As a result of development in medical diagnostic technology, the incidence of double primary cancer is increasing. In case that one primary cancer was identified by pathologic finding and has suggestive multiple metastatic lesions of the cancer, it is often difficult that the lesions are considered as another synchronous primary malignancy, not as metastasis of primary cancer. Here, we report a rare case of synchronous double primary renal cell carcinoma that was diagnosed initially as metastatic duodenal adenocarcinoma. A 66-year-old man presented with a palpable abdominal mass. Abdominal computed tomography showed duodenal wall thickening and hypervascular masses in the liver and left kidney. Esophagogastroduodenoscopy revealed an ulcerative lesion in the duodenal bulb, and endoscopic biopsy identified moderately-differentiated adenocarcinoma. We regarded the findings as duodenal adenocarcinoma with metastasis to liver and kidney. So he was treated with fluoropyrimidine-based chemotherapy for the diagnosis of duodenal adenocarcinoma with liver and renal metastases. After 10 months, he developed a right forearm mass. Morphological and immunohistochemical analysis of an incisional biopsy of the forearm mass were consistent with a diagnosis of metastatic renal cell carcinoma. Even when there is a pathologically confirmed malignancy, clinicians must consider the possibility of synchronous double primary malignancy in metastatic lesions.

In this study, we propose an efficient two-phase heuristic policy, called an acceptance tolerance control policy, for Infrastructure as a Service (IaaS) cloud services that considers both the service provider and customer in terms of profit and satisfaction, respectively. Each time an IaaS cloud service is requested, this policy determines whether the service is accepted or rejected by calculating the potential for realizing the two performance objectives. Moreover, it uses acceptance tolerance to identify the possibility for error with the chosen decision while compensating for both future fluctuations in customer demand and error possibilities based on past decisions. We conducted a numerical experiment to verify the performance of the proposed policy using several actual IaaS cloud service specifications and comparing it with other heuristics.