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Background: Hepatic cavernous hemangioma is the most common type of benign liver tumor. Although ruptures and hemorrhages of hepatic hemangioma are rare complications, they are associated with high mortality. Most practitioners only pay more attention to abdominal hemorrhages caused by the rupture of hepatic hemangiomas. However, spontaneous intracapsular hemorrhages can often be neglected and poorly understood.Case Presentation: A 65-year-old man was referred to our institution with right upper quadrant pain, which had occurred suddenly and without a history of recent trauma. The blood test results were normal. Magnetic resonance imaging (MRI) of the abdomen showed a cystic mass in the right liver lobe. Considering the possibility of hepatic cystadenoma with hemorrhage, the patient underwent a right hepatic lobectomy. The pathological findings unexpectedly revealed intratumoral hemorrhage of hepatic hemangioma. The patient recovered well and was discharged eight days after surgery.Conclusions: Intracapsular hemorrhage of hepatic cavernous hemangioma is challenging to diagnose and has a high potential risk of rupture. MRI is beneficial for diagnosing subacute internal hemorrhage cases, and it is recommended to undergo surgery for patients with a definitive diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: Radiation-induced osteosarcomas (R-OS) have historically been high-grade, locally invasive tumors with a poor prognosis. The purpose of this study was to perform a comprehensive literature review and analysis of reported cases dealing with R-OS in the pediatric population to identify the characteristics, prognostic factors, optimal treatment modalities, and overall survival of these patients. Methods and Materials: A MEDLINE/PubMed search of articles written in the English language dealing with OSs occurring after radiotherapy (RT) in the pediatric population yielded 30 studies from 1981 to 2004. Eligibility criteria included patients <21 years of age at the diagnosis of the primary cancer, cases satisfying the modified Cahan criteria, and information on treatment outcome. Factors analyzed included the type of primary cancer treated with RT, the radiation dose and beam energy, the latency period between RT and the development of R-OS, and the treatment, follow-up, and final outcome of R-OS. Results: The series included 109 patients with a median age at the diagnosis of primary cancer of 6 years (range, 0.08–21 years). The most common tumors treated with RT were Ewing’s sarcoma (23.9%), rhabdomyosarcoma (17.4%), retinoblastoma (12.8%), Hodgkin’s disease (9.2%), brain tumor (8.3%), and Wilms’ tumor (6.4%). The median radiation dose was 47 Gy (range, 15–145 Gy). The median latency period from RT to the development of R-OS was 100 months (range, 36–636 months). The median follow-up after diagnosis of R-OS was 18 months (1–172 months). The 3- and 5-year cause-specific survival rate was 43.6% and 42.2%, respectively, and the 3- and 5-year overall survival rate was 41.7% and 40.2%, respectively. Variables, including age at RT, primary site, type of tumor treated with RT, total radiation dose, and latency period did not have a significant effect on survival. The 5-year cause-specific and overall survival rate for patients who received treatment for R-OS involving chemotherapy alone, surgery alone, and surgery plus chemotherapy was 17.3% and 17.3%, 56.6% and 50.3%, and 71.0% and 68.3%, respectively (p < 0.0001, log–rank test). Conclusion: The type of treatment for R-OS was the most significant factor for cause-specific and overall survival. Patients who develop R-OS should be aggressively treated, because the outcome is not as dismal as once thought. [Copyright &y& Elsevier]

• An new multi-task learning framework is devised for differential diagnosis of subjective cognitive decline and mild cognive impairment. • The proposed multi-task learning algorithm combines functional and structural brain information. • The proposed method can discover the most disease-related brain regions and their connectivity. • The extensive experiments demonstrate good classification performance against competing techniques. Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique. [Display omitted] [ABSTRACT FROM AUTHOR]