Numerous surgical procedures have been developed for urinary diversion in patients who have undergone a radical cystectomy for bladder cancer or, less frequently, a benign condition. Because urinary diversion procedures are complex, early and late postsurgical complications frequently occur. Possible complications include alterations in bowel motility, anastomotic leaks, fluid collections (abscess, urinoma, lymphocele, and hematoma), fistulas, peristomal herniation, ureteral strictures, calculi, and tumor recurrence. Computed tomography (CT) is an accurate method for evaluating such events. Multiplanar reformatting and three-dimensional volume rendering of multidetector CT image data are particularly useful for achieving an accurate and prompt diagnosis of complications and obtaining information that is essential for adequate surgical management. In addition, knowledge of urinary diversion procedures, normal postsurgical appearances, and optimal CT technique for postsurgical evaluations is essential for detecting complications and avoiding misdiagnosis.

Meningiomas account for approximately 15% of all intracranial tumors and are the most common non-glial primary tumors of the central nervous system. Most meningiomas are benign neoplasms with characteristic imaging features. Primary extradural meningiomas account for only 1-2% of all meningiomas. They must be differentiated from intradural meningiomas with secondary extradural extension and/or metastases. The vast majority of extradural meningiomas are found in the skull or in the head and neck region. We report on an extremely rare case of primary extradural meningioma that was located in the scapula. The lesion was resected. Radiographic findings and pathologic features are discussed. To the best of our knowledge, this form of presentation of an extradural meningioma has not been previously described.

Background: The International Prognostic Scoring System (IPSS) for MDS has recently been revised (IPSS-R). However both scoring systems were developed after exclusion of therapy-related cases and data on its usefulness in treatment-related MDS (tMDS) is limited. Aims and Methods: We analyzed 1837 pts from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed 1975-2015. Complete data to calculate the IPSS/-R was available in 1511 pts. The impact of prognostic features was analyzed by uni- and multivariable models and estimated by a measure of concordance for censored data (Dxy). Results: Median age was 68 years. 1% of pts had 5q-syndrome, 13% RCUD, 4% RARS, 27% RCMD/-RS, 18% RAEB 1, 18% RAEB 2, 4% CMML 1, 2% CMML 2, 3% MDS-U, and 7% AML (RAEB-T) according to WHO-classification. Regarding cytogenetics 38% exhibited good, 14% intermediate, and 48% poor-risk according to IPSS, and 2% very good, 36% good, 17% intermediate, 15% poor, and 31% very poor according to IPSS-R. Prognostic risk groups were 12% IPSS low, 34% int 1, 36% int 2, and 18% high, while the IPSS-R was very low in 8%, low in 20%, intermediate in 17%, high in 23%, and very high in 32%. The most frequent primary diseases were NHL 28%, breast cancer 16%, myeloma 6%, prostate cancer 6%, Hodgkins disease 5%, and 4% gastrointestinal tumors. Patients received chemotherapy in 75% and radiotherapy in 47%. Regarding chemotherapeutic drugs, most pts received combination regimens containing alkylating agents in 65%, topoisomerase inhibitors in 44%, antitubulin agents in 26%, and antimetabolites in 26%. Median follow-up from MDS diagnosis was 59 months, median survival 16 months. Since a disease altering treatment is, at least in higher risk disease, which is overrepresented in tMDS, standard of care, we decided to analyze treated as well as untreated pts to avoid a selection bias. This included stem cell transplantation in 16% with a median survival of 24 months. Features with influence on survival and time to AML in univariable analysis included FAB, WHO, IPSS, IPSS-R, cytogenetics, hb, platelets, marrow and peripheral blasts, ferritin, LDH, fibrosis, ß2-microglobulin, and use of alkylating agents for the treatment of primary disease. For hemoglobin, platelets, LDH, fibrosis, and ß2-microglobulin the influence was stronger on survival. Year of diagnosis, age, gender, neutrophil count, WBC, use of chemo or radiotherapy as well as other chemotherapeutic agents had no marked influence on both outcomes. According to our results, both the IPSS (Dxy 0.29 for survival, 0.32 for AML) and IPSS-R (Dxy 0.34, 0.32 for AML) perform moderately in tMDS, but not as well as in primary MDS (pMDS). Therefore, existing prognostic models need to be adjusted to tMDS. However, this appears to be not without difficulties. The scores tested, as well as most prognostic variables themselves perform inferior compared to pMDS. It becomes even more complicated since tMDS in itself is even more heterogeneous than pMDS. Scores and variables perform differently depending on the primary disease or therapy. The IPSS/-R and its variables perform for example better in pts with solid tumors compared to hematologic diseases or in pts who have received radio- instead of chemotherapy, but also in pts after prostate compared to breast cancer. In addition to the integration of further variables, new cutoffs, or the weighting of existing variables, we are currently testing the possibility of separate score versions for different tMDS subgroups. Separate score versions for survival and time to AML would also give differing weights to most features. Hemoglobin, platelets and cytogenetics would get more weight for survival, while marrow blasts would be more important regarding AML. Conclusion: In contrast to early descriptions of tMDS, with poor risk cytogenetics in the vast majority of pts and a uniformly poor prognosis, surprisingly we find good risk karyotypes in a relatively large number of pts. Although, poor risk cytogenetics are still overrepresented, this indicates, different types of tMDS exist. Our analysis shows that many variables exhibit prognostic influence in tMDS and the IPSS or preferably IPSS-R can be applied in these pts. However, the prognostic power of both scores is inferior compared to pMDS, making an optimized tMDS score reasonable. Currently data from further IWG centers is integrated in our database and further analyses are performed to propose a tMDS specific score. Figure 1. Figure 1. Disclosures Komrokji: Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau; Incyte: Consultancy; Celgene: Consultancy, Research Funding. Sekeres:TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Celgene: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Onconova: Consultancy. Valent:Novartis: Consultancy, Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Pfizer: Honoraria; Celgene: Honoraria. Platzbecker:Boehringer: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Esteve:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

