Neuroendocrine tumors are slow-growing tumors and associated with prolonged overall survival even in the presence of untreated liver metastases. The presence of liver metastases may be responsible for severe symptoms with impairment of quality of life. Liver resection has been proposed to achieve better symptom control and/or improve overall survival, but this concerns less than 20% of patients with liver metastases. In addition, the chance to be really cured after liver resection is around 40%, which prompts consideration of liver transplantation as the only potential curative treatment. Time has come to move beyond the traditional debate around the best candidates and prognostic factors for liver transplantation. This review gives the opportunity to discuss new insights: (1) outcome of liver transplantation for neuroendocrine liver metastases as compared with hepatocellular carcinoma, (2) outcome of salvage liver transplantation as a secondary procedure after surgical resection of neuroendocrine liver metastases, (3) outcome of palliative liver transplantation for neuroendocrine liver metastases, and (4) the chance to be cured after liver transplantation for neuroendocrine liver metastases.

Conclusion:This work implements a Machine-Learning tool to asses and classifies the plasmodium species using the stained thin blood smear images. The outcome of the proposed tool confirms that, it works well on considered microscopic image database and helps to attain better classification accuracy. In future, the proposed approach can be used to examine the clinical grade thin blood smear images of malaria infected patients.

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