E. Saada-Bouzid10, JY. Blay11, A. Italiano12 1 Radiology Department, Bergonie Institute, Bordeaux, France, 2Medical Oncology, Centre Le´on Be´rard, Lyon, France, 3Medical Oncology, Centre Georges-Franc¸ois Leclerc (Dijon), Dijon, France, 4Medical Oncology, Institut Universitaire du Cancer -ToulouseOncopole, Toulouse, Haute-Garonne, France, 5Medical Oncology, Hopital SaintAntoine, Paris, France, 6Medical Oncology, Hoˆpital Saint Louis, Paris, France, 7Medical Oncology, Gustave Roussy - Cancer Campus, Villejuif, France, 8Medical Oncology, ICO Institut de Cancerologie de l’Ouest Rene´ Gauducheau, Saint-Herblain, France, 9Medical Oncology, Institut Curie, Paris, France, 10Medical Oncology, Hopital Lacassagne, Nice, France, 11Medicine, Centre Le´on Be´rard, Lyon, France, 12Early Phase Trials Unit, Institute Bergonie´, Bordeaux, France Background: Response of desmoid tumors (DT) to chemotherapy (CT) is evaluated according to RECIST criteria in daily practice and clinical trials. However, MRI demonstrates early change in heterogeneity in responding tumor, due to decrease in cellular area and increase in fibro-necrotic content, before dimensional response. Heterogeneity can be quantified through radiomics approach. Aim was to develop radiomics-based response criteria and to compare their performances with usual criteria. Methods: 43 patients (28 women, median age: 38.2) were prospectively included in this ancillary multicentre study of a randomized phase II trial (NCT01876082) as they presented with progressive DT, contrast-enhanced MRI at baseline (MRI-0) and early evaluation (3 months later, MRI-1). After signal intensities normalization, voxel size standardization and segmentation of whole DT volume on fat-suppressed contrastenhanced T1-weighted imaging, 90 baseline and delta 3D-radiomics features (RF) were extracted. Using least absolute shrinkage and selection operator with Cox regression model and cross validation, a radiomics score based on selected and weighted RF was generated. The performances of prognostic models based on radiomics score, RECIST, mRECIST, EASL, Cheson, Choi and modified-Choi criteria from MRI-0 to MRI-1 to predict progression-free survival (PFS) were assessed with concordance-index. All the results were adjusted for ECOG performance status, initial tumor volume, prior CTs, current CT and B-catenin mutation. Usual response criteria were evaluated in both disease control and objective response settings. Results: There were 11 progressions. The radiomics score included 4 variables (1 baseline RF and 3 delta-RF). A high radiomics score indicated a poorer prognosis. The radiomics score significantly correlated with PFS (adjusted hazard ratio ¼ 3.39, 95%CI ¼ [1.47-7.79], p ¼ 0.0043) while none of the usual response criteria was (range of p-values ¼ [0.166-0.750]). The prognostic model based on radiomics score had the highest concordance-index (0.794, 95%CI ¼ [0.678-0.910]). Conclusions: Quantifying heterogeneity with MRI at baseline and its early change through a dedicated radiomics score can improve the response evaluation for DT treated with CT. Legal entity responsible for the study: Antoine Italiano. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.

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Radiomics of gastrointestinal stromal tumours, risk classification based on computed tomography images: A pilot study

M.J. Timbergen1, M.P.A. Starmans2, M. Vos3, M. Renckens4, D.J. Gru¨nhagen1, G.J.L.H. van Leenders5, W.J. Niessen2, C. Verhoef1, S. Sleijfer6, S. Klein2, J.J. Visser4 1 Surgical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands, 2Radiology and Nuclear Medicine, Medical Informatics, Erasmus MC, Rotterdam, Netherlands, 3 Medical Oncology and Surgical Oncology, Erasmus MC Daniel den Hoed Cancer Center, Rotterdam, Netherlands, 4Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands, 5Pathology, Erasmus MC, Rotterdam, Netherlands, 6Medical Oncology, Erasmus MC Daniel den Hoed Cancer Center, Rotterdam, Netherlands Background: Gastrointestinal stromal tumors (GISTs) are rare mesenchymal tumors of the gastrointestinal (GI) tract. The size, the presence of a c-KIT gene mutation and the mitotic index are used to determine the prognosis and to direct systemic treatment choices. This study evaluated the use of radiomics, a technique which uses algorithms for diagnosing and predicting the c-KIT mutational status and mitotic index of GISTs from medical imaging features. Methods: A combination of machine learning methods was used to distinguish treatment-naive GISTs from other GI tumors resembling GISTs on imaging, based on clinical and molecular characteristics, as well as imaging features extracted from the contrast-enhanced venous phase computed tomography scans. Evaluation was performed in a 100x random-split cross-validation with 20% of the data for testing. Results: A total of 242 tumors were used for distinguishing GISTs (n ¼ 123) from nonGISTs (n ¼ 119) including leiomyoma (n ¼ 25), schwannoma (n ¼ 21), gastric carcinoma (n ¼ 25), lymphoma (n ¼ 23) and leiomyosarcoma (n ¼ 25).The non-GISTs

doi:10.1093/annonc/mdz283 | v699

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