ABSTRACT
Based on Quantum illumination (QI) protocol, researcshers have developed prototypes of quantum radar and demonstrated its quantum enhancement. Nevertheless, there are still difficulties in the practical application for QI radar, especially the trade-off between the detection range and quantum enhancement, as well as the construction of the optimized receiver. Some studies have suggested that the potential solutions to these difficulties are to deploy the quantum limited amplifiers in QI radars, and have envisioned different amplification schemes. In this paper, we establish a universal and effective method to evaluate the signal-to-noise ratio of QI radar. It connects QI radar theory with classical radar signal processing theory, providing support for researchers to evaluate the performance of various QI radar schemes from a radar perspective. Based on this method, we prove that any quantum limited phase-insensitive amplification scheme will seriously weaken the quantum enhancement of QI radar. Furthermore, we also demonstrate that the QI radar with phase-sensitive amplified idler has no advantage over the optimal classical illumination. These results can help us avoid some unreasonable QI radar schemes. In addition, we believe that the proposed method can also be applied to explore other potential QI radar schemes and contribute to promoting the application development of QI radar.
ABSTRACT
It has been proved that quantum illumination (QI) radar has the quantum advantages in error-probability exponent. However, the error-probability exponent is not a recognized figure of merit in the radar literature, nor does it correspond in a straightforward manner to any such figure of merit. Signal to noise ratio (SNR) gain is an important criterion in radar theory. While, the theoretical analysis of quantum enhancement in SNR gain of QI radar has not been reported. In this paper, we compare the physical fundamental of matched filter (MF), which can achieve the optimal SNR gain under white noise in classical radar theory, and phase conjugation (PC) receiver. Furthermore, the quantum enhancement of SNR gain in QI radar is studied. It is shown that QI radar with practical receivers can achieve about 3dB quantum advantage in SNR gain. In addition, in the case of extremely weak signal, it can potentially achieve tens of dB enhancement in SNR gain compared with the MF based classical radar.
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Purpose: Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT). Patients and Methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed. Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature. Conclusion: The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.
ABSTRACT
BACKGROUND: Since it's a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. METHODS: The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). RESULTS: The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. CONCLUSIONS: GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.
Subject(s)
Carcinoma, Hepatocellular/surgery , Liver Neoplasms/surgery , Nomograms , Carcinoma, Hepatocellular/pathology , Female , Humans , Liver Neoplasms/pathology , Male , Middle AgedABSTRACT
BACKGROUND: Most hepatocellular carcinoma (HCC) patients' liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. METHODS: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method. RESULTS: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis. CONCLUSIONS: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.
