RESUMO
BACKGROUND: Total tumor volume (TTV) can provide a simplified parameter in describing the tumor burden by incorporating the size and number of tumor nodules into one continuous variable. The aim of the study was to evaluate the prognostic value of TTV in resection of hepatocellular carcinoma (HCC). METHODS: Patients who underwent liver resection for HCC between 2012 and 2017 were retrospectively analyzed. Patients were divided into a group with TTV ≤65.5 cm³ (which nearly equal to a single tumor with a diameter of 5 cm), and another group with TTV > 65.5 cm³. RESULTS: Two hundred and four patients were included in this study (108 patients had TTV ≤ 65.5cm3, and 96 patients had TTV > 65.5 cm³). Ninety patients (44.1%) were within Milan and 114 patients (55.9%) were beyond Milan criteria. Eighteen patients (15.8%) of beyond Milan criteria had TTV ≤ 65.5 cm³, with a median survival of 32 months which is comparable to a median survival of patients with TTV< 65.5 cm³ (38 months, P = 0.38). TTV-based Cancer of Liver Italian Program (CLIP) score gained the highest value of likelihood ratio 114.7 and the highest Concordance-index 0.73 among other prognostic scoring and staging systems. In multivariate analysis, independent risk factors for diminished survival were serum AFP level >400 ng/ml, TTV >65.5 cm³, microvascular invasion, postoperative decompensation (all P values < 0.05). CONCLUSION: TTV is a feasible prognostic measure to describe the tumor burden in patients with HCC. TTV-CLIP score may provide good prognostic value for resection of HCC than other staging systems.
RESUMO
This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.