• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1811-1815
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• 2014

Background: In clinical trials with no upper age limit, the proportion of older patients is usually small, probably reflecting the more conservative approach adopted by clinicians when treating the elderly. An exploratory analysis of elderly patients in the RECORD-1 Trial showed that patients ${\geq}$ 65 y.o. had superior median PFS than overall RECORD-1 population (5.4 months and 4.9 months, respectively). We investigated the efficacy, relative benefit and safety of Everolimus (EVE) as sequential therapy after failure of VEGFr-TKI therapy for older patients with metastatic renal cell cancer (mRCC), in daily practice. Materials and Methods: 172 consecutive IRB approved patients with mRCC (median age 65, M:F 135/37, 78% clear cell) who received salvage EVE at 39 tertiary institutions between October 2009 and August 2011 were included in this analysis. Some 31% had progressed on sunitinib, 22% on sorafenib, 1% on axitinib, 41% on sequential therapy, and 5% had received other therapy. Patients with brain metastases were not included and 95% of the patients had a ECOG (Eastern Cooperative Oncology Group) performance status (PS) of 0 or 1. Previous radiotherapy was an exclusion criterion, but prior chemotherapy was permitted. Adequate organ function and hematologic parameters were mandatory. EVE administration was approved by the institutional review board at each participating institution and signed informed consent was obtained from all patients. Results: Median time of the whole cohort to last follow-up was 3.5 months (range 0.4-15.2 months). Forty four percent were continuing to take EVE at last followup. There were 86 (50%) patients ${\geq}$ 65 y.o. and 86 (50%) <65 y.o. The percentage of patients who showed PR/SD was higher in the older group than in the younger one (5.9%/61.2% vs 1.2%/46.5%, respectively). Median survival of older patients was also significantly longer (3.5 +/- 0.31 vs 3.1 +/- 0.34, hazard ratio=0.45, CI; 0.255-0.802). Analysis using Cox regression model adjusted for gender, PS, number of metastases, site of metastases, histology, smoking history and age detected an association between age and PFS (p=0.011). The frequency of adverse events in elderly patients treated with EVE was no greater than that in younger patients, although such toxicity may have had a greater impact on their quality of life. Conclusions: Older patients should not generally be excluded from accepted therapies (mTOR inhibitors after failure of VEGFr-TKI therapy) for mRCC.

• Korean Journal of Comparative Education
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• v.27 no.4
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• pp.187-209
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• 2017

The purpose of this study was to highlight the necessity of a conceptual reestablishment of world university evaluations. The hitherto most well-known and validated world university evaluation systems such as Times Higher Education (THE), Quacquarelli Symonds (QS) or Academic Ranking of World Universities (ARWU) primarily assess big universities with quantitative evaluation indicators and performance results in the rankings. Those Systems have instigated a kind of elitism in higher education and neglect numerous small or local institutions of higher education, instead of providing stakeholders with comprehensive information about the real possibilities of tertiary education so that they can choose an institution that is individually tailored to their needs. Also, the management boards of universities and policymakers in higher education have partly been manipulated by and partly taken advantage of the elitist ranking systems with an economic emphasis, as indicated by research-centered evaluations and industry-university cooperation. To supplement such educational defects and to redress the lack of world university evaluation systems, a new system called 'U-Multirank' has been implemented with the financial support of the European Commission since 2012. U-Multirank was designed and is enforced by an international team of project experts led by CHE(Centre for Higher Education/Germany), CHEPS(Center for Higher Education Policy Studies/Netherlands) and CWTS(Centre for Science and Technology Studies at Leiden University/Netherlands). The significant features of U-Multirank, compared with e.g., THE and ARWU, are its qualitative, multidimensional, user-oriented and individualized assessment methods. Above all, its website and its assessment results, based on a mobile operating system and designed simply for international users, present a self-organized and evolutionary model of world university evaluation systems in the digital and global era. To estimate the universal validity of the redefinition of the world university evaluation system using U-Multirank, an epistemological approach will be used that relies on Edgar Morin's Complexity Theory and Karl Popper's Philosophy of Science.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1196-1206
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• 2021

Purpose To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30-50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learning-based AI algorithm for CDSS.