Because of its multifactorial nature, predicting the presence of cancer using a single biomarker is difficult. We aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma (HCC) using real-world data obtained during clinical practice. To establish a predictive model, we developed a machine-learning framework which developed optimized classifiers and their respective hyperparameter, depending on the nature of the data, using a grid-search method. We applied the current framework to 539 and 1043 patients with and without HCC to develop a predictive model for the diagnosis of HCC. Using the optimal hyperparameter, gradient boosting provided the highest predictive accuracy for the presence of HCC (87.34%) and produced an area under the curve (AUC) of 0.940. Using cut-offs of 200 ng/mL for AFP, 40 mAu/mL for DCP, and 15% for AFP-L3, the accuracies of AFP, DCP, and AFP-L3 for predicting HCC were 70.67% (AUC, 0.766), 74.91% (AUC, 0.644), and 71.05% (AUC, 0.683), respectively. A novel predictive model using a machine-learning approach reduced the misclassification rate by about half compared with a single tumor marker. The framework used in the current study can be applied to various kinds of data, thus potentially become a translational mechanism between academic research and clinical practice.
A severe peak tailing was observed for adenosine 5'-monophosphate in flow injection analysis with stainless steel tubing and water/methanol mixture (1:1, v/v) as carrier. The cause of the peak tailing was investigated by focusing on the chemical structure of the analytes, the material used for the analytical systems and the composition of the carrier. We clarified that the peak tailing was caused by the interaction between phosphate residues in the analytes and stainless steel. The severe peak tailing did not occur with stainless steel tubing when the phosphate compounds were analyzed with carrier containing phosphoric acid or phosphate buffer. The findings indicate that such ill peak profiles are usually not considerable in conventional HPLC separation because phosphoric acid or phosphate buffer is quite commonly used in eluents. In LC-MS, however, the use of phosphoric acid and phosphate buffer is usually avoided because of their non-volatility; therefore this interaction between stainless steel and phosphate compound becomes predominant and results in severe peak tailings. We also found an effective method for avoiding the interaction. When stainless parts, such as LC tubing and ESI spray capillary, were treated with phosphoric acid prior to analysis, the peak profiles of the phosphate compounds were dramatically improved, even when non-phosphate buffer is used as carrier.
We have developed Mass++, a plug-in style visualization and analysis tool for mass spectrometry. Its plug-in style enables users to customize it and to develop original functions. Mass++ has several kinds of plug-ins, including rich viewers and analysis methods for proteomics and metabolomics. Plug-ins for supporting vendors' raw data are currently available; hence, Mass++ can read several data formats. Mass++ is both a desktop tool and a software development platform. Original functions can be developed without editing the Mass++ source code. Here, we present this tool's capability to rapidly analyze MS data and develop functions by providing examples of label-free quantitation and implementing plug-ins or scripts. Mass++ is freely available at http://www.first-ms3d.jp/english/ .
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