To develop a prognostic scoring system tailored for therapy-related myelodysplastic syndromes (tMDS), we put together a database containing 1933 patients (pts) with tMDS from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed between 1975-2015. Complete data to calculate the IPSS and IPSS-R were available in 1603 pts. Examining different scoring systems, we found that IPSS and IPSS-R do not risk stratify tMDS as well as they do primary MDS (pMDS), thereby supporting the need for a tMDS-specific score (Kuendgen et al., ASH 2015). The current analysis focuses on cytogenetic information as a potential component of a refined tMDS score, based on this large, unique patient cohort. Of the 1933 pts, 477 had normal karyotype (KT), 197 had missing cytogenetics, while 467 had a karyotype not readily interpretable. Incomplete karyotype descriptions will be reedited for the final evaluation. Of the remaining 1269 pts the most frequent cytogenetic abnormalities (abn) were: -7, del(5q), +mar, +8, del(7q), -5, del(20q), -17, -18, -Y, del(12p), -20, and +1 with >30 cases each. Frequencies are shown in Table 1. Some abn were observed mostly or solely within complex KTs, such as monosomies, except -7. Others, like del(20q) or -Y, are mainly seen as single or double abn, while del(5q), -7, or del(7q) are seen in complex as well as non-complex KTs. The cytogenetic profile overlapped with that of pMDS (most frequent abn: del(5q), -7/del(7q), +8, -18/del(18q), del(20q), -5, -Y, -17/del(17p), +21, and inv(3)/t(3q) (Schanz et al, JCO 2011)), with notable differences including overrepresentation of complete monosomies, a higher frequency of -7 or t(11q23), and a more frequent occurrence of cytogenetic subtypes in complex KTs, which was especially evident in del(5q) occurring as a single abn in 16%, compared to 70% within a complex KT. IPSS-R cytogenetic groups were distributed as follows: Very Good (2%), Good (35%), Int (17%), Poor (15%), Very Poor (32%). Regarding the number of abn (including incomplete KT descriptions) roughly 30% had a normal KT, 20% 1, 10% 2, and 40% ≥3 abn, compared to pMDS: 55% normal KT, 29% 1, 10% 2, and 6% ≥3 abn. To be evaluable for prognostic information, abn should occur in a minimum of 10 pts. As a single aberration this was the case for -7, +8, del(5q), del(20q), del(7q), -Y, and t(11;varia) (q23;varia). Of particular interest, there was no apparent prognostic difference between -7 and del(7q); del(5q) as a single abn was associated with a relatively good survival, while the prognosis was poor with the first additional abn; t(11q23) occurred primarily as a single abn and was associated with an extremely poor prognosis, and prognosis of pts with ≥4 abn was dismal independent of composition (Table 1). To develop a more biologically meaningful scoring system containing homogeneous and prognostically stable groups, we will further combine subgroups with different abn leading to the same cytogenetic consequences. For example, deletions, unbalanced translocations, derivative chromosomes, dicentric chromosomes of 17p, and possibly -17 all lead to a loss of genetic material at the short arm of this respective chromosome affecting TP53. Further information might be derived from analyses of the minimal common deleted regions. For some abn, like del(11q), del(3p), and del(9q), this can be refined to one chromosome band only (table 1). Conclusion: Development of a robust scoring system for all subtypes of tMDS is challenging using existing variables. This focused analysis on the cytogenetic score component shows that favorable KTs are evident in a substantial proportion of pts, in contrast to historic data describing unfavorable cytogenetics in the majority of pts. Although complex and monosomal KTs are overrepresented, this suggests the existence of distinct tMDS-subtypes, although some of these cases might not be truly therapy-induced despite a history of cytotoxic treatment. The next steps will be to analyze the prognosis of the different groups, develop a tMDS cytogenetic score, and examine minimal deleted regions to identify candidate genes for development of tMDS, as well as to describe the possible influence of different primary diseases and treatments (radio- vs chemotherapy, different drugs) on induction of cytogenetic subtypes. Our detailed analysis of tMDS cytogenetics should reveal important prognostic information and is likely to help understand mechanisms of MDS development. Disclosures Komrokji: Novartis: Consultancy, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Sole:Celgene: Membership on an entity's Board of Directors or advisory committees. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Steensma:Amgen: Consultancy; Genoptix: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Millenium/Takeda: Consultancy; Ariad: Equity Ownership. Schlenk:Pfizer: Honoraria, Research Funding; Amgen: Research Funding. Valent:Amgen: Honoraria; Deciphera Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Deciphera Pharmaceuticals: Research Funding. Giagounidis:Celgene Corporation: Consultancy. Giagounidis:Celgene Corporation: Consultancy. Platzbecker:Celgene Corporation: Honoraria, Research Funding; TEVA Pharmaceutical Industries: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen-Cilag: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. Lübbert:Janssen-Cilag: Other: Travel Funding, Research Funding; Celgene: Other: Travel Funding; Ratiopharm: Other: Study drug valproic acid.

